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.. image:: https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white
    :alt: PyTorch
    :target: https://pytorch.org/get-started/locally/

.. image:: https://img.shields.io/badge/-Albumentations-017F2F?style=flat&logo=github&labelColor=gray
    :alt: Albumentations
    :target: https://github.com/albumentations-team/albumentations
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.. image:: https://img.shields.io/badge/-Pyscaffold-017F2F?style=flat&logo=github&labelColor=gray
    :alt: Pyscaffold
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    :target: https://github.com/pyscaffold/pyscaffold
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==============
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PLI-Transforms
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==============
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A collection of data transformations for 3D-PLI images with available implementations for data augmentation in machine-learning pipelines.
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.. image:: img/example.png
    :alt: Alternative text for the image
    :scale: 80%
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https://arxiv.org/abs/2401.17207  Example 3D-PLI data augmentations using the provided transformations of 3D-PLI paramter maps. Original and transformed parameter maps are visualized as transmttance, retardation and fiber orientation map (FOM).
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------------
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Installation
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Basic installation of the ``pli-transforms`` package:
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.. code-block:: bash

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    pip install git+https://jugit.fz-juelich.de/inm-1/bda/software/data_processing/pli-transforms.git
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To access some data augmentations under ``pli_transforms.augmentations.pytorch`` a running installation of `PyTorch <https://pytorch.org/>`_ is required.
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-------
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Content
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TBD


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How to Cite
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If you use this work in your research, please cite it as follows:

.. code-block:: latex

   @article{oberstrass2024,
     title = {Self-{{Supervised Representation Learning}} for {{Nerve Fiber Distribution Patterns}} in {{3D-PLI}}},
     author = {Oberstrass, A. and others},
     year = {2024},
     journal = {arXiv preprint arXiv:2401.17207},
     eprint = {2401.17207},
     archiveprefix = {arxiv}
   }