Metadata-Version: 2.1
Name: cornucopia
Version: 0.1.0
Summary: An abundance of augmentation layers
Home-page: UNKNOWN
Author: Yael Balbastre
Author-email: yael.balbastre@gmail.com
License: MIT
Project-URL: Source Code, https://github.com/balbasty/cornucopia
Description: <picture align="center">
          <source media="(prefers-color-scheme: dark)" srcset="docs/icons/cornucopia_lightorange.svg">
          <source media="(prefers-color-scheme: light)"  srcset="docs/icons/cornucopia_orange.svg">
          <img alt="Cornucopia logo" src="docs/icons/cornucopia_orange.svg">
        </picture>
        
        The `cornucopia` package provides a generic framework for preprocessing,
        augmentation, and domain randomization; along with an abundance of specific layers,
        mostly targeted at (medical) imaging. `cornucopia` is written using a PyTorch
        backend, and therefore runs **on the CPU or GPU**.
        
        Cornucopia is *intended* to be used on the GPU for on-line augmentation.
        A quick [benchmark](examples/benchmark.ipynb) of affine and elastic augmentation
        shows that while cornucopia is slower than [TorchIO](https://github.com/fepegar/torchio)
        on the CPU (~ 3s vs 1s), it is greatly accelerated on the GPU (~ 50ms).
        
        Since gradients are not expected to backpropagate through its layers, it can
        theoretically be used within any dataloader pipeline,
        independent of the downstream learning framework (pytorch, tensorflow, jax, ...).
        
        ## Installation
        
        
        ## Installation
        
        ### Dependencies
        
        - `pytorch >= 1.8`
        - `numpy`
        - `nibabel`
        - `torch-interpol`
        - `torch-distmap`
        
        ### Conda
        
        ```sh
        conda install cornucopia -c balbasty -c pytorch 
        ```
        
        ### Pip
        
        ```sh
        pip install cornucopia
        ```
        
        ## Documentation
        
        Read the [documentation](https://cornucopia.readthedocs.io) and in particular:
        - [installation](https://cornucopia.readthedocs.io/en/latest/install/)
        - [get started](https://cornucopia.readthedocs.io/en/latest/start/)
        - [examples](https://cornucopia.readthedocs.io/en/latest/examples/overview/)
        - [API](https://cornucopia.readthedocs.io/en/latest/api/overview/)
        
        ## Other augmentation packages
        
        There are other great, and much more mature, augmentation packages 
        out-there (although few run on the GPU). Here's a non-exhaustive list:
        - [MONAI](https://github.com/Project-MONAI/MONAI)
        - [TorchIO](https://github.com/fepegar/torchio)
        - [Albumentations](https://github.com/albumentations-team/albumentations) (2D only)
        - [Volumentations](https://github.com/ZFTurbo/volumentations) (3D extension of Albumentations)
        
        ## Contributions
        
        If you find this project useful and wish to contribute, please reach out!
        
Platform: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.6
Description-Content-Type: text/markdown
