Metadata-Version: 2.1
Name: icon-registration
Version: 0.2.1
Summary: A package for image registration regularized by inverse consistency
Home-page: https://github.com/uncbiag/ICON
Author: Hastings Greer
Author-email: t@hgreer.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/uncbiag/ICON/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.4
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: torchvision
Requires-Dist: tqdm
Requires-Dist: matplotlib
Requires-Dist: footsteps
Requires-Dist: scikit-learn
Requires-Dist: itk
Requires-Dist: girder-client

![Demo figure](notebooks/paper_figures/Intro_NewLabels-2.png)


# ICON: Learning Regular Maps through Inverse Consistency

[<img src="https://github.com/uncbiag/ICON/actions/workflows/test-action.yml/badge.svg">](https://github.com/uncbiag/ICON/actions)[<img src="https://img.shields.io/pypi/v/icon_registration.svg?color=blue">](https://pypi.org/project/icon-registration)


This is the official repository for  

**ICON: Learning Regular Maps through Inverse Consistency.**   
Hastings Greer, Roland Kwitt, Francois-Xavier Vialard, Marc Niethammer.  
_ICCV 2021_ https://arxiv.org/abs/2105.04459

## Cite this work
```
@InProceedings{Greer_2021_ICCV,
    author    = {Greer, Hastings and Kwitt, Roland and Vialard, Francois-Xavier and Niethammer, Marc},
    title     = {ICON: Learning Regular Maps Through Inverse Consistency},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3396-3405}
}
```

## Video Presentation

[<img src="https://img.youtube.com/vi/7kZsJ3zWDCA/maxresdefault.jpg" width="50%">](https://youtu.be/7kZsJ3zWDCA)


## Running our code

We are available on PyPI!
```bash
pip install icon-registration
```

To run our pretrained model in the cloud on 4 sample image pairs from OAI knees (as above), visit [our google colab notebook](https://colab.research.google.com/drive/1Pd3ua_NZTem3xtBvDxertzi7u3E233ZL?usp=sharing)

----------------

To train from scratch on the synthetic triangles and circles dataset:

```bash
git clone https://github.com/uncbiag/ICON
cd ICON

pip install -e .

python training_scripts/2d_triangles_example.py
```




