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Backend of the EcoTaxa application

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# EcoTaxa, back end

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EcoTaxa is a web application destined to process the large number of images generated by such quantitative imaging instruments. It leverages deep learning and an efficient user interface to allow taxonomists to classify thousands of images per day. In addition, it can store a large quantity of metadata together with the images, with very few constraints on its content. These metadata can be used by the operators to sort through the images and by the machine learning backend to suggest identifications. Finally, EcoTaxa can export the metadata and the identifications together, in a versatile text table or following the DarwinCore Archive standard, for further data exploitation.

## Howto

### Run EcoTaxa

This is the backend part of the application, which is coupled with a [front-end](https://github.com/ecotaxa/ecotaxa_front).

In `docker` you can find build scripts.

The simplest way to set up a running instance is to follow the [all_in_one instructions](https://github.com/ecotaxa/ecotaxa_front/tree/master/docker/all_in_one). This will give you a solution but probably not scale to be a production environment.

### Help develop EcoTaxa

In this directory:

- `py` is for Python back-end
- `QA` contains all tests & measurements on the code (all functional tests are in `QA/py/tests`).

The UI itself is a server-side Flask app, in the `../ecotaxa` folder.

The image vault is shared between this back-end and the front-end.

The back-end reads the `config.cfg` file from the front end and derives from there.

To launch the docker DB server:

-    `cd docker`
-    `docker-compose up`

More detailed instructions are in the [documentation](https://github.com/ecotaxa/ecotaxa_back/tree/master/documentation)

## Metadata

### Citation

If you use EcoTaxa in your work, please cite it as

> Marc Picheral, Sébastien Colin, and Jean-Olivier Irisson (2017) *EcoTaxa, a tool for the taxonomic classification of images*. http://ecotaxa.obs-vlfr.fr

### License

This code is released under the [GPLv3](license.txt).

### Contributors

Specifications and supervision

- March Picheral, [Laboratoire d'Océanographie de Villefranche (LOV)][lov], CNRS [0000-0001-8172-5473](https://orcid.org/0000-0001-8172-5473)
- Jean-Olivier Irisson, [Laboratoire d'Océanographie de Villefranche (LOV)][lov], Sorbonne Université [0000-0003-4920-3880](https://orcid.org/0000-0003-4920-3880)

Current developers

- Béatrice Caraveo, [Laboratoire d'Océanographie de Villefranche (LOV)][lov], Sorbonne Université
- Laurent Salinas, DevInitIf (previously  [Laboratoire d'Océanographie de Villefranche (LOV)][lov], Sorbonne Université)
- Julie Coustenoble, [Laboratoire d'Océanographie de Villefranche (LOV)][lov], Sorbonne Université

Past contributors

- Sebastien Colin, Station Biologique de Roscoff, Sorbonne Université
- Laurent Navarro, AltiDev
- Laurent Reese, Laboratoire d'Océanographie de Villefranche (LOV), Sorbonne Université

[lov]: https://edmo.seadatanet.org/report/490

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