oplab-pipeline 1.1.0

Toolchain for AUV dive processing, camera calibration and image correction

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# oplab_pipeline

oplab_pipeline is a python toolchain to process AUV dives from raw data into navigation and imaging products. The software is capable of:

• Process navigation: fuses AUV or ROV sensor data using state of the art filters and geolocalises recorded imagery.

• Camera and laser calibration: performs automatic calibration pattern detection to calibrate monocular or stereo cameras. Also calibrates laser sheets with respect to the cameras.

• Image correction: performs pixel-wise image corrections to enhance colour and contrast in underwater images.

Please review the latest changes in the [CHANGELOG.md](CHANGELOG.md).

## Installation

For __production__, to install this package run: `bash pip install -U git+https://github.com/ocean-perception/oplab_pipeline.git `

This will make the commands auv_nav, auv_cal and correct_images available in the terminal. For more details refer to the documentation.

For __development__, clone the repository, navigate to the oplab-pipeline folder and run `bash pip install -U --user -e . `

## Documentation The documentation is hosted in [read the docs](https://oplab-pipeline.readthedocs.io).

## Citation If you use this software, please cite the following article:

> Yamada, T, Prügel‐Bennett, A, Thornton, B. Learning features from georeferenced seafloor imagery with location guided autoencoders. J Field Robotics. 2020; 1– 16. https://doi.org/10.1002/rob.21961

## License Copyright (c) 2020-2022, University of Southampton. All rights reserved. This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].

See LICENSE.md file in the project root for full license information.

## Contributing Please document the code using [Numpy Docstrings](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html). If you are using VSCode, there is a useful extension that helps named [Python Docstring Generator](https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring). Once installed, make sure you select Numpy documentation in the settings.

Run pre-commit install to install [pre-commit](https://pre-commit.com/) into your git hooks. pre-commit will now run on every commit. If you don’t have pre-commit installed, run pip install pre-commit.

[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png [cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY–NC–SA%204.0-lightgrey.svg