oplab-pipeline 0.2.3a0

Toolchain for AUV dive processing, camera calibration and image correction

[](https://github.com/ocean-perception/oplab_pipeline/actions/workflows/oplab_pipeline.yml) [](https://codecov.io/gh/ocean-perception/oplab_pipeline) [](https://oplab-pipeline.readthedocs.io/en/latest/?badge=latest)

# 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 cd into the oplab-pipeline folder and run pip3 install ., resp. if you are using Anaconda run pip install . from the Anaconda Prompt (Anaconda3). This will make the commands auv_nav, auv_cal and correct_images available in the terminal. For more details refer to the documentation.

## 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, University of Southampton. All rights reserved.

Licensed under the BSD 3-Clause License. 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.