Skip to main content

Toolchain for AUV dive processing, camera calibration and image correction

Project description

[![oplab_pipeline](https://github.com/ocean-perception/oplab_pipeline/actions/workflows/oplab_pipeline.yml/badge.svg)](https://github.com/ocean-perception/oplab_pipeline/actions/workflows/oplab_pipeline.yml) [![Code Coverage](https://codecov.io/gh/ocean-perception/oplab_pipeline/branch/master/graph/badge.svg?token=PJBfl6qhp5)](https://codecov.io/gh/ocean-perception/oplab_pipeline) [![Documentation Status](https://readthedocs.org/projects/oplab-pipeline/badge/?version=latest)](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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

oplab_pipeline-0.2.3a0.tar.gz (212.8 kB view hashes)

Uploaded Source

Built Distribution

oplab_pipeline-0.2.3a0-py3-none-any.whl (281.6 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page