CoralNet-Toolbox

The CoralNet-Toolbox is an unofficial codebase that can be used to augment processes associated with those on CoralNet. It uses ✨Ultralytics🚀 as a base, which is an open-source library for computer vision and deep learning built in PyTorch. For more information on their AGPL-3.0 license, see here.

Quick Start

Running the following command will install the coralnet-toolbox, which you can then run from the command line:

# cmd

# Install
pip install "git+https://github.com/Jordan-Pierce/CoralNet-Toolbox.git"

# Run 
coralnet-toolbox

For further instructions, see How to Install; for information on how to use, check out the docs.

Tools

Enhance your CoralNet experience with these tools:

🔍 API: Get predictions from any CoralNet source model
📥 Download: Retrieve source data from CoralNet
📤 Upload: Add images and annotations to CoralNet
✏️ Annotate: Create patches manually or from annotations
👁️ Visualize: See points/patches on images
🧩 Patches: Extract from annotated images
📍 Points: Sample using various methods (Uniform, Random, Stratified
🧠 Classification: Build local patch-based classifiers
🔮 Inference: Use trained models for predictions

Watch the Video

How to Install

Anaconda

It's recommended to use Anaconda to create an environment for the toolbox:

# cmd

# Create and activate an environment
conda create --name coralnet-toolbox python=3.8 -y
conda activate coralnet-toolbox

CUDA

Once this has finished, if you have CUDA, you should install the versions of cuda-nvcc and cudatoolkit that you need, and then install the corresponding versions torch and torchvision:

# cmd

# Example for CUDA 11.8
conda install cuda-nvcc -c nvidia/label/cuda-11.8.0 -y
conda install cudatoolkit=11.8 -c nvidia/label/cuda-11.8.0 -y

# Example for torch 2.0.0 and torchvision 0.15.1 w/ CUDA 11.8
pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 --extra-index-url https://download.pytorch.org/whl/cu118

If CUDA is installed on your computer, and torch was built with it properly, you should see a rabbit icon in the toolbox instead of a turtle.

See here for more details on PyTorch versions.

Install

Finally, install and run the toolbox:

# cmd

# Install
pip install "git+https://github.com/Jordan-Pierce/CoralNet-Toolbox.git"

# Run
coralnet-toolbox

GitHub Repository

If you prefer to clone the repository and run the toolbox from the source code, you can do so with the following after creating an Anaconda environment:

# cmd

# Clone and enter the repository
git clone https://github.com/Jordan-Pierce/CoralNet-Toolbox.git
cd CoralNet-Toolbox

# Install the latest
pip install -e .

# Run
coralnet-toolbox

What Happened to the Old Repository?

The previous repository can be found in the Toolshed folder. The instructions for installing and running the toolshed are the same as above; after creating an Anaconda environment, you can install the toolshed with the following link:

# cmd

# Change directories
cd CoralNet-Toolbox/toolshed

# Install the latest
pip install -e .

# Run
python main.py

Alternatively, you can work with the functions within a script:

# python

import argparse
from coralnet_toolshed.Download import download

# Create an empty parser
args = argparse.Namespace()

# Add an argument
args.username = "username"
args.password = "password"
args.source_id = 3420

# Run the function
download(args)

And also command line:

python coralnet_toolshed/Download.py --username username --password password --source_id 3420

About CoralNet

Coral reefs are vital ecosystems that support a wide range of marine life and provide numerous benefits to humans. However, they are under threat due to climate change, pollution, overfishing, and other factors. CoralNet is a platform designed to aid researchers and scientists in studying these important ecosystems and their inhabitants.

CoralNet allows users to upload photos of coral reefs and annotate them with detailed information about the coral species and other features present in the images. The platform also provides tools for analyzing the annotated images, and create patch-based image classifiers.

The CoralNet Toolbox is an unofficial tool developed to augment processes associated with analyses that use CoralNet and Coral Point Count (CPCe).

Conclusion

In summary, this repository provides a range of tools that can assist with interacting with CoralNet and performing various tasks related to analyzing annotated images. These tools can be useful for researchers and scientists working with coral reefs, as well as for students and hobbyists interested in learning more about these important ecosystems.

Citation

If used in project or publication, please attribute your use of this repository with the following:

@misc{CoralNet-Toolbox,
  author = {Pierce, Jordan and Edwards, Clint and Vieham, Shay and Rojano, Sarah and Cook, Sophie and Costa, Bryan and Sweeney, Edward and Battista, Tim},
  title = {CoralNet-Toolbox},
  year = {2023},
  howpublished = {\url{https://github.com/Jordan-Pierce/CoralNet-Toolbox}},
  note = {GitHub repository}
}

References

The following papers inspired this repository:

Pierce, J., Butler, M. J., Rzhanov, Y., Lowell, K., &amp; Dijkstra, J. A. (2021).
Classifying 3-D models of coral reefs using structure-from-motion and multi-view semantic segmentation.
Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.706674

Pierce, J. P., Rzhanov, Y., Lowell, K., &amp; Dijkstra, J. A. (2020).
Reducing annotation times: Semantic Segmentation of coral reef survey images.
Global Oceans 2020: Singapore – U.S. Gulf Coast. https://doi.org/10.1109/ieeeconf38699.2020.9389163

Beijbom, O., Edmunds, P. J., Roelfsema, C., Smith, J., Kline, D. I., Neal, B. P., Dunlap, M. J., Moriarty, V., Fan, T.-Y., Tan, C.-J., Chan, S., Treibitz, T., Gamst, A., Mitchell, B. G., &amp; Kriegman, D. (2015).
Towards automated annotation of benthic survey images: Variability of human experts and operational modes of automation.
PLOS ONE, 10(7). https://doi.org/10.1371/journal.pone.0130312

Disclaimer

This repository is a scientific product and is not official communication of the National Oceanic and Atmospheric Administration, or the United States Department of Commerce. All NOAA GitHub project code is provided on an 'as is' basis and the user assumes responsibility for its use. Any claims against the Department of Commerce or Department of Commerce bureaus stemming from the use of this GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.

License

Software code created by U.S. Government employees is not subject to copyright in the United States (17 U.S.C. §105). The United States/Department of Commerce reserve all rights to seek and obtain copyright protection in countries other than the United States for Software authored in its entirety by the Department of Commerce. To this end, the Department of Commerce hereby grants to Recipient a royalty-free, nonexclusive license to use, copy, and create derivative works of the Software outside of the United States.