The Koster Seafloor Observatory is an open-source, citizen science and machine learning approach to analyse subsea movies.
The system processes underwater footage and its associatead metadata into biologically-meaningful information. The format of the underwater media is standardised (typically .mp4 or .jpg) and the associated metadata should be captured in three csv files (“movies”, “sites” and “species”) following the Darwin Core standards (DwC).
This repository contains scripts and resources to:
- move and process underwater footage and its associated data (e.g. location, date, sampling device).
- make this data available for citizen science to help you with annotating the data.
- train and evaluate machine learning models. (customise Yolov5 models using Ultralytics.)
The system is built around a series of easy-to-use Jupyter notebook tutorials. Each tutorial allows users to perform a specific task of the system (e.g. upload footage to the citizen science platform or analyse the classified data). The notebooks rely on the koster utility functions.
Users can run these tutorials via Google Colab (by clicking on the Colab links in the table below), locally or on SNIC.
| Name | Description | Try it! |
|---|---|---|
| 1. Check footage and metadata | Check format and contents of footage and sites, media and species csv files |   |
| 2. Upload new media to the system* | Upload new underwater media to the cloud/server and update the csv files | |
| 3. Upload clips to Zooniverse | Prepare original footage and upload short clips to Zooniverse | |
| 4. Upload frames to Zooniverse | Extract frames of interest from original footage and upload them to Zooniverse | |
| 5. Train ML models | Prepare the training and test data, set model parameters and train models | |
| 6. Evaluate ML models | Use ecologically-relevant metrics to test the models | |
| 7. Publish ML models | Publish the model to a public repository | |
| 8. Analyse Zooniverse classifications | Pull up-to-date classifications from Zooniverse and report summary stats/graphs | |
| 9. Download and format Zooniverse classifications | Pull up-to-date classifications from Zooniverse and format them for further analysis | Coming soon |
| 10. Run ML models on footage | Automatically classify new footage | Coming soon |
* Project-specific tutorial
If you want to fully use our system (Binder has computing limitations), you will need to download this repository on your local computer or server. (Or use SNIC, see instructions below)
Clone this repository using
git clone --recurse-submodules https://github.com/ocean-data-factory-sweden/kso.gitDepending on which system you are using (Windows/Linux/MacOS), you might need to install some extra tools. If this is the case, you will get a message of what you need to install in the next steps. For example, on a windows system it will request you to install the Microsoft Build Tools C++ with a version higher than 14.0. You can install it from https://visualstudio.microsoft.com/visual-cpp-build-tools/. In the install menu, you only need to select the "Windows SDK".
- Open the Anaconda Prompt
- Navigate to the folder where you have cloned the repository or unzipped the manually downloaded repository. Then go into the kso folder. (
cd kso) - Create an Anaconda environment with python 3.8:
conda create -n <name env> python=3.8
- Enter the environment:
conda activate <name env>
- Install numpy to prevent an error that will otherwise occur in the next step.
pip install numpy==1.22
- Install all the requirements. If you do not have a GPU, run the following:
pip install -r yolov5_tracker/requirements.txt -r yolov5_tracker/yolov5/requirements.txt -r kso_utils/requirements.txt
Have a GPU? Find out which pytorch installation you need here (https://pytorch.org/), depending on your device and CUDA version. Add the recommended command to the gpu_requirements_user.txt file in the same way as the current example. Then run:
pip install -r yolov5_tracker/requirements.txt -r yolov5_tracker/yolov5/requirements.txt -r kso_utils/requirements.txt -r gpu_requirements_user.txt
If you are using another virtual environment package, install the same requirements inside your fresh environment (Python 3.8).
After installing all the requirements, run the following command in your environment:
ipython kernel install --user --name=<name env>
Now open the Jupyter notebook and select/change kernel to run the notebooks from your environment.
Before using Option 2, users should have login credentials and have setup the Chalmers VPN on their local computers
Information for Windows users: Click here Information for MAC users: Click here
To use the Jupyter Notebooks within the Alvis HPC cluster, please visit Alvis Portal and login using your SNIC credentials.
Once you have been authorized, click on "Interactive Apps" and then "Jupyter". This open the server creation options.
Creating a Jupyter session requires a custom environment file, which is available on our shared drive /mimer/NOBACKUP/groups/snic2022-22-1210/jupter_envs. Please copy this file (jupyter-kso.sh) to your Home Directory in order to use the custom environment we have created.
Here you can keep the settings as default, apart from the "Number of hours" which you can set to the desired limit. Then choose kso-jupyter.sh from the Runtime dropdown options.
This will directly queue a server session using the correct container image, first showing a blue window and then you should see a green window when the session has been successfully started and the button "Connect to Jupyter" appears on the screen. Click this to launch into the Jupyter notebook environment.
Important note: The remaining time for the server is shown in green window as well. If you have finished using the notebook server before the alloted time runs out, please select "Delete" so that the resources can be released for use by others within the project.
If you will work in a new project you will need to:
- Create initial information for the database: Input the information about the underwater footage files, sites and species of interest. You can use a template of the csv files and move the directory to the "db_starter" folder.
- Link your footage to the database: You will need files of underwater footage to run this system. You can download some samples and move them to
db_starter. You can also store your own files and specify their directory in the tutorials.
If you would like to expand and improve the KSO capabilities, please follow the instructions above to set the project up on your own local computer.
When you start adding changes, please create your own branch on top of the current 'dev' branch. Before submitting a Merge Request, please:
- Run Black on the code you have edited
black filename - Clean up your commit history on your branch, so that every commit represents a logical change. (so squash and edit commits so that it is understandable for others)
- For the commit messages, we ask that you please follow the conventional commits guidelines to facilitate code sharing. Also, please give a description of the logic behind the commit in the body of the message.
- Rebase on top of dev. (never merge, only use rebase)
- Submit a Pull Request and link at least 2 reviewers
If you use this code or its models in your research, please cite:
Anton V, Germishuys J, Bergström P, Lindegarth M, Obst M (2021) An open-source, citizen science and machine learning approach to analyse subsea movies. Biodiversity Data Journal 9: e60548. https://doi.org/10.3897/BDJ.9.e60548
You can find out more about the project at https://www.zooniverse.org/projects/victorav/the-koster-seafloor-observatory.
We are always excited to collaborate and help other marine scientists. Please feel free to contact us (matthias.obst(at)marine.gu.se) with your questions.
If you experience issues uploading movies to Zooniverse, it might be related to the libmagic package. In Windows, the following commands seem to work:
pip install python-libmagic
pip install python-magic-bin