This repository contains code and configuration for processing and analysing images of plankton samples. It's experimental, serving as much as a proposed template for new projects than as a project in itself.
It's a companion project to an R-shiny based image annotation app that is not yet released, written by researchers and data scientists at the UK Centre for Ecology and Hydrology in the early stages of a collaboration that was placed on hold.
Use anaconda or miniconda to create a python environment using the included environment.yml
conda env create -f environment.ymlPlease note that this is specifically pinned to python 3.9 due to dependency versions; we make experimental use of the CEFAS plankton model available through SciVision, which in turn uses an older version of pytorch that isn't packaged above python 3.9.
.env contains environment variable names for S3 connection details for the JASMIN object store. Fill these in with your own credentials. If you're not sure what the ENDPOINT should be, please reach out to one of the project contributors listed below.
Get started by cloning this repository and running
pip install -e .
python -m pytest or py.test
scripts/intake_metadata.py is a proof of concept that creates a configuration file for an intake catalogue - a utility to make reading analytical datasets into analysis workflows more reproducible and less effortful.
Experiment testing workflows by using this plankton model from SciVision to extract features from images for use in similarity search, clustering, etc.
The notebooks/ directory contains Markdown (.md) representations of the notebooks.
To create Jupyter notebooks (.ipynb), run the following command with the conda environment activated:
jupytext --sync notebooks/*If you modify the contents of a notebook, run the command after closing the notebook to re-sync the .ipynb and .md representations before committing.
For more information see the Jupytext docs.
Jo Walsh Alba Gomez Segura Ezra Kitson
Please see CONTRIBUTING.md