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Tests

SAMBA (Segment Anything Model Based App) is a trainable segmentation tool for materials science that uses deep learning for fast, high-quality labels and random forests for robust, generalizable segmentations. It is accessible in the browser (https://www.sambasegment.com), without the need to download any external dependencies. This repo is a local version of the website which contains the frontend for the website (React + TSX) and the backend (Python + Flask). The frontend handles labelling and the backend sends back SAM embeddings (if requested) and segmentations.

Check out the tutorial/user manual if help is needed!

Local Installation Instructions

These instructions are for installing and running the model locally. They assume a UNIX enviroment (mac or linux), but adapting for Windows is straightforward. Note you will need 2 terminals, one for the frontend local server and one for the backend local server.

Preliminaries

Download one of the ViT checkpoints for the SAM model - I chose the smallest, vit_b: ViT-B SAM model. Copy this into the backend/ directory.

Install & run the backend

  1. Setup a virtual environment in Python and activate it (not necessary but recommended)
  2. Install libraries from backend/requirements.txt:
pip install -r backend/requirements.txt
  1. With your virtual environment activated and inside the backend/ directory, run
python -m flask --app server run

The server is now setup and listening for requests from our frontend!

Install & run the frontend

  1. Install the JS libraries needed to build and run the frontend. Install Yarn (and npm first if needed)
npm install --g yarn
  1. Build and run:
yarn && yarn start
  1. Navigate to http://localhost:8081/ or http://localhost:8080/ depending on the port (it should do this automatically).

Testing Instructions

  1. Run (with your virtual enviroment activated!)
python backend/tests.py $FIJI_PATH

where $FIJI_PATH is the absolute path to your FIJI installation.

Citing

If you use SAMBA in one your works, please cite its JOSS publication via CITATION.cff or clicking the 'cite this repository' button at the top of the page:

Docherty et al., (2024). SAMBA: A Trainable Segmentation Web-App with Smart Labelling. Journal of Open Source Software, 9(98), 6159, https://doi.org/10.21105/joss.06159