This web app allows you to label images, draw bounding boxes, shapes, collect information in forms with dropdowns, checkboxes and inputs.

The labeling UI provides a lot of features for drawing polygon shapes, editing them with assisted tracing with auto-tracing based on edges or an external ML model.

Use it when you need to segment and label multiple images, either yourself or by a group. This tool makes it easy to gather and later export the data in a format compatible with LabelMe. You can use this tool as an alternative to self-hosted tools like LabelMe, js-segment-annotator, etc or hosted services like LabelBox.

Demo of the labeling interface with all data served statically (no persistence, reverts on refresh).

Bounding box labeling:

Segmentation with polygons:

Automatic tracing:

Assisted segmentation with Tensor Flow Serving:

Project configuration and custom labeling UI:

Install npm packages for client, server and the top-level folder:

The server will run migrations on the first run if the database file doesn't exist already.

Run in the development mode:

Build the client app:

Now you can run the server app in prod mode serving the client build:

The following environment variables can be tweaked:

• PORT - the part the app is served on (dev, prod)
• API_PORT - to differentiate the port for the API to run on (should be only used in dev)
• UPLOADS_PATH - absolute path where the app stores uploaded images, defaults to server's folder 'uploads'
• DATABASE_FILE_PATH - absolute path of the file where the app stores the SQLite data. Defaults to database.sqlite in the server folder
• ADMIN_PASSWORD - sets a simple password on all non-labeler actions (stored in a hased form).

The default Dockerfile points to /uploads and /db/db.sqlite for persisted data, make sure to prepare those in advance to be mounted over. Here is an example mounting a local host directory:

Now build the container:

Run attaching the mounts:

Access the site at localhost:5000.

• Checkout the docker-compose.yml for detailed configuration.
• Need to set & export environment variable CURRENT_UID before running.

This project has been developed as part of my internship at the NCSOFT Vision AI Lab in the beginning of 2019.