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https://github.com/Slava/label-tool
Web application for image labeling and segmentation
https://github.com/Slava/label-tool
boundingbox computer-vision computer-vision-tools data-labeling image-annotation image-label image-labeling image-labeling-tool labelme machine-learning segmentation sematic-segmentation training-data
Last synced: 7 days ago
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Web application for image labeling and segmentation
- Host: GitHub
- URL: https://github.com/Slava/label-tool
- Owner: Slava
- License: mit
- Created: 2019-03-18T04:33:20.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-09T16:19:39.000Z (almost 2 years ago)
- Last Synced: 2024-08-02T15:50:59.805Z (3 months ago)
- Topics: boundingbox, computer-vision, computer-vision-tools, data-labeling, image-annotation, image-label, image-labeling, image-labeling-tool, labelme, machine-learning, segmentation, sematic-segmentation, training-data
- Language: JavaScript
- Homepage: http://slv.io/label-tool/demo/
- Size: 10.4 MB
- Stars: 345
- Watchers: 8
- Forks: 74
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Image Labeling Tool
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](https://github.com/wkentaro/labelme). You can use this tool as an alternative to self-hosted tools like [LabelMe](https://github.com/wkentaro/labelme), [js-segment-annotator](https://github.com/kyamagu/js-segment-annotator), etc or hosted services like [LabelBox](https://www.labelbox.com/).
## [Labeling Demo](http://slv.io/label-tool/demo/)
Demo of the labeling interface with all data served statically (no persistence, reverts on refresh).
## Screenshots
Bounding box labeling:
Segmentation with polygons:
Automatic tracing:
Assisted segmentation with Tensor Flow Serving:
Project configuration and custom labeling UI:
## Development
Install npm packages for client, server and the top-level folder:
```bash
yarn install
cd server && yarn install && cd ..
cd client && yarn install && cd ..
```
The server will run migrations on the first run if the database file doesn't exist already.
Run in the development mode:
```bash
env PORT=3000 API_PORT=3001 yarn start
```
## Build For Production
Build the client app:
```bash
cd client && yarn run build && cd ..
```
Now you can run the server app in prod mode serving the client build:
```bash
env PORT=80 NODE_ENV=production node server/src/index.js
```
## Config
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).
## Run in Docker
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:
```bash
mkdir ~/containersmnt/
mkdir ~/containersmnt/db/
mkdir ~/containersmnt/uploads/
```
Now build the container:
```bash
docker build -t imslavko/image-labeling-tool .
```
Run attaching the mounts:
```bash
docker run -p 5000:3000 -u $(id -u):$(id -g) -v ~/containersmnt/uploads:/uploads -v ~/containersmnt/db:/db -d imslavko/image-labeling-tool
```
Access the site at `localhost:5000`.
### Run with docker-compose
- Checkout the `docker-compose.yml` for detailed configuration.
- Need to set & export environment variable CURRENT_UID before running.
```bash
# if it needs to build the docker image,
CURRENT_UID=$(id -u):$(id -g) docker-compose up -d --build
# if it only needs to run,
CURRENT_UID=$(id -u):$(id -g) docker-compose up -d
```
## Project Support and Development
This project has been developed as part of my internship at the [NCSOFT](http://global.ncsoft.com/global/) Vision AI Lab in the beginning of 2019.