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SixK / coco-annotator-ng

JsBroks COCO Annotator modernized
MIT License
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COCO Annotator next generation

This version of COCO Annotator is a strait port from JsBroks COCO Annotator official version to vue3.2+

Before going further, if you already use JsBroks COCO Annotator and want to switch to this version, you will have to change user password encryption methode in mongo database (Werkzeug 3 break change).
For this, you will have to install an old and compatible Werkzeug python library and use the change_password_hash_type.py python script:

pip install werkzeug==2.0.3
pip install pymongo
python change_password_hash_type.py

By default change_password_hash_type.py only change password hash type for the admin user.
Edit this file and run it again to migrate others users accounts.
You will have to know all users password to migrate all passwords.
You can then use any recent werkzeug version.

Features

Now we can talk about what this version will provide:

what features you will loose or bugs are introduced:

Build docker images

There is actually no pre-built docker images.
You will have to build docker images by yourself.
Note that docker images are actually using around 15Gb disk space. Make sure to have at least 30Gb disk space to build all images.

Building docker images:

First build base images: 

    bash ./build_base_image.sh

Then build compnents images depending of your needs.

Production images with no IA support:

docker compose -f ./docker-compose.build.yml build
docker compose -f ./docker-compose.yml up

Dev images with IA support:

docker compose -f ./docker-compose.dev.yml build
docker compose -f ./docker-compose.dev.yml up

Production images with IA support:

docker compose -f /docker-compose.gpu.yml build
docker compose -f /docker-compose.gpu.yml up

Features • Wiki • Getting Started • Issues • License

COCO Annotator is a web-based image annotation tool designed for versatility and efficiently label images to create training data for image localization and object detection. It provides many distinct features including the ability to label an image segment (or part of a segment), track object instances, labeling objects with disconnected visible parts, efficiently storing and export annotations in the well-known COCO format. The annotation process is delivered through an intuitive and customizable interface and provides many tools for creating accurate datasets.


Join our growing discord community of ML practitioner


Image annotations using COCO Annotator

Checkout the video for a basic guide on installing and using COCO Annotator.


Note: This video is from v0.1.0 and many new features have been added.


If you enjoy my work please consider supporting me


Features

Several annotation tools are currently available, with most applications as a desktop installation. Once installed, users can manually define regions in an image and creating a textual description. Generally, objects can be marked by a bounding box, either directly, through a masking tool, or by marking points to define the containing area. COCO Annotator allows users to annotate images using free-form curves or polygons and provides many additional features were other annotations tool fall short.

For examples and more information check out the wiki.

Vue3 porting attempt

At this state, source code tested only using docker-compose.dev.yml. Lot of eslint errors appears, but application is functionnal

Using SAM (Segment Anything) - Deprecated to SAM-HQ

This coco annotator version is a vue3 port from original jsbrok coco-annotator based on vue2. This version still has some bugs mainly introduced by vue3 new behaviour or vue3 conversion, so use it at your own risks. Most of libraries has been updated to more recent versions.

To use SAM you will need a Cuda capable graphic card (or modify sources to use CPU. Untested). First rebuild a new base image using the following command from build_base_image.sh:

docker build -f ./backend/Dockerfile . -t jsbroks/coco-annotator:python-env

Download SAM model :

cd models;bash sam_model.sh

Then rebuild/build coco-annotator images using docker-compose.dev.yml or docker-compose.gpu.yml files :

docker compose -f ./docker-compose.dev.yml build

You can then run coco-annotator:

docker compose -f ./docker-compose.dev.yml up

Now select or create a new annotation.
Select the new SAM button in the left pannel (under DEXTR button).
Click on the object you want to create mask.
A new mask should be created.

Using SAM-HQ (Segment Anything in High Quality)

SAM-HQ is a drop in place of SAM with more precision: https://github.com/SysCV/sam-hq

Simply download SAM-HQ model:

cd models;bash sam_hq_model.sh

Rebuild docker images :

docker compose -f ./docker-compose.dev.yml build

You can then run coco-annotator:

docker compose -f ./docker-compose.dev.yml up

SAM-HQ is now default, but you still can use original SAM modifying backend/webserver/Dockerfile
to comment line with sam-hq and uncomment line before installing segment-anything. `

 RUN pip install segment-anything

RUN git clone https://github.com/SysCV/sam-hq.git && cd sam-hq && pip install -e . `

And modify docker-compose.dev.yml:

`

Download SAM model (if not already done) rebuild docker images and restart coco-annotator using docker-compose.

Using SAM2 (Segment Anything 2)

Sam2 is the latest Segmentation tool from Meta: https://github.com/facebookresearch/sam2/

Download Sam2 model:

cd models;bash sam2_model.sh

Rebuild docker images :

docker compose -f ./docker-compose.dev.yml build

You can then run coco-annotator:

docker compose -f ./docker-compose.dev.yml up

In coco-annotator select a class, click on Sam2 button then click an object.
Object should be segmented.

Default model is sam2.1_hiera_base_plus. It seem's to be a good compromise between speed and efficacity.
You can use any other Sam2 model if needed.
Simply download the model and adapt docker-compose lines.

Using ZIM (ZIM Anything) (Zero-Shot Image Matting for Anything)

Zim is the latest Segmentation tool from NAVER Cloud Corp: https://github.com/naver-ai/ZIM/

Download Zim model:

cd models;bash zim_model.sh

Rebuild docker images :

docker compose -f ./docker-compose.dev.yml build

You can then run coco-annotator:

docker compose -f ./docker-compose.dev.yml up

In coco-annotator select a class, click on Zim button then click an object.
Object should be segmented.

Default model is Vit_b. It seem's to be a good compromise between speed, memory and efficacity.
You can use any other Zim model if needed.
Simply download the model and adapt docker-compose lines.

Demo

Login Information
Username: admin
Password: password

https://annotator.justinbrooks.ca/

Backers

If you enjoy the development of coco-annotator or are looking for an enterprise annotation tool, consider checking out DataTorch.

https://datatorch.io · support@datatorch.io · Next generation of coco-annotator

Built With

Thanks to all these wonderful libaries/frameworks:

Backend

Frontend

License

MIT

Citation

  @MISC{cocoannotator,
    author = {Justin Brooks},
    title = {{COCO Annotator}},
    howpublished = "\url{https://github.com/jsbroks/coco-annotator/}",
    year = {2019},
  }