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141 lines
5.3 KiB
Markdown
141 lines
5.3 KiB
Markdown
# Computer Vision Annotation Tool (CVAT)
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[](https://gitter.im/opencv-cvat)
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CVAT is completely re-designed and re-implemented version of [Video Annotation Tool from Irvine, California](http://carlvondrick.com/vatic/) tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team.
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## Documentation
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- [User's guide](cvat/apps/documentation/user_guide.md)
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- [XML annotation format](cvat/apps/documentation/xml_format.md)
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- [AWS Deployment Guide](cvat/apps/documentation/AWS-Deployment-Guide.md)
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## Screencasts
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- [Annotation mode](https://youtu.be/6h7HxGL6Ct4)
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- [Interpolation mode](https://youtu.be/U3MYDhESHo4)
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- [Attribute mode](https://youtu.be/UPNfWl8Egd8)
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- [Segmentation mode](https://youtu.be/6IJ0QN7PBKo)
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## LICENSE
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Code released under the [MIT License](https://opensource.org/licenses/MIT).
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## INSTALLATION
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The instructions below should work for `Ubuntu 16.04`. It will probably work on other Operating Systems such as `Windows` and `macOS`, but may require minor modifications.
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### Install [Docker CE](https://www.docker.com/community-edition) or [Docker EE](https://www.docker.com/enterprise-edition) from official site
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Please read official manual [here](https://docs.docker.com/engine/installation/linux/docker-ce/ubuntu/).
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### Install the latest driver for your graphics card
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The step is necessary only to run tf_annotation app. If you don't have a Nvidia GPU you can skip the step.
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```bash
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sudo add-apt-repository ppa:graphics-drivers/ppa
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sudo apt-get update
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sudo apt-cache search nvidia-* # find latest nvidia driver
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sudo apt-get install nvidia-* # install the nvidia driver
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sudo apt-get install mesa-common-dev
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sudo apt-get install freeglut3-dev
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sudo apt-get install nvidia-modprobe
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```
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Reboot your PC and verify installation by `nvidia-smi` command.
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### Install [Nvidia-Docker](https://github.com/NVIDIA/nvidia-docker)
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The step is necessary only to run tf_annotation app. If you don't have a Nvidia GPU you can skip the step. See detailed installation instructions on repository page.
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### Install docker-compose (1.19.0 or newer)
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```bash
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sudo pip install docker-compose
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```
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### Build docker images
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To build all necessary docker images run `docker-compose build` command. By default, in production mode the tool uses PostgreSQL as database, Redis for caching.
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### Run containers without tf_annotation app
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To start all containers run `docker-compose up -d` command. Go to [localhost:8080](http://localhost:8080/). You should see a login page.
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### Run containers with tf_annotation app
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If you would like to enable tf_annotation app first of all be sure that nvidia-driver, nvidia-docker and docker-compose>=1.19.0 are installed properly (see instructions above) and `docker info | grep 'Runtimes'` output contains `nvidia`.
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Run following command:
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```bash
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docker-compose -f docker-compose.yml -f docker-compose.nvidia.yml up -d --build
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```
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### Create superuser account
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You can [register a user](http://localhost:8080/auth/register) but by default it will not have rights even to view list of tasks. Thus you should create a superuser. The superuser can use admin panel to assign correct groups to the user. Please use the command below:
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```bash
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docker exec -it cvat sh -c '/usr/bin/python3 ~/manage.py createsuperuser'
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```
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Type your login/password for the superuser [on the login page](http://localhost:8080/auth/login) and press **Login** button. Now you should be able to create a new annotation task. Please read documentation for more details.
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### Stop all containers
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The command below will stop and remove containers, networks, volumes, and images
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created by `up`.
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```bash
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docker-compose down
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```
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### Advanced settings
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If you want to access you instance of CVAT outside of your localhost you should specify [ALLOWED_HOSTS](https://docs.djangoproject.com/en/2.0/ref/settings/#allowed-hosts) environment variable. The best way to do that is to create [docker-compose.override.yml](https://docs.docker.com/compose/extends/) and put all your extra settings here.
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```yml
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version: "2.3"
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services:
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cvat:
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environment:
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ALLOWED_HOSTS: .example.com
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ports:
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- "80:8080"
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```
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### Annotation logs
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It is possible to proxy annotation logs from client to ELK. To do that run the following command below:
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```bash
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docker-compose -f docker-compose.yml -f analytics/docker-compose.yml up -d --build
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```
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### Share path
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You can use a share storage for data uploading during you are creating a task. To do that you can mount it to CVAT docker container. Example of docker-compose.override.yml for this purpose:
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```yml
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version: "2.3"
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services:
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cvat:
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environment:
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CVAT_SHARE_URL: "Mounted from /mnt/share host directory"
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volumes:
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cvat_share:/home/django/share:ro
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volumes:
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cvat_share:
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driver_opts:
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type: none
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device: /mnt/share
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o: bind
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```
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You can change the share device path to your actual share. For user convenience we have defined the enviroment variable $CVAT_SHARE_URL. This variable contains a text (url for example) which will be being shown in the client-share browser.
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