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Image annotation site and API to enable the distributed annotation of museum images.
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README.md

Coyote

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An open source image annotation app enabling the distributed annotation and comprehensive representation of images. Long descriptions can range from one sentence to several paragraphs. The software was developed by the Museum of Contemporary Art Chicago to support a distributed workflow for describing their images and publishing those descriptions to the web.

Example

An image like this would traditionally be described by alt text like A red, white, and blue fabric canopy presses against walls of room; portable fans blow air into the room through a doorway.:

MCAChicago sample image T.Y.F.F.S.H

Kris Martin, Belgian, b. 1972. T.Y.F.F.S.H., 2011. Collection: Museum of Contemporary Art. Photo: Nathan Keay, © MCA Chicago.

Coyote aims to provide more comprehensive representations:

This is an installation that viewers are invited to walk inside of. From this viewpoint you are looking through a doorway at a slight distance, as if standing inside of a large cave and looking out of its narrow entrance at the world outside. The walls of this cave are alternating stripes of red, white, and blue material that seems to be made of some kind of thin fabric. These colored stripes spiral around toward the entrance, as if being sucked out of the opening. The inside of the cave is more shadowed and the area outside is brightly lit. Gradually you notice that there are in fact two openings lined up in front of each other, straight ahead of you: the first one is a tall rectangle—the red, white and blue fabric is wrapped through the edges of a standard doorway; beyond that it continues to spiral around toward another circular opening. The center of this circle is much brighter, as if one had finally escape from the cave. At the center of that circular opening you see two large white fans facing your direction, blowing air into the cave-like opening. Beyond the fans you see a brown, square form, which is the bottom of a huge wicker basket. This basket, lying on its side, helps to reveal the truth about what you are seeing: You are standing inside of a huge hot air balloon, which is lying on its side. Blown by the fans, the fabric billows out to press out against the existing w alls of a large room, the malleable shape of the balloon conforming to the rectangular surfaces of an existing building–the gallery that contains it.

More information about image description projects at the MCA and elsewhere is available at coyote.pics, along with contact information for the project team. You can also view or hear image descriptions on the MCA website.

Table of contents

Developer Setup

Set up the Docker environment

The app runs in a self-contained Docker environment, which you can use for development. For more details see local development with Docker Compose.

Using Docker ensures the environment you develop in very closely matches the environment to which we deploy! Please use it for local development - it is set up to be as painless as possible.

Follow these steps:

git clone https://github.com/coyote-team/coyote.git
cd coyote
docker-compose build             # first, build images and containers
docker-compose run web bin/setup # set up the environment for development
docker-compose up web            # start the web app in development
docker-compose up test           # run the test suite

That's it! It's really that simple. You should be able to visit http://localhost:3000/ to see the app, and the tests should run (and pass). The seed script builds a simple user, so you can login as admin@example.com.

Once the app is running you can interact with it using commands like this:

docker ps                                    # list running containers
docker-compose exec web pumactl restart      # restart Puma
docker-compose exec web bin/rails console    # access Rails console
docker-compose exec web bin/rails db:migrate # update the Postgres server managed by Docker

Error starting userland proxy: listen tcp 0.0.0.0:5432: bind: address already in use

If you encounter an error starting Docker processes like the above, you may have your own version of Postgres running in the background. You'll need to stop it in an environment-appropriate way, e.g. sudo service postgresql stop on Linux or brew services stop postgresql on Mac OS.

Configuring secure credentials

The app uses Rails secure credentials with sane development-time defaults. This allows us to ship Dockerized versions of the app with zero configuration, for both development and production.

If you are deploying this application yourself, you will want to use your own production credentials. You can find an example configuration in config/credentials.yml.example. To deploy your own version of Coyote, you will want to:

  1. Copy the example credentials file (e.g. pbcopy < config/credentials.yml.example)
  2. Remove the existing production credentials: rm config/credentials/production*
  3. Generate your own production credentials: rails credentials:edit --environment production
  4. Paste in the example credentials you copied in step 1, and overwrite their values to more useful stuff

Testing Mailers in Development Mode

The secure credentials work with mailcatcher out of the box:

gem install mailcatcher     # runs independently of the app, so this gem is not part of our Gemfile
mailcatcher -f              # optionally run as `mailcatcher -f` to run in the foreground
open http://127.0.0.1:1080/ # mail delivery console

Re-seeding the Database

You can regenerate development environment data by running bundle exec rake dev_only:reseed.

Asynchronous Workers

Coyote uses Cloudtasker, which has an identical API to Sidekiq, but processes jobs by pushing them up to Google Tasks, which in turn call back down to the web server. This prevents the request/response cycle from blocking, and allows us to use the flexible Cloud Run environment to expand and contract with worker demand the same way it does with traffic.

docker-compose will ensure the Cloudtasker worker is running, but you can also run it manually:

bundle exec cloudtasker

The biggest difference between this and Sidekiq is that you will notice Cloudtasker tasks running in your web server logs, and not the terminal where you ran Cloudtasker. And that's about it.

Development Workflow

Topic Branches & Review Apps

We use Heroku Pipelines and a modified "git flow" workflow for our development process. It's very important you follow the process outlined below!

  1. Create a topic branch off of master related to your work, typically something like

    git checkout master
    git checkout -b {name}/{issue-numer}-{description} # (e.g.) `flip/208-fix-dev-workflow
  2. Write your code and then run tests, either locally (rspec) or via CI (simply push your topic branch to Github: git push origin flip/208-fix-dev-workflow)

  3. When CI has run on Github, create a pull request ("PR") and request a review from one or more team members who aren't you

  4. Heroku will deploy a review app for every pull request you create. The review apps automatically generate seed data to use with testing. Reviewers can use them to test new functionality with one of the seed user accounts.

Deploying to Staging

Deploying to staging happens automatically whenever the master branch is modified (in this, master is similar to develop in git flow). Staging is available at staging.coyote.pics.

IMPORTANT NOTE: when you deploy to staging, the production database is cloned and then migrations are run on staging. This means every commit to master will overwrite the staging database.

Deploying to Production

Deploying to production is as simple as merging master into production. Doing this will automatically deploy your code and run migrations TO THE PRODUCTION INSTANCE, so be 100% sure you know what you're doing and test a lot on staging first.

Documentation

YARD documentation is hosted at coyote-team.github.io.

Fresh documentation can be generated locally by running bundle exec rake coyote:docs OUT=docs/apipie. The OUT parameter is for apipie.

Coyote design refinements are documented in design_refinements.pdf.

API

Coyote's API is based on the JSON API standard. Coyote-specific JSON API documentation is generated by apipie and can be viewed here.

Data Model

Coyote is a multi-tenant application, and user records have the ability to belong to one or many tenants (which are called "organizations"). To get a handle on how it all hooks together, checkout the DATA_MODEL.md document included in this repository.

Links

More info regarding accessibility:

Contributors

License

MPLv2

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