Clearboard receives a stream of images, detects a blackboard in it, extracts it and serves an enhanced view of what is written on the blackboard.

Make sure you have a recent version of Docker and Docker Compose installed on your laptop:

$ docker -v
  Docker version 20.10.2, build 2291f61

$ docker-compose -v
  docker-compose version 1.27.4, build 40524192

⚠️ You may need to run the following commands with sudo but this can be avoided by assigning your user to the docker group.

The easiest way to start working on the project is to use our Makefile :

$ make bootstrap

This command builds the app and lambda-enhance containers, installs dependencies and runs them. It's a good idea to use this command each time you are pulling code from the project repository to avoid dependency-releated issues.

The app container is the FastAPI web server that serves the API to:

• serve an upload policy to the frontend
• serve a CloudFront signed url to retrieve the processed image from the destination bucket

You should be able to access the API overview interface at (http://localhost:8070).

You need to create a .env file in the backend repository to specify parameters used in clearboard/config.py. At the moment there are 2 parameters:

• MEDIA_ROOT the root folder for saving pictures
• ORIGINS used when adding a middle ware that whitelist origins that can contact the api, if you need to whitelist several addresses, use , to separate them.

Example : MEDIA_ROOT="/data/media" ORIGINS="http://localhost:3000,https://www.example.com"

All python scripts for the FastAPI server are in the clearboard folder:

• main.py is the main script that is running on the docker, it managed all the api routes
• config.py load the env vars
• models.py handle models used in main.py
• coord_loader.py script implementing functions to load and save coordinates in file
• black_n_white.py, color.py, contrast.py, parallax.py filters already implemented and available

The lambda-enhance container holds the Python script that processes images in an AWS lambda. It is not implemented yet.

The lambda is triggered by S3 each time an image is uploaded to it. It processes the image and deposits the result in the destination bucket.

The lambda container can run locally in docker compose for development purposes:

$ make lambda

You can then test it with a trigger request including a payload mimicking an AWS S3 trigger. For example, if your source bucket contains a file with key "image.png", the following curl mimicks the trigger sent by S3 when the file was uploaded:

curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" \
    -d '{"Records":[{"s3":{"bucket":{"name": "production-clearboard-source-fun"}, "object":{"key": "image.png"}}}]}'

Note that you can see all available commands in our Makefile with :

$ make help

This project is intended to be community-driven, so please, do not hesitate to get in touch if you have any question related to our implementation or design decisions.

We try to raise our code quality standards and expect contributors to follow the recommandations from our handbook.

This work is released under the MIT License (see LICENSE).