In this post we’ll share how we used TensorFlow’s object detection API to build a custom image annotation service for eyeson. Below you can seen an example where Philipp is making the “thinking” 🤔 pose during a meeting which automatically triggers a GIF reaction. Background and Overview The idea for this project came up in one of our stand-up meetings. After we introduced GIF reactions to eyeson we thought about marrying that feature with some machine learning.
This tutorial provides a practical example of how to setup a video platform using the eyeson API service. The application uses the eyeson ruby gem, the web application framework Sinatra and hosting platform Heroku. By following the steps in the how-to you will have a website up and running, providing an entry point for a shared video meeting room. The participants can join the meeting without any registration or sign up, and will return to the application website after exiting the meeting.