Imagine, an AI video monitoring framework

Imagine is a crystal lang web platform for AI processing and monitoring video streams.

• takes UDP H.264 video streams as input (needs to be web compatible format)
• runs frames through TensorFlow Lite models
• outputs detection data to a websocket and optionally a Redis channel or webhook
  • detection coordinates only
  • optionally the PNG image (websocket and webhook)
  • optionally the PNG image with markup applied (websocket and webhook)
• grabs segments from the video stream and makes it available as MPEG-DASH for browser viewing
  • this will be pushed to a local volume for serving
  • and optionally can be pushed to a S3 for serving video at scale

This is intended to run independently of any applications that make use of the output. KISS

Documentation

Use the provided docker-compose to launch the application.

It's not intended to be public facing as it will not scale (without wasting a lot processing power) but is designed so that it can be used in scalable applications. Obviously works fine as a frontend for your personal Raspberry Pi projects.

Pre-prepared TF Lite models

These are a bunch of models available that will work with Coral Edge TPUs and the example object detection model

• https://github.com/google-coral/test_data

Converting TF Models for use

• List of pre-built detection models
• guide for converting these models to run on TF Lite
  • Download the tooling: pip3 install tf-models-official
  • pip install tensorflow-object-detection-api