This demo combines several Redis data structures and Redis Modules to process a AnimalRecognitionDemostream of images and filter out the images that contain cats.

It uses:

• Redis Streams to capture the input video stream: all
• RedisGears to process this stream
• RedisAI to classify the images with MobilenetV2

It forwards the images that contain cats to a stream: cats

Docker and Python 2

To run the demo:

$ git clone https://github.com/RedisGears/AnimalRecognitionDemo.git $ cd AnimalRecognitionDemo # If you don't have it already, install https://git-lfs.github.com/ (On OSX: brew install git-lfs) $ git lfs install && git lfs fetch && git lfs checkout $ docker-compose up 

If something went wrong, e.g. you skipped installing git-lfs, you need to force docker-compose to rebuild the containers

$ docker-compose up --force-recreate --build 

Open a second terminal for the video capturing:

$ pip install -r camera/requirements.txt $ python camera/read_camera.py 

• http://localhost:3000 shows all the captured frames
• http://localhost:3001 shows only the framse with cats

This demo is designed to be easy to setup, so it relies heavily on docker. You can get better performance and a higher FPS by runninng this demo outside docker. To control the FPS, edit the gear.py file.