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license Animal Recognition Forum Discord

AnimalRecognitionDemo

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

It uses RedisAI Integration in RedisGears with asynchronous function so the server is not blocked while RedisGears is triggering an inference session in RedisAI.

Architecture

Architecture Architecture

Requirements

Docker and Python 3

Running the Demo

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

For running the demo with make, run:

make start
make camera

Then open the UI to watch the result streams.

To end the demo, then to stop the containers:

make stop

Run make help for a few more options.

For running the demo manually, run:

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

Or run the camera process in test mode (without streaming from your camera):

ANIMAL=[cat|dog] python camera/read_camera.py --test

UI

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

Limitations

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.

About

An example of using Redis Streams, RedisGears and RedisAI for Realtime Video Analytics (i.e. filtering cats)

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