During the COVID-19 pandemic, Redisafe is developed to be a secured AI platform with the purpose of detecting PPE on medical staff for safety protocol to prevent the spread of virus. This project combines several Redis data structures and Redis Modules to process a stream of images and classify them with the Tensorflow detection model.
It uses:
- Redis Streams to capture the input video stream:
camera:0
- RedisAI to classify the images with TensorFlow mask detection model.
- Express.js to serve the client side.
It forwards the classifies images to a stream: results
Python 3.6
- OpenCV
- Redis - python client
- RedisAI - python client
- Numpy
To run the demo:
$ git clone https://github.com/sagban/redisPPEScan.git
$ cd redisPPEScan
$ pip install -r requirements.txt # Now install the python dependencies
Start the redisAI detction model:
$ cd app/mask_detection
$ python detection.py
Open a second terminal for the video capturing:
$ python camera/read_camera.py
Open a third terminal for the express.js client application:
$ cd client
$ npm install
$ node server.js
http://localhost:3000
shows all the classified frames.
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.
Sagar Bansal VI LY