Real-Time Hand Gesture Recognition Webapp

Team Members

• Ana Sofia
• Samuel Shally
• Lemon

Project Description

Our project is a Real-Time Hand Gesture Recognition Webapp. The web application is designed to recognize hand gestures in real-time using computer vision and machine learning techniques. Users can interact with the application by performing various hand gestures, and at the end, you will see what image corresponds with the gesture you did the most. Beware that, since it takes 1 frame per second, it may seem a bit slow.

Setup Instructions

1. Create .env file with the following fields:

   MONGO_URI="mongodb://localhost:27017/"
   MONGO_DBNAME={example-name}
   FLASK_APP=app.py
   FLASK_ENV=development
   

2. Cd to the repository:

   cd path/to/your/localrepository

3. Install Docker Desktop:

   Docker Desktop Installation Guide

4. Run the following command to build and start the application using Docker Compose:

   docker-compose up --build

5. Adjust mediapipe package in requirements.txt to match your python version

   latest: mediapipe==0.10.8

6. Access the web-app:

   Open your web browser and go to http://localhost:5002.

Optional: Pull from the latest hosted images

docker pull samuelshally/4-containerized-app-exercise-samuel-web_app:latest

docker pull samuelshally/4-containerized-app-exercise-samuel-machine_learning_client:latest

docker pull samuelshally/mongo:latest