Pytch

Pytch is an innovative open-source platform designed to revolutionize the world of soccer analytics. Developed at Texas A&M University's Department of Computer Science and Engineering, this project utilizes state-of-the-art machine learning and computer vision techniques to offer accessible, in-depth soccer match analysis to fans, analysts, and enthusiasts.

Features

• Analytics: Pytch provides a range of soccer analytics including shotmaps, heatmaps, and passmaps.
• User Uploads: Users can upload soccer footage to receive detailed analytics.
• Custom Visualizations: Supports custom visualizations, allowing users to tailor their analytics experience.
• Open-Source: Encourages community collaboration and innovation.

Getting Started

Prerequisites

• Ensure you have Python and Node.js installed on your machine.
• Basic knowledge of machine learning and computer vision concepts is beneficial.

frontend setup

• cd frontend

• Fill out env vars: cp .env.example .env

• Install packages: npm install or pnpm i

• database setup

  • Have docker running
    • start server: docker-compose up -d
    • if you want to stop: docker-compose down or in the docker app
  • push schema to db server: npx prisma db push or pnpm prisma db push
  • view database: npx prisma studio or pnpm prisma studio

• start frontend: npm run dev or pnpm dev

backend setup

• pip install cython
• cd tracker/track
• python -m pip install --upgrade pip==22.0.4
• pip install -r requirements.txt
• rm -rf ByteTrack
• git clone git@github.com:ifzhang/ByteTrack.git --force
• cd ByteTrack
• pip install -r requirements.txt
• python setup.py develop
• cd ../..

Project Structure

• db/: Contains database-related configurations and files.
• docs/: Includes comprehensive documentation for the project.
• frontend/: Houses the front-end code of the application.
• tracker/: contains Yolov8 identification, ByteTrack, Team Detection, and Localization start with :flask --app app run -p 8080

Contributing

Contributions are what make the open-source community an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Team

• Gabriel Diaz - Computer Vision Engineer
• Ryan Kutz - Computer Vision Engineer
• Anthony Pasala - Backend Engineer
• Joseph Quismorio - Frontend Development Head
• Ryan Son - Project Manager
• Shurui Xu - Database and Analytics Engineer