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FitSnap

AI powered clothing size prediction along with overlays to try out clothes virtually using AR tech.

HackX 3.0

🎓 Narsee Monjee Institute of Management Studies (NMIMS), Navi Mumbai

📌 Navi Mumbai, Maharashtra

🚀 24hr Hackathon Project

🥈 2nd
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FitSnap - Personalized Virtual Wardrobe

FitSnap is an AI-powered application that not only predicts your shoulder width, chest, and waist measurements based on your height but also offers a unique social experience. Engage with a Tinder-like interface to swipe right on outfits you love from other users and virtually try them on by overlaying the selected clothing onto your photo.

Features

  • Accurate Measurement Predictions: Utilizes advanced AI models to estimate key body measurements.
  • Personalized Size Recommendations: Suggests optimal clothing sizes tailored to your body profile.
  • Social Outfit Discovery: Explore and like outfits from other users with an intuitive swipe interface.
  • Virtual Try-On: Overlay liked outfits onto your photo using cutting-edge image processing techniques.

Virtual Try-On Technology

FitSnap's virtual try-on feature employs a combination of advanced technologies:

  1. Clothing Segmentation with U2NET: Segments clothing items from images to isolate them for overlay.
  2. Pose Estimation with MediaPipe: Detects and aligns body joints to ensure accurate placement of clothing.
  3. Image Processing with OpenCV: Overlays segmented clothing onto user photos, adjusting for pose and alignment.

Installation

  1. Clone the Repository:
    git clone https://github.com/jaykerkar0405/FitSnap.git
    
  2. Navigate to the Project Directory:
    cd FitSnap
    
  3. Install Dependencies:
    • For the backend:
      cd backend
      pip install -r requirements.txt
      
    • For the frontend:
      cd frontend
      npm install
      

Usage

  1. Start the Backend Server:
    cd backend
    python app.py
    
  2. Launch the Frontend Application:
    cd frontend
    npm start
    
  3. Access the Application: Open your browser and navigate to http://localhost:3000 to use FitSnap.

Contributing

We welcome contributions! Please follow these steps:

  1. Fork the Repository: Click on the 'Fork' button at the top right of this page.
  2. Create a New Branch: Use git checkout -b feature-branch-name.
  3. Make Your Changes: Implement your feature or fix.
  4. Commit Changes: Use git commit -m 'Description of your changes'.
  5. Push to Your Fork: Use git push origin feature-branch-name.
  6. Submit a Pull Request: Navigate to the original repository and click on 'New Pull Request'.

Acknowledgements

Special thanks to the contributors: Sundaram Krishnan, Yash Kolekar, Aayush Nair, Jay Kerkar.