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PastryVision

Demo Image

PastryVision is a computer vision project designed to help small pastry shops modernize their operations. By automatically detecting and tallying items in a customer's cart, the system enables a self-serve workflow that enhances efficiency while reducing costs. It eliminates the need for plastic packaging and barcode printing, promotes pastry freshness, and operates locally without requiring web hosting. With its simple and user-friendly setup, PastryVision empowers pastry shops to deliver a modern customer experience effortlessly.

Running the Project

  1. Start/Stop the frontend client
  2. Start/Stop the backend server

PastryVision Workflow

  1. The frontend camera scans the pastry items on customer's tray.
  2. The customer takes a snapshot of their tray.
  3. The snapshot is sent to the backend to detect and identify the pastry items on the tray.
  4. The backend automatically tallies the quantities and costs of the tray and sends this information to the frontend.
  5. The customer can confirm the cost breakdown provided from the backend and proceed to pay or cancel their transaction.

Tech Stack

References

  1. MediaPipe
  2. React & ML Object Detection Example
  3. Vite Configuration Guide
  4. TailwindCSS Documentation
  5. MediaPipe Object Detection Example
  6. Training your own model using mediapipe
  7. Bread dataset on roboflow
  8. FastAPI - to build our backend
  9. OpenCV - computer vision operations library
  10. Python multipart parser
  11. Mediapipe Examples GitHub Repo
  12. BB Brain (example solution in Japan)
  13. Viscovery

About the Team

PastryVision was developed for the CVI620 NZA class at Seneca Polytechnic (Fall 2024), under Professor Savita Seharawat's guidance.

Team Members