Image Recognition Project

This repository provides a basic structure for an image recognition system with a Python backend and a Node.js frontend.

Backend

• Framework: FastAPI
• Purpose: Accept image uploads, detect objects (faces, cars, license plates), store embeddings in a Postgres database with vector search, and return identification results.
• Key files:
  • backend/app.py – main FastAPI application.
  • backend/detection/object_detection.py – placeholder for detection logic.
  • backend/models/db.py – simplified database interface using pgvector.
  • backend/requirements.txt – Python dependencies.

Run the backend with:

pip install -r backend/requirements.txt
uvicorn backend.app:app --reload

Frontend

• Framework: Node.js with Express
• Purpose: Provide a simple web UI to upload images and display results from the backend.
• Key files:
  • frontend/server.js – Express server handling uploads and forwarding to the backend.
  • frontend/public/index.html – Upload form.
  • frontend/package.json – Node.js dependencies.

Run the frontend with:

npm install --prefix frontend
node frontend/server.js

How It Works

1. The user uploads an image via the frontend.
2. The frontend sends the image to the backend /upload endpoint.
3. The backend detects objects, extracts embeddings, and checks them against a Postgres database with vector search (e.g., using the pgvector extension).
4. Results are returned to the frontend and displayed to the user.

This project serves as a starting point; you will need to implement actual object detection and database logic to suit your requirements.