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Pool Area Occupancy AI

Web-based AI system analyzing resorts' pool areas. It detects people and chairs, and calculates occupancy. It also provides insights (e.g., demographics) with a multi-modal LLM.

The project consists of:

  • Frontend: A barebone interface supporting file uploads and gallery image selection.
  • Backend: Provides statistics and insights using YOLO, Gemini, and some geometric processing.

Pool Area AI System

Prerequisites

  1. Docker: Install Docker and Docker Compose.
  2. YOLO: The code downloads YOLO11x (i.e., the latest at the time of writing).
  3. Gemini API Key: Add your API key to your environment.

How to use

  1. Clone the repository:

    git clone https://github.com/your-repo/chair-occupancy-ai.git
cd chair-occupancy-ai

  2. Add your Gemini API key to the .env file:

    echo "GEMINI_API_KEY=your_api_key_here" > .env

  3. Start the application:

    ./run.sh

  4. Access the frontend at localhost.

  5. Upload an image or select one from the gallery.

  6. Alternatively, you can access the backend API directly at /docs for testing.

API Endpoints

  • Analyze Image: POST /analyze-image

    • Input: Image file.
    • Output: Detection results and labeled image.

  • AI Analysis: POST /llm-analyze

    • Input: Image file.
    • Output: GenAI insights.

Tech Stack

  • AI Models: YOLO, Google Gemini
  • Backend: FastAPI, Python
  • Frontend: HTML, CSS, JavaScript
  • Docker: For containerization