Crowd Detection and Wait-time Predictions

This project demonstrates real-time video processing using the YOLOv8 object detection model to count people and simulate POS (Point of Sale) operations. The application features an interactive front-end to select video streams from multiple outlets and provides live updates on the detected people count, bill simulations, and estimated wait times.

Features

• Real-time Video Analysis: Detect and count people in video streams using the YOLOv8 model.
• POS Simulation: Simulates bill processing for a food outlet with dynamic wait time estimation.
• Interactive UI: Users can select between different outlets and monitor the results in real-time.
• Flask + SocketIO Integration: Seamless communication between the back-end and front-end for real-time updates.
• Themed UI Design: Modern, responsive interface with a dark mode theme.

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Getting Started

Follow these steps to set up and run the application on your local machine.

Prerequisites

• Python 3.8 or later
• Virtual environment (recommended)
• A YOLOv8 model file (yolov8n.pt)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/live-yolov8-pos.git
   cd live-yolov8-pos

2. Install the required dependencies:

   pip install -r requirements.txt

3. Set up pyngrok for public access (optional):

   • Install ngrok and set up your API key:

     from pyngrok import ngrok
     ngrok.set_auth_token("YOUR_NGROK_AUTH_TOKEN")

4. Ensure the video files exist in the specified paths:

   • Update the video_paths dictionary in app.py with your video file paths.

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Running the Application

1. Start the Flask application:

   python app.py

2. Access the application:

   • Locally: http://127.0.0.1:5000
   • Via ngrok (if configured): Check the terminal output for the public URL.

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Usage

1. Open the application in your web browser.
2. Select an outlet from the homepage to start processing its video feed.
3. Monitor:
   • People Count: Real-time count of detected individuals.
   • POS Simulation: Bill count and estimated wait time.
4. Return to the homepage to select a different outlet.

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Technical Details

YOLOv8 Model

• This project uses the YOLOv8 model for object detection.
• The model is initialized with:

  model = YOLO('yolov8n.pt')

POS Simulation

• The POS system simulates bill processing, where:
  • Base Wait Time: 2 minutes per person
  • Additional Time: 1.5 minutes per bill
  • Food Preparation Time: 7 minutes

Flask-SocketIO

• Used for real-time communication between the server and the front-end to push frame updates and POS data.

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Customization

1. Video Streams:
   • Update the video_paths dictionary in app.py to point to your video files.
2. Wait Time Calculation:
   • Modify constants wait_time_per_person, additional_time_per_bill, and food_prep_time.

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Requirements

Create a requirements.txt file to manage dependencies:

Flask
Flask-SocketIO
numpy
opencv-python
pyngrok
ultralytics