🚗 ParkVision

A computer vision-based smart parking lot monitoring system that detects and tracks parking slot occupancy in real-time using OpenCV.

✨ Features

• Real-time Parking Detection - Analyzes video feeds to detect occupied and free parking slots
• Visual Slot Marker Tool - Interactive tool to define parking slot boundaries
• Edge & Pixel Analysis - Uses Canny edge detection and color variance to determine occupancy
• REST API - FastAPI endpoint to query parking status programmatically
• Live Statistics - Displays real-time count of free and occupied slots
• Web Dashboard - Real-time web interface with Flask & SocketIO

📁 Project Structure

ParkVision/
├── src/                       # Core Python scripts
│   ├── parking_analyzer.py    # Main detection script
│   ├── slot_marker.py         # Interactive slot marking tool
│   └── api.py                 # FastAPI REST endpoint
├── config/                    # Configuration files
│   ├── parking_slots.json     # Slot coordinates (camera 1)
│   └── parking_slots2.json    # Slot coordinates (camera 2)
├── videos/                    # Video files (not tracked in git)
│   ├── carPark.mp4
│   └── 2.mp4
├── web/                       # Flask web application
│   ├── app.py                 # Main Flask app with SocketIO
│   ├── static/                # CSS and JavaScript
│   └── templates/             # HTML templates
├── screenshots/               # Project screenshots
├── requirements.txt           # Python dependencies
├── LICENSE                    # MIT License
└── README.md

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• A parking lot video file (e.g., carPark.mp4)

Installation

1. Clone the repository

   git clone https://github.com/MathewX470/ParkVision.git
   cd ParkVision

2. Create a virtual environment (recommended)

   python -m venv venv
   
   # Windows
   venv\Scripts\activate
   
   # Linux/macOS
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

📖 Usage

Step 1: Mark Parking Slots

Before analyzing a video, you need to define the parking slot boundaries:

python src/slot_marker.py

Controls:

Key	Action
Drag	Draw a rectangle to create a slot
F	Duplicate the last slot
W/S	Adjust vertical offset
A/D	Adjust horizontal offset
U	Undo last slot
P	Save and exit

The slot coordinates are saved to config/parking_slots.json.

Step 2: Run the Parking Analyzer

Analyze a parking lot video to detect occupancy:

python src/parking_analyzer.py

• Green slots = Free 🟢
• Red slots = Occupied 🔴
• Press ESC to exit

Step 3: Run the Web Dashboard

Start the Flask web server with real-time updates:

python web/app.py

Access the dashboard at: http://localhost:5000

Step 4: Query via API (Optional)

Start the FastAPI server to get parking status via REST:

uvicorn src.api:app --reload

Access the API at: http://localhost:8000/parking/status

Example Response:

{
  "total_slots": 15,
  "free": 5,
  "occupied": 10
}

⚙️ How It Works

ParkVision uses a pixel-based analysis approach to determine slot occupancy:

1. Edge Detection - Applies Canny edge detection to identify car outlines
2. Color Variance - Calculates standard deviation of pixel values
3. Threshold Comparison - Empty slots (asphalt) have low edge density and uniform color, while occupied slots (cars) show high edge density and varied colors

# Detection thresholds (adjustable)
edge_threshold = 8      # Edge density percentage
std_threshold = 35      # Color variance threshold

🎛️ Configuration

You can tune the detection sensitivity by modifying these parameters in parking_analyzer.py:

Parameter	Default	Description
edge_threshold	8	Minimum edge density % to consider occupied
std_threshold	35	Minimum color std dev to consider occupied

📸 Screenshots

Interactive Map

Slot Detection View 1

Slot Detection View 2

🛠️ Tech Stack

• OpenCV - Computer vision and image processing
• NumPy - Numerical operations
• FastAPI - REST API framework
• Flask-SocketIO - Real-time WebSocket support (optional)

🚀 Future Enhancements

• Vehicle Size Detection - Classify vehicles as compact, sedan, SUV, or truck
• Smart Slot Recommendations - Suggest optimal parking spots based on vehicle size
• License Plate Recognition - Identify and track vehicles by license plate
• Parking Duration Tracking - Monitor how long vehicles have been parked
• Mobile App Integration - iOS/Android app for real-time parking availability
• Payment System Integration - Automated billing based on parking duration
• Multi-camera Support - Seamless stitching of multiple camera feeds
• Historical Analytics - Track parking patterns and peak hours
• Reservation System - Allow users to reserve parking spots in advance
• Accessibility Features - Highlight handicap-accessible spots

👥 Contributors

Mathew Punnen Chandy

Vishak R

Johan Thomas Roby

Ashish Jacob

🤝 Contributing

Contributions are welcome! Feel free to:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.