README: Smart Parking System (Car Number Plate Registration/Recognition)

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Project Overview

This project is a smart parking system developed using Python, Tkinter, PHP-MyAdmin, and MySQL database. It uses Opencv and Haar Cascade classifiers for detecting car number plates, processes the information, and integrates with a database to manage car details. The system supports automatic retrieval of registered car information, new car registration, and a monthly pass feature.

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Features

• Automatic Detection:

  • Detects car number plates using Haar Cascade files and OpenCV.
  • Automatically fetches details of registered cars from the database.

• New Car Registration:

  • Allows new cars to register themselves directly into the system.

• Monthly Pass Management:

  • Offers a feature for car owners to subscribe to a monthly pass.

• Database Integration:

  • Uses MySQL for seamless backend connectivity and data storage.

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Technologies Used

• Programming Languages: Python, Tkinter for frontend
• Database: MySQL
• Libraries/Frameworks: OpenCV, Haar Cascade
• Other Tools: XAMPP

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System Requirements

• Python 3.x
• MySQL 5.x or higher
• Required Python Libraries: OpenCV, NumPy
• A local or hosted server environment (e.g., XAMPP, WAMP)

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Setup Instructions

1. Clone the Repository:

   git clone <repository_url>
   cd <repository_folder>

2. Set Up the Database:

   • Import the provided SQL file (database.sql) into your MySQL server.
   • Update the database credentials in the PHP connection file.

3. Install Python Dependencies:

   pip install pymysql opencv-python numpy

4. Run the Application:

   • Start the PHP server.
   • Execute the Python script for number plate recognition.
   • Access the interface through the server URL.

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Usage

• Upload image of car front part for detecting car number plates.
• The system will fetch the car details if the number is registered.
• For new registrations, use the registration interface.
• Manage monthly passes directly from the system.

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Future Enhancements

• Integration with a live camera stream.
• Integration with cloud-based databases.
• Enhanced detection using deep learning models like YOLO.

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Contributors

• Manpreet Singh

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Feel free to contribute to this project or provide feedback to enhance its functionality!