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This project leverages computer vision techniques to analyze parking lot occupancy. The goal is to detect available parking spaces in real-time using image and video input.

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Ahmet904-beep/computer-vision-parking-lot

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Computer Vision Parking Lot 🚗🔍

GitHub Repo Releases

Welcome to the Computer Vision Parking Lot project! This repository focuses on using computer vision techniques to analyze parking lot occupancy. The goal is to detect available parking spaces in real-time using image and video input. This README will guide you through the project's features, installation, usage, and more.

Table of Contents

  1. Introduction
  2. Features
  3. Technologies Used
  4. Installation
  5. Usage
  6. Contributing
  7. License
  8. Contact
  9. Releases

Introduction

Parking in urban areas can be challenging. With the rise of smart cities, efficient parking management has become crucial. This project aims to simplify the parking experience by utilizing computer vision. By analyzing video feeds or images, we can identify open parking spaces and help drivers find them quickly.

Features

  • Real-time Detection: Analyze video streams to detect available parking spots.
  • Image Processing: Process images to identify occupancy.
  • Data Visualization: Visualize parking lot occupancy trends over time.
  • Transfer Learning: Use pre-trained models for improved accuracy.
  • User-Friendly Interface: Simple command-line interface for ease of use.

Technologies Used

This project incorporates a variety of technologies, including:

  • Python: The primary programming language.
  • OpenCV: For image processing and computer vision tasks.
  • TensorFlow/Keras: For building and training machine learning models.
  • Matplotlib: For data visualization.
  • Google Colab: For cloud-based coding and collaboration.

Installation

To get started, follow these steps:

  1. Clone the Repository:

    git clone https://github.com/Ahmet904-beep/computer-vision-parking-lot.git
    cd computer-vision-parking-lot
  2. Install Required Packages: Use pip to install the necessary libraries:

    pip install -r requirements.txt
  3. Download the Pre-trained Model: You can find the pre-trained model in the Releases section. Download the latest version and place it in the appropriate directory.

Usage

To use the parking lot analysis tool, run the following command:

python main.py --input <video_or_image_path>

Replace <video_or_image_path> with the path to your video or image file. The program will process the input and display the detected parking spaces.

Example

python main.py --input parking_lot_video.mp4

Contributing

We welcome contributions! If you have suggestions or improvements, please follow these steps:

  1. Fork the repository.
  2. Create a new branch:
    git checkout -b feature/YourFeature
  3. Make your changes and commit them:
    git commit -m "Add your message here"
  4. Push to the branch:
    git push origin feature/YourFeature
  5. Open a pull request.

License

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

Contact

For questions or suggestions, feel free to reach out:

Releases

You can find the latest releases of this project here. Please download the necessary files and execute them as needed.

Conclusion

The Computer Vision Parking Lot project aims to enhance the parking experience using advanced computer vision techniques. By leveraging real-time data, we can significantly improve how we manage parking spaces. We invite you to explore the project, contribute, and help us make parking easier for everyone.

Thank you for visiting! 🚀

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This project leverages computer vision techniques to analyze parking lot occupancy. The goal is to detect available parking spaces in real-time using image and video input.

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