rishit-katiyar / arucomarkerdetector

A Python script using OpenCV to detect ArUco markers in a video stream from a camera, calculating distances between markers and displaying information in real-time

ArUco Marker Detection

Installation 🚀

Requirements 📋

Before diving into the installation process of the ArUco Marker Detector, ensure that your system meets the following requirements:

• Python 3.6+: The ArUco Marker Detector is compatible with Python 3.6 and higher versions. You can download the latest version of Python from the official website here.
• OpenCV: OpenCV (Open Source Computer Vision Library) is a vital dependency for image processing and computer vision tasks. You can install OpenCV using pip, the Python package installer.
• NumPy: NumPy is a fundamental package for scientific computing with Python and is required for array manipulation and numerical operations. It's essential to have NumPy installed to use the ArUco Marker Detector effectively.

Steps 🛠️

Follow these detailed steps to install the ArUco Marker Detector on your machine:

1. Clone the Repository: Begin your journey by cloning this repository to your local machine using Git. Open your terminal or command prompt and execute the following command:

   git clone https://github.com/Rishit-katiyar/ArUcoMarkerDetector.git

2. Navigate to Project Directory: Once you've successfully cloned the repository, navigate to the project directory using the cd command:

   cd ArUcoMarkerDetector

3. Install Python: If Python is not already installed on your system, you can download and install it from the official Python website here. Follow the installation instructions provided for your operating system.

4. Install OpenCV: OpenCV can be installed using pip, the Python package installer. Execute the following command to install the OpenCV library:

   pip install opencv-python

5. Install NumPy: Similarly, NumPy can be installed using pip. Execute the following command to install the NumPy package:

   pip install numpy

6. Verify Installation: After installing the dependencies, it's essential to verify the installation to ensure everything is set up correctly. You can do this by running a simple Python script that imports the required libraries. Create a new Python script (e.g., verify_installation.py) and add the following code:

   import cv2
   import numpy as np
   
   print("OpenCV version:", cv2.__version__)
   print("NumPy version:", np.__version__)

   Save the script and execute it using the Python interpreter. If the installation was successful, you should see the versions of OpenCV and NumPy printed to the console.

7. Congratulations!: 🎉 You have successfully installed the ArUco Marker Detector and its dependencies on your system. You're now ready to delve into the exciting world of detecting, tracking, and visualizing ArUco markers in images or videos.

8. Additional Resources: For more information on OpenCV and NumPy, you can refer to their official documentation:

   • OpenCV Documentation
   • NumPy Documentation

9. Troubleshooting: If you encounter any issues during the installation process, don't panic! We've got you covered. Check out the troubleshooting section below for common solutions to potential problems.

10. Feedback and Contributions: 🤝 We value your feedback and welcome contributions from the community. If you have suggestions for improvement or want to contribute to the project, please open an issue or submit a pull request on GitHub.

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

12. Acknowledgements: Special thanks to the developers and contributors of OpenCV and NumPy for their outstanding work and contributions to the fields of computer vision and scientific computing.

13. Stay Updated: Don't forget to star the repository on GitHub and follow us for updates and announcements! ⭐️

14. Happy Coding!: 🚀 We hope you find the ArUco Marker Detector useful for your projects and experiments. Happy coding! 😊