Title: Drone Control and Monitoring with Computer Vision (CV) This Python script uses the OpenCV library and the MediaPipe library for real-time pose estimation using your computer's camera. It detects and visualizes pose landmarks on the video and camera

Prerequisites

Before running the code, make sure you have the following libraries installed:

• OpenCV (pip install opencv-python)
• MediaPipe (pip install mediapipe)

Usage

1. Clone or download this repository to your local machine.
2. Open a terminal or command prompt.
3. Navigate to the project directory.
4. Run the script using the following command:
5. The webcam feed will open, and you will see the pose landmarks drawn on your body in real time.

Introduction:

Welcome to the repository for our exciting drone control and monitoring project using Computer Vision (CV)! In this project, we utilize CV techniques to control drones via hand gestures and monitor various aspects such as speed and hand/plam movements. This README will guide you through the project structure and provide essential information for understanding and replicating our work.

Project's Overview:

Our project focuses on leveraging CV algorithms to interact with drones in innovative ways. Here's a brief overview of the key components:

Hand Gesture Control:

We've developed a system that allows users to control the drone using hand gestures. By analyzing hand movements captured by a camera, the drone can be directed to perform specific actions. #Speed Calculation:

One of the functionalities includes calculating the speed of a person or object. This data is then used to adjust the speed of the drone accordingly, enhancing its tracking capabilities.

Hand and Palm Extraction:

We've implemented algorithms to extract values related to hand and palm positions. This information can be utilized for various purposes such as object detection, gesture recognition, etc. #Basic Computer Vision Projects:

Additionally, we've included some basic CV projects like face detection to showcase the versatility and potential applications of CV technology.

Getting Started:

To replicate our project or integrate it into your own system, follow these steps:

Clone the repository to your local machine. Install the necessary dependencies listed in the requirements.txt file. Explore the individual scripts to understand their functionality and adapt them to your requirements. Refer to the documentation within each script for usage instructions and customization options. Contributing: We welcome contributions from the community to improve and expand this project. If you have any ideas, suggestions, or bug fixes, feel free to submit a pull request or open an issue on GitHub.

License:

This project is licensed under the MIT License. Feel free to use and modify the code according to the terms of the license.

Acknowledgments:

We'd like to thank the open-source CV community for their valuable contributions and resources that made this project possible.

For any questions or feedback, please don't hesitate to reach out. Happy coding!

Best regards