CV_Pose_Detection Project Description This repository contains a computer vision project focused on real-time pose detection. The goal is to detect and track human skeletal keypoints from video streams or static images, enabling a wide range of applications from gesture recognition to fitness analysis. The project will leverage cutting-edge computer vision models and libraries to achieve accurate and efficient results.

Features Real-Time Pose Estimation: Detects human pose in live video feeds.

Keypoint Tracking: Tracks individual body keypoints (e.g., nose, shoulders, elbows) over time.

Multi-Person Detection: Capable of detecting multiple individuals in a single frame.

Cross-Platform Compatibility: Designed to run on various operating systems.

Getting Started Prerequisites Python 3.x

A package manager like pip

Installation To set up the project locally, follow these steps.

Clone this repository to your local machine:

git clone https://github.com/your-username/CV_pose_detection.git

Navigate into the project directory:

cd CV_pose_detection

Install the required dependencies:

pip install -r requirements.txt

Usage Once the installation is complete, you can run the main script to start pose detection.

python main.py --source video.mp4

Note: Replace main.py with the name of your primary script and video.mp4 with your input source.

Contributing Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

Fork the Project

Create your Feature Branch (git checkout -b feature/AmazingFeature)

Commit your Changes (git commit -m 'Add some AmazingFeature')

Push to the Branch (git push origin feature/AmazingFeature)

Open a Pull Request

License Distributed under the MIT License. See LICENSE for more information.

This README will be updated as the project progresses.