Human Pose Detection using MediaPipe

This project detects human poses in images, videos, and live webcam streams using MediaPipe that makes use of Blaze Pose detection method and is deployed with Streamlit.

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📌 Features

✅ Detects 33 body landmarks using BlazePose
✅ Works on images, videos, and live webcam streams
✅ Streamlit UI for easy interaction
✅ Real-time pose tracking with OpenCV
✅ Simple deployment & lightweight inference

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🛠️ Files Structure

📂 Human-Pose-Detection │── 📂 Images/ # Stores sample images │── 📂 Videos/ # Stores sample videos │── 📜 HME_live.py # Pose detection on live webcam feed │── 📜 HME_onimage.py # Pose detection on images │── 📜 HME_onvid.py # Pose detection on videos │── 📜 app.py # Streamlit app to run the project │── 📜 requirements.txt # Required dependencies │── 📜 README.md # Project documentation

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⚙️ Installation & Setup

🔹 1. Clone the Repository git clone https://github.com/your-repo/Human-Pose-Detection.git

cd Human-Pose-Detection

🔹 2. Install Dependencies

pip install -r requirements.txt

🔹 3. Run the Streamlit App

streamlit run app.py

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📷 How It Works

🔹 BlazePose is used for real-time pose detection.

🔹 OpenCV processes frames from images/videos/webcam.

🔹 Streamlit provides an interactive UI for users.

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🛠️ Future Enhancements

🚀 Add pose classification for exercises (e.g., Yoga, Workouts)

🚀 Deploy as a Web App

🚀 Integrate gesture recognition