Human Pose Estimation using Machine Learning

🔍 Overview

This project detects human poses in images and videos using machine learning and provides a Streamlit-based web interface for an interactive experience. The model identifies key body parts and maps them to analyze human posture and movements.

---

🚀 Features

✅ Streamlit Web App – Run pose estimation via a web-based UI
✅ Real-time Pose Detection – Analyze live webcam feed
✅ Image & Video Processing – Detect poses in static images and pre-recorded videos
✅ Pre-trained Model – Uses TensorFlow for accurate pose estimation

---

🛠️ Installation

1️⃣ Clone the Repository

git clone https://github.com/YeshitaMotwani/Human-Pose-Estimation.git
cd Human-Pose-Estimation

2️⃣ Set Up a Virtual Environment (Recommended)

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

---

💻 Running the Streamlit Web App

Run the App

streamlit run estimation_app.py

📌 This will open the web interface in your default browser.

Using the Web UI:

1. Upload an Image/Video to detect human poses.
2. Choose Webcam Mode for real-time pose estimation.
3. View detection results instantly in your browser!

---

🖼️ Pose Estimation on Images

To process an image without Streamlit, run:

python pose_estimation.py --image_path path/to/your/image.jpg

📌 Replace path/to/your/image.jpg with the actual image file path.

---

📹 Pose Estimation on Videos

For processing a video file, use:

python pose_estimation_Video.py --video_path path/to/your/video.mp4

📌 Replace path/to/your/video.mp4 with the actual video file path.

---

📡 Real-Time Pose Estimation (Webcam Mode)

Run the script to analyze live webcam feed:

python estimation_app.py

📌 Ensure your webcam is connected and properly configured.

---

📂 Output Files

🔹 Processed Images → Saved in the output/ folder
🔹 Processed Videos → Saved in the output/ folder

---

📜 Model & Data

• The project uses a pre-trained pose estimation model (graph_opt.pb) for detecting human body keypoints.
• The model is optimized for real-time inference and works efficiently on CPU & GPU.

---

📦 Dependencies

The project requires the following Python libraries:

• Streamlit (for the web UI)
• OpenCV (for image processing)
• TensorFlow (for pose detection)
• NumPy (for array computations)

📌 All dependencies are listed in requirements.txt. Install them with:

pip install -r requirements.txt

---

🤝 Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m "Added new feature").
4. Push to your fork and submit a Pull Request.

---

🙌 Acknowledgements

Special thanks to the open-source community and the developers of TensorFlow, OpenCV, and Streamlit for making this project possible.

---

✨ Now your GitHub README will look professional and informative! 🚀

Would you like me to add any images, GIFs, or badges to make it even better? 😊