Streamlit Webrtc Example

What is Streamlit Webrtc Example ?

Streamlit Webrtc Example is a web-based application built using Streamlit and WebRTC (Web Real-Time Communication) technologies. It is designed to demonstrate real-time webcam interactions, specifically focusing on pose estimation and tracking. This example allows users to track and count squats using their webcam, making it a useful tool for fitness and exercise monitoring.

Features

• Webcam Access: Utilizes the user's webcam for real-time video capture. • Pose Estimation: Detects human poses and tracks specific movements like squats. • Squat Counting: Automatically counts the number of squats performed. • Real-Time Feedback: Provides instant feedback on the user's exercises. • Customizable Thresholds: Allows users to adjust detection sensitivity. • Video Recording: Optionally records the workout session for review. • Dashboard Support: Displays statistics and workout summaries.

How to use Streamlit Webrtc Example ?

1. Install Required Dependencies: Ensure you have Streamlit and the necessary WebRTC packages installed in your environment.
2. Run the Application: Execute the Streamlit app using streamlit run your_script.py.
3. Enable Webcam Access: Grant permission for the app to access your webcam when prompted.
4. Adjust Settings: Fine-tune any customizable settings such as pose detection thresholds.
5. Start Your Workout: Perform squats or other exercises while the app tracks your movements in real time.
6. Review Results: After your workout, review the statistics and recorded video (if enabled).

Frequently Asked Questions

1. How do I install the required dependencies?
You can install the necessary packages using pip: pip install streamlit webrtc. Ensure your environment is set up correctly before running the app.

2. Is my webcam data stored securely?
No, the app does not store your webcam data unless you explicitly enable video recording. Even then, recordings are saved locally on your device.

3. Why is the squat counting inaccurate sometimes?
Inaccuracies may occur due to poor lighting, obstructions, or incorrect pose detection thresholds. Adjust the sensitivity settings or improve your environment for better accuracy.

4. Can I customize the app for other exercises?
Yes, the core pose estimation model can be modified to track different exercises. You would need to adjust the model and detection logic accordingly.