Streamlit Webrtc Example

What is Streamlit Webrtc Example ?

Streamlit Webrtc Example is a web application built using Streamlit, designed to demonstrate real-time video processing capabilities. It integrates WebRTC (Web Real-Time Communication) to access and process video streams directly from the user's webcam. This example is particularly focused on pose estimation, enabling applications such as squat tracking and counting. It provides a user-friendly interface for users to interact with their webcam feed while leveraging AI to analyze movements in real time.

---

Features

• Real-time Video Processing: Access and analyze live video feeds from your webcam.
• Pose Estimation: Detect and track human poses in real time using AI models.
• Squat Counting: Automatically count squats based on detected movements.
• Webcam Integration: Seamlessly connect to your device's camera for input.
• Data Visualization: Display movement data and squat counts on the interface.
• User-Friendly Interface: Easy-to-use controls for starting and stopping the tracking.

---

How to use Streamlit Webrtc Example ?

1. Install Required Dependencies: Ensure you have Streamlit and WebRTC libraries installed.

   pip install streamlit opencv-python numpy
   

2. Run the Application: Execute the Streamlit script to launch the app.

   streamlit run app.py
   

3. Access the App: Open your web browser and navigate to the provided URL (typically http://localhost:8501).
4. Allow Webcam Access: Grant permission for the app to access your webcam.
5. Start Tracking: Click the "Start" button to begin tracking your movements.
6. Perform Squats: Perform squats in front of your webcam, and the app will automatically count them.

---

Frequently Asked Questions

1. What is pose estimation, and how does it work?
Pose estimation is the ability to detect and track human body parts (e.g., joints and limbs) in images or video streams. In this app, AI models analyze the video feed to identify movements and count squats.

2. Can I use this app without a webcam?
No, the app requires a webcam to function, as it relies on real-time video input for pose estimation and squat tracking.

3. How accurate is the squat counting?
Accuracy depends on factors like lighting, camera quality, and movement clarity. For best results, ensure proper lighting and a clear view of your full body in the webcam frame.

4. Is my video data saved anywhere?
No, the app processes video data in real time and does not store it. All processing is done locally on your device.

5. Can I modify this example for other exercises?
Yes, you can customize the pose estimation model and tracking logic to detect other exercises like push-ups or lunges. This requires adjustments to the AI model and movement detection criteria.