Streamlit Webrtc Example

What is Streamlit Webrtc Example ?

Streamlit Webrtc Example is an open-source application built using Streamlit and WebRTC (Web Real-Time Communication) technologies. It is designed for real-time object detection in video feeds, enabling users to identify objects within live or recorded video streams. This tool leverages AI models to analyze video frames and detect objects with high accuracy. Streamlit's simplicity and WebRTC's real-time capabilities make it an ideal combination for rapid development and deployment of video-based AI applications.

Features

• Real-time Object Detection: Analyzes video frames in real-time to identify objects.
• WebRTC Integration: Utilizes WebRTC for low-latency video streaming directly in the browser.
• Customizable AI Models: Supports integration with various pre-trained AI models for object detection.
• User-Friendly Interface: Provides an intuitive interface for users to interact with the video feed and view detection results.
• Cross-Platform Compatibility: Works seamlessly across modern web browsers.

How to use Streamlit Webrtc Example ?

1. Install Required Packages: Run pip install streamlit webrtc to install the necessary dependencies.
2. Clone or Download the Example: Obtain the Streamlit Webrtc Example code from a repository or create a new Streamlit app.
3. Run the Application: Execute streamlit run your_script.py to launch the app.
4. Access the App: Open a web browser and navigate to http://localhost:8501.
5. Allow Camera Access: Grant permission for the app to access your webcam.
6. Start Detection: Click the "Start Detection" button to begin real-time object detection in the video feed.
7. Interact with Results: View detected objects in real-time and adjust settings as needed.

Frequently Asked Questions

What browsers are supported?
Streamlit Webrtc Example is compatible with modern browsers like Chrome, Firefox, and Edge.

Can I customize the AI model?
Yes, you can integrate custom AI models or use pre-trained models like YOLO or TensorFlow-based detectors.

How do I improve performance?
For better performance, use a high-resolution camera, optimize your AI model, and ensure a stable internet connection.