By using Streamlit and streamlit-webrtc, we can create web-based real-time computer vision apps only with ~10 or 20 additional lines of Python code.

To turn computer vision models into real-time demos, we have conventionally used OpenCV modules such as cv2.VideoCapture and cv2.imshow(). However, such apps are difficult or impossible to share with friends, run on smartphones, or integrate with modern interactive widgets and other data views and inputs.

Web-based apps don't have such problems.

Streamlit provides an easy way to build web apps quickly, and streamlit-webrtc allows to use real-time video streams.
You can create real-time video apps with modern interactive views and inputs, and host these apps on the cloud to use from any devices with browsers.

In this talk, I will demonstrate the development process using these libraries and show a variety of examples so that we see how easy and useful they are and can make use of them in daily development and research.streamlit-webrtc extends Streamlit to be capable of dealing with real-time video and audio streams.
With a combination of these libraries, developers can rapidly create real-time computer vision and audio processing apps for which OpenCV has typically been used.