This article is based on an older version of the library and out-of-date.
See this new tutorial ✌️
Streamlit is a great framework for data scientists, machine learning researchers and developers, and
streamlit-webrtc extends it to be able to deal with real-time video (and audio) streams.
It means you can implement your computer vision algorithms only in Python on server-side and users can use them as web apps with real-time video inputs from their webcams or smartphones.
This article is a short tutorial explaining how to use this library to develop such kinds of apps.
$ pip install streamlit
If you are not familiar with Streamlit, I recommend you to see its tutorial. It takes just a few minutes.
streamlit-webrtc. This tutorial is based on the version
0.6.3. The API might be changed in future releases.
$ pip install streamlit-webrtc $ # pip install streamlit-webrtc==0.6.3 # Specify the exact version if necessary
opencv-python as well.
$ pip install opencv-python
First of all, let's simply put the
streamlit_webrtc's custom component on the page as an example below.
# simple_loopback.py import streamlit as st from streamlit_webrtc import webrtc_streamer webrtc_streamer(key="example")
key argument is not optional unlike other Streamlit components. An arbitrary unique string must be set to it.
Then run the app with
streamlit run command as below.
$ streamlit run simple_loopback.py
After opening the app on the browser, click the "Start" button.
Then a video sourced from your webcam is displayed.
(You may be asked for permission to access the webcam and microphone. If so, allow it.)
This is the simplest example of
streamlit-webrtc - capturing video from a local webcam, sending it to the Python process, and sending it back to the frontend to display the preview.
Next, we will see how to inject computer vision code into the process.
Here is the next example, the Canny filter with OpenCV.
# canny_filter.py import cv2 from streamlit_webrtc import VideoTransformerBase, webrtc_streamer class VideoTransformer(VideoTransformerBase): def transform(self, frame): img = frame.to_ndarray(format="bgr24") img = cv2.cvtColor(cv2.Canny(img, 100, 200), cv2.COLOR_GRAY2BGR) return img webrtc_streamer(key="example", video_transformer_factory=VideoTransformer)
Run this code and see the result.
$ streamlit run canny_filter.py
You can see the Canny filter is applied to the video stream in real time.
webrtc_streamer can take
video_transformer_factory argument, which is a
Callable that returns an instance of a class which has
transform(self, frame) method.
If you like type annotation, you can use an ABC (abstract base class),
VideoTransformerBase, which defines this interface, like the example above.
Anyway, as the example above shows, you can implement arbitrary frame filters inside the
transform() method, which transforms each frame coming from the video stream.
transform() takes an argument
frame and returns a numpy array
frame is an instance of
av package and the returned value
img is a numpy array representing the output frame picture.
Note that the video stream is processed in a forked thread independent of the Streamlit's script execution flow in the main thread, and
transform() is called from the forked thread.
One of the biggest benefits of using Streamlit is interactive controls such as sliders and radio buttons.
streamlit-webrtc works well in combination with them.
See the next example.
# interactive.py import cv2 import streamlit as st from streamlit_webrtc import VideoTransformerBase, webrtc_streamer class VideoTransformer(VideoTransformerBase): def __init__(self): self.threshold1 = 100 self.threshold2 = 200 def transform(self, frame): img = frame.to_ndarray(format="bgr24") img = cv2.cvtColor( cv2.Canny(img, self.threshold1, self.threshold2), cv2.COLOR_GRAY2BGR ) return img ctx = webrtc_streamer(key="example", video_transformer_factory=VideoTransformer) if ctx.video_transformer: ctx.video_transformer.threshold1 = st.slider("Threshold1", 0, 1000, 100) ctx.video_transformer.threshold2 = st.slider("Threshold2", 0, 1000, 200)
and run this code.
$ streamlit run interactive.py
This example is based on the previous one and extended to use sliders to control the Canny filter parameters.
ctx object returned from
webrtc_streamer() is used here to refer to the
.video_transformer attribute. This attribute is an instance of
VideoTransformer instantiated by
Then the values from sliders are passed to be used in
transform() via the instance attributes,
This is because
transform() is called and running in another thread in async way as stated above.
Make sure that this value passing is thread-safe, and be careful especially when you pass complex objects.
While only a simple example with the Canny filter has been shown in this article,
you can develop more interesting, any kinds of computer vision apps.
For example, I'm hosting a sample Object Detection app with MobileNet SSD at
https://share.streamlit.io/whitphx/streamlit-webrtc-example/main/app.py (Streamlit Sharing)
https://streamlit-webrtc-example.herokuapp.com/ (Heroku free plan).
https://github.com/whitphx/streamlit-webrtc-example is a repository of this app.
You can also try this app on your local environment with the commands below:
$ pip install streamlit-webrtc opencv-python $ streamlit run https://raw.githubusercontent.com/whitphx/streamlit-webrtc-example/main/app.py
This forum topic may be useful to know more about this library.
We have been working on localhost, and deploying apps on remote hosts might be a next step.
It is a nice usage of
streamlit-webrtc as it allows users to easily try the app remotely, with their local webcams or smartphones.
There are some things you need to be aware of to do it, especially with
Video streaming does not work in some network environments.
For example, in some office networks, there are firewalls which drop the WebRTC packets.
getUserMedia() API to access local webcams, and this method does not work in an insecure context.
This document says
A secure context is, in short, a page loaded using HTTPS or the file:/// URL scheme, or a page loaded from localhost.
So, when hosting your app on a remote server, it must be served via HTTPS.
One of the easiest way to do it is to use some hosting services like Heroku, which provides HTTPS serving by default. Especially for apps using
streamlit, Streamlit Sharing can be a choice.
The Object Detection sample app shown above is actually using both Heroku and Streamlit Sharing. You can see the deployment settings of this app for those services in the repository.
If you have to serve the app on hosts inside local networks, you can use some reverse proxy software like nginx.
In prototyping phase, https://github.com/suyashkumar/ssl-proxy might be a simple but useful option.