streamlit-webrtc

Handling and transmitting real-time video/audio streams over the network with Streamlit

<table> <tr> <td width="48%"> <a href="https://share.streamlit.io/whitphx/streamlit-webrtc-example/main/app.py"> <img src="https://global.discourse-cdn.com/streamlit/original/2X/a/af111a7393c77cb69d7712ac8e71ca862feaeb24.gif" /> </a> </td> <td width="48%"> <a href="https://share.streamlit.io/whitphx/style-transfer-web-app/main/app.py"> <img src="https://global.discourse-cdn.com/streamlit/original/2X/b/b3cb8aa60eb746366e06726a9137720583c02c3a.gif" /> </a> </td> </tr> </table>

Sister project: streamlit-fesion to execute video filters on browsers with Wasm.

Examples

⚡️Showcase including following examples and more: 🎈Online demo

• Object detection
• OpenCV filter
• Uni-directional video streaming
• Audio processing

⚡️Real-time Speech-to-Text: 🎈Online demo

It converts your voice into text in real time. This app is self-contained; it does not depend on any external API.

⚡️Real-time video style transfer: 🎈Online demo

It applies a wide variety of style transfer filters to real-time video streams.

⚡️Video chat

(Online demo not available)

You can create video chat apps with ~100 lines of Python code.

⚡️Tokyo 2020 Pictogram: 🎈Online demo

MediaPipe is used for pose estimation.

Install

$ pip install -U streamlit-webrtc

Quick tutorial

See also the sample pages, pages/*.py, which contain a wide variety of usage.

See also "Developing Web-Based Real-Time Video/Audio Processing Apps Quickly with Streamlit".

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Create app.py with the content below.

from streamlit_webrtc import webrtc_streamer

webrtc_streamer(key="sample")

Unlike other Streamlit components, webrtc_streamer() requires the key argument as a unique identifier. Set an arbitrary string to it.

Then run it with Streamlit and open http://localhost:8501/.

$ streamlit run app.py

You see the app view, so click the "START" button.

Then, video and audio streaming starts. If asked for permissions to access the camera and microphone, allow it.

Media toggle controls

When the app sends local camera or microphone input, webrtc_streamer() shows camera and microphone toggle buttons next to the Start/Stop button. These controls let users turn their outgoing camera or microphone track on and off without stopping the WebRTC session.

Set media_toggle_controls=False to hide these toggle buttons.

from streamlit_webrtc import webrtc_streamer

webrtc_streamer(key="example", media_toggle_controls=False)

When a user turns off the camera or microphone with these buttons, the WebRTC track stays active. As described in MDN's MediaStreamTrack.enabled documentation, disabled audio tracks send silence, and disabled video tracks send black frames; the session does not stop or renegotiate.

Next, edit app.py as below and run it again.

from streamlit_webrtc import webrtc_streamer
import av


def video_frame_callback(frame):
    img = frame.to_ndarray(format="bgr24")

    flipped = img[::-1,:,:]

    return av.VideoFrame.from_ndarray(flipped, format="bgr24")


webrtc_streamer(key="example", video_frame_callback=video_frame_callback)

Now the video is vertically flipped.

As an example above, you can edit the video frames by defining a callback that receives and returns a frame and passing it to the video_frame_callback argument (or audio_frame_callback for audio manipulation). The input and output frames are the instance of av.VideoFrame (or av.AudioFrame when dealing with audio) of PyAV library.

You can inject any kinds of image (or audio) processing inside the callback. See examples above for more applications.

Pass parameters to the callback

You can also pass parameters to the callback.

In the example below, a boolean flip flag is used to turn on/off the image flipping.

import streamlit as st
from streamlit_webrtc import webrtc_streamer
import av


flip = st.checkbox("Flip")


def video_frame_callback(frame):
    img = frame.to_ndarray(format="bgr24")

    flipped = img[::-1,:,:] if flip else img

    return av.VideoFrame.from_ndarray(flipped, format="bgr24")


webrtc_streamer(key="example", video_frame_callback=video_frame_callback)

Pull values from the callback

Sometimes we want to read the values generated in the callback from the outer scope.

Note that the callback is executed in a forked thread running independently of the main script, so we have to take care of the following points and need some tricks for implementation like the example below (See also the section below for some limitations in the callback due to multi-threading).

• Thread-safety
  • Passing the values between inside and outside the callback must be thread-safe.
• Using a loop to poll the values
  • During media streaming, while the callback continues to be called, the main script execution stops at the bottom as usual. So we need to use a loop to keep the main script running and get the values from the callback in the outer scope.

The following example is to pass the image frames from the callback to the outer scope and continuously process it in the loop. In this example, a simple image analysis (calculating the histogram like this OpenCV tutorial) is done on the image frames.

threading.Lock is one standard way to control variable accesses across threads. A dict object img_container here is a mutable container shared by the callback and the outer scope and the lock object is used at assigning and reading the values to/from the container for thread-safety.

import threading

import cv2
import streamlit as st
from matplotlib import pyplot as plt

from streamlit_webrtc import webrtc_streamer

lock = threading.Lock()
img_container = {"img": None}


def video_frame_callback(frame):
    img = frame.to_ndarray(format="bgr24")
    with lock:
        img_container["img"] = img

    return frame


ctx = webrtc_streamer(key="example", video_frame_callback=video_frame_callback)

fig_place = st.empty()
fig, ax = plt.subplots(1, 1)

while ctx.state.playing:
    with lock:
        img = img_container["img"]
    if img is None:
        continue
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    ax.cla()
    ax.hist(gray.ravel(), 256, [0, 256])
    fig_place.pyplot(fig)

Callback limitations

The callbacks are executed in forked threads different from the main one, so there are some limitations:

• Streamlit methods (st.* such as st.write()) do not work inside the callbacks.
• Variables inside the callbacks cannot be directly referred to from the outside.
• The global keyword does not work expectedly in the callbacks.
• You have to care about thread-safety when accessing the same objects both from outside and inside the callbacks as stated in the section above.

Cleanup on Stop (session lifecycle)

webrtc_streamer() accepts on_video_ended and on_audio_ended arguments — zero-argument callables that fire when the corresponding input media track ends (the user clicks "STOP", closes the page, or the connection drops). They are the recommended hook for tearing down per-session resources that the frame callbacks allocated, such as worker threads, model handles, file writers, queues, or st.session_state entries.

import streamlit as st
from streamlit_webrtc import webrtc_streamer


def video_frame_callback(frame):
    # ... process the frame, possibly initializing per-session state on first call ...
    ret