Skip to content


Switch branches/tags

Streamlit - Drawable Canvas

Streamlit component which provides a sketching canvas using Fabric.js.

Streamlit App

PyPI PyPI - Downloads

Buy Me A Coffee


  • Draw freely, lines, circles, boxes and polygons on the canvas, with options on stroke & fill
  • Rotate, skew, scale, move any object of the canvas on demand
  • Select a background color or image to draw on
  • Get image data and every drawn object properties back to Streamlit !
  • Choose to fetch back data in realtime or on demand with a button
  • Undo, Redo or Delete canvas contents
  • Save canvas data as JSON to reuse for another session


pip install streamlit-drawable-canvas

Example Usage

Copy this code snippet:

import pandas as pd
from PIL import Image
import streamlit as st
from streamlit_drawable_canvas import st_canvas

# Specify canvas parameters in application
drawing_mode = st.sidebar.selectbox(
    "Drawing tool:", ("point", "freedraw", "line", "rect", "circle", "transform")

stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 3)
if drawing_mode == 'point':
    point_display_radius = st.sidebar.slider("Point display radius: ", 1, 25, 3)
stroke_color = st.sidebar.color_picker("Stroke color hex: ")
bg_color = st.sidebar.color_picker("Background color hex: ", "#eee")
bg_image = st.sidebar.file_uploader("Background image:", type=["png", "jpg"])

realtime_update = st.sidebar.checkbox("Update in realtime", True)


# Create a canvas component
canvas_result = st_canvas(
    fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
    background_color=bg_color, if bg_image else None,
    point_display_radius=point_display_radius if drawing_mode == 'point' else 0,

# Do something interesting with the image data and paths
if canvas_result.image_data is not None:
if canvas_result.json_data is not None:
    objects = pd.json_normalize(canvas_result.json_data["objects"]) # need to convert obj to str because PyArrow
    for col in objects.select_dtypes(include=['object']).columns:
        objects[col] = objects[col].astype("str")

You will find more detailed examples on the demo app.


    fill_color: str
    stroke_width: int
    stroke_color: str
    background_color: str
    background_image: Image
    update_streamlit: bool
    height: int
    width: int
    drawing_mode: str
    initial_drawing: dict
    display_toolbar: bool
    point_display_radius: int
    key: str
  • fill_color : Color of fill for Rect in CSS color property. Defaults to "#eee".
  • stroke_width : Width of drawing brush in CSS color property. Defaults to 20.
  • stroke_color : Color of drawing brush in hex. Defaults to "black".
  • background_color : Color of canvas background in CSS color property. Defaults to "" which is transparent. Overriden by background_image. Changing background_color will reset the drawing.
  • background_image : Pillow Image to display behind canvas. Automatically resized to canvas dimensions. Being behind the canvas, it is not sent back to Streamlit on mouse event. Overrides background_color. Changes to this will reset canvas contents.
  • update_streamlit : Whenever True, send canvas data to Streamlit when object/selection is updated or mouse up.
  • height : Height of canvas in pixels. Defaults to 400.
  • width : Width of canvas in pixels. Defaults to 600.
  • drawing_mode : Enable free drawing when "freedraw", object manipulation when "transform", otherwise create new objects with "line", "rect", "circle" and "polygon". Defaults to "freedraw".
    • On "polygon" mode, double-clicking will remove the latest point and right-clicking will close the polygon.
  • initial_drawing : Initialize canvas with drawings from here. Should be the json_data output from other canvas. Beware: if you try to import a drawing from a bigger/smaller canvas, no rescaling is done in the canvas and the import could fail.
  • point_display_radius : To make points visible on the canvas, they are drawn as circles. This parameter modifies the radius of the displayed circle.
  • display_toolbar : If False, don't display the undo/redo/delete toolbar.


import streamlit as st
from streamlit_drawable_canvas import st_canvas

canvas_result = st_canvas()
  • display_toolbar : Display the undo/redo/reset toolbar.
  • key : An optional string to use as the unique key for the widget. Assign a key so the component is not remount every time the script is rerun.



  • JS side
cd frontend
npm install
  • Python side
conda create -n streamlit-drawable-canvas python=3.7
conda activate streamlit-drawable-canvas
pip install -e .


Both webpack dev server and Streamlit should run at the same time.

  • JS side
cd frontend
npm run start
  • Python side
streamlit run

Cypress integration tests

  • Install Cypress: cd e2e; npm i or npx install cypress (with --force if cache problem)
  • Start Streamlit frontend server: cd streamlit_drawable_canvas/frontend; npm run start
  • Start Streamlit test script: streamlit run e2e/
  • Start Cypress app: cd e2e; npm run cypress:open



Do you like Quick, Draw? Well what if you could train/predict doodles drawn inside Streamlit? Also draws lines, circles and boxes over background images for annotation.