st.camera_input

Display a widget that returns pictures from the user's webcam.

Function signature
st.camera_input(label, key=None, help=None, on_change=None, args=None, kwargs=None, *, disabled=False)
Parameters
label (str)	A short label explaining to the user what this widget is used for.
key (str or int)	An optional string or integer to use as the unique key for the widget. If this is omitted, a key will be generated for the widget based on its content. Multiple widgets of the same type may not share the same key.
help (str)	A tooltip that gets displayed next to the camera input.
on_change (callable)	An optional callback invoked when this camera_input's value changes.
args (tuple)	An optional tuple of args to pass to the callback.
kwargs (dict)	An optional dict of kwargs to pass to the callback.
disabled (bool)	An optional boolean, which disables the camera input if set to True. The default is False. This argument can only be supplied by keyword.
Returns
(None or UploadedFile)	The UploadedFile class is a subclass of BytesIO, and therefore it is "file-like". This means you can pass them anywhere where a file is expected.

Examples

import streamlit as st

picture = st.camera_input("Take a picture")

if picture:
     st.image(picture)

To read the image file buffer as bytes, you can use getvalue() on the UploadedFile object.

import streamlit as st

img_file_buffer = st.camera_input("Take a picture")

if img_file_buffer is not None:
    # To read image file buffer as bytes:
    bytes_data = img_file_buffer.getvalue()
    # Check the type of bytes_data:
    # Should output: <class 'bytes'>
    st.write(type(bytes_data))

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Important

st.camera_input returns an object of the UploadedFile class, which a subclass of BytesIO. Therefore it is a "file-like" object. This means you can pass it anywhere where a file is expected, similar to st.file_uploader.

Image processing examples

You can use the output of st.camera_input for various downstream tasks, including image processing. Below, we demonstrate how to use the st.camera_input widget with popular image and data processing libraries such as Pillow, NumPy, OpenCV, TensorFlow, torchvision, and PyTorch.

While we provide examples for the most popular use-cases and libraries, you are welcome to adapt these examples to your own needs and favorite libraries.

Pillow (PIL) and NumPy

Ensure you have installed Pillow and NumPy.

To read the image file buffer as a PIL Image and convert it to a NumPy array:

import streamlit as st
from PIL import Image
import numpy as np

img_file_buffer = st.camera_input("Take a picture")

if img_file_buffer is not None:
    # To read image file buffer as a PIL Image:
    img = Image.open(img_file_buffer)

    # To convert PIL Image to numpy array:
    img_array = np.array(img)

    # Check the type of img_array:
    # Should output: <class 'numpy.ndarray'>
    st.write(type(img_array))

    # Check the shape of img_array:
    # Should output shape: (height, width, channels)
    st.write(img_array.shape)

OpenCV (cv2)

Ensure you have installed OpenCV and NumPy.

To read the image file buffer with OpenCV:

import streamlit as st
import cv2
import numpy as np

img_file_buffer = st.camera_input("Take a picture")

if img_file_buffer is not None:
    # To read image file buffer with OpenCV:
    bytes_data = img_file_buffer.getvalue()
    cv2_img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)

    # Check the type of cv2_img:
    # Should output: <class 'numpy.ndarray'>
    st.write(type(cv2_img))

    # Check the shape of cv2_img:
    # Should output shape: (height, width, channels)
    st.write(cv2_img.shape)

TensorFlow

Ensure you have installed TensorFlow.

To read the image file buffer as a 3 dimensional uint8 tensor with TensorFlow:

import streamlit as st
import tensorflow as tf

img_file_buffer = st.camera_input("Take a picture")

if img_file_buffer is not None:
    # To read image file buffer as a 3D uint8 tensor with TensorFlow:
    bytes_data = img_file_buffer.getvalue()
    img_tensor = tf.io.decode_image(bytes_data, channels=3)

    # Check the type of img_tensor:
    # Should output: <class 'tensorflow.python.framework.ops.EagerTensor'>
    st.write(type(img_tensor))

    # Check the shape of img_tensor:
    # Should output shape: (height, width, channels)
    st.write(img_tensor.shape)

Torchvision

Ensure you have installed Torchvision (it is not bundled with PyTorch) and PyTorch.

To read the image file buffer as a 3 dimensional uint8 tensor with torchvision.io:

import streamlit as st
import torch
import torchvision

img_file_buffer = st.camera_input("Take a picture")

if img_file_buffer is not None:
    # To read image file buffer as a 3D uint8 tensor with `torchvision.io`:
    bytes_data = img_file_buffer.getvalue()
    torch_img = torchvision.io.decode_image(
        torch.frombuffer(bytes_data, dtype=torch.uint8)
    )

    # Check the type of torch_img:
    # Should output: <class 'torch.Tensor'>
    st.write(type(torch_img))

    # Check the shape of torch_img:
    # Should output shape: torch.Size([channels, height, width])
    st.write(torch_img.shape)

PyTorch

Ensure you have installed PyTorch and NumPy.

To read the image file buffer as a 3 dimensional uint8 tensor with PyTorch:

import streamlit as st
import torch
import numpy as np

img_file_buffer = st.camera_input("Take a picture")

if img_file_buffer is not None:
    # To read image file buffer as a 3D uint8 tensor with PyTorch:
    bytes_data = img_file_buffer.getvalue()
    torch_img = torch.ops.image.decode_image(
        torch.from_numpy(np.frombuffer(bytes_data, np.uint8)), 3
    )

    # Check the type of torch_img:
    # Should output: <class 'torch.Tensor'>
    st.write(type(torch_img))

    # Check the shape of torch_img:
    # Should output shape: torch.Size([channels, height, width])
    st.write(torch_img.shape)

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