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| title: st.camera_input | |
| slug: /develop/api-reference/widgets/st.camera_input | |
| description: st.camera_input displays a widget to upload images from a camera | |
| <Autofunction function="streamlit.camera_input" /> | |
| To read the image file buffer as bytes, you can use `getvalue()` on the `UploadedFile` object. | |
| ```python | |
| 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)) | |
| ``` | |
| <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`. | |
| </Important> | |
| ## 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](https://pillow.readthedocs.io/en/stable/installation.html), [NumPy](https://numpy.org/), [OpenCV](https://pypi.org/project/opencv-python-headless/), [TensorFlow](https://www.tensorflow.org/), [torchvision](https://pytorch.org/vision/stable/index.html), and [PyTorch](https://pytorch.org/). | |
| 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](https://pillow.readthedocs.io/en/stable/installation.html) and [NumPy](https://numpy.org/). | |
| To read the image file buffer as a PIL Image and convert it to a NumPy array: | |
| ```python | |
| 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](https://pypi.org/project/opencv-python-headless/) and [NumPy](https://numpy.org/). | |
| To read the image file buffer with OpenCV: | |
| ```python | |
| 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](https://www.tensorflow.org/install/). | |
| To read the image file buffer as a 3 dimensional uint8 tensor with TensorFlow: | |
| ```python | |
| 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](https://pypi.org/project/torchvision/) (it is not bundled with PyTorch) and [PyTorch](https://pytorch.org/). | |
| To read the image file buffer as a 3 dimensional uint8 tensor with `torchvision.io`: | |
| ```python | |
| 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](https://pytorch.org/) and [NumPy](https://numpy.org/). | |
| To read the image file buffer as a 3 dimensional uint8 tensor with PyTorch: | |
| ```python | |
| 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) | |
| ``` | |