notebooks/style-transfer-webcam/README.md

# Live Style Transfer with OpenVINO™
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*Binder and Google Colab are a free services where the webcam will not work, and performance on the video will not be good. For the best performance run the notebook locally.*

![style transfer](https://user-images.githubusercontent.com/109281183/208703143-049f712d-2777-437c-8172-597ef7d53fc3.gif)

Artistic style transfer blends a single style to any given image. The real-time style transfer model is a neural network trained to apply a single style to images. Different networks can be trained for different styles you may wish to apply.

## Notebook Contents

There are five pre-trained style transfer models you can use with this notebook with the following styles: Mosaic, Rain Princess, Candy, Udnie and Pointilism. The models are downloaded from [ONNX Model Repository](https://github.com/onnx/models). They are based on the research paper [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://arxiv.org/abs/1603.08155) and [Instance Normalization](https://arxiv.org/abs/1607.08022). The final steps in this notebook show live inference results using video from a webcam and video file.

## Installation Instructions

This is a self-contained example that relies solely on its own code.</br>
We recommend  running the notebook in a virtual environment. You only need a Jupyter server to start.
For details, please refer to [Installation Guide](../../README.md).

## See Also

* [OpenVINO notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
* [Model Conversion API](https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html)
* [Image Processing Demo](https://docs.openvino.ai/2024/omz_demos_image_processing_demo_cpp.html)