In this tutorial, we will learn how to use the neural-style-transfer library to compose images in the style of other images using just a few lines of code. The Neural Style Transfer technique is an optimization method that uses two images as input. One image refers to the style of the input. The other is the input image you wish to style.
A model merges the two images and creates a transformed image with the referenced style. looks interesting right.. but it’s required lots of stuff which includes image pre-processing code, model training code, optimization functions, and lots of other small functions, but to get rid of all these things we have an amazing library called neural-style-transfer which does all this stuff for us in just a few lines of code so in this blog will show you how someone can use this beautiful library.
First, install the library pip install neural-style-transfer Import the library and create a class object from neuralstyletransfer.style_transfer import NeuralStyleTransfer nst = NeuralStyleTransfer Now we need to load content and reference style images using the given command.
Here I have used an image that was downloaded from another URL. However, images can be loaded directly from your local machine by specifying an absolute path and pathType=’local’. content_url = ‘https://i.ibb.co/6mVpxGW/content.png’
style_url = ‘https://i.ibb.co/30nz9Lc/style.jpg’ nst.LoadContentImage(content_url, pathType=’url’)
nst.LoadStyleImage(style_url, pathType=’url’) Content image Content image Style image Great! Now we can start training models.. All you need is to simply call the apply function, model start optimization and output. output = nst.apply(contentWeight=1000, styleWeight=0. 01, epochs =600) Output variable holds the final image. You can then save it using the Pillow Library. from PIL import Image output.save(‘output.jpg’) this process takes some time based on your machine’s hardware I suggest using a GPU machine to make the process much faster.
You can specify different hyperparameters in the apply function depending on what you need. ContentWeight – This parameter can be used to specify the weight for the content image. If it’s set lower than the result (transformed) image, it may not show clearly.
To make the image more sharper, you can raise the content weight. StyleWeight – This parameter allows us to specify the style image’s weight. Usually, it is 0. 01, but you have the option to change it to suit your needs. You can adjust the number of epochs for model training by using this parameter. It is possible to set a value such as 500, 600, or more if your requirements are more pressing.
The training process prints out the style and content loss, so you can choose your epoch based on this information. The output image is the transformed image. Check out this colab-notebook to see how it looks. Neural Style Transfer with a Python library originally appeared in on Medium. People are responding and highlighting this story. Published via