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License GitHub release

neural-style-art (NST - Neural Style Transfer )

Neural Style Transfer refers to a kind of software algorithm that manages images or videos, to transfer the visual style of another image. NST algorithms are characterized by their use of deep neural networks for image transformation.

special thanks

This project is inspired by the following articles which, if you are interested in the subject, I recommend you read a lot, they are extremely interesting. Special thanks to the article Neural Style Transfer: Creating Art with Deep Learning using tf.keras and eager execution by Raymond Yuan, thanks to these project provided a lot of support and inspiration. Please visit their repositories you may be interested to you. Neural Style Transfer with Swift for TensorFlow, Walking through Neural Style Transfer with Weights & Biases and Neural Style Transfer (Part 1)

Requirements

The project can be used with python2.7 for to build. The most recommended is to use version 2.7 within a virtual environment, since this version is already deprecated, you can install python2.7 on a virtual environment you can use virtualenv or pipenv. To run the version of the script with tkinter you need the tk package. Also to run this project successfully, you need to have jupyter notebook installed on your computer.

Notes

  • Tensor : (Algebraic entity of several components, which generalizes the concepts of scalar, vector and matrix)
  • Tensorflow :( Automatic learning through a range of tasks with the ability to build and train neural networks to detect and decipher patterns and correlations, analogous to learning and reasoning used by humans )
  • Keras: ( Neural Network Manipulator, Helps experiment with Deep Learning Networks ) Keras is a model level library, providing high level building blocks for the development of models of deep learning. It does not handle low-level operations such as products tensioners, convolutions, etc. Instead, it is based on a specialized and well-optimized library of manipulating tensors to do so, serving as Keras "backend engine". Instead of choosing a single library of tensors and make the Keras implementation link to that library, Keras handles the problem in a modular way, and several different backend motors can be connected to Keras without any problem

Pip packages

  • jupyterlab
  • matplotlib
  • numpy
  • pandas
  • Pyllow
  • tensorflow
  • tensorflow-gpu
  • keras

Works Optimizer Adam from tensorflow Again Xd

# works Adam YEA!
best, best_loss = run_style_transfer(content_path, style_path, num_iterations=5)

png

Image.fromarray(best)

png

def show_results(best_img, content_path, style_path, show_large_final=True):
    plt.figure(figsize=(10, 5))
    content = load_img(content_path) 
    style = load_img(style_path)
    
    plt.subplot(1, 2, 1)
    imshow(content, 'Content Image')

    plt.subplot(1, 2, 2)
    imshow(style, 'Style Image')

    if show_large_final: 
        plt.figure(figsize=(10, 10))

    plt.imshow(best_img)
    plt.title('Output Image')
    plt.show()
show_results(best, content_path, style_path)

png

png

Aditional Note:

This project is a fork of https://github.com/tensorflow/models/tree/master/research/nst_blogpost

When you increase the number of iterations of the execution of the transfer style function, the need for computing is greater and for optimal results you need a little more computing resources in my case take advantage of a virtual machine in azure (Ubuntu 18.04, core i7, 16GB RAM), depending on the number of iterations is the time to run the script.

# Connect to the server using SSH tunneling
$ ssh [email protected] -NL 1234:localhost:1234 
$ jupyter notebook --no-browser --port 1234