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Hands-On Image Processing with Python

You're reading from   Hands-On Image Processing with Python Expert techniques for advanced image analysis and effective interpretation of image data

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343731
Length 492 pages
Edition 1st Edition
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Author (1):
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Sandipan Dey Sandipan Dey
Author Profile Icon Sandipan Dey
Sandipan Dey
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Table of Contents (20) Chapters Close

Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
1. Getting Started with Image Processing FREE CHAPTER 2. Sampling, Fourier Transform, and Convolution 3. Convolution and Frequency Domain Filtering 4. Image Enhancement 5. Image Enhancement Using Derivatives 6. Morphological Image Processing 7. Extracting Image Features and Descriptors 8. Image Segmentation 9. Classical Machine Learning Methods in Image Processing 10. Deep Learning in Image Processing - Image Classification 11. Deep Learning in Image Processing - Object Detection, and more 12. Additional Problems in Image Processing 1. Other Books You May Enjoy Index

Neural style transfers with cv2 using a pre-trained torch model


In this section, we will discuss how to use deep learning to implement a neural style transfer(NST). You will be surprised at the kind of artistic images we can generate using it. Before diving into further details about the deep learning model, let's discuss some of the basic concepts.

Understanding the NST algorithm

The NST algorithm was first revealed in a paper on the subject by Gatys et alia in 2015. This technique involves a lot of fun! I am sure you will love implementing this, and will be amazed at the outputs that you'll create.

It attempts to merge two images based on the following parameters:

  • A content image (C)
  • A style image (S)

 

The NST algorithm uses these parameters to create a third, generated image (G). The generated image G combines the content of the image C with the style of image S.

Here is an example of what we will actually be doing: 

Surprised? I hope you liked the filter applied on the Mona Lisa! Excited to...

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