Book Image

Hands-On Image Processing with Python

By : Sandipan Dey
Book Image

Hands-On Image Processing with Python

By: Sandipan Dey

Overview of this book

Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python. The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. By the end of this book, we will have learned to implement various algorithms for efficient image processing.
Table of Contents (20 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
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...