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

Face morphing


In Chapter 1, Getting Started with Image Processing, we discussed a naive face morphing technique based on simple α-blending, which looks terrible if the faces to be morphed are not aligned.

 

Let's conclude the last chapter by discussing a sophisticated face morphing technique, namely Beier-Neely morphing, which visually looks way smoother and better than α-blending for non-aligned faces. Here is the algorithm:

  1. Read in two image files, A and B.
  2. Specify the correspondence between source image and destination image interactively (by computing facial key points with PyStasm) using a set of line segment pairs. Save the line segment pair to lines file.
  3. Read the lines file. The lines file contains the line segment pairs SiA, SiB
  4. Compute destination line segments by linearly interpolating between Siand Siby warp fraction. These line segments define the destination shape.
  5. Warp image A to its destination shape, computing a new image A'. 
  6. Warp picture B to its destination shape, computing...