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

Unsupervised machine learning – clustering, PCA, and eigenfaces


In this section, we will discuss a few popular machine learning algorithms along with their applications in image processing. Let's start with a couple of clustering algorithms and their applications in color quantization and the segmentation of images. We will use the scikit-learn library's implementation for these clustering algorithms.

K-means clustering for image segmentation with color quantization

In this section, we will demonstrate how to perform a pixel-wise Vector Quantization (VQ) of the pepper image, reducing the number of colors required to show the image from 250 unique colors down to four colors, while preserving the overall appearance quality. In this example, pixels are represented in a 3D space and k-means is used to find four color clusters.

In image processing literature, the codebook is obtained from k-means (the cluster centers) and is called the color palette. In a color palette, using a single byte, up to...