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

Supervised machine learning – image classification


In this section, we will discuss the image classification problem. The input dataset that we will use is MNIST (http://yann.lecun.com/exdb/mnist/), which is a classic dataset in machine learning, consisting of 28 x 28 grayscale images of handwritten digits.

 

 

The original training dataset contains 60,000 examples (the handwritten digit images along with the labels to train machine learning models), and the test dataset contains 10,000 examples (the handwritten digit images along with the labels as ground-truths, to test the accuracy of the models learned). Given a set of handwritten digits and images along with their labels (0-9), the goal will be to learn a machine learning model that can automatically identify the digit from an unseen image and assign a label (0-9) to the image:

  1. A few supervised machine learning (multi-class classification) models (classifiers) will be trained using the training dataset
  2. Then, they will be used to predict...