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

Summary


In this chapter, we first provided a basic introduction to image processing and basic concepts regarding the problems that we try to solve in image processing. We then discussed different tasks and steps with image processing, and the leading image processing libraries in Python, which we are going to use for coding in this book. Next, we talked about how to install different libraries for image processing in Python, and how to import them and call the functions from the modules. We also covered basic concepts about image types, file formats, and data structures to store image data with different Python libraries. Then, we discussed how to perform image I/O and display in Python using different libraries. Finally, we discussed how to perform basic image manipulations with different Python libraries. In the next chapter, we will deep dive into sampling, quantization, convolution, the Fourier transform, and frequency domain filtering on images.