This book covers how to solve image processing problems using popular Python image processing libraries (such as PIL, scikit-image, python-opencv, scipy ndimage, and SimpleITK), machine learning libraries (scikit-learn), and deep learning libraries (TensorFlow, Keras). It will enable the reader to write code snippets to implement complex image processing algorithms, such as image enhancement, filtering, restoration, segmentation, classification, and object detection. The reader will also be able to use machine learning and deep learning models to solve complex image processing problems.
The book will start with the basics and guide the reader to go to an advanced level by providing Python-reproducible implementations throughout the book. The book will start from the classical image processing techniques and explore the journey of evolution of the image processing algorithms all the way through to the recent advances in image processing/computer vision with deep learning. Readers will learn how to use the image processing libraries, such as PIL, scikit-image, and scipy ndimage in Python, which will enable them to write code snippets in Python 3 and quickly implement complex image processing algorithms, such as image enhancement, filtering, segmentation, object detection, and classification. The reader will learn how to use machine learning models using the scikit-learn library and later explore deep CNN such as VGG-19 with TensorFlow/Keras, use the end-to-end deep learning YOLO model for object detection, and DeepLab V3+ for semantic segmentation and neural-style transfer models. The reader will also learn a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. By the end of this book, the reader will learn to implement various algorithms for efficient image processing.
This book follows a highly practical approach that will take its readers through a set of image processing concepts/algorithms and help them learn, in detail, how to use leading Python library functions to implement these algorithms.
The images used in this book as inputs and the outputs can be found at https://www.packtpub.com/sites/default/files/downloads/9781789343731_ColorImages.pdf.
This book is for engineers/applied researchers, and also for software engineers interested in computer vision, image processing, machine learning, and deep learning, especially for readers who are adept at Python programming and who want to explore various topics on image processing in detail and solve a range of complex problems, starting from concept through to implementation. A math and programming background, along with some basic knowledge of machine learning, are prerequisites.
Chapter 1, Getting Started with Image Processing, covers image processing and its applications, different Python libraries, image input/output, data structures, file formats, and basic image manipulations.
Chapter 2, Sampling, Fourier Transform, and Convolution, covers 2D Fourier transform, sampling, quantization, discrete Fourier transform, 1D and 2D convolution and filtering in the frequency domain, and how to implement them with Python using examples. You will learn the simple signal processing tools that are needed in order to understand the following units.
Chapter 3, Convolution and Frequency Domain Filtering, demonstrates how convolution is carried out on images using Python. Topics such as filtering in the frequency domain are also covered.
Chapter 4, Image Enhancement, covers some of the most basic tools in image processing, such as mean/median filtering and histogram equalization, which are still among the most powerful. We will describe these and provide a modern interpretation of these basic tools.
Chapter 5, Image Enhancement using Derivatives, covers further topics associated with image enhancement, in other words, the problem of improving the appearance or usefulness of an image. Topics covered include edge detection with derivatives and Laplacian, sharpening, and pseudo coloring. All the concepts will be described with the help of examples involving Python.
Chapter 6, Morphological Image Processing, covers binary operations and the use of filter rank module to perform operations such as morphological contrast enhancements, noise removal, and computing local entropy. We will also see how a morphology module is used.
Chapter 7, Extracting Image Features and Descriptors, describes several techniques for extracting features from images/compute image descriptors.
Chapter 8, Image Segmentation, outlines the basic techniques for partitioning an image, from a simple threshold to more advanced graph cuts.
Chapter 9, Classical Machine Learning Methods in Image Processing, introduces a number of different machine learning techniques for image classification and object detection/recognition.
Chapter 10, Deep Learning in Image Processing – Image Classification, describes why the image processing/computer vision community gradually transitioned from the classical feature-based machine learning models to deep learning models.
Chapter 11, Deep Learning in Image Processing - Object Detection, and more, describes a number of remarkable applications of the CNNs for object detection, semantic segmentation, and image style transfer. A few popular models, such as YOLO and object proposals, will be demonstrated. How to use transfer learning to avoid learning a very deep neural net from scratch will also be outlined.
Chapter 12, Additional Problems in Image Processing, describes a number of additional image processing problems and various algorithms for solving them. Problems include seam carving (for context-aware image resizing), image quilting (for image resizing with non-parametric sampling and texture transfer), poisson (gradient) image editing (blending) to seamlessly blend one image within another, image morphing (to transform one image to another), image inpainting (to restore a degraded image), and some variational image processing techniques (for image denoising, for example).
- A basic knowledge of Python is required to run the codes, along with access to image datasets and the GitHub link.
- A basic Math background is also needed to understand the concepts.
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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Image-Processing-with-Python. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789343731_ColorImages.pdf.
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."
A block of code is set as follows:
viewer = viewer.ImageViewer(im) viewer.show()
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
[default]
exten => s,1,Dial(Zap/1|30)
exten => s,2,Voicemail(u100)
exten => s,102,Voicemail(b100)
exten => i,1,Voicemail(s0)Any command-line input or output is written as follows:
>>> pip install numpy >>> pip install scipy
Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."
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