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

Hough transform – detecting lines and circles


In image processing, Hough transform is a feature extraction technique that aims to find instances of objects of a certain shape using a voting procedure carried out in a parameter space. In its simplest form, the classical Hough transform can be used to detect straight lines in an image. We can represent a straight line using polar parameters (ρ, θ), where ρ is the length of the line segment and θ is the angle in between the line and the axis. To explore (ρ, θ) parameter space, it first creates a 2D-histogram. Then, for each value of ρ and θ, it computes the number of non-zero pixels in the input image that are close to the corresponding line and increments the array at position (ρ, θ) accordingly. Hence, each non-zero pixel can be thought of as voting for potential line candidates. The most probable lines correspond to the parameter values that obtained the highest votes, that is, the local maxima in a 2D histogram. The method can be extended...