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

Edge detection using derivatives and filters (Sobel, Canny, and so on)


As discussed earlier, the pixels that construct the edges in an image are the ones where there are sudden rapid changes (discontinuities) in the image intensity function, and the goal of edge detection is to identify these changes. Hence, edge detection is a pre-processing technique where the input is a 2D (gray-scale) image and the output is a set of curves (that are called the edges). The salient features of an image are extracted in the edges detection process; an image representation using edges is more compact than one using pixels. The edge detectors output the magnitude of the gradients (as a gray-scale image), and now, to get the edge pixels (as a binary image), we need to threshold the gradient image. Here, a very simple fixed gray-level thresholding is used (assigning all negative-valued pixels to zero with the numpy's clip() function); to obtain the binary images, we can use more sophisticated methods (such...