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 discussed a few advanced image processing problems. We started with the seam carving algorithm and demonstrated a couple of applications of the algorithm in context-aware image resizing and object or artifact removal from images with the scikit-image library.

Next, we discussed seamless cloning with an application to copy one object from one image to another using Python and OpenCV. Then we discussed the biharmonic inpainting algorithm and applied it to restore damaged pixels in an image using the scikit-image library. After that, we discussed variational methods in image processing with an application in image denoising with scikit-image again. Next, we discussed the image quilting algorithm and its application in texture synthesis and transfer of images. Finally, we ended this chapter with a discussion on an advanced face morphing algorithm. By the end of this chapter, the reader should be able to write Python codes for all these tasks.