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

Questions


  1. Show with a binary image that morphological opening and closing are dual operations. (Hint: apply opening on an image foreground and closing on the image background with the same structuring element)
  2. Automatically crop an image using the convex hull of the object in it (the problem is taken from https://stackoverflow.com/questions/14211340/automatically-cropping-an-image-with-python-pil/51703287#51703287). Use the following image and crop the white background:

The desired output image is shown as follows—the bounding rectangle to crop the image is to be found automatically:

  1. Use the maximum() and minimum() functions from skimage.filters.rank to implement morphological opening and closing with a grayscale image.