Book Image

Python Image Processing Cookbook

By : Sandipan Dey
Book Image

Python Image Processing Cookbook

By: Sandipan Dey

Overview of this book

With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing. With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems. By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.
Table of Contents (11 chapters)

Compressing an image using wavelets

In this recipe, you will learn how to use wavelets to transform an image and discard the lower-order bits from the output of the transform, so that most of its values are zero (or very small), but most of the signal (pixels) is preserved. We shall use the mahotas library functions for the demonstration.

Getting ready

In this recipe, we will use the cameraman grayscale image as input. Let's get started by importing the required libraries and modules:

import numpy as np
import mahotas
from mahotas.thresholding import soft_threshold
from matplotlib import pyplot as plt
import os

How to do it.....