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)

Classifying images with VGG19/Inception V3/MobileNet/ResNet101 (with PyTorch)

In this recipe, you are going to learn how to use torchvision's pretrained (on Imagenet) deep learning models for a few famous models. ImageNet is an image database organized as per the WordNet hierarchy. Hundreds/thousands of images belong to each node in the hierarchy.

The following plot shows the top-1 accuracy achieved by a few popular deep neural nets participated in the ImageNet challenge, starting from AlexNet (Krizhevsky et al., 2012) on the far left, to the best performing Inception-v4 (Szegedy et al., 2016) on the far right:

The top-1 accuracy is defined as the average number of times the correct label for an image was the highest probability class predicted by the CNN for that image. At the other end of the scale, the top-1 error shows the error that occurs when the model-predicted...