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 traffic signs using a deep learning model (with PyTorch)

In this recipe, you will learn how to train a custom neural network from scratch using PyTorch and use the model's predictions to classify traffic signs. We shall use the German Traffic Sign Recognition Benchmark (GTSRB) dataset (https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html) as the input images for training/testing. The images are labeled with 43 different traffic signs. The dataset contains 39,209 training and 12,630 test images. For this recipe, it is also recommended that you use a computer with GPU(s) in it to make the training process faster.

Getting ready

First, download the compressed pickled dataset...