Book Image

Hands-On Computer Vision with Julia

By : Dmitrijs Cudihins
Book Image

Hands-On Computer Vision with Julia

By: Dmitrijs Cudihins

Overview of this book

Hands-On Computer Vision with Julia is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because it’s easy to use and lets you write easy-to-compile and efficient machine code. . This book begins by introducing you to Julia's image processing libraries such as Images.jl and ImageCore.jl. You’ll get to grips with analyzing and transforming images using JuliaImages; some of the techniques discussed include enhancing and adjusting images. As you make your way through the chapters, you’ll learn how to classify images, cluster them, and apply neural networks to solve computer vision problems. In the concluding chapters, you will explore OpenCV applications to perform real-time computer vision analysis, for example, face detection and object tracking. You will also understand Julia's interaction with Tesseract to perform optical character recognition and build an application that brings together all the techniques we introduced previously to consolidate the concepts learned. By end of the book, you will have understood how to utilize various Julia packages and a few open source libraries such as Tesseract and OpenCV to solve computer vision problems with ease.
Table of Contents (11 chapters)
9
Assessments

Using test images

The TestImages.jl package and dataset provides easy access to a small number of free images out of the box. It is the way to go when trying out different computer vision techniques and algorithms.

The benefits of using the TestImages dataset are the following:

  • Images are of different file types, such as JPG, PNG, and TIF
  • Images are of different sizes, such as 512x512 and 256x256
  • Images are of different color schemes, such as RGB and grayscale

It is very easy to start with the TestImages package. You just need to load the TestImages package and use the testimage function to load the image by name:

  using TestImages
img = testimage("mandril_color");
save("mandril_color.png", img);

Our code example would result in loading a mandrill image from the TestImages dataset. You can save this in your current working directory.