Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By : Joseph Howse, Joe Minichino
Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.
Table of Contents (13 chapters)

To get the most out of this book

The reader is expected to have at least basic proficiency in the Python programming language.

A Windows, macOS, or Linux development machine is recommended. You can refer to Chapter 1, Setting Up OpenCV, for instructions about setting up OpenCV 4, Python 3, and other dependencies.

This book takes a hands-on approach to learning and includes 77 sample scripts, along with sample data. Working through these examples as you read the book will help enforce the concepts.

The code for this book is released under the BSD 3-Clause open source license, which is the same as the license used by OpenCV itself. The reader is encouraged to use, modify, improve, and even publish their changes to these example programs.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packt.com.
  2. Select the Support tab.
  3. Click on Code Downloads.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Learning-OpenCV-4-Computer-Vision-with-Python-Third-Edition. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Code in Action

Code in Action videos for this book can be viewed at http://bit.ly/2STXnRN.

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781789531619_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "OpenCV provides the VideoCapture and VideoWriter classes, which support various video file formats."

A block of code is set as follows:

import cv2

grayImage = cv2.imread('MyPic.png', cv2.IMREAD_GRAYSCALE)
cv2.imwrite('MyPicGray.png', grayImage)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

import cv2

cameraCapture = cv2.VideoCapture(0)
fps = 30 # An assumption
size = (int(cameraCapture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(cameraCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
videoWriter = cv2.VideoWriter(
'MyOutputVid.avi', cv2.VideoWriter_fourcc('M','J','P','G'), fps, size)

In general, command-line input or output is written as follows:

$ pip install opencv-contrib-python

Alternatively, for Windows, command-line input or output may be written as follows:

> pip install opencv-contrib-python

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Now, under System variables, select Path and click on the Edit... button."

Warnings or important notes appear like this.
Tips and tricks appear like this.