Technical requirements

This chapter assumes that you are using one of the following operating systems:

• Windows 10 or a later version
• macOS 10.15 (Catalina) or a later version
• Debian 10 (Buster) or a later version, or a derivative such as the following:
  • Ubuntu 18.04 (Bionic Beaver) or a later version
  • Linux Mint 19 or a later version
• Arch Linux, or a derivative such as Manjaro

For editing Python scripts and other text files, this book's authors simply recommend that you should have a good text editor. Visual Studio (VS) Code is the most suitable editor because of its simplicity and almost all OS distributions support this editor. Other examples include the following:

• Notepad++ for Windows
• BBEdit (free version) for macOS
• GEdit for the GNOME desktop environment on Linux
• Kate for the KDE Plasma desktop environment on Linux

We also recommend that you have a Git client installed since this will be the easiest way to obtain the book’s latest sample code, which is hosted on GitHub...

What's new in OpenCV 5

If you are an OpenCV veteran, you might want to know more about OpenCV 5's changes before you decide to install it. Here are some of the highlights:

• The C++ implementation of OpenCV has been updated to C++14. OpenCV's Python bindings wrap the C++ implementation, so as Python users, we may gain some performance advantages from this update, even though we are not using C++ directly.
• Support for Python 2 has been dropped because this Python version is obsolete and no longer maintained. If you have any old Python 2 projects, you should upgrade them to Python 3.
• For many algorithms, support for named parameters has been added in order to make the initialization code more concise and readable.
• For the pip package installer, the opencv-python and opencv-contrib-python packages are now maintained by the core OpenCV team rather than a third party. The opencv-contrib-python-nonfree package has been discontinued due to patent restrictions.
• The SIFT algorithm...

Optimizing OpenCV for specific hardware

Generally, the setup instructions in this chapter are compatible with any hardware architecture that is supported by OpenCV and the operating system. For example, the setup instructions that target Linux operating systems will work equally well on x64, x86, ARM, or RISC-V hardware (except that the optional OpenNI package is unavailable for RISC-V).

That said, OpenCV supports many optional optimizations for specific hardware. By following the instructions in this chapter, you will get an OpenCV installation that uses a default set of optimizations for your hardware. The defaults are generally good, but for some applications, you could get even better performance with a more highly customized installation of OpenCV. Here are a few examples of use cases:

• For x64, x86, or ARM processors, OpenCV can optionally integrate with Intel Thread Building Blocks (TBB), a multithreading library. OpenCV can use TBB to speed up certain parallel algorithms, notably...

Choosing and using the right setup tools

We are free to choose various setup tools depending on our operating system and how much configuration we want to do.

Regardless of the choice of operating system, Python offers some built-in tools that are useful for setting up a development environment. These tools include a package manager called pip and a virtual environment manager called venv. Some of this chapter's instructions will cover pip specifically, but if you would like to learn about venv, please refer to the official Python documentation at https://docs.python.org/3/library/venv.html.

You should consider using venv if you plan to maintain a variety of Python projects that might have conflicting dependencies – for example, projects that depend on different versions of OpenCV. Each of venv's virtual environments has its own set of installed libraries, and we can switch between these environments without reinstalling anything. Within a given virtual environment,...

Running the official sample code

Running a few sample scripts is a good way to test whether OpenCV has been set up correctly. Some samples are included in OpenCV's source code archive. If you have not already obtained the source code, go to https://opencv.org/releases/ and download one of the following archives:

• For Windows, download the latest archive, labeled Windows. It is a self-extracting ZIP. Run it and, when prompted, enter any destination folder, which we will refer to as <opencv_unzip_destination>. Find the Python samples in <opencv_unzip_destination>/opencv/sources/samples/python.
• For other systems, download the latest archive, labeled Sources. It is a ZIP file. Unzip it to any destination folder, which we will refer to as <opencv_unzip_destination>. Find the Python samples in <opencv_unzip_destination>/samples/python.

Some of the sample scripts require command-line arguments. However, the following scripts (among others) should work without any...

Finding documentation, help, and updates

OpenCV's documentation can be found at http://docs.opencv.org/, where you can either read it online or download it for offline reading. If you write code on airplanes or other places without internet access, you will definitely want to keep offline copies of the documentation.

The documentation includes a combined API reference for OpenCV's C++ API and its Python API. When you look up a class or function, be sure to read the section under the heading Python.

OpenCV's Python module is named cv2. The 2 in cv2 has nothing to do with the version number of OpenCV; we really are using OpenCV 5. Historically, OpenCV had a cv Python module that wrapped a now-obsolete C version of the library. The cv module was removed in OpenCV 4.

Still, in the official OpenCV 5 samples and in some other authors’ samples, you might see valid Python code such as the following, which imports the cv2 module and gives it a local alias, cv:

import...

Finding this book’s sample code

We will walk through a set of relevant code samples in each chapter of this book. For now, let’s just take note of the samples’ location and format.

All of this book’s samples can be found online in the Git repository at https://github.com/PacktPublishing/Learning-OpenCV-5-Computer-Vision-with-Python-Fourth-Edition/. The contents are organized into various folders by chapter. For some chapters, an interactively editable version of the code is provided as a Juptyer notebook. For example, Chapter 5 has a Jupyter notebook located in the repository at chapter05/chapter05.ipynb.

If you are new to Git or want to expand your Git skills, take a look at the free ebook Pro Git, by Scott Chacon and Ben Straub. You can find it online at https://git-scm.com/book/. The book’s second chapter, Git Basics, is especially helpful for beginners.

Typically, when you start studying a chapter, you might want to pull down the latest code from...

Summary

By now, we should have an OpenCV installation that will serve our needs for the diverse projects described in this book. Depending on which approach we took, we may also have a set of tools and scripts that can be used to reconfigure and rebuild OpenCV for our future needs.

We also know where to find this book’s sample code and OpenCV's official Python samples. The official samples cover a different range of functionalities outside this book's scope, but they are useful as additional learning aids.

In the next chapter, we will familiarize ourselves with the most basic functions of the OpenCV API, namely displaying images and videos, capturing videos through a webcam, and handling basic keyboard and mouse inputs.