With the aim of making the most of this book, you have to take into account two simple but key considerations:

1. Some basic knowledge of Python programming is assumed as all the scripts and examples in this book are in Python.
2. The NumPy and OpenCV-Python packages are highly interconnected (you will learn why in this book). In spite of NumPy examples being fully explained, the learning curve can be softened if some NumPy knowledge is acquired before starting this book.

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.packt.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 & Errata.
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/Mastering-OpenCV-4-with-Python. 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!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781789344912_ColorImages.pdf.

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: "The code for build_sample_image() is provided next."

A block of code is set as follows:

 channels = cv2.split(img)
    eq_channels = []
    for ch in channels:
        eq_channels.append(cv2.equalizeHist(ch))

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

Hu moments (original): '[ 1.92801772e-01 1.01173781e-02 5.70258405e-05 1.96536742e-06 2.46949980e-12 -1.88337981e-07 2.06595472e-11]'
 Hu moments (rotation): '[ 1.92801772e-01 1.01173781e-02 5.70258405e-05 1.96536742e-06 2.46949980e-12 -1.88337981e-07 2.06595472e-11]'
 Hu moments (reflection): '[ 1.92801772e-01 1.01173781e-02 5.70258405e-05 1.96536742e-06 2.46949980e-12 -1.88337981e-07 -2.06595472e-11]'

Any command-line input or output is written as follows:

$ mkdir opencv-project
$ cd opencv-project

Bold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."