1. The list of software needed for this book is as follows:
   • Anaconda distribution v5.0.1
   • OpenCV v3.3.0
   • TensorFlow v1.4.0
   • Keras v2.1.2

2. To run all of the code effectively, Ubuntu 16.04 is preferable, with Nvidia GPU and at least 4 GB of RAM. The code will also run without GPU support.

You can download the example code files for this book from your account at www.packtpub.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.packtpub.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/Practical-Computer-Vision. 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/PracticalComputerVision_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: "This will install the Python libraries in the $HOME/anaconda3 folder since we are using Python 3. A Python 2 version is also available and the installation process is similar. To use Anaconda, the newly installed libraries need to be added in $PATH, this can be done every time a new shell is launched."

A block of code is set as follows:

import numpy as np 
import matplotlib.pyplot as plt 
import cv2

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

from sklearn.metrics import f1_score
true_y = .... # ground truth values
pred_y = .... # output of the model

f1_value = f1_score(true_y, pred_y, average='micro')

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

sudo apt-get install build-essential
sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

Bold: Indicates a new term, an important word, or words that you see onscreen.