Open source computer vision projects, such as OpenCV 3, enable all kinds of users to harness the forces of machine vision, machine learning, and artificial intelligence. By mastering these powerful libraries of code and knowledge, professionals and hobbyists can create smarter, better applications wherever they are needed.
This is exactly where this book is focused, guiding you through a set of hands-on projects and templates, which will teach you to combine fantastic techniques in order to solve your specific problem.
As we study computer vision, let's take inspiration from these words:
"I saw that wisdom is better than folly, just as light is better than darkness."
Let's build applications that see clearly, and create knowledge.
Chapter 1, Getting the Most out of Your Camera System, discusses how to select and configure camera systems to see invisible light, fast motion, and distant objects.
Chapter 2, Photographing Nature and Wildlife with an Automated Camera, shows how to build a "camera trap", as used by nature photographers, and process photos to create beautiful effects.
Chapter 3, Recognizing Facial Expressions with Machine Learning, explores ways to build a facial expression recognition system with various feature extraction techniques and machine learning methods.
Chapter 4, Panoramic Image Stitching Application Using Android Studio and NDK, focuses on the project of building a panoramic camera app for Android with the help of OpenCV 3's stitching module. We will use C++ with Android NDK.
Chapter 5, Generic Object Detection for Industrial Applications, investigates ways to optimize your object detection model, make it rotation invariant, and apply scene-specific constraints to make it faster and more robust.
Chapter 6, Efficient Person Identification Using Biometric Properties, is about building a person identification and registration system based on biometric properties of that person, such as their fingerprint, iris, and face.
Chapter 7, Gyroscopic Video Stabilization, demonstrates techniques for fusing data from videos and gyroscopes, how to stabilize videos shot on your mobile phone, and how to create hyperlapse videos.
As a basic setup, the complete book is based on the OpenCV 3 software. If a chapter does not have a specific OS requirement, then it will run on Windows, Linux, and Mac. As authors, we encourage you to take the latest master branch from the official GitHub repository (https://github.com/Itseez/opencv/) for setting up your OpenCV installation, rather then using the downloadable packages at the official OpenCV website (http://opencv.org/downloads.html), since the latest master branch contains a huge number of fixes compared to the latest stable release.
For hardware, the authors expect that you have a basic computer system setup, either a desktop or a laptop, with at least 4 GB of RAM memory available. Other hardware requirements are mentioned below.
The following chapters have specific requirements that come on top of the OpenCV 3 installation:
Chapter 1, Getting the Most out of Your Camera System:
Software: OpenNI2 and FlyCapture 2.
Hardware: PS3 Eye camera or any other USB webcam, an Asus Xtion PRO live or any other OpenNI-compatible depth camera, and a Point Grey Research (PGR) camera with one or more lenses.
Remarks: The PGR camera setup (with FlyCapture 2) will not run on Mac. Even if you do not have all the required hardware, you can still benefit from some sections of this chapter.
Chapter 2, Photographing Nature and Wildlife with an Automated Camera:
Software: Linux or Mac operating system.
Hardware: A portable laptop or a single-board computer (SBC) with battery, combined with a photo camera.
Chapter 4, Panoramic Image Stitching Application Using Android Studio and NDK:
Software: Android 4.4 or later, Android NDK.
Hardware: Any mobile device that supports Android 4.4 or later.
Chapter 7, Gyroscopic Video Stabilization:
Software: NumPy, SciPy, Python, and Android 5.0 or later, and the Android NDK.
Hardware: A mobile phone that supports Android 5.0 or later for capturing video and gyroscope signals.
As authors, we acknowledge that installing OpenCV 3 on your system can sometimes be quite cumbersome. Therefore, we have added a series of basic installation guides for installing OpenCV 3, based on the latest OpenCV 3 master branch on your system, and getting the necessary modules for the different chapters to work. For more information, take a look at https://github.com/OpenCVBlueprints/OpenCVBlueprints/tree/master/installation_tutorials.
Keep in mind that the book also uses modules from the OpenCV "contrib" (contributed) repository. The installation manual will have directions on how to install these. However, we encourage you to only install those modules that we need, because we know that they are stable. For other modules, this might not be the case.
This book is ideal for you if you aspire to build computer vision systems that are smarter, faster, more complex, and more practical than the competition. This is an advanced book, intended for those who already have some experience in setting up an OpenCV development environment and building applications with OpenCV. You should be comfortable with computer vision concepts, object-oriented programming, graphics programming, IDEs, and the command line.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "You can find the OpenCV software by going to http://opencv.org and clicking on the download link."
A block of code is set as follows:
Mat input = imread("/data/image.png", LOAD_IMAGE_GRAYSCALE); GaussianBlur(input, input, Size(7,7), 0, 0); imshow("image", input); waitKey(0);
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
Mat input = imread("/data/image.png", LOAD_IMAGE_GRAYSCALE);
GaussianBlur(input, input, Size(7,7), 0, 0);
Any command-line input or output is written as follows:
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Clicking the Next button moves you to the next screen."
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