[체험판] OpenCV 3.x with Python By Example Second Edition
Make the most of OpenCV and Python to build applications for object recognition and augmented reality
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<[체험판] OpenCV 3.x with Python By Example Second Edition> ▶Book Description
Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease.
We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples.
This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.
▶What You Will Learn
⦁ Detect shapes and edges from images and videos
⦁ How to apply filters on images and videos
⦁ Use different techniques to manipulate and improve images
⦁ Extract and manipulate particular parts of images and videos
⦁ Track objects or colors from videos
⦁ Recognize specific object or faces from images and videos
⦁ How to create Augmented Reality applications
⦁ Apply artificial neural networks and machine learning to improve object recognition
⦁ Learn how to apply complex visual effects to images with OpenCV 3.x and Python
⦁ Extract features from an image and use them to develop advanced applications
⦁ Build algorithms to help you understand image content and perform visual searches
⦁ Get to grips with advanced techniques in OpenCV such as machine learning, artificial neural network, 3D reconstruction, and augmented reality
▶Who This Book Is For
This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV and Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on.
▶What this book covers
⦁ Chapter 1, Applying Geometric Transformations to Images, explains how to apply geometric transformations to images. In this chapter, we will discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. The chapter will begin with the procedure of installing OpenCV-Python on multiple platforms, such as Mac OS X, Linux, and Windows. You will also learn how to manipulate an image in various ways, such as resizing and changing color spaces.
⦁ Chapter 2, Detecting Edges and Applying Image Filters, shows how to use fundamental imageprocessing operators and how we can use them to build bigger projects. We will discuss why we need edge detection and how it can be used in various different ways in computer vision applications. We will discuss image filtering and how we can use it to apply various visual effects to photos.
⦁ Chapter 3, Cartoonizing an Image, shows how to cartoonize a given image using image filters and other transformations. We will see how to use the webcam to capture a live video stream. We will discuss how to build a real-time application, where we extract information from each frame in the stream and display the result.
⦁ Chapter 4, Detecting and Tracking Different Body Parts, shows how to detect and track faces in a live video stream. We will discuss the face detection pipeline and see how we can use it to detect and track different parts of the face, such as eyes, ears, mouth, and nose.
⦁ Chapter 5, Extracting Features from an Image, is about detecting the salient points (called keypoints) in an image. We will discuss why these salient points are important and how we can use them to understand the image's content. We will talk about the different techniques that can be used to detect salient points and extract features from an image.
⦁ Chapter 6, Seam Carving, shows how to do content-aware image resizing. We will discuss how to detect interesting parts of an image and see how we can resize a given image without deteriorating those interesting parts.
⦁ Chapter 7, Detecting Shapes and Segmenting an Image, shows how to perform image segmentation. We will discuss how to partition a given image into its constituent parts in the best possible way. You will also learn how to separate the foreground from the background in an image.
⦁ Chapter 8, Object Tracking, shows you how to track different objects in a live video stream. At the end of this chapter, you will be able to track any object in a live video stream that is captured through the webcam.
⦁ Chapter 9, Object Recognition, shows how to build an object recognition system. We will discuss how to use this knowledge to build a visual search engine.
⦁ Chapter 10, Augmented Reality, shows how to build an augmented reality application. By the end of this chapter, you will be able to build a fun augmented reality project using the webcam.
⦁ Chapter 11, Machine Learning by Artificial Neural Network, shows how to build advanced image classifiers and object recognition using the latest OpenCV implementations. By the end of this chapter, you will be able to understand how neural networks work and how to apply them to machine learning to build advance images tools.
Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementations.
⦁ Gabriel Garrido
Gabriel Garrido is a multifaceted and versatile software engineer with more than 7 years of experience in developing web applications for companies such as Telefonica, Trivago, and Base7Booking. He has a degree in computer science from the University of Granada, Spain.
He is passionate about coding, focusing on its quality and spending hours working on personal projects based on technologies such as computer vision, artificial intelligence, and augmented reality. Taking part in hackathons is one of his hobbies. He has won a couple of prizes for implementing beta software for a Google Cardboard hackathon and another for a travel assistant at a TNOOZ hackathon.
⦁ Prateek Joshi
Prateek Joshi is an artificial intelligence researcher, an author of eight published books, and a TEDx speaker. He has been featured in Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and many more publications. He is the founder of Pluto AI, a venture-funded Silicon Valley start-up building an intelligence platform for water facilities. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, Global Big Data Conference, Machine Learning Developers Conference, Sensors Expo, and more. His tech blog has more than 1.6 million page views from over 200 countries, and he has more than 7,400 followers. He graduated from the University of Southern California with a master's degree specializing in Artificial Intelligence. He has previously worked at NVIDIA and Microsoft Research. You can learn more about him on his personal website at www.prateekj.com.
▶TABLE of CONTENTS
1: APPLYING GEOMETRIC TRANSFORMATIONS TO IMAGES
2: DETECTING EDGES AND APPLYING IMAGE FILTERS
3: CARTOONIZING AN IMAGE
4: DETECTING AND TRACKING DIFFERENT BODY PARTS
5: EXTRACTING FEATURES FROM AN IMAGE
6: SEAM CARVING
7: DETECTING SHAPES AND SEGMENTING AN IMAGE
8: OBJECT TRACKING
9: OBJECT RECOGNITION
10: AUGMENTED REALITY
11: MACHINE LEARNING BY AN ARTIFICIAL NEURAL NETWORK
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