This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV-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 You Will Learn
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 will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications.
This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner's level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples.
The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation.Style and approach
This is a conversational-style book filled with hands-on examples that are really easy to understand. Each topic is explained very clearly and is followed by a programmatic implementation so that the concept is solidified. Each topic contributes to something bigger in the following chapters, which helps you understand how to piece things together to build something big and complex.
Prateek Joshi is a computer vision researcher with a primary focus on content-based analysis. He is particularly interested in intelligent algorithms that can understand images to produce scene descriptions in terms of constituent objects. He has a master's degree from the University of Southern California, specializing in computer vision. He was elected to become a member of the Honor Society for academic excellence and an ambassador for the School of Engineering. Over the course of his career, he has worked for companies such as Nvidia, Microsoft Research, Qualcomm, and a couple of early stage start-ups in Silicon Valley. His work in this field has resulted in multiple patents, tech demos, and research papers at major IEEE conferences. He has won many hackathons using a wide variety of technologies related to image recognition. He enjoys blogging about topics such as artificial intelligence, abstract mathematics, and cryptography. His blog has been visited by users in more than 200 countries, and he has been featured as a guest author in prominent tech magazines.
This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks.What You Will LearnRealize different classification and regression techniquesUnderstand the concept of clustering and how to use it to automatically segment dataSee how to build an intelligent recommender systemUnderstand logic programming and how to use itBuild automatic speech recognition systemsUnderstand the basics of heuristic search and genetic programmingDevelop games using Artificial IntelligenceLearn how reinforcement learning worksDiscover how to build intelligent applications centered on images, text, and time series dataSee how to use deep learning algorithms and build applications based on itIn Detail
Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications.
During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide!Style and approach
This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
This book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. Readers are expected to be familiar with OpenCV's concepts and Python libraries. Basic knowledge of Python programming is expected and assumed.What You Will LearnGenerate real-time visual effects using different filters and image manipulation techniques such as dodging and burningRecognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensorLearn feature extraction and feature matching for tracking arbitrary objects of interestReconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniquesTrack visually salient objects by searching for and focusing on important regions of an imageDetect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs)Recognize street signs using a multi-class adaptation of support vector machines (SVMs)Strengthen your OpenCV2 skills and learn how to use new OpenCV3 featuresIn Detail
OpenCV is a native cross platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android. Developers using OpenCV build applications to process visual data; this can include live streaming data from a device like a camera, such as photographs or videos. OpenCV offers extensive libraries with over 500 functions
This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this book teach the reader how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.
By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications.Style and approach
This book covers independent hands-on projects that teach important computer vision concepts like image processing and machine learning for OpenCV with multiple examples.
This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code.What You Will LearnExplore classification algorithms and apply them to the income bracket estimation problemUse predictive modeling and apply it to real-world problemsUnderstand how to perform market segmentation using unsupervised learningExplore data visualization techniques to interact with your data in diverse waysFind out how to build a recommendation engineUnderstand how to interact with text data and build models to analyze itWork with speech data and recognize spoken words using Hidden Markov ModelsAnalyze stock market data using Conditional Random FieldsWork with image data and build systems for image recognition and biometric face recognitionGrasp how to use deep neural networks to build an optical character recognition systemIn Detail
Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.
With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.
You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.Style and approach
You will explore various real-life scenarios in this book where machine learning can be used, and learn about different building blocks of machine learning using independent recipes in the book.
Intended for novices to the world of OpenCV and computer vision, as well as OpenCV veterans that want to learn about what's new in OpenCV 3, this book is useful as a reference for experts and a training manual for beginners, or for anybody who wants to familiarize themselves with the concepts of object classification and detection in simple and understandable terms. Basic knowledge about Python and programming concepts is required, although the book has an easy learning curve both from a theoretical and coding point of view.What You Will LearnInstall and familiarize yourself with OpenCV 3's Python APIGrasp the basics of image processing and video analysisIdentify and recognize objects in images and videosDetect and recognize faces using OpenCVTrain and use your own object classifiersLearn about machine learning concepts in a computer vision contextWork with artificial neural networks using OpenCVDevelop your own computer vision real-life applicationIn Detail
OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance.
Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.Style and approach
This book is a comprehensive guide to the brand new OpenCV 3 with Python to develop real-life computer vision applications.