Mastering OpenCV 4 with Python (häftad)
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Häftad (Paperback / softback)
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Packt Publishing Limited
Black & white illustrations
235 x 190 x 25 mm
1000 g
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403:B&W 7.5 x 9.25 in or 235 x 191 mm Perfect Bound on White w/Matte Lam
Mastering OpenCV 4 with Python (häftad)

Mastering OpenCV 4 with Python

A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7

Häftad Engelska, 2019-03-29
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Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality. Key Features Develop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4) and Python Apply machine learning and deep learning techniques with TensorFlow and Keras Discover the modern design patterns you should avoid when developing efficient computer vision applications Book DescriptionOpenCV is considered to be one of the best open source computer vision and machine learning software libraries. It helps developers build complete projects in relation to image processing, motion detection, or image segmentation, among many others. OpenCV for Python enables you to run computer vision algorithms smoothly in real time, combining the best of the OpenCV C++ API and the Python language. In this book, you'll get started by setting up OpenCV and delving into the key concepts of computer vision. You'll then proceed to study more advanced concepts and discover the full potential of OpenCV. The book will also introduce you to the creation of advanced applications using Python and OpenCV, enabling you to develop applications that include facial recognition, target tracking, or augmented reality. Next, you'll learn machine learning techniques and concepts, understand how to apply them in real-world examples, and also explore their benefits, including real-time data production and faster data processing. You'll also discover how to translate the functionality provided by OpenCV into optimized application code projects using Python bindings. Toward the concluding chapters, you'll explore the application of artificial intelligence and deep learning techniques using the popular Python libraries TensorFlow, and Keras. By the end of this book, you'll be able to develop advanced computer vision applications to meet your customers' demands. What you will learn Handle files and images, and explore various image processing techniques Explore image transformations, including translation, resizing, and cropping Gain insights into building histograms Brush up on contour detection, filtering, and drawing Work with Augmented Reality to build marker-based and markerless applications Work with the main machine learning algorithms in OpenCV Explore the deep learning Python libraries and OpenCV deep learning capabilities Create computer vision and deep learning web applications Who this book is forThis book is designed for computer vision developers, engineers, and researchers who want to develop modern computer vision applications. Basic experience of OpenCV and Python programming is a must.
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Övrig information

Alberto Fernandez Villan is a software engineer with more than 12 years of experience in developing innovative solutions. In the last couple of years, he has been working in various projects related to monitoring systems for industrial plants, applying both Internet of Things (IoT) and big data technologies. He has a Ph.D. in computer vision (2017), a deep learning certification (2018), and several publications in connection with computer vision and machine learning in journals such as Machine Vision and Applications, IEEE Transactions on Industrial Informatics, Sensors, IEEE Transactions on Industry Applications, IEEE Latin America Transactions, and more. As of 2013, he is a registered and active user (albertofernandez) on the Q&A OpenCV forum.


Table of Contents Setting up OpenCV Image basics in OpenCV Handling files and images Constructing basic shapes in OpenCV Image processing techniques Constructing and Building Histograms Thresholding techniques Contours Detection, filtering, and drawing Augmented reality and 3D Visualization Machine Learning and Deep Learning in OpenCV Face detection, tracking and recognition Introduction to deep learning Mobile and web computer vision with Python and OpenCV