Book Image

Mastering OpenCV 4 with Python

By : Alberto Fernández Villán
5 (1)
Book Image

Mastering OpenCV 4 with Python

5 (1)
By: Alberto Fernández Villán

Overview of this book

OpenCV 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.
Table of Contents (20 chapters)
Free Chapter
1
Section 1: Introduction to OpenCV 4 and Python
6
Section 2: Image Processing in OpenCV
12
Section 3: Machine Learning and Deep Learning in OpenCV
16
Section 4: Mobile and Web Computer Vision

What this book covers

Chapter 1, Setting Up OpenCV, shows how to install everything you need to start programming with Python and OpenCV. You'll also be introduced to general terminology and concepts to contextualize what you will learn, establishing and setting the bases in relation to the main concepts of computer vision using OpenCV.

Chapter 2, Image Basics in OpenCV, demonstrates how to start writing your first scripts, in order to introduce you to the OpenCV library.

Chapter 3, Handling Files and Images, shows you how to cope with files and images, which are necessary for building your computer vision applications.

Chapter 4, Constructing Basic Shapes in OpenCV, covers how to draw shapes—from basic ones to some that are more advanced—using the OpenCV library.

Chapter 5, Image Processing Techniques, introduces most of the common image processing techniques you will need for your computer vision projects.

Chapter 6, Constructing and Building Histograms, shows how to both create and understand histograms, which are a powerful tool for understanding image content.

Chapter 7, Thresholding Techniques, introduces the main thresholding techniques you will need for your computer vision applications as a key process of image segmentation.

Chapter 8, Contour Detection, Filtering, and Drawing, shows how to deal with contours, which are used for shape analysis and for both object detection and recognition.

Chapter 9, Augmented Reality, teaches you how to build your first augmented reality application.

Chapter 10, Machine Learning with OpenCV, introduces you to the world of machine learning. You will see how machine learning can be used in your computer vision projects.

Chapter 11, Face Detection, Tracking, and Recognition, demonstrates how to create face processing projects using state-of-the-art algorithms, in connection with face detection, tracking, and recognition.

Chapter 12, Introduction to Deep Learning, introduces you to the world of deep learning with OpenCV and also some deep learning Python libraries (TensorFlow and Keras).

Chapter 13, Mobile and Web Computer Vision with Python and OpenCV, shows how to create computer vision and deep learning web applications using Flask.