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Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3

By : Joseph Howse, Joe Minichino
4.1 (14)
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Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3

4.1 (14)
By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.
Table of Contents (13 chapters)
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Creating modules

To help us build an interactive demo of a depth camera, we will reuse much of the Cameo project that we developed in Chapter 2, Handling Files, Cameras, and GUIs, and Chapter 3, Processing Images with OpenCV. As you will recall, we designed Cameo to support various kinds of input, so we can easily adapt it to support depth cameras in particular. We will add code that analyzes the depth layers in an image in order to find the main region, such as the face of a person sitting in front of the camera. Having found this region, we will paint everything else black. This type of effect is sometimes used in chat applications to hide the background so that users have more privacy.

Some of the code to manipulate depth-camera data will be reusable outside Cameo.py, so we should separate it into a new module. Let's create a depth.py file in the same directory as Cameo...

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