Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By : Joseph Howse, Joe Minichino
Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

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)

Detecting people with HOG descriptors

OpenCV comes with a class called cv2.HOGDescriptor, which is capable of performing people detection. The interface has some similarities to the cv2.CascadeClassifier class that we used in Chapter 5, Detecting and Recognizing Faces. However, unlike cv2.CascadeClassifier, cv2.HOGDescriptor sometimes returns nested detection rectangles. In other words, cv2.HOGDescriptor might tell us that it detected one person whose bounding rectangle is located completely inside another person's bounding rectangle. This situation really is possible; for example, a child could be standing in front of an adult, and the child's bounding rectangle could be completely inside the adult's bounding rectangle. However, in a typical situation, nested detections are probably errors, so cv2.HOGDescriptor is often used along with code to filter out any nested...