Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

By : Joseph Howse, Joe Minichino
5 (2)
Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

5 (2)
By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science in the field of artificial intelligence, encompassing diverse use cases 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 5 and Python 3. You'll start by setting up OpenCV 5 with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying images, videos, and camera feeds. From taking you through image processing, video analysis, 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. You'll tackle two popular challenges: face detection and face recognition. You'll also learn about object classification and machine learning, which will enable you to create and use object detectors and even track moving objects in real time. Later, you'll develop your skills in augmented reality and real-world 3D navigation. Finally, you'll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age, and you'll deploy your solutions to the Cloud. By the end of this book, you'll have the skills you need to execute real-world computer vision projects.
Table of Contents (12 chapters)
Free Chapter
1
Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning
Appendix A: Bending Color Space with the Curves Filter

Getting Haar cascade data

Your installation of OpenCV 5 should contain a subfolder called data. The path to this folder is stored in an OpenCV variable called cv2.data.haarcascades.

The data folder contains XML files that can be loaded by an OpenCV class called cv2.CascadeClassifier. An instance of this class interprets a given XML file as a Haar cascade, which provides a detection model for a type of object such as a face. cv2.CascadeClassifier can detect this type of object in any image. As usual, we could obtain a still image from a file, or we could obtain a series of frames from a video file or a video camera.

From the data folder, we will use the following cascade files:

haarcascade_frontalface_default.xml
haarcascade_eye.xml

As their names suggest, these cascades are for detecting faces and eyes. They require a frontal, upright view of the subject. We will use them later when building a face detector.

If you are curious about how these cascade files are generated, you can find...