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

Using OpenCV to perform face detection

With cv2.CascadeClassifier, it makes little difference whether we perform face detection on a still image or a video feed. The latter is just a sequential version of the former: face detection on a video is simply face detection applied to each frame. Naturally, with more advanced techniques, it would be possible to track a detected face continuously across multiple frames and determine that the face is the same one in each frame. However, it is good to know that a basic sequential approach also works.

Let's go ahead and detect some faces.

Performing face detection on a still image

The first and most basic way to perform face detection is to load an image and detect faces in it. To make the result visually meaningful, we will draw rectangles around faces in the original image. Remembering that the face detector is designed for upright, frontal faces, we will use an image of a row of people, specifically woodcutters, standing shoulder to shoulder...