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
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


This chapter has dealt with video analysis and, in particular, a selection of useful techniques for tracking objects.

We began by learning about background subtraction with a basic motion detection technique that calculates frame differences. Then, we moved on to more complex and efficient background subtraction algorithms – namely, MOG and KNN – which are implemented in OpenCV's cv2.BackgroundSubtractor class.

We then proceeded to explore the MeanShift and CamShift tracking algorithms, using color histograms and back-projections. We also familiarized ourselves with the Kalman filter and its usefulness in smoothing the results of a tracking algorithm. Then, we put all of our knowledge together in a sample surveillance application, which is capable of tracking pedestrians (or other moving objects) in a video.

Finally, we saw that we can easily replace one set of tracking techniques with another, in our object-oriented implementation of a pedestrian tracker. We...