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

Tracking pedestrians

Up to this point, we have familiarized ourselves with the concepts of motion detection, object detection, and object tracking. You are probably anxious to put this newfound knowledge to good use in a real-life scenario. Let's do just that by tracking pedestrians in a video from a surveillance camera.

You can find a surveillance video inside the OpenCV repository at samples/data/vtest.avi. A copy of this video is located inside this book's GitHub repository at vidoesvidoes/pedestrians.avi.

Let's lay out a plan and then implement the application!

Planning the flow of the application

The application will adhere to the following logic:

  1. Capture frames from a video file.
  2. Use the first 20 frames to populate the history of a background subtractor.
  3. Based on background subtraction, use the 21st frame to identify moving foreground objects. We will treat these as pedestrians. For each pedestrian, assign an ID and an initial tracking window, and then calculate...