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)

Other Book You May Enjoy

If you enjoyed this book, you may be interested in this other book by Packt:

Hands-On Computer Vision with TensorFlow 2
Benjamin Planche, Eliot Andres

ISBN: 978-1-78883-064-5

  • Create your own neural networks from scratch
  • Classify images with modern architectures including Inception and ResNet
  • Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net
  • Tackle problems faced when developing self-driving cars and facial emotion recognition systems
  • Boost your application’s performance with transfer learning, GANs, and domain adaptation
  • Use recurrent neural networks (RNNs) for video analysis
  • Optimize and deploy your networks on mobile devices and in the browser