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)

Training an ANN classifier in multiple epochs

Let's create an ANN that attempts to classify animals based on three measurements: weight, length, and number of teeth. This is, of course, a mock scenario. Realistically, no one would describe an animal with just these three statistics. However, our intent is to improve our understanding of ANNs before we start applying them to image data.

Compared to the minimal example in the previous section, our animal classification mock-up will be more sophisticated in the following ways:

  • We will increase the number of neurons in the hidden layer.
  • We will use a larger training dataset. For convenience, we will generate this dataset pseudorandomly.
  • We will train the ANN in multiple epochs, meaning that we will train and retrain it multiple times with the same dataset each time.
The number of neurons in the hidden layer is an important...