Book Image

Machine Learning for OpenCV 4 - Second Edition

By : Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler
Book Image

Machine Learning for OpenCV 4 - Second Edition

By: Aditya Sharma, Vishwesh Ravi Shrimali, Michael Beyeler

Overview of this book

OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition. You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system. By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4.
Table of Contents (18 chapters)
Free Chapter
1
Section 1: Fundamentals of Machine Learning and OpenCV
6
Section 2: Operations with OpenCV
11
Section 3: Advanced Machine Learning with OpenCV

Using OpenVINO Model Zoo with OpenCV

In the previous sections, we briefly discussed OpenVINO Model Zoo and how we can use OpenVINO IE with OpenCV. In this section, we will learn more about Model Zoo and what it offers.

OpenVINO Model Zoo is a collection of optimized pre-trained models that can be directly imported into OpenVINO for inference. The importance of this feature lies in the fact that one of the major reasons behind OpenVINO's speedup is the optimized model file that it takes for inference. The underlying inference principle is still the same as most deep learning inference toolkits and languages, such as OpenCV. OpenCV's dnn module uses this speedup principle of OpenVINO by using it as the default backend for all inference tasks.

While it is possible to convert the model files into .xml and .bin files, it's not very easy. There are mainly two problems...