Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By : David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot
Book Image

Building Computer Vision Projects with OpenCV 4 and C++

By: David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

Overview of this book

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books: •Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá •Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi
Table of Contents (28 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Automatic object inspection classification example


In Chapter 5, Automated Optical Inspection, Object Segmentation, and Detection, we looked at an example of automatic object inspection segmentation where a carrier tape contained three different types of object: nuts, screws, and rings. With computer vision, we will be able to recognize each one of these so that we can send notifications to a robot or put each one in a different box. The following is a basic diagram of the carrier tape:

In Chapter 5Automated Optical Inspection, Object Segmentation, and Detection, we pre-processed the input images and extracted the regions of interest, isolating each object using different techniques. Now, we are going to apply all the concepts we explained in the previous sections in this example to extract features and classify each object, allowing the robot to put each one in a different box. In our application, we are only going to show the labels of each image, but we could send the positions in the...