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

Isolating objects in a scene


In this chapter, we are going to introduce the first step in an AOI algorithm and try to isolate different parts or objects in a scene. We are going to take the example of the object detection and classification of three object types (screw, packing ring, and nut) and develop them in this chapter and Chapter 6, Learning Object Classification.

Imagine that we are in a company that produces these three objects. All of them are in the same carrier tape. Our objective is to detect each object in the carrier tape and classify each one to allow a robot to put each object on the correct shelf:

In this chapter, we are going to learn how to isolate each object and detect its position in the image in pixels. In the next chapter, we are going to learn how to classify each isolated object to recognize if it is a nut, screw, or a packing ring.

 

In the following screenshot, we show our desired result, where there are a few objects in the left image. In the right image, we have...