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You're reading from OpenCV 3 Computer Vision with Python Cookbook Leverage the power of OpenCV 3 and Python to build computer vision applications

Product type Paperback

Published in Mar 2018

Publisher Packt

ISBN-13 9781788474443

Length 306 pages

Edition 1st Edition

Languages

Python

Tools

OpenCV

Concepts

Computer Vision

Authors (2):

Aleksandr Rybnikov

Aleksei Spizhevoi

View More author details

Table of Contents (11) Chapters

Preface

1. I/O and GUI FREE CHAPTER

2. Matrices, Colors, and Filters

3. Contours and Segmentation

4. Object Detection and Machine Learning

5. Deep Learning

6. Linear Algebra

7. Detectors and Descriptors

8. Image and Video Processing

9. Multiple View Geometry

10. Other Books You May Enjoy

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QR code detector

QR codes, like AruCo markers, are another type of specifically designed object, used to store information and describe 3D space. You can find QR codes almost everywhere, from food packages to museums and robotized factories.

In this recipe, we will understand how to detect QR codes and remove perspective distortions to get a canonical view of the codes. This task may sound easy to complete, but it requires a lot of OpenCV functionality. Let's find out how to do it.

Getting ready

Before you proceed with this recipe, you will need to install the OpenCV 3.x Python API package.

How to do it...

...

The rest of the chapter is locked

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Authors (2)

Aleksei Spizhevoi

Alexey Spizhevoy has over 7 years of experience in computer vision R&D. He has worked for 5 years at Itseez, the main OpenCV contributor, before it was acquired by Intel. He has contributed to video stabilization and photo stitching modules into OpenCV library. He has successfully participated in numerous Computer Vision projects in such areas as 3D reconstruction, video conferencing, object detection and tracking, semantic segmentation, driving assistance, and others. He holds a master's degree in computer science, and he is currently pursuing PhD.

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Rybnikov

Aleksandr Rybnikov has over 5 years of experience in C++ programming, including 3 years in the Computer Vision (CV) domain. He worked at Itseez, a company that supported and developed OpenCV, and then at Intel. He enriched OpenCV's dnn module by adding support of another two Deep Learning (DL) frameworks and many features, along with improving the existing functionality. As an engineer, he participated in CV and DL projects such as iris recognition, object detection, semantic segmentation, 6-DOF pose estimation, and digital hologram reconstruction. He has a master's degree in physics.

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