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Mastering OpenCV 4
Mastering OpenCV 4

Mastering OpenCV 4: A comprehensive guide to building computer vision and image processing applications with C++ , Third Edition

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Profile Icon Roy Shilkrot Profile Icon Millán Escrivá
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Mastering OpenCV 4

Explore Structure from Motion with the SfM Module

Structure from motion (SfM) is the process of recovering both the positions of cameras looking at a scene, and the sparse geometry of the scene. The motion between the cameras imposes geometric constraints that can help us recover the structure of objects, hence why the process is called SfM. Since OpenCV v3.0+, a contributed ("contrib") module called sfm was added, which assists in performing end-to-end SfM processing from multiple images. In this chapter, we will learn how to use the SfM module to reconstruct a scene to a sparse point cloud, including camera poses. Later, we will also densify the point cloud, adding many more points to it to make it dense by using an open Multi-View Stereo (MVS) package called OpenMVS. SfM is used for high-quality three-dimensional scanning, visual odometry for autonomous navigation...

Technical requirements

These technologies and installations are required to build and run the code in this chapter:

  • OpenCV 4 (compiled with the sfm contrib module)
  • Eigen v3.3+ (required by the sfm module)
  • Ceres solver v2+ (required by the sfm module)
  • CMake 3.12+
  • Boost v1.66+
  • OpenMVS
  • CGAL v4.12+ (required by OpenMVS)

The build instructions for the components listed, as well as the code to implement the concepts in this chapter, will be provided in the accompanying code repository. Using OpenMVS is optional, and we may stop after getting the sparse reconstruction. However, the full MVS reconstruction is much more impressive and useful; for instance, for 3D printing replicas.

Any set of photos with sufficient overlap may be sufficient for 3D reconstruction. For example, we may use a set of photos I took of the Crazy Horse memorial head in South Dakota that is bundled with this...

Core concepts of SfM

Before we delve into the implementation of a SfM pipeline, let's revisit some key concepts that are an essential part of the process. The foremost class of theoretical topics in SfM is epipolar geometry (EG), the geometry of multiple views or MVG, which builds upon knowledge of image formation and camera calibration; however, we will only brush over these basic subjects. After we cover a few basics in EG, we will shortly discuss stereo reconstruction and look over subjects such as depth from disparity and triangulation. Other crucial topics in SfM, such as Robust Feature Matching, are more mechanical than theoretical, and we will cover them as we advance in coding the system. We intentionally leave out some very interesting topics, such as camera resectioning, PnP algorithms, and reconstruction factorization, since these are handled by the underlying...

Implementing SfM in OpenCV

OpenCV has an abundance of tools to implement a full-fledged SfM pipeline from first principles. However, such a task is very demanding and beyond the scope of this chapter. The former edition of this book presented just a small taste of what building such a system will entail, but luckily now we have at our disposal a tried and tested technique integrated right into OpenCV's API. Although the sfm module allows us to get away with simply providing a non-parametric function with a list of images to crunch and receive a fully reconstructed scene with a sparse point cloud and camera poses, we will not take that route. Instead, we will see in this section some useful methods that will allow us to have much more control over the reconstruction and exemplify some of the topics we discussed in the last section, as well as be more robust to noise.

This...

Summary

This chapter focused on SfM and its implementation with OpenCV's sfm contributed module and OpenMVS. We explored some theoretical concepts in multiple view geometry, and several practical matters: extracting key feature points, matching them, creating and analyzing the match graph, running the reconstruction, and finally performing MVS to densify the sparse 3D point cloud.

In the next chapter, we will see how to use OpenCV's face contrib module to detect facial landmarks in photos, as well as detecting the direction a face is pointing with the solvePnP function.

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Key benefits

  • Learn about the new features that help unlock the full potential of OpenCV 4
  • Build face detection applications with a cascade classifier using face landmarks
  • Create an optical character recognition (OCR) model using deep learning and convolutional neural networks

Description

Mastering OpenCV, now in its third edition, targets computer vision engineers taking their first steps toward mastering OpenCV. Keeping the mathematical formulations to a solid but bare minimum, the book delivers complete projects from ideation to running code, targeting current hot topics in computer vision such as face recognition, landmark detection and pose estimation, and number recognition with deep convolutional networks. You’ll learn from experienced OpenCV experts how to implement computer vision products and projects both in academia and industry in a comfortable package. You’ll get acquainted with API functionality and gain insights into design choices in a complete computer vision project. You’ll also go beyond the basics of computer vision to implement solutions for complex image processing projects. By the end of the book, you will have created various working prototypes with the help of projects in the book and be well versed with the new features of OpenCV4.

Who is this book for?

This book is for those who have a basic knowledge of OpenCV and are competent C++ programmers. You need to have an understanding of some of the more theoretical/mathematical concepts, as we move quite quickly throughout the book.

What you will learn

  • Build real-world computer vision problems with working OpenCV code samples
  • Uncover best practices in engineering and maintaining OpenCV projects
  • Explore algorithmic design approaches for complex computer vision tasks
  • Work with OpenCV's most updated API (v4.0.0) through projects
  • Understand 3D scene reconstruction and Structure from Motion (SfM)
  • Study camera calibration and overlay AR using the ArUco Module
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Table of Contents

11 Chapters
Cartoonifier and Skin Color Analysis on the RaspberryPi Chevron down icon Chevron up icon
Explore Structure from Motion with the SfM Module Chevron down icon Chevron up icon
Face Landmark and Pose with the Face Module Chevron down icon Chevron up icon
Number Plate Recognition with Deep Convolutional Networks Chevron down icon Chevron up icon
Face Detection and Recognition with the DNN Module Chevron down icon Chevron up icon
Introduction to Web Computer Vision with OpenCV.js Chevron down icon Chevron up icon
Android Camera Calibration and AR Using the ArUco Module Chevron down icon Chevron up icon
iOS Panoramas with the Stitching Module Chevron down icon Chevron up icon
Finding the Best OpenCV Algorithm for the Job Chevron down icon Chevron up icon
Avoiding Common Pitfalls in OpenCV Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.7
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5 star 0%
4 star 33.3%
3 star 0%
2 star 66.7%
1 star 0%
H. Vladimir Rios Feb 23, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I liked very much that the book is structured in several self contained project. Several of them are very useful for my work and research.I did not like the images, in general of low quality.
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Galib F. Rahman Jun 11, 2019
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You are better off using PDFs online
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not covered
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