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Mastering OpenCV 4

You're reading from   Mastering OpenCV 4 A comprehensive guide to building computer vision and image processing applications with C++

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Product type Paperback
Published in Dec 2018
Publisher
ISBN-13 9781789533576
Length 280 pages
Edition 3rd Edition
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Authors (2):
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Roy Shilkrot Roy Shilkrot
Author Profile Icon Roy Shilkrot
Roy Shilkrot
David Millán Escrivá David Millán Escrivá
Author Profile Icon David Millán Escrivá
David Millán Escrivá
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Toc

Table of Contents (12) Chapters Close

Preface 1. Cartoonifier and Skin Color Analysis on the RaspberryPi 2. Explore Structure from Motion with the SfM Module FREE CHAPTER 3. Face Landmark and Pose with the Face Module 4. Number Plate Recognition with Deep Convolutional Networks 5. Face Detection and Recognition with the DNN Module 6. Introduction to Web Computer Vision with OpenCV.js 7. Android Camera Calibration and AR Using the ArUco Module 8. iOS Panoramas with the Stitching Module 9. Finding the Best OpenCV Algorithm for the Job 10. Avoiding Common Pitfalls in OpenCV 11. Other Books You May Enjoy

Technical requirements

The following technologies and installations are required to build the code in this chapter:

  • OpenCV v4 (compiled with the face contrib module)
  • Boost v1.66+

Build instructions for the preceding components listed, as well as the code to implement the concepts presented in this chapter, will be provided in the accompanying code repository.

To run the facemark detector, a pre-trained model is required. Although training the detector model is certainly possible with the APIs provided in OpenCV, some pre-trained models are offered for download. One such model can be obtained from https://raw.githubusercontent.com/kurnianggoro/GSOC2017/master/data/lbfmodel.yaml, supplied by the contributor of the algorithm implementation to OpenCV (during the 2017 Google Summer of Code (GSoC)).

The facemark detector can work with any image; however, we can use a prescribed dataset...

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