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OpenCV By Example

OpenCV By Example

By : Joshi, Millán Escrivá, Vinícius G. Mendonça
3.8 (5)
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OpenCV By Example

OpenCV By Example

3.8 (5)
By: Joshi, Millán Escrivá, Vinícius G. Mendonça

Overview of this book

Open CV is a cross-platform, free-for-use library that is primarily used for real-time Computer Vision and image processing. It is considered to be one of the best open source libraries that helps developers focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you are completely new to the concept of Computer Vision or have a basic understanding of it, this book will be your guide to understanding the basic OpenCV concepts and algorithms through amazing real-world examples and projects. Starting from the installation of OpenCV on your system and understanding the basics of image processing, we swiftly move on to creating optical flow video analysis or text recognition in complex scenes, and will take you through the commonly used Computer Vision techniques to build your own Open CV projects from scratch. By the end of this book, you will be familiar with the basics of Open CV such as matrix operations, filters, and histograms, as well as more advanced concepts such as segmentation, machine learning, complex video analysis, and text recognition.
Table of Contents (13 chapters)
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12
Index

Chapter 7. Detecting Face Parts and Overlaying Masks

In the previous chapter, we learned about object classification and how machine learning can be used to achieve it. In this chapter, we will learn how to detect and track different face parts. We will start the discussion by understanding the face detection pipeline and how it's built from the ground up. We will then use this framework to detect face parts, such as eyes, ears, mouth, and nose. We will then learn how to overlay funny masks on these face parts in a live video.

In this chapter, we will cover the following topics:

  • Working with Haar cascades
  • Integral images and why we need them
  • Building a generic face detection pipeline
  • Detecting and tracking face parts, such as eyes, ears, nose, and mouth in a live video stream from the webcam
  • Automatically overlaying facemasks, sunglasses, and a funny nose on a person's face in a video

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