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OpenCV 3.x with Python By Example
OpenCV 3.x with Python By Example

OpenCV 3.x with Python By Example: Make the most of OpenCV and Python to build applications for object recognition and augmented reality , Second Edition

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Profile Icon Gabriel Garrido Calvo Profile Icon Prateek Joshi
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€18.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (2 Ratings)
Paperback Jan 2018 268 pages 2nd Edition
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€25.99 €28.99
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€37.99
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Renews at €18.99p/m
Arrow left icon
Profile Icon Gabriel Garrido Calvo Profile Icon Prateek Joshi
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (2 Ratings)
Paperback Jan 2018 268 pages 2nd Edition
eBook
€25.99 €28.99
Paperback
€37.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€25.99 €28.99
Paperback
€37.99
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Free Trial
Renews at €18.99p/m

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OpenCV 3.x with Python By Example

Chapter 2. Detecting Edges and Applying Image Filters

In this chapter, we are going to see how to apply cool visual effects to images. We will learn how to use fundamental image processing operators, discuss edge detection, and see how we can use image filters to apply various effects to photos.

By the end of this chapter, you will know:

  • What 2D convolution is, and how to use it
  • How to blur an image
  • How to detect edges in an image
  • How to apply motion blur to an image
  • How to sharpen and emboss an image
  • How to erode and dilate an image
  • How to create a vignette filter
  • How to enhance image contrast

2D convolution


Convolution is a fundamental operation in image processing. We basically apply a mathematical operator to each pixel, and change its value in some way. To apply this mathematical operator, we use another matrix called a kernel. The kernel is usually much smaller in size than the input image. For each pixel in the image, we take the kernel and place it on top so that the center of the kernel coincides with the pixel under consideration. We then multiply each value in the kernel matrix with the corresponding values in the image, and then sum it up. This is the new value that will be applied to this position in the output image.

Here, the kernel is called the image filter and the process of applying this kernel to the given image is called image filtering. The output obtained after applying the kernel to the image is called the filtered image. Depending on the values in the kernel, it performs different functions such as blurring, detecting edges, and so on. The following figure...

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Key benefits

  • Learn how to apply complex visual effects to images with OpenCV 3.x and Python
  • Extract features from an image and use them to develop advanced applications
  • Build algorithms to help you understand image content and perform visual searches
  • Get to grips with advanced techniques in OpenCV such as machine learning, artificial neural network, 3D reconstruction, and augmented reality

Description

Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples. This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.

Who is this book for?

This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV and Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on.

What you will learn

  • Detect shapes and edges from images and videos
  • How to apply filters on images and videos
  • Use different techniques to manipulate and improve images
  • Extract and manipulate particular parts of images and videos
  • Track objects or colors from videos
  • Recognize specific object or faces from images and videos
  • How to create Augmented Reality applications
  • Apply artificial neural networks and machine learning to improve object recognition

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 17, 2018
Length: 268 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788396905
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Product Details

Publication date : Jan 17, 2018
Length: 268 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788396905
Category :
Languages :
Tools :

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Table of Contents

11 Chapters
Applying Geometric Transformations to Images Chevron down icon Chevron up icon
Detecting Edges and Applying Image Filters Chevron down icon Chevron up icon
Cartoonizing an Image Chevron down icon Chevron up icon
Detecting and Tracking Different Body Parts Chevron down icon Chevron up icon
Extracting Features from an Image Chevron down icon Chevron up icon
Seam Carving Chevron down icon Chevron up icon
Detecting Shapes and Segmenting an Image Chevron down icon Chevron up icon
Object Tracking Chevron down icon Chevron up icon
Object Recognition Chevron down icon Chevron up icon
Augmented Reality Chevron down icon Chevron up icon
Machine Learning by an Artificial Neural Network Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(2 Ratings)
5 star 50%
4 star 0%
3 star 0%
2 star 50%
1 star 0%
Andrew Zappella Jun 09, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent book! I highly recommend it! The book helps the reader navigate the complex world of computer vision through OpenCV and Python. It gradually introduces the concepts and tools necessary to do object recognition and object tracking. It lends itself very well as an introductory book for more advanced artificial intelligence computer vision topics.
Amazon Verified review Amazon
Reading Thusly Jul 01, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Good:Good attempt at producing an up to date (openCV3.X) reference for python. There arent man. Pyimagesearch is probably the best.Good coding examples, probably even for a beginner that has been through codeacademy or something like it.Bad:The grammar...I mean what the heck editors? I didnt even know what they were talking about sometimes because it was like they didnt even put the variables they were talking about into the sentence. Makes the book feel amateurish amd harder to take seriously.
Amazon Verified review Amazon
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