OpenCV 3.x with python by example : make the most of OpenCV and Python to build applications for object recognition and augmented reality / Gabriel Garrido, Prateek Joshi.

Format:

Book

Author/Creator:

Garrido, Gabriel, author.

Joshi, Prateek, author.

Language:

English

Subjects (All):

Python (Computer program language)--Textbooks.

Python (Computer program language).

Physical Description:

1 online resource (268 pages)

Edition:

Second edition.

Place of Publication:

Birmingham, England : Packt Publishing, 2018.

System Details:

text file

Summary:

Learn the techniques for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications using examples on different functions of OpenCV. About This Book 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 Who This Book Is 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 In Detail 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 Ope...

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Packt Upsell

Table of Contents

Preface

Chapter 1: Applying Geometric Transformations to Images

Installing OpenCV-Python

Windows

macOS X

Linux (for Ubuntu)

Virtual environments

Troubleshooting

OpenCV documentation

Reading, displaying, and saving images

What just happened?

Loading and saving an image

Changing image format

Image color spaces

Converting color spaces

Splitting image channels

Merging image channels

Image translation

Image rotation

Image scaling

Affine transformations

Projective transformations

Image warping

Summary

Chapter 2: Detecting Edges and Applying Image Filters

2D convolution

Blurring

Size of the kernel versus blurriness

Motion blur

Under the hood

Sharpening

Understanding the pattern

Embossing

Edge detection

Erosion and dilation

Afterthought

Creating a vignette filter

What's happening underneath?

How do we move the focus around?

Enhancing the contrast in an image

How do we handle color images?

Chapter 3: Cartoonizing an Image

Accessing the webcam

Extending capture options

Keyboard inputs

Interacting with the application

Mouse inputs

Interacting with a live video stream

How did we do it?

Cartoonizing an image

Deconstructing the code

Chapter 4: Detecting and Tracking Different Body Parts

Using Haar cascades to detect things

What are integral images?

Detecting and tracking faces

Understanding it better

Fun with faces

Removing the alpha channel from the overlay image.

Detecting eyes

Fun with eyes

Positioning the sunglasses

Detecting ears

Detecting a mouth

It's time for a moustache

Detecting pupils

Chapter 5: Extracting Features from an Image

Why do we care about keypoints?

What are keypoints?

Detecting the corners

Good features to track

Scale-invariant feature transform (SIFT)

Speeded-up robust features (SURF)

Features from accelerated segment test (FAST)

Binary robust independent elementary features (BRIEF)

Oriented FAST and Rotated BRIEF (ORB)

Chapter 6: Seam Carving

Why do we care about seam carving?

How does it work?

How do we define interesting?

How do we compute the seams?

Can we expand an image?

Can we remove an object completely?

Chapter 7: Detecting Shapes and Segmenting an Image

Contour analysis and shape matching

Approximating a contour

Identifying a pizza with a slice taken out

How to censor a shape?

What is image segmentation?

Watershed algorithm

Chapter 8: Object Tracking

Frame differencing

Colorspace based tracking

Building an interactive object tracker

Feature-based tracking

Background subtraction

Chapter 9: Object Recognition

Object detection versus object recognition

What is a dense feature detector?

What is a visual dictionary?

What is supervised and unsupervised learning?

What are support vector machines?

What if we cannot separate the data with simple straight lines?

How do we actually implement this?

What happened inside the code?

How did we build the trainer?

Chapter 10: Augmented Reality

What is the premise of augmented reality?

What does an augmented reality system look like?.

Geometric transformations for augmented reality

What is pose estimation?

How to track planar objects

How to augment our reality

Mapping coordinates from 3D to 2D

How to overlay 3D objects on a video

Let's look at the code

Let's add some movements

Chapter 11: Machine Learning by an Artificial Neural Network

Machine learning (ML) versus artificial neural network (ANN)

How does ANN work?

How to define multi-layer perceptrons (MLP)

How to implement an ANN-MLP classifier?

Evaluate a trained network

Classifying images

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Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (EBC, viewed February 22, 2018).

OCLC:

1022796137