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OpenCV 4 for Secret Agents

By : Joseph Howse, Ponnusamy

Buy this Book

OpenCV 4 for Secret Agents

By: Joseph Howse, Ponnusamy

Buy this Book

Overview of this book

OpenCV 4 is a collection of image processing functions and computer vision algorithms. It is open source, supports many programming languages and platforms, and is fast enough for many real-time applications. With this handy library, you’ll be able to build a variety of impressive gadgets. OpenCV 4 for Secret Agents features a broad selection of projects based on computer vision, machine learning, and several application frameworks. To enable you to build apps for diverse desktop systems and Raspberry Pi, the book supports multiple Python versions, from 2.7 to 3.7. For Android app development, the book also supports Java in Android Studio, and C# in the Unity game engine. Taking inspiration from the world of James Bond, this book will add a touch of adventure and computer vision to your daily routine. You’ll be able to protect your home and car with intelligent camera systems that analyze obstacles, people, and even cats. In addition to this, you’ll also learn how to train a search engine to praise or criticize the images that it finds, and build a mobile app that speaks to you and responds to your body language. By the end of this book, you will be equipped with the knowledge you need to advance your skills as an app developer and a computer vision specialist.

Preface

Who this book is for

What this book covers

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Free Chapter

Section 1: The Briefing

Preparing for the Mission

Technical requirements

Setting up a development machine

Setting up Android Studio and OpenCV

Setting up Unity and OpenCV

Setting up a Raspberry Pi

Finding OpenCV documentation, help, and updates

Alternatives to Raspberry Pi

Summary

Searching for Luxury Accommodations Worldwide

Technical requirements

Planning the Luxocator app

Creating, comparing, and storing histograms

Training the classifier with reference images

Acquiring images from the web

Acquiring images from Bing Image Search

Preparing images and resources for the app

Integrating everything into the GUI

Running Luxocator and troubleshooting SSL problems

Building Luxocator for distribution

Summary

Section 2: The Chase

Training a Smart Alarm to Recognize the Villain and His Cat

Technical requirements

Understanding machine learning in general

Planning the Interactive Recognizer app

Understanding Haar cascades and LBPH

Implementing the Interactive Recognizer app

Planning the cat-detection model

Implementing the training script for the cat-detection model

Planning the Angora Blue app

Implementing the Angora Blue app

Building Angora Blue for distribution

Further fun with finding felines

Summary

Controlling a Phone App with Your Suave Gestures

Technical requirements

Planning the Goldgesture app

Understanding optical flow

Setting up the project in Android Studio

Getting a cascade file and audio files

Specifying the app's requirements

Laying out a camera preview as the main view

Tracking back-and-forth gestures

Playing audio clips as questions and answers

Capturing images and tracking faces in an activity

Summary

Equipping Your Car with a Rearview Camera and Hazard Detection

Technical requirements

Planning The Living Headlights app

Detecting lights as blobs

Estimating distances (a cheap approach)

Implementing The Living Headlights app

Testing The Living Headlights app at home

Testing The Living Headlights app in a car

Summary

Creating a Physics Simulation Based on a Pen and Paper Sketch

Technical requirements

Planning the Rollingball app

Detecting circles and lines

Setting up OpenCV for Unity

Configuring and building the Unity project

Creating the Rollingball scene in Unity

Creating Unity assets and adding them to the scene

Creating the launcher scene in Unity

Tidying up and testing

Summary

Section 3: The Big Reveal

Seeing a Heartbeat with a Motion-Amplifying Camera

Technical requirements

Planning the Lazy Eyes app

Understanding what Eulerian video magnification can do

Extracting repeating signals from video using the fast Fourier transform

Compositing two images using image pyramids

Implementing the Lazy Eyes app

Configuring and testing the app for various motions

Summary

Stopping Time and Seeing like a Bee

Technical requirements

Planning the Sunbaker app

Understanding the spectrum

Finding specialized cameras

Installing Spinnaker SDK and PySpin

Capturing images from industrial cameras using PySpin

Adapting the Lazy Eyes app to make Sunbaker

Summary

Making WxUtils.py Compatible with Raspberry Pi

Learning More about Feature Detection in OpenCV

Running with Snakes (or, First Steps with Python)

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Understanding optical flow

Optical flow is the pattern of apparent motion between two consecutive frames of video. We select feature points in the first frame and try to determine where those features have gone in the second frame. This search is subject to a few caveats:

We make no attempt to distinguish between camera motion and subject motion.
We assume that a feature's color or brightness remains similar between frames.
We assume that neighboring pixels have similar motions.

OpenCV's calcOpticalFlowPyrLK function implements the Lucas-Kanade method of computing optical flow. Lucas-Kanade relies on a 3 x 3 neighborhood (that is, 9 pixels) around each feature. Taking each feature's neighborhood from the first frame, we try to find the best matching neighborhood in the second frame, based on least squares error. OpenCV's implementation of Lucas-Kanade uses...