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OpenCV 4 for Secret Agents - Second Edition

 

OpenCV 4 for Secret Agents - Second Edition

Autor: Joseph Howse
Broj strana: 336
ISBN broj: 9781789345360
Izdavač: PACKT PUBLISHING
Godina izdanja: 2019.

Pregleda (30 dana / ukupno): 67 / 67

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  • Detect motion and recognize gestures to control a smartphone game
  • Detect car headlights and estimate their distance
  • Detect and recognize human and cat faces to trigger an alarm
  • Amplify motion in a real-time video to show heartbeats and breaths
  • Make a physics simulation that detects shapes in a real-world drawing
  • Build OpenCV 4 projects in Python 3 for desktops and Raspberry Pi
  • Develop OpenCV 4 Android applications in Android Studio and Unity

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.

  • Build OpenCV 4 apps with Python 2 and 3 on desktops and Raspberry Pi, Java on Android, and C# in Unity
  • Detect, classify, recognize, and measure real-world objects in real-time
  • Work with images from diverse sources, including the web, research datasets, and various cameras

Table of contents

1 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

2 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

3 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

4 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

5 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

6 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

7 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

8 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

 

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