Book Image

OpenCV 4 for Secret Agents - Second Edition

By : Joseph Howse
Book Image

OpenCV 4 for Secret Agents - Second Edition

By: Joseph Howse

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.
Table of Contents (16 chapters)
Free Chapter
1
Section 1: The Briefing
4
Section 2: The Chase
9
Section 3: The Big Reveal
12
Making WxUtils.py Compatible with Raspberry Pi
13
Learning More about Feature Detection in OpenCV
14
Running with Snakes (or, First Steps with Python)

Summary

Like the previous chapter, this chapter has dealt with classification tasks, as well as interfaces among OpenCV, a source of images, and a GUI. This time, our classification labels have more objective meanings (a species or an individual's identity), so the classifier's success or failure is more obvious. To meet the challenge, we used much bigger sets of training images, we preprocessed the training images for greater consistency, and we applied two tried-and-true classification techniques in sequence (either Haar cascades or LBP cascades for detection and then LBPH for recognition).

The methodology presented in this chapter, as well as the entire Interactive Recognizer app and some of the other code, generalizes well to other original work in detection and recognition. With the right training images, you could detect and recognize many more animals in many...