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OpenCV 4 for Secret Agents

OpenCV 4 for Secret Agents

By : Joseph Howse, Ponnusamy
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OpenCV 4 for Secret Agents

OpenCV 4 for Secret Agents

By: Joseph Howse, Ponnusamy

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)
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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)

Training the classifier with reference images

"Can you identify this coastline? Given time, yes."
– Photo caption, Dante Stella (http://www.dantestella.com/technical/hex352.html)

A small selection of reference images is included in this book's GitHub repository in a folder called Chapter002/images. Feel free to experiment with the classifier by adding more reference images, since a larger set may yield more reliable results. Bear in mind that our classifier relies on average similarity, so the more times you include a given color scheme in the reference images, the more heavily you are weighting the classifier in favor of that color scheme.

At the end of HistogramClassifier.py, let's add a main method to train and serialize a classifier using our reference images. We will also run the classifier on a couple of the...

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