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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)

Detecting lights as blobs

To the human eye, light can appear both very bright and very colorful. Imagine a sunny landscape or a storefront lit by a neon sign; they are bright and colorful! However, a camera captures a range of contrast that is much narrower and not as intelligently selected, so that the sunny landscape or neon-lit storefront can look washed out. This problem of poorly controlled contrast is especially bad in cheap cameras or cameras that have small sensors, such as webcams. As a result, bright light sources tend to be imaged as big white blobs with thin rims of color. These blobs also tend to mimic a lens's iris—typically, a polygon approximating a circle.

The thought of all lights becoming white and circular makes the world seem like a poorer place, if you ask me. Nonetheless, in computer vision, we can take advantage of such a predictable pattern...

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