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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)

Estimating distances (a cheap approach)

Suppose we have an object sitting in front of a pinhole camera. Regardless of the distance between the camera and the object, the following equation holds true:

We may use any unit (such as pixels) in the equation's left-hand side and any unit (such as meters) in its right-hand side (on each side of the equation, the division cancels the unit). Moreover, we may define the object's size based on anything linear that we can detect in the image, such as the diameter of a detected blob or the width of a detected face rectangle.

Let's rearrange the equation to illustrate that the distance to the object is inversely proportional to the object's size in the image:

Let's assume that the object's real size and the camera's focal length are constant (a constant focal length means that the lens does not zoom and...

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