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)

Planning the Angora Blue app

Angora Blue reuses the same detection and recognition models that we created earlier. It is a relatively linear and simple app because it has no GUI and does not modify any models. It just loads the detection and recognition models from file and then silently runs a camera until a face is recognized with a certain level of confidence. After recognizing a face, the app sends an email alert and exits. To elaborate, we may say the app has the following flow of execution:

  1. Load face detection and face recognition models from file for both human and feline subjects.
  2. Capture a live video from a camera. For each frame of video, it can do the following:
  • Detect all human faces in the frame. Perform recognition on each human face. If a face is recognized with a certain level of confidence, it sends an email alert and exits the app.
  • Detect all cat faces in...