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)

Implementing the training script for the cat-detection model

"Praline: I've never seen so many aerials in me life. The man told me, their equipment could pinpoint a purr at 400 yards and Eric, being such a happy cat, was a piece of cake."
– Fish License sketch, Monty Python's Flying Circus, Episode 23 (1970)

This segment of the project uses tens of thousands of files, including images, annotation files, scripts, and intermediate and final outputs of the training process. Let's organize all of this new material by giving our project a subfolder, cascade_training, which will ultimately have the following contents:

  • cascade_training/CAT_DATASET_01: This is the first half of the Microsoft Cat Dataset 2008.
  • cascade_training/CAT_DATASET_02: This is the second half of the Microsoft Cat Dataset 2008.
  • cascade_training/faces: This...