Learning OpenCV 3 Computer Vision with Python - Second Edition

Unleash the power of computer vision with Python using OpenCV
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Learning OpenCV 3 Computer Vision with Python - Second Edition

Joe Minichino, Joseph Howse

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Unleash the power of computer vision with Python using OpenCV
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Book Details

ISBN 139781785283840
Paperback266 pages

Book Description

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance.

Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.

Table of Contents

Chapter 1: Setting Up OpenCV
Choosing and using the right setup tools
Installing the Contrib modules
Running samples
Finding documentation, help, and updates
Summary
Chapter 2: Handling Files, Cameras, and GUIs
Basic I/O scripts
Project Cameo (face tracking and image manipulation)
Cameo – an object-oriented design
Summary
Chapter 3: Processing Images with OpenCV 3
Converting between different color spaces
The Fourier Transform
Creating modules
Edge detection
Custom kernels – getting convoluted
Modifying the application
Edge detection with Canny
Contour detection
Contours – bounding box, minimum area rectangle, and minimum enclosing circle
Contours – convex contours and the Douglas-Peucker algorithm
Line and circle detection
Detecting shapes
Summary
Chapter 4: Depth Estimation and Segmentation
Creating modules
Capturing frames from a depth camera
Creating a mask from a disparity map
Masking a copy operation
Depth estimation with a normal camera
Object segmentation using the Watershed and GrabCut algorithms
Summary
Chapter 5: Detecting and Recognizing Faces
Conceptualizing Haar cascades
Getting Haar cascade data
Using OpenCV to perform face detection
Summary
Chapter 6: Retrieving Images and Searching Using Image Descriptors
Feature detection algorithms
Summary
Chapter 7: Detecting and Recognizing Objects
Object detection and recognition techniques
Detecting cars
Summary
Chapter 8: Tracking Objects
Detecting moving objects
Background subtractors – KNN, MOG2, and GMG
CAMShift
The Kalman filter
Summary
Chapter 9: Neural Networks with OpenCV – an Introduction
Artificial neural networks
The structure of an ANN
ANNs in OpenCV
Handwritten digit recognition with ANNs
Possible improvements and potential applications
Summary

What You Will Learn

  • Install and familiarize yourself with OpenCV 3's Python API
  • Grasp the basics of image processing and video analysis
  • Identify and recognize objects in images and videos
  • Detect and recognize faces using OpenCV
  • Train and use your own object classifiers
  • Learn about machine learning concepts in a computer vision context
  • Work with artificial neural networks using OpenCV
  • Develop your own computer vision real-life application

Authors

Table of Contents

Chapter 1: Setting Up OpenCV
Choosing and using the right setup tools
Installing the Contrib modules
Running samples
Finding documentation, help, and updates
Summary
Chapter 2: Handling Files, Cameras, and GUIs
Basic I/O scripts
Project Cameo (face tracking and image manipulation)
Cameo – an object-oriented design
Summary
Chapter 3: Processing Images with OpenCV 3
Converting between different color spaces
The Fourier Transform
Creating modules
Edge detection
Custom kernels – getting convoluted
Modifying the application
Edge detection with Canny
Contour detection
Contours – bounding box, minimum area rectangle, and minimum enclosing circle
Contours – convex contours and the Douglas-Peucker algorithm
Line and circle detection
Detecting shapes
Summary
Chapter 4: Depth Estimation and Segmentation
Creating modules
Capturing frames from a depth camera
Creating a mask from a disparity map
Masking a copy operation
Depth estimation with a normal camera
Object segmentation using the Watershed and GrabCut algorithms
Summary
Chapter 5: Detecting and Recognizing Faces
Conceptualizing Haar cascades
Getting Haar cascade data
Using OpenCV to perform face detection
Summary
Chapter 6: Retrieving Images and Searching Using Image Descriptors
Feature detection algorithms
Summary
Chapter 7: Detecting and Recognizing Objects
Object detection and recognition techniques
Detecting cars
Summary
Chapter 8: Tracking Objects
Detecting moving objects
Background subtractors – KNN, MOG2, and GMG
CAMShift
The Kalman filter
Summary
Chapter 9: Neural Networks with OpenCV – an Introduction
Artificial neural networks
The structure of an ANN
ANNs in OpenCV
Handwritten digit recognition with ANNs
Possible improvements and potential applications
Summary

Book Details

ISBN 139781785283840
Paperback266 pages
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