OpenCV Computer Vision with Python

Learn to capture videos, manipulate images, and track objects with Python using the OpenCV Library

OpenCV Computer Vision with Python

Starting
Joseph Howse

Learn to capture videos, manipulate images, and track objects with Python using the OpenCV Library
$17.99
$29.99
RRP $17.99
RRP $29.99
eBook
Print + eBook
$12.99 p/month

Want this title & more? Subscribe to PacktLib

Enjoy full and instant access to over 2000 books and videos – you’ll find everything you need to stay ahead of the curve and make sure you can always get the job done.
+ Collection
Free Sample

Book Details

ISBN 139781782163923
Paperback122 pages

About This Book

  • Set up OpenCV, its Python bindings, and optional Kinect drivers on Windows, Mac or Ubuntu
  • Create an application that tracks and manipulates faces
  • Identify face regions using normal color images and depth images

Who This Book Is For

This book is written for Python developers who are new to computer vision and want a practical guide to teach them the essentials. Some knowledge of image data would be beneficial. You will need access to at least one webcam and will require a second one for certain exercises.

Table of Contents

Chapter 1: Setting up OpenCV
Choosing and using the right setup tools
Running samples
Finding documentation, help, and updates
Summary
Chapter 2: Handling Files, Cameras, and GUIs
Basic I/O scripts
Project concept
An object-oriented design
Summary
Chapter 3: Filtering Images
Creating modules
Channel mixing – seeing in Technicolor
Curves – bending color space
Highlighting edges
Custom kernels – getting convoluted
Modifying the application
Summary
Chapter 4: Tracking Faces with Haar Cascades
Conceptualizing Haar cascades
Getting Haar cascade data
Creating modules
Defining a face as a hierarchy of rectangles
Tracing, cutting, and pasting rectangles
Adding more utility functions
Tracking faces
Modifying the application
Summary
Chapter 5: Detecting Foreground/Background Regions and Depth
Creating modules
Capturing frames from a depth camera
Creating a mask from a disparity map
Masking a copy operation
Modifying the application
Summary

What You Will Learn

  • Install OpenCV and related software, such as Python, NumPy, SciPy, OpenNI, and SensorKinect—on Windows, Mac, or Ubuntu
  • Capture, display, and save photos, as well as real-time videos
  • Handle window events and input events using OpenCV's HighGUI module or Pygame
  • Understand OpenCV's image format and how to perform efficient operations on OpenCV images with NumPy and SciPy
  • Apply an effect only to certain depths of an image by using data from a depth sensor, such as Kinect
  • Track faces, eyes, noses, and mouths by using prebuilt datasets
  • Apply "curves" and other color transformations to simulate the look of old photos, movies, or video games

In Detail

OpenCV Computer Vision with Python shows you how to use the Python bindings for OpenCV. By following clear and concise examples, you will develop a computer vision application that tracks faces in live video and applies special effects to them. If you have always wanted to learn which version of these bindings to use, how to integrate with cross-platform Kinect drivers, and how to efficiently process image data with NumPy and SciPy, then this book is for you.

This book has practical, project-based tutorials for Python developers and hobbyists who want to get started with computer vision with OpenCV and Python. It is a hands-on guide that covers the fundamental tasks of computer vision, capturing, filtering, and analyzing images, with step-by-step instructions for writing both an application and reusable library classes.

Authors

Table of Contents

Chapter 1: Setting up OpenCV
Choosing and using the right setup tools
Running samples
Finding documentation, help, and updates
Summary
Chapter 2: Handling Files, Cameras, and GUIs
Basic I/O scripts
Project concept
An object-oriented design
Summary
Chapter 3: Filtering Images
Creating modules
Channel mixing – seeing in Technicolor
Curves – bending color space
Highlighting edges
Custom kernels – getting convoluted
Modifying the application
Summary
Chapter 4: Tracking Faces with Haar Cascades
Conceptualizing Haar cascades
Getting Haar cascade data
Creating modules
Defining a face as a hierarchy of rectangles
Tracing, cutting, and pasting rectangles
Adding more utility functions
Tracking faces
Modifying the application
Summary
Chapter 5: Detecting Foreground/Background Regions and Depth
Creating modules
Capturing frames from a depth camera
Creating a mask from a disparity map
Masking a copy operation
Modifying the application
Summary

Book Details

ISBN 139781782163923
Paperback122 pages
Read More