Book Image

OpenCV Computer Vision with Python

By : Joseph Howse
Book Image

OpenCV Computer Vision with Python

By: Joseph Howse

Overview of this book

<p>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.</p> <p>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.</p>

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OpenCV Computer Vision with Python
Joseph Howse
Apr, 2013 | 122 pages
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Table of Contents (14 chapters)
OpenCV Computer Vision with Python
OpenCV Computer Vision with Python
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Setting up OpenCV
Setting up OpenCV
Choosing and using the right setup tools
Running samples
Finding documentation, help, and updates
Summary
2
Handling Files, Cameras, and GUIs
Handling Files, Cameras, and GUIs
Basic I/O scripts
Project concept
An object-oriented design
Summary
3
Filtering Images
Filtering Images
Creating modules
Channel mixing – seeing in Technicolor
Curves – bending color space
Highlighting edges
Custom kernels – getting convoluted
Modifying the application
Summary
4
Tracking Faces with Haar Cascades
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
5
Detecting Foreground/Background Regions and Depth
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
Integrating with Pygame
Integrating with Pygame
Installing Pygame
Documentation and tutorials
Subclassing managers.WindowManager
Modifying the application
Further uses of Pygame
Summary
Generating Haar Cascades for Custom Targets
Generating Haar Cascades for Custom Targets
Gathering positive and negative training images
Finding the training executables
Creating the training sets and cascade
Testing and improving <cascade>
Summary
Index
Index

Summary

We now have an application that uses a depth camera, facial tracking, copy operations, masks, and image filters. By developing this application, we have gained practice in leveraging the functionality of OpenCV, NumPy, and other libraries. We have also practiced wrapping this functionality in a high-level, reusable, and object-oriented design.

Congratulations! You now have the skill to develop computer vision applications in Python using OpenCV. Still, there is always more to learn and do! If you liked working with NumPy and OpenCV, please check out these other titles from Packt Publishing:

  • NumPy Cookbook, Ivan Idris

  • OpenCV 2 Computer Vision Application Programming Cookbook, Robert Laganière, which uses OpenCV's C++ API for desktops

  • Mastering OpenCV with Practical Computer Vision Projects, (by multiple authors), which uses OpenCV's C++ API for multiple platforms

  • The upcoming book, OpenCV for iOS How-to, which uses OpenCV's C++ API for iPhone and iPad

  • OpenCV Android Application Programming...

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