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>
Table of Contents (14 chapters)
OpenCV Computer Vision with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Summary


We now have two versions of Cameo. One version tracks faces in a single camera feed and, when faces are found, swaps them by copying and resizing. The other version tracks faces in two camera feeds and, when faces are found in each, copies and resizes faces from one feed to replace faces in the other. Additionally, in both versions, one camera feed is made visible and effects are applied to it.

These versions of Cameo demonstrate the basic functionality that we proposed two chapters ago. The user can displace his or her face onto another body, and the result can be stylized to give it a more unified feel. However, the transplanted faces are still just rectangular cutouts. So far, no effort is made to cut away non-face parts of the rectangle or to align superimposed and underlying components such as eyes. The next chapter examines some more sophisticated techniques for facial blending, particularly using depth vision.