Book Image

OpenNI Cookbook

By : Soroush Falahati
Book Image

OpenNI Cookbook

By: Soroush Falahati

Overview of this book

The release of Microsoft Kinect, then PrimeSense Sensor, and Asus Xtion opened new doors for developers to interact with users, re-design their application’s UI, and make them environment (context) aware. For this purpose, developers need a good framework which provides a complete application programming interface (API), and OpenNI is the first choice in this field. This book introduces the new version of OpenNI. "OpenNI Cookbook" will show you how to start developing a Natural Interaction UI for your applications or games with high level APIs and at the same time access RAW data from different sensors of different hardware supported by OpenNI using low level APIs. It also deals with expanding OpenNI by writing new modules and expanding applications using different OpenNI compatible middleware, including NITE. "OpenNI Cookbook" favors practical examples over plain theory, giving you a more hands-on experience to help you learn. OpenNI Cookbook starts with information about installing devices and retrieving RAW data from them, and then shows how to use this data in applications. You will learn how to access a device or how to read data from it and show them using OpenGL, or use middleware (especially NITE) to track and recognize users, hands, and guess the skeleton of a person in front of a device, all through examples.You also learn about more advanced aspects such as how to write a simple module or middleware for OpenNI itself. "OpenNI Cookbook" shows you how to start and experiment with both NIUI designs and OpenNI itself using examples.
Table of Contents (14 chapters)
OpenNI Cookbook
About the Author
About the Reviewers

Syncing image and depth sensors to read new frames from both streams at the same time

A device, by default, captures and sends frames from each sensor independently. This means there is no guarantee that both sensors capture a snapshot of the environment at the same time, but there may be a lot of cases where you want to reduce the delay between capturing two frames from two different sensors. For example, if you want to use both image and depth streams to retrieve the color of an object recognized by the depth stream from the color stream you need to read the data from both the streams; this means one stream could have a different capture time from the other. But using frame syncing that is available for image and depth streams, you can at least decrease this difference in capture time to the lowest possible value.

In this recipe, we are going to show you how you can enable frame syncing and how much this option can reduce the difference between capture times.

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