Thursday, April 10, 2008

Visualizer, Part 1: Introduction

On and off over the past couple of months, I've been working on an OpenGL visualization application, called Visualizer (original, I know), for viewing high resolution core imagery and data. Visualizer is built on and with numerous technologies: Java, OSGi, JOGL, Eclipse, Ant, etc. Along the way, I've collected a fair number of tips, tricks, and tidbits that I'm going to share here. I hope to make Visualizer posts a regular feature of the blog, at least until the tidbits run out. If you like it, please let me know by commenting.

A quick aside here: Those of you in geosciences might recognize this concept as Corelyzer. My goal was to create a simplified, streamlined version of Corelyzer that would be easy to deploy on laptops/desktops; a sort of Corelyzer "lite" edition if you will. Visualizer doesn't have nearly as many features as the current version of Corelyzer, but it also doesn't suffer some of Corelyzer's warts.

I thought I'd start off with quick introduction of what Visualizer is for folks who aren't familiar with Corelyzer. During scientific geological drilling, hundreds or thousands of core samples are taken, imaged, and analyzed. This results in several (tens of) gigabytes of data and imagery. Collecting all of this data is of little use unless you have a way to view it. This is where tools like Corelyzer and Visualizer come in. They provide a way to view the images and data in context:

This picture illustrates Corelyzer in action. Stretched across the two 30" cinema screens is a high resolution image of core drilled in Antarctica. The user can zoom in or out and pan left or right to view the all of the imagery collected on the expedition.

So now that you know a bit of the background, let's look at the requirements (in no particular order) I had for Visualizer:
  • Cross platform - I have users on Mac, Windows, and Linux so I need a solution that will work for all of them.
  • Easy to deploy - Visualizer should work out of the box with no complicated setup for most users.
  • Easy to update - Needs a fairly automated mechanism to distribute updates and new features.
  • Open, extensible platform - Building on an open, extensible platform will allow me or others to quickly add new features or customize to a specific groups needs.
  • Reasonable performance - Visualizer should be able to handle 100m or so of imagery and data with reasonable performance on commodity hardware.
In the next blog post, I'll talk about the overall design of Visualizer and specific technologies employed. Future blog entries will include: "OSGi & Native Libraries", "Poor Man's Eclipse PDE Build", "Nifty Ant Tricks", "Automated Update Checks", and anything else that I can think of.


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