At SonoPlot, we just undertook a full rewrite of our robotic control software from Objective-C to Swift. While at first it might appear crazy to rework a large, stable project in a brand-new language, we did so after carefully examining sources of bugs in our Objective-C application and determining that Swift would prevent a large percentage of them. While we've only just started, we've learned enough so far that I thought there would be value in sharing this.

I always find it more effective to learn new programming concepts by building projects using them, so I decided to do the same for Apple's new Swift language. I also wanted to see how well it would interact with my open source GPUImage framework. As a result, I made GPUImage fully Swift-compatible and I've built and committed to the GitHub repository a couple of Swift sample applications. I wanted to write down some of the things that I learned when building these.

I recently pushed a significant set of changes to the GPUImage repository,, and wanted to explain them in a little more detail, especially since this changes one part of the manual photo filtering process. These changes should dramatically reduce the overall memory usage of the framework, help to prevent memory-related crashes, and fix a number of subtle bugs that have plagued the framework since I started it.

With the launch of iOS 7, and its use of blurs throughout the interface, there's been a lot of interest in fast ways of blurring content. GPUImage has had a reasonably performant, but somewhat limited Gaussian blur implementation for a while, but I've not been happy with it. I've completely rewritten this Gaussian blur, and it now supports arbitrary blur radii while still being tuned for the iOS GPUs. At higher blur radii, I'm slower than Core Image, though, and I don't quite understand why.