I'm new to computer imaging and vision, and the algorithms and applications of this domain are a new frontier for my curiosity. The halide-excursions project is an attempt to create a large, open source repository of Halide computer-vision, computational-photography, and image processing functions. Anyone and everyone is more than welcome to join.
Halide is a new language for image processing and computer vision. It was developed by several PhD students at MIT and it is actively maintained. There's a developer mailing list with less than a handful of messages a day (so it is easy to lazily eavesdrop on the conversation) where you can communicate directly with the Halide developers. The response time is very short and the guys on the other end are very nice and eager to help.
Halide's succinct, functional syntax is very appealing and perhaps this is what drew me in. If, like me, you are starting with little knowledge in the domain, and the word 'kernel' makes you think about the Linux kernel; and 'convolution' is a long forgotten concept from the university, then it might take a little more energy to get to smooth sailing. But that's part of the fun. Moving from Wikipedia to implementation is always a nice feeling and I find Halide a great platform to do this. Here's the gradient magnitude of the Sobel operator (source code in halide-excursions):
The source code comes with a bunch of tutorials, sample "applications" and a large set of unit tests that can serve as a jumping board to start learning and messing around. There's no user guide or official language specification, but there is doxygen-generated documentation. All in all, I think there's plenty of resources to get you started.
Halide is supported on several OSs and cores (incl. GPUs) and promises the same performance as hand-optimized native code, with less code lines,less mess and with portability. Hand optimized code - using vectorization and parallelization intrinsics and a sort of other tricks - is hard to read, hard to maintain, hard/impossible to port and makes exploring the scheduling optimization space very slow. Halide's ability to separate the algorithm from the scheduling policy is very appealing and works well, most of the time. For example, when implementing the Viola-Jones face detection algorithm, I found that implementing the classifier cascade phase in Halide cannot be done optimally because of Halide's poor support for control code. In a future post I'll provide more examples showing where Halide shines, and where a hybrid native-Halide solution works better.
Until then, I hope you join the project.
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