OpenCV is arguably the defacto free, open-source computer vision library, but setting it up for usage in a JVM project can be hard because OpenCV itself is written in C++, so there are a bunch of system-dependent things that you need to download/compile/install before you can use it.
JavaCV, written by Bytedeco is a library that makes it more bearable to use OpenCV from JVM projects by providing a bunch of wrapper classes and logic around OpenCV (there’s a lot more to it, see their page for details).
Still, because JavaCV depends on JavaCPP for common and OpenCV C++ wrappers, and JavaCPP requires you to set your target platform (what platform you want to run on), I thought getting started could be easier still.
Play is one of two officially-supported web frameworks from Typesafe, the company behind Scala (the other is Spray). It runs on its own webserver, is non-blocking, and encourages the use of idiomatic Scala. It is often compared with Rails because of its emphasis on convention over configuration and because it’s a full-on framework that comes with most of the bells and whistles needed to build a full-featured webapp. Spray is considered by many to be the defacto API-centric alternative to Play, offering a Sinatra-esque DSL for routing and being slimmer to boot (from a files + LOC perspective).
After looking around I began suspecting that Play comes with the ability to be slimmed down. By combining the String Interpolating Routing DSL and Compile-time dependency injection of Play 2.4, I was able to build a Scala app that would give Sinatra a run for its money in terms of the whole brevity thing.
If you’ve been working with Scala for a while, you might have come across a few “problems” with the built in
Enumeration that’s provided out-of-the-box. This is especially true if you have colleagues who come from a Java background and yearn for the Java-style
Enum that gave them lots of power and flexibility.
A quick search on the internet for “Scala enumeration alternative” will yield a lot of results (perhaps on StackOverflow) where people have cooked up their own implementation of enumerations, usually built on
sealed traits. Personally, I found most of them to be either too inconvenient to use, too over-powered, or too complicated, and I really didn’t want to have to copy-paste enum-related code into all my projects.
Thus Enumeratum was born.
Last week, I decided to take a stab at learning Scala macros. I had played around with macros when I wrote Scheme for a living (yes, believe it or not, these places exist…and existed long before Clojure made Lisp hip again), but the complexity of Scala’s macros always put me off (if you don’t believe me, check out the example given in the offical docs for a simple print macro).
In Scala, things are not so simple, but with the introduction of quasiquotes and some refinements brought by Scala 2.11, things are smoother. Still, for a guy like me, the documentation was both sparse and DRY. Since I learn best when I’m actively engaged in building something, I decided to try writing the run-of-the-mill unless-when macros in Scala.
This post aims to summarise my journey towards implementing unless-when and hopefully along the way make Scala macros accessible, at least at an introductory level, for Most People. There are already a few Scala macro blog posts out there but another one can’t hurt.
A couple days ago, I released v0.1.3 of Schwatcher, which introduces the ability to monitor events on file paths using a composable Rx Observable interface. “What does that even mean and why should you care?” is what this blog post tries to answer.
The original version of Schwatcher allowed you to tell a
MonitorActor what callback you want to fire when a certain type of event happened on a file path. This is fine and there are people out there using it in production as is. The limitation to this approach is that (at least by default), the events are difficult to treat as data and thus difficult to compose.
With Rx, we turn file path events into an asynchronous stream/channel. Essentially, you tell a
RxMonitor object what path and event type you want to monitor and when an event happens, it will get pushed into its
observable (the stream). You can then choose to filter, map, or fold over this data stream, creating new data streams. If you wish to cause side-effects, you can add one or more
observers to these data streams.
Version 0.1.3 of Schwatcher has been released.
This version brings a new Observable interface that exposes a “stream” (or channel) of
EventAtPaths that can be composed. Using this interface, you no longer need to register callbacks - you simply register paths and get notifications for events on them either by subscribing to the Observable or by composing.
For more information on how to use Observables (especially how they compose in awesome ways), checkout the Rx homepage
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Create a backported Ruby 1.9.3p484 Ubuntu package. For the purposes of this post, we will assume that you are OK with not creating a signed package (required if uploading to launchpad). If you wish to upload to launchpad (for example to share your backport with the world), it may be useful for you to check out this site after reading through this guide.
At my day job, I had to upgrade Ruby on very old production boxes that were running Oneiric AMD64. Upgrading the OS was a chore that would be happening later but we needed to start using the latest Ruby as soon as possible.
Unfortunately, there is no Ruby 1.9.3p484 Debian package readily available for Oneiric, not even as a backport.
Version 0.0.4 of Schwatcher has been released.
- No longer uses Akka Agent to hold CallbackRegistry (thanks crdueck). This should result in a small performance increase because of more ‘direct’ memory access inside MonitorActor.
- Refactored testing for better coverage and maintainability
- Scala 2.10.3 support in testing