Just a quick share of lessons learned about Android’s Frame-by-Frame animations. Some of the functionality is poorly documented, as many people point out, so the web is the only place for answers. Having looked for some answers to these questions and couldn’t find any – here’s what I found out myself.
Update [2/3/11]: A new post on this topic gives a more broad view of my experience.
I believe that every builder-hacker should have their own little Swiss-army-knife server that just does everything they need, but as a webservice. You can basically do anything as a service nowadays: image/audio/video manipulation, mock-cloud data storage, offload heavy computation, and so on.
Tornado, the lightweight Python webserver is perfect for this, and since so many of the projects these days have Python binding (see python-tesseract), it should be a breeze to integrate them with minimal work.
Let’s see how it’s done
Just wanted to share a thing I made – a simple 2D hand pose estimator, using a skeleton model fitting. Basically there has been a crap load of work on hand pose estimation, but I was inspired by this ancient work. The problem is setting out to find a good solution, and everything is very hard to understand and implement. In such cases I like to be inspired by a method, and just set out with my own implementation. This way, I understand whats going on, simplify it, and share it with you!
Anyway, let’s get down to business.
Edit (6/5/2014): Also see some of my other work on hand gesture recognition using smart contours and particle filters
A very basic feature that I was missing in my Android phone is the ability to save a number while I’m in a call. Sure you can go to the home screen, and open up any app with a textbox, but I decided to create a dedicated application.
So, meet Number Saver!
UPDATE: Number Saver got a new look
Basic usage guide:
When you open the app you will get the main screen:
From here you are able to enter the number, copy to the clipboard, and clear the clipboard if it is full. Also, when you open the app, if there is a text in the clipboard it will already be in the text box. So you can press Dial to instantly dial the number
Also, during an active call you will notice the note and pencil icon. Stretching the notification area will give you the option to launch the application.
This app is available for free through the Android market. Donation is always welcome …
As a part of my work, I was asked to create a semi-simulator for JNLP (Web-Start) loader.
The intention was to get the JNLP link, get all the necessary JARS and resources, and send the main class to another process that will run it as a host.
I had no previous in
troduction with JNLP what so ever… Well, first stage: look at the file.
Looking at the file revealed a simple XML file that contained all the necessary data.
So of course, first things first – Get all the JARS. But how?
I have been playing around with NyARToolkit’s CPP implementation in the last week, and I got some nice results. I tried to keep it as “casual” as I could and not get into the crevices of every library, instead, I wanted to get results and fast.
First, NyARToolkit is a derivative of the wonderful ARToolkit by the talented people @ HIT Lab NZ & HIT Lab Uni of Washington. NyARToolkit however was ported to many other different platforms, like Java, C# and even Flash (Papervision3D?), and in the process making it object oriented, instead of ARToolkit procedural approach. NyARToolkit have made a great job, so I decided to build from there.
NyART don’t provide any video capturing, and no 3D rendering in their CPP implementation (they do in the other ports), so I set out to build it on my own. OpenCV is like a second language to me, so I decided to take its video grabbing mechanism wrapper for Win32. For 3D rendering I used the straightforward GLUT library which does an excellent job ridding the programmer from all the Win#@$#@ API mumbo-jumbo-CreateWindowEx crap.
So let’s dive in….
Just wanted to report on a breakthrough in my iPhone-CV digging. I found a true realtime frame grabber for the iPhone preview frame (15fps of ~400×300 video), and successfully integrated this video feed with a pure C++ implementation of the MeanShift tracking algorithm. The whole setup runs at realtime, under a few constraints of course, and gives nice results.
Update: Apple officially supports camera video pixel buffers in iOS 4.x using AVFoundation, here’s sample code from Apple developer.
So lets dig in…
As my search for the best platform to roll-out my new face detection concept continues, I decided to give ol’ Qt framework a go.
I like Qt. It’s cross-platform, a clear a nice API, straightforward, and remindes me somewhat of Apple’s Cocoa.
My intention is to get some serious face detection going on mobile devices. So that means either the iPhone, which so far did a crummy job performance-wise, or some other mobile device, preferably linux-based.
This led me to the decision to go with Qt. I believe you can get it to work on any linux-ish platform (limo, moblin, android), and since Nokia baught Trolltech – it’s gonna work on Nokia phones soon, awesome!
Lets get to the details, shall we?
Recently I was working on an iPhone app for work, for demo purposes, but my company cheaped out on the Apple iPhone Developers registration.