With this Google Summer of Code’s firm pencils down deadline approaching rapidly, I thought I’d write a status report for what is mostly going to be the ‘official’ result of my work. I won’t drop dead right after the deadline passes or this posting is published, but this is just one of the formalities I have to take care of, the sooner the better.

During the course of this Google Summer of Code, I’ve been extensively working on the OS X support of the Kivy framework. As I have explained previously, Kivy’s set of features is based on the concept of providers, meaning that every functionality required by the framework is encapsulated in a provider that defines an abstract interface to the user (i.e. developers) of the framework. Each such interface then has a number of specialized implementations using (in almost all cases) a third-party or system supplied software library that was designed to do work in the field that the respective provider operates in.

Now my task of this GSoC was titled “Tight Mac OS X Integration”, which means that we wanted to reduce the number of external libraries (not system libraries) that we were using in Kivy, as those have to be compiled for and bundled with our OS X executable. Not only does this add to the overall memory requirements (storage wise), it also requires extra steps to be taken when it comes to maintenance and deployment.

In concrete terms, during this GSoC I implemented the following providers:

• Window (based on SDL; this was an internal decision tito and I made as this would be reusable on iOS as well)
• Image (based on Apple’s Core Graphics and Quartz APIs; I actually provided two versions of this, see below)
• Text (based on Apple’s Quartz APIs; I too provided two implementations for this)
• Audio (based on Apple’s Cocoa APIs)
• Video (based on Apple’s QTKit APIs)

Now for the image and text providers, I wrote two versions, respectively: One Python based version that uses PyObjC (available by default on OS X) and one ObjC based version to which I bridge using Cython. The advantage of the PyObjC versions are that they’re just single Python files that can be run on any recent Mac without inquiring any bundling or presence of additional tools, or even compile-link cycles. The drawback is that I cannot use them on iOS, as PyObjC is way too bloated for what I need, not well maintained and not functional on iOS anyway. That’s why I have a second version of those providers that actually works on iOS, as can be witnessed in my last blog posting. I will branch these off into the iOS support branch and bring them in back later into master when Kivy support for iOS is officially supported.

So what have we got now? Seven new provider implementations for 5 different core tasks. One of which (Window, SDL) will actually be reusable on all our supported platforms, not just OS X. The other four use native system APIs that already exist on any mac, so there is absolutely no memory footprint (storage wise) added and as soon as this hits master, we will see a dramatic reduction of size and bloat of the Kivy installer (and it will make my life as the OS X maintainer a whole lot easier). In numbers (and this is a back-of-the-envelope calculation), we’ll probably be reducing the size from 100 MB (uncompressed) to about 10 MB (uncompressed). They also benefit from the functionality that OS X inherently provides, such as audio and video codecs (the list of which can be added to by the user by installing things like Perian).

Since midterm, that means we’ve got audio and video providers, a new audio example, text and image PyObjC-based providers, significant visual improvements in terms of text rendering and under the hood changes for text and image display. This is a before/after shot for text rendering. There have also been some other artifacts around the characters that I’ve also gotten rid of. This and a couple fixes from tito now also make it work in the text input widget.

Here’s a video showing the video provider in action (The stutter comes from the screen capture. That video displays smoothly on my Mac).

This was the first time I’ve worked with Objective C or Apple’s APIs, so naturally a lot of work went into researching the different APIs, learning how Objective C works and how I can make use of it (I will write another posting to describe an alternative approach to using Objective C from Python that I came up with). I have a great sense of personal accomplishment in terms of teaching myself and therefore learning new things in this area and this is one of the major things I really like about Google’s Summer of Code project.

As a side note, I recently moved to the US for the remainder of the year to write my master’s thesis and I had to take care of a huge amount of (paper)work for that (this is kind of a pioneer project). So there’s some glitches remaining that I’ll certainly be looking into as soon as I get the time and then merge all of this new code into master and make it available to the user.

For instance, amongst other things, the text display isn’t a pixel perfect match with the other platforms yet and the video provider has problems with larger files that gstreamer handles properly (but at least it handles some other formats that gstreamer doesn’t, I just don’t want to break existing apps that rely on gstreamer supported formats to work at this point).

Anyway I’ll continue to dig into these remaining tasks before and after the deadline and I’d like to take a moment to thank a few people for what has been a terrific Google Summer of Code (probably my last as a student, unfortunately): Paweł Sołyga for being my mentor, Christian Moore for taking care this project could exist under the NUIGroup organisation umbrella and Mathieu Virbel for all the discussions and the help.

I recently had the opportunity to do some research with the goal of being able to run Python on any iOS device (iPhone, iPad, iPod touch). The idea is to only write some Python code (and nothing else) and deploy that to different platforms without changing it (e.g. Windows, Linux, Mac OS X, Android, iOS).

If you’re interested, here’s a preview/draft document that at a very high and easy to understand level very roughly summarizes what had to be done.

Now I’m not saying that this is THE way to develop cross-platform software, especially for devices such as tablets. The goal just was to see whether or not it’s technically possible and feasible to write applications for iOS using Python only. Fortunately, it seems possible and actually the programs run pretty snappy. They also use the GPU for rendering using OpenGL ES 2.0. Also, there was no jailbreak necessary.

Consider this work in progress. There’s still many things on the TODO list, I just wanted to share the early results with you and let you know that it is in fact possible. The code is on github and I’m using the kivy framework. I’m looking for opportunities to present this in much more depth in a journal or at a conference. If you know of any opportunities, please send me a mail (address in the PDF).

Update: I mentioned the code to be on github, but didn’t provide any actual links as I was in a hurry when I wrote the blog post. Here are the links: Python for iOS repo (compiles Python 2.7 for ARM, based off of cobbal’s repo): https://github.com/dennda/python-for-iphone Kivy iOS support branch: https://github.com/tito/kivy/tree/ios-support Objective C test app that embeds Python and runs a Kivy example: https://github.com/dennda/python-for-iphone-test You will also need SDL 1.3.

And last but not least I’d like to repeat what I wrote in the PDF and thank my friend Mathieu Virbel (from the kivy team) for all the help. I especially enjoyed the hack session we had at UDS.

At the end of 2010 I went to Paris (France) for an internship at a local company that produces multitouch hardware. I chose the job myself because I thought it would be interesting. What I implemented was markerless object recognition & improved tracking for the computer vision framework we’re developing (movid.org).

The video below shows the results of the prototype. The program is able to recognize objects based on their shape and size and does not need additional fiducial markers. It also takes into account object rotation (as long as you don’t have a perfectly circular object), even for square objects. You get an angle between 0 and 360 degrees.

The demo runs on an LLP setup. Only lasers, no diffuse illumination or similar approaches added.

The program that you actually see is just a visualization of the recognition and tracking, written in PyMT.

The quality of the video suffers from the fact that we had only 10 minutes to capture it before the table was transferred to an exhibition. The calibration was just quick and dirty, which is why I had to press that button to register an object (on the bottom) with a mouse instead of touching it.

What you see is a WIP project prototype. The code can be found in the GitHub repository on the master branch.

Markerless Object Recognition & Tracking (Movid) from Christopher Denter on Vimeo.

I recently wrote an interesting little GLSL shader program for a course at my university. I was given the topic Non-Photorealistic Fiber Rendering. What we call ‘Fibers’ are the nerve pathways that go through the human brain. There are quite many of them, and the goal is not to harm them while performing a tumor resection, for example. Otherwise functional areas of your brain (speaking, walking, etc.) might not be functional anymore after surgery. Since you cannot see the fibers (or bundles thereof) when you open up the head and look at the brain, it is important to visualize them. I did that using an approach that does not aim to provide realism, but comprehensibility (akin to the schematic drawings or similar illustrations found in medical books).

So my task was to implement an approach for halo rendering introduced by Everts et al. I combined it with another approach to depict contours from Otten et al. The result was quite nice already. To get an even better impression of spatial depth relationships, I had the idea of adding a modified approach to ambient occlusion to my program. I am pretty satisfied with the results:

Without Ambient Occlusion, following the combined Everts & Otten approach (click to enlarge):

With added Ambient Occlusion (click to enlarge):

Obviously this just focuses on the rendering of the fibers. Surrounding anatomy like brain tissue or potential tumors are not depicted. What you see is a set of fibers that ‘connects’ your eyes to your brain.

The tool shown there works on OSX, Ubuntu and Windows using the MeVisLab framework. The framework also allows Python scripting.

OK, small news very quick: We released PyMT version 0.5.1 which addresses quite a bunch of problems that were discovered since the release of PyMT 0.5. See the changelog and the website.

I’m sure you’ve sensed all the buzz about Ubuntu going multitouch. I truly think that this is some great news, being the multitouch and HCI enthusiast that I am. But what if you want to test your multitouch hardware? Or if you want to actually develop multitouch applications? Here’s something for you: PyMT has just been released in version 0.5!

We’ve been working hard to make this reality, and many a new feature has been added and quite a few bugs have been squashed. I suggest you read the full changelog and, if you already have a PyMT 0.4 application, also the migration guide.

One of the coolest new things with this release is the availability of portable binary packages for Windows and OS X. Those come bundled with everything you need (on Windows, even Python) to get started. You simply download the package for your platform and run it. We didn’t provide a portable package for Ubuntu, but it’s ridiculously easy to install PyMT there anyways. On Ubuntu 10.10, all you need is:

sudo apt-get install python-pymt

PyMT has native support for multitouch devices on Linux that are supported by the kernel, all Windows 7 multitouch devices, all of Apple’s multitouch accessories and much more. If you know basic python, PyMT is the easiest way to create multitouch applications or to just test your hardware.

In future releases we’re planning to fully use a rewrite of our current OpenGL abstraction and other performance-critical parts (that we start doing in C) that will allow for much higher application speed, less battery consumption and OpenGL ES/3.0 compatibility so that we can smoothly run on portable slate/pad devices.

Lastly, see what people have done with it (planet readers, click the images to get to the videos):

I hope that sparked your interest. We also hope you enjoy PyMT. If there are any questions, bugs, problems or feature requests, let us know. There’s a mailing list, a google code issue tracker and our IRC channel at irc.freenode.net in #pymt.

Google’s Summer of Code 2010 comes to an end for me today. It has been a great time working on awesome projects like PyMT and Movid. My task was to enhance PyMT’s text input methods. One of the joys of this task was that it allowed me to work on a relatively wide scope of things. Here’s a brief list of what I worked on:

• I added a new spelling provider to PyMT that abstracts from individual spellchecking libraries. That means you can use your favorite spellchecking library, which is important considering that PyMT is cross-platform.
• I added two actual spelling providers that implement this protocol: One enchant spelling provider (usable after installing enchant) and one provider using OS X’s native AppKit spellcheckers (so you get that out of the box on OS X).
• Mathieu once wrote a basic virtual keyboard with spelling suggestions which I adapted, cleaned and merged.
• PyMT obviously already had some text input widgets, which I improved (e.g. MTTextArea).
• I began working on a version of MTTextInput with added spellchecking (like OO.org with red lines drawn for incorrectly spelled words), but that needs some more love.
• One of the more concrete objectives of my task was a Swype-like keyboard for PyMT. I created a prototype for that, see the video below.
• Another concrete objective was a split keyboard (split into two parts, one half for the left, one for the right hand) that adjusts to your hand’s properties (e.g. size). To achieve this, a substantial amount of changes was needed to our vision tracking application (Movid):
• For the keyboard to adjust to the user’s hands, a handtracking algorithm was needed that I implemented for Movid. It detects the fingertips of the hand as well as the hand’s center. These are just seen as a certain type of ‘blobs’ internally.
• These blobs need to be tracked over a sequence of frames from the camera. Additionally, we also want to find simple touches (without all the hand information). For that, I added and integrated BlobFinder and BlobTracker modules that obey a common format so they’re easily interchangable.
• When your camera senses a blob on the touch surface, the application needs to perform a mapping to get the blob into screen coordinates. We do that using a calibration module, which I had started before SoC. I finished it and merged it back into our master branch.
• As an extra feature, I added a PyMT module that you can use to calibrate your tracker from within your client application, eliminating the need to switch applications. I also added a Flash GUI for the calibration so that you can easily do it on any remote computer via our web interface.
• To actually send the handtracking to the client application, Mathieu added a TUIO2 module to Movid. I started a PyMT input provider for TUIO2. Both of which is work in progress, but I believe we’re the first project to adapt TUIO2 (there’s not even a reference implementation yet).
• The result of that can be seen in the second video below. Also, make sure to read the vimeo description.
• Other than that we now also provide portable binary packages for PyMT 0.5 for both Windows and OSX. I created the OSX package, so it’s no longer a major pain to install. You just download and run it.
• And, of course, many more fixes!

Some of that is already in PyMT 0.5. All of the Movid stuff will be in the first release. In future releases we shall see much improved versions of these prototypes and hopefully even context aware word suggestions.

Here are the two promised videos, if you’re reading this through a planet, please go directly to my blog.

Prototype WipeToType Keyboard for PyMT from Christopher Denter on Vimeo.

Ergonomic multitouch keyboard prototype from Christopher Denter on Vimeo.

Thanks to all the people who made this possible. Thanks Google, Christian, Pawel, Mathieu and Thomas, for being (a) fantastic mentor(s). It has been a great pleasure and privilege to work with you in GSoC 2010 and I sure will continue to work on both projects.

I just had the opportunity to take a video of my multitouch table with my software in action. Both hardware and software were built for my bachelor’s thesis which I handed in in march. The software that you see at the end is written in Python with PyMT, using the VTK library.

Medical Multitouch from Christopher Denter on Vimeo.

Reading through a planet? Click here!

For more information, see the video description. PS: Although it supports all platforms, it currently runs on ubuntu. :-)

Let me know what you think!

Many things happened in the last few weeks. I just want to quickly outline them in case you’ve been wondering what I’ve been doing.

PyMT Spelling

Starting with PyMT 0.5 (to be released in August), we added support for spelling correction and word suggestions. This is based on my GSoC work. The code has been polished and integrated into the master branch, which will soon lead to the 0.5 release. I’m also currently working on a text input widget that indicates incorrectly spelled words as you type (just as OpenOffice would). This much is working. In a next step, I plan to add a feature that lets you select from a list of suggestions for the word that you just tapped.

WipeToType

One of the tasks of my GSoC proposal is the implementation of a Swype-like keyboard. What this means is that you just wipe over the keys that make up the word you want to type and it automatically determines which word you intended to enter. It is clearly far beyond the scope of a single, multitouch-oriented GSoC proposal to implement something as clever as a Swype clone (especially since this also requires A LOT of backend code for the intelligence). However, something remotely similar and usable should be doable and is what I’m looking for. A while back I started something like this and quickly sketched a modified version of the virtual keyboard.

This still needs much more love, but keeping in mind that I did this in a really short amount of time I think I can say that we’re getting somewhere.

FITG in Lille

Thomas, Mathieu and I have had the chance to meet in Lille at the FITG conference and present PyMT in a talk and several workshops. This was a great opportunity and in fact, it was the biggest real-life meeting of core developers and users so far.

The conference itself was a great success, both for the organizers and us. We had many people come to us and ask questions concerning PyMT and Movid. After our talk (which I think was well-received) we decided to give an additional workshop so that people interested could play with PyMT and get help from us. The room was pretty crowded and people were standing. The workshop presented a basic PyMT overview and first steps in a ‘hello world’ fashion (At least I think that’s what Mathieu was talking about. He spoke french and Thomas and I were answering questions or translating things in English). In the evening we gathered all the people that were still at the conference and went to a nice little restaurant to chat.

The next day we gave two more workshops. The idea to do the first one came up while we had breakfast. We decided to implement a simple version of the game at linerider.com with PyMT. When we arrived an hour later, we just picked a python 2D physics library that was easy to install and started live and from scratch, without any actual code having been written (or even thought about) beforehand. Luckily it all turned out well. After almost exactly one hour (in which Mathieu helped people in the audience, Thomas pointed to and explaining stuff at the projection and I coded and talked) we had finished what we were looking for in just 60 lines of unoptimized python code. The last workshop was about advanced OpenGL. Mathieu presented some of his insights that he had gathered while optimizing PyMT’s performance (great advances have been done here, by the way).

I stayed four days in total and it was absolutely worth it. Lille is a wonderful city and the conference was fantastic. The venue itself was just mind-blowing to begin with. We had a lot of fun together and obviously worked on PyMT as well. It’s even more fun if we’re in the same room! Sincere thanks to everyone involved in making these days as awesome as they were!

Reworked TextArea

For our talk in Lille, we used a very nice presentation tool (PreseMT) which is, obviously, written with PyMT. While using it for my own bachelor’s colloquium (I’m officially a Bachelor of Science now, by the way) I noticed that entering text suffered from severe limitations of the text input widget. Given that I had no multitouch-capable device around to enter text, I did it all with my hardware keyboard. I added to PyMT’s TextArea widget the ability to resize automatically depending on the text that was entered (which is what you want in PreseMT). Furthermore, the widget now properly reacts to several special keys like the arrow keys, home, end, del, pgup and pgdown.

Portable PyMT

It is no secret that installing PyMT on OSX is a major pain. This is not our fault, though. The problem simply is that installing almost anything in non-app format involves a non-trivial compilation process using MacPorts and the like. Unfortunately, one of our dependencies (gstreamer) is not easily installed this way.

Since we really don’t want our users to go through all of this, we decided to distribute portable versions of PyMT for OSX, Linux and Windows. I did the OSX version and hope to be able to finish it soon so that it can be reviewed. With it, you just download a zip file, unzip it and go. It contains everything that is needed to run PyMT.

In the course of this, I also fixed the compilation of our OpenGL-dependant cython modules for OSX.

Conclusion

Hopefully you will see a wonderful PyMT release next month. We’ve added many new features, improvements and fixed a lot of bugs. Some of my GSoC work will also go into it. In terms of GSoC, I will finish the aforementioned spelling-aware text input widget. I also intend to improve the quality of the results of the WipeToType keyboard and implement the things left on my GSoC proposal.

The NUIGroup Google Summer of Code students (I was lucky enough to become one of them for PyMT this year) are asked to summarize their weekly activities in blog format. Given that the first week has passed I figured I should just quickly outline what I have been working on up to now.

My proposal aims at developing more advanced text input methods for PyMT.

Work on PyMT

Some of the ideas I will realize draw heavily upon spelling correction and suggestion. It is therefore necessary that PyMT can interact with a spelling backend. Given that PyMT should be kept modular, I first implemented an abstract new core provider for spelling suggestions to become independent of a specific library. I then realized two concrete implementations of this provider:

• An enchant spelling backend. This uses the enchant spelling library which can itself be used with different kinds of dictionaries.
• A spelling backend based on OSX’s AppKit spell checker.

After the foundation was laid out I adapted a virtual keyboard with spelling support that Mathieu once developed to the new API and added it to the code base. All of this is not yet finished and needs some more love before I can merge it back into the master branch. You can check the branch I’m currently working on here.

Work on Movid

While spellchecking is important for some of my upcoming widgets, some other text input approaches make use of additional information provided by the tracking application. For example, one idea I had was to split the keyboard in half and dedicate one half to each hand. The halves would then automatically orient themselves following the respective hand’s position and orientation. Theoretically, further information such as properties of the user’s hands (length of fingers, etc.) could be taken into account to lay out the keyboards. For this I obviously need some kind of hand and fingertip tracking. Luckily I implemented that for Movid already:

However, since Movid is still not ready for end users due to a missing calibration utility and a proper (generic!) blob tracker (which means I can’t use it yet either), I continued my work on both of those. Again, both of which are not finished, but I can see the light at the end of the tunnel (or rather, the light below my fingers):

I hope that we can finish all of this and push out a first version of Movid for end users soon. And obviously, I want to test my text input widgets on my multitouch table and not in the mouse simulator.

This concludes my work for week one. If you have any questions or are interested in PyMT or Movid, feel free to join our IRC channel at #pymt and #movid on irc.freenode.net.