Having never skateboarded before, I saw the Archos SK8 electric skateboard for about 80€ at a sale and thought why not give it a try. This got me into this whole electric skateboarding thing.

After the latest migration of WebKitGTK test bots to use the new SDK based on Flatpak, the old development environment based on jhbuild became deprecated. It can still be used with export WEBKIT_JHBUILD=1, though, but support for this way of working will gradually fade out.

Just a quick note to let the world know that PhotoTeleport 0.13 has been released.

Due to the current circumstances, I had to record the lectures on augmented reality, which I am typically holding live. This was much more work than anticipated..
On the other hand, this means that I can make them available via Youtube now.

So, if you ever wanted to learn about the basic algorithms behind Augmented Reality, now is your chance.

The lecture is structured in two parts

• Camera Calibration, and
• Object Pose Estimation.

You can click on the TOC links below to directly jump to a specific topic.

Camera Calibration

• Mathematical camera model
• Lens distortions
• Calibration targets
• Calibration poses

Object Pose Estimation

• Fiducial marker detection
• Natural feature detection
• Contour based tracking

Yes, this is yet another post in the internet talking about using exceptions versus error returns. The topic has been flaming up at my workplace for quite some time now, and I felt that writing a blog post about it during the week-end would help me focus my thoughts and give me time to explain my point with the due care. In case you didn't know, I'm against using exceptions for error handling (maybe having spent many years working with Qt has had an effect on this); that does not mean that I never write code using exceptions: I certainly do my good share of try ... catch when dealing with third-party code (including the STL), but you won't find a throw in my programs.

Mappero Geotagger has now moved from its previous page from this site to a new, separate website built with the awesome Nikola static website generator.

The main reason for this change is that I didn't have an online space where to host the application binaries, and I wanted to experiment with a different selling method. Now, downloads are (poorly) hidden behind a payment page, whereas in multiple places of the website I also mention that I can provide the application for free to whomever asks for it. While it might seem weird at first, I do honestly believe that this will not stop people from buying it: first of all, many people just think it's fair to pay for a software applications, and secondly, for some people writing an e-mail and establishing a personal contact with a stranger is actually harder than paying a small amount of money. And in all sincerity, the majority of the income I've had so far for Mappero Geotagger came from donations, rather than purchases; so, not much to lose here.

QBS and MXE

Anyway, since this is primarily a technical blog, I want to share my experiences with cross-building from Linux to Windows. As you might remember, some time ago I switched the build system of Mappero from qmake to QBS, and I haven't regretted it at all. I've managed to build the application in Linux (of course), macOS, as a Debian package on the Ubuntu PPA builders, on Windows with AppVeyor and, last but not least, on Linux for Windows using the mingw setup provided by the MXE project.

QBS worked surprisingly well also in this case, though I had to fight with a small bug on the toolchain detection, which is hopefully going to be fixed soon. For the few of you who are interested in achieving something similar, here's the steps I ran to configure QBS for mingw:

Sorry for using that many environment variables ☺. After qbs is configured, it's just a matter of running

to build the application. You will get a nice window binary and, once you collect all the needed library dependencies, you'll be able to run it on Windows. Or WINE ☺.

As part of this effort, I also had to build libraw, so I didn't miss the occasion to contribute its recipe to MXE. I'm also trying to get a change accepted, that would make MXE support the dynamic OpenGL selection available since Qt 5.4.

I am quite frustrated with corona graphs in the news, since most reporters seem to have skipped math classes back then. For instance, just plotting the number of confirmed infections at the respective dates does not tell you anything due to the different time point of outbreak. So lets see whether I can do better:

https://paroj.github.io/arewedeadyet/

With the site above, I tried to improve on a few things:

• the Charts are live: they update themselves each time you load the site.
• The curves are normalized by the time-point of outbreak so you can compare the course in different countries.
• You can select the countries that you want to compare.
• Different metrics are computed that allow comparing the corona countermeasures and impact across countries with different population size.

Today I've released Imaginario 0.9. The big feature coming with this new release is a face tagging flow which I believe will be the fastest and simplest you've ever used, despite it being all manual. I even sat down and spent some quality time with Blender to prepare a video to show it off:

While some people might actually think that I spent more time for making the video than for implementing the face tagging feature itself, this couldn't be farther from the truth: the face tagging branch has been being worked on for at least three months (of course, that's my spare time — so it's actually less than one hour per day) and consisted of more than 40 commits (after squashing all the fixups), whereas for the video I spent no more than a couple of hours.

I would appreciate if the curious could go and try it out, and let me know about any issues you should find: there are built packages for Linux (AppImage), macOS and Windows. I do also have an Ubuntu PPA where nightly images are built, but I'm not sure if I can recommend that one, since I've not been using it myself and have no idea whether those packages actually even start. But you are welcome to try :-)

Your feedback will help me do better, so please don't be shy!

Lets say you have mesh data in the typical format, triangulated, vertex buffer and index buffer. E. g. something like

>>> vertices

[[[ 46.27500153  19.2329998   48.5       ]]

 [[  7.12050009  15.28199959  59.59049988]]

 [[ 32.70849991  29.56100082  45.72949982]]

 ..., 

>>> indices

[[1068 1646 1577]
 [1057  908  938]
 [ 420 1175  237]
 ..., 

Typically you would need to feed it into OpenGL to get an image out of it. However, there are occasions when setting up OpenGL would be too much hassle or when you deliberately want to render on the CPU.

In this case we can use the OpenCV to do the rendering in two function calls as:

img = np.full((720, 1280, 3), 64, dtype=np.uint8)

pts2d = cv2.projectPoints(vertices, rot, trans, K, None)[0].astype(int)
cv2.polylines(img, pts2d[indices], True, (255, 255, 255), 1, cv2.LINE_AA)

• Wire-frame rendering

See the documentation of cv2.projectPoints for the meaning of the parameters.

Note how we only project each vertex once and only apply the mesh topology afterwards. Here, we just use the numpy advanced indexing as pts2d[indices] to perform the expansion.

This is pretty fast too. The code above only takes about 9ms on my machine.

In case you want filled polygons, this is pretty easy as well

for face in indices:
    cv2.fillConvexPoly(img, pts2d[face], (64, 64, 192))

• Filled polygon rendering

However, as we need to a python loop in this case and also have quite some overdraw, it is considerable slower at 20ms.

Of course you can also combine both to get an image like in the post title.

From here on you can continue to go crazy and compute face normals to do culling and shading.

• But you can only get that far without a depth buffer

So after having made fun of people for “wearing toothbrushes”, I finally came to buy such headphones for myself.