I have a tendency to have ideas that seem simple until I attempt the implementation. Then, all of the complexities jump out at me, and I give up and move on to the next idea. Once in a while, however, I’ll go so far as to prototype something or play around before abandoning ship. This here’s an example of the latter.

The Big Idea hit when I looking up a recruiter who had emailed me out of the blue. He shared the name of someone I went to school with, and I wanted to see if it was, in fact, the same person. In this case, a quick Google Images search on the name and job title indicated that it was not the same person, so I didn’t have to fake remembered camaraderie.

While searching, though, I thought it interesting the variety of faces that showed up for that name. Hm. Wouldn’t it be cool, thought I, if I could enter a name into my simple little web service, and it would spit back the “average face” of the first ten or twenty matching images from Google? After writing a few pages of code, scrapping them, switching languages and libraries, writing a few pages of code, scrapping them, switching languages and libraries again, writing a few more pages of code, I then ditched the whole enterprise.

In the process, I did use some morphing software to test the underlying concept. Here is the average face for my picture mixed in with the first seven Samuel Goldstein images that were clear enough, angled correctly, and of sufficient size to work with:

For what it’s worth, here are a few of the software challenges to automating something like this:

• Extracting the portrait from the background. This isn’t critical, but will simplify subsequent tasks.
• Scaling the heads to be the same size.
• Aligning the images more or less. I was going to use the eyes as the critical alignment points; if they couldn’t be within a certain degree of accuracy, this would suggest incompatible images (e.g., one a 3/4 portrait, the other straight on).
• Detail extraction. This is finding key points that match on each image. Experimentally, when combining images by hand, it may be sufficient to match:
  • 4 points for each eye, marking the boundaries
  • 3 points marking the extremities of the nostrils and bottom center of septum
  • 5 points marking ear boundaries: top point where they connect to the head, top of extension, center, bottom of lobe, point where lobe connects to head
  • 5 points marking top outer edges, outer angle, and center of mandible
  • 5 points mapping hairline
  • 5 points mapping top of hair
  • 7 points along the superciliary ridge
• Interpolate these points on each pair of images, then on each interpolated pair, and so on until a single final interpolation is achieved
• Render the image to a useful format
• View image, and laugh (or cry)

A few other lessons learned:

The typical picture returned by Google images search for a name will be a thumbnail portrait. It’ll be small — maybe 150 by 300 pixels or so. While that’s enough data to recognize a face, it’s not a lot of data when you start manipulating it. Ideally, for nicer results, source images should be a minimum of two or three times that size.

Google gives back different results depending on whether you surround the name with quotes or not; it also makes a big difference if you pass size or color parameters. The “face” search is a good parameter to use, although when searching for “Samuel Goldstein” face search inexplicably had lots of Hitlers and Osama Bin Ladens. The “safe search” feature is also strongly recommended for this project — again, when searching for “Samuel Goldstein” without safe search yielded a variety of unexpected vaginas.

Bing image search gives different results (not too surprisingly), but they also have some anomalies. My search brought back many of the same pictures, along with an inexplicable collection of calabashes and LP labels.

If any ambitious programmers go ahead and implement this, please send me the link when you’re done!

I needed to change my iPhone lock screen image. With no further aesthetic commentary, here are three Retina-ready, iPhone 5-sized images you can use. They’ll also work on non-Retina or lower-resolution iPhone screens, you’ll just have to select a portion of the image (or scale it down). If you don’t know how to install Wallpaper images on your iPhone, the first page I Googled gave a pretty good step-by-step.

(Click on the thumbnail to see the full-size versions; right-click to download them)

Enjoy!

Cambot is a project based on the Teensy development board. Cambot creates an infra-red “trip beam” that triggers a pre-focused Nikon D90.

To create an IR beam and detector that work in direct sunlight, Cambot pulses the IR source every 10ms, and compares detected signal between the on and off states. If it’s greater than a threshold, it’s considered a valid signal. The sensor also has a vary small aperture which is additionally shielded with a filter to reduce the ambient IR. When powered on, Cambot goes into calibration mode, which lights up an LED when it detects a good signal. This is critical for lining up the IR sources and detector — once a good signal is sustained for 3 seconds, Cambot goes into “armed” mode. When armed, breaking the beam will turn off the IR source, half-press the camera’s shutter for 10ms so the camera can compute exposure, and then fires off a burst of pictures before re-arming itself.

While there’s nothing in Cambot that couldn’t have been implemented with, say, 555 timers, counters, and gates, having the ability to drive digital logic with C code makes things much more flexible. When trying to determine the proper threshold values for arming and triggering, the ability to output hex data over the USB connector to a host computer was invaluable.

Here’s Cambot’s first run, when the trip-beam was positioned over a tempting milkweed blossom in the back yard.

If there’s any interest, I’ll post a circuit diagram and source code.

I’ve played with the Javascript interface to Photoshop for a couple of years. Conceptually, it’s great — a simple, powerful, interpreted language like Javascript, with an API to interface to one of the best image-processing packages available. In practice, it’s not as good as it is in concept, but it’s still pretty good. The API doesn’t include all of Photoshop’s functionality directly, and there are a lot of things you need to execute as fairly obscure event actions. These event actions aren’t documented, but can be determined by activating a plug-in which logs everything that you do using the Photoshop GUI — you can then read through these logs, and copy the actions you need.

Still, there are some real advantages to using this Javascript interface, as opposed to something designed for the purpose like, say, Processing. You can use the Photoshop UI for controlling inputs to your script (set foreground colors, select portions of the image, select specific layers, etc.), and output your manipulations directly into Photoshop layers.

I’ll be posting here shortly a library I’ve created for easily building dialog panels for setting script options. I find that most manipulations I want to do have a set of variables, and I’d rather not tweak the code each time I want to change them.

This library was originally written under Photoshop 10 (aka CS3). Under version 11 (aka CS4), it was less stable. Sometimes it would crash out at odd places complaining that I was referencing properties of undefined objects. Because there have been memory leaks and other issues with the Javascript interpreter, these seemingly random failures were annoying but not too surprising. When it came to version 12 (aka CS5), I was rarely able to run my scripts at all. What made it frustrating was the apparent randomness of the crashes. I could print a variable to the console, and the very next line would crash out with an “undefined object” error when referencing that variable.
To make a long story short, I was able to track down the issue. It turns out that in iterations, declaring variables matters. That is to say:
for (i in someCollection)
{
$.writeln(i['someAttribute']);
}

will cause random crashes, but
for (var i in someCollection)
{
$.writeln(i['someAttribute']);
}

runs beautifully. Now, I “knew” that the var keyword is optional and used for specifying scope, but I never had any idea that there could be an issue within the scope of a simple loop. Obviously, Javascript didn’t know that I intended i to be a variable on each iteration — perhaps it thinks I meant for i to be a 1957 Chevy Belair on some iterations.

In any case, having cracked the code as it were, I have proceeded to enhance and add to my library. After a little more testing, I’ll be posting it here or on GitHub.

We’ve had some new hatches in the Butterfly Fort, and there are at least six chrysalids which will be eclosing in the next couple of days¹. This reawakens my interest in time-lapse photography.

I used to have a setup with a Harbortronics D2000 which I hooked up to my Nikon Coolpix 950 (and, later, Coolpix 995). It was good for a lot of things, but I succeeded in burning a nice streak across the sensor of the camera when the sun passed directly through the scene — the Coolpix line didn’t have a physical shutter, so the lens focused the sun onto the delicate sensor for the full time it was in view.

I’ve been using the Brinno Gardenwatch Cam that I received as a gift
a few years ago. It’s a dedicated, all-in-one time-lapse device. Once I learned a few things, I was able to use it successfully. First off, it really needs to be run on Nickel-metal hydride batteries. Next, you have to listen carefully when turning it on, because it’s not always obvious when you’ve powered it on and then off again by holding down the button a bit too long.

The Gardenwatch Cam does a decent job. It creates AVI format movies. It has 7 interval settings ranging from 1 minute to 24 hours. It has two focus settings, one for close up, and one for landscape.

With the monarchs, though, I want to be able to get in closer, and have sharper images than the Gardenwatch cam will give me. I still have a Nikon D70 which served me well for many years, but has been supplanted by the D90 in recent years. I also have a small assortment of lenses that I’ve accumulated over the past fifteen years. I’m thinking that the six megapixels of the D70 should be far more than adequate for doing some nice macro time-lapse work.

So the only problem is intervalometry — how do I trigger the camera to take pictures? Nikon sells intervalometers for most cameras, but the D70 is notably excluded from that list. There are a number of people making kits (or generously giving away their designs). I thought I might be able to rig something together.

I was successful. Taking an ancient Gateway Solo 9300 notebook that I’d bought for a king’s ransom back in 1998 or 1999, I installed Ubuntu 11.4 desktop on it. This was a mistake. The machine has a 366MHz Pentium II processor, and 128 MB of memory — lesser specs than your iPhone². It got part way through the boot sequence, and locked up (terminal swapping? driver issues? I don’t know). I then installed a version of Ubuntu 8 Server for which I happened to have a CD. After installation, I booted into Matrix-esque screen garbage, but after some fighting with boot parameters was able to get running cleanly. Next, I installed gPhoto2. Putting the D70 into PPT mode, I hooked it up to the notebook with a USB cable.

Voilà! Now, all it took was a few commands:

Find the camera:

gphoto2 --auto-detect

Store pictures on the camera’s compact flash card:

gphoto2 --set-config capturetarget=1

And take pictures at a 30-second interval:

gphoto2 --capture-image --interval 30


If I get any worthy results, I’ll post ’em on Archie’s Garden.

¹ There sure are a lot of great insect words. “Eclose” and “chrysalid” are just two among many.
² The two generations of iPhone had 128 MB of memory, and a 412 MHz ARM processor.