There’s a small but dedicated group of folks out there who spend all year planning their Christmas decorations. These aren’t simple lawn ornaments or displays, either, but have evolved into complex lightning performances that require quite a bit of computer control. For some things, hooking up a relay to a microcontroller can get the job done, but [Andy] has turned to computer vision to solve some of the more time-consuming aspects of these displays.
Specifically, [Andy] has a long string of programmable RGB LED lights to wrap around a Christmas tree, but didn’t want to spend time manually mapping out each light’s location. So he used OpenCV to register the locations of the LEDs from three different camera angles, and then used a Python script to calculate their position in the 3D space. This means that he will easily be able to take the LEDs down at the end of the holidays and string them back up next year without having to do the tedious manual mapping ever again.
While [Andy] notes that he may have spent more time writing the software to map out the LEDs than manually doing it himself, but year-after-year it may save him a lot of time and effort, not to mention the benefits of a challenge like writing this software in the first place. If you want to get started on your own display this year, all you really need is some lights and a MIDI controller.
Ever wonder what your favorite board game sounds like? Neither did we. Thankfully [Sara Adkins] did, and created a step sequencer called Let’s Go that uses the classic board game Go as input.
In the game Go, two players place black and white tokens on a grid, vying for control of the board. As the game progresses, the configuration of game pieces gets more complex and coincidentally begins to resemble Conway’s Game of Life (or a weird QR Code). Sara saw music in the evolving arrangement of circles and transformed the ancient board game into a modern instrument so others could hear it too.
To an observer, [Sara’s] adaptation looks fairly indistinguishable from the version played in China 2,500 years ago — with the exception of an overhead webcam and nearby laptop, of course. The laptop uses OpenCV to digitize the board layout. It feeds that information via Open Sound Control (OSC) into popular music creation software Max MSP (though an open-source version could probably be implemented in Pure Data), where it’s used to control a step sequencer. Each row on the board represents an instrumental voice (melodic for white pieces, percussive for black ones), and each column corresponds to a beat.
Every new game is a new piece of music that starts out simple and gradually increases in complexity. The music evolves with the board, and adds a new dimension for players to interact with the game. If you want to try it out yourself, [Sara] has the project fully documented on her website, and all of the code is available on GitHub. Now we’re just left wondering what other games sound like — [tinkartank] already answered that question for chess, but what about Settlers of Catan?
Plenty of people don’t bother to read the current newspaper, let alone editions that were published over 100 years ago. But there’s a wealth of important historical information buried in these dusty old publications, assuming you can find a way to reliably digitize and index it all. You might think the solution is as simple as running images of the paper through optical character recognition (OCR) software, but as [John Scancella] explains, the problem is a bit more complicated than that.
Stretching the text vertically highlights the columns.
Ultimately, the issue largely comes down to formatting. The OCR software reasonably assumes all the text is in orderly horizontal lines, because in the vast majority of cases, it would be. That’s how you’re reading these words now. But as anyone who’s seen an old time newspaper knows, that’s not how things were necessarily written back then. Pages consisted of multiple narrow columns of stories separated by vertical lines; if the OCR tries to read the page from left to right, the resulting text is a mishmash of several unrelated topics.
The answer is to break all those articles into their own images, but doing that manually at any sort of scale simply isn’t an option. So [John] has been working on a system that uses OpenCV to identify the columns of text and isolate them. He details the multi-step process down in his write-up, and even provides the Python code should you want to give it a spin. But the short version is that the image is converted to grayscale and the OpenCV dilate function is used to stretch the text in the Y dimension. This produces big blobs of white that can easily be picked out with findContours() and snipped into individual images.
It’s not a perfect solution, and there are still a few pitfalls. For one, the name of the paper needs to be removed from the front page before the stretching operation happens. But it’s clearly a step in the right direction, and the results certainly look very promising. Anything that makes OCR more accurate or easier to implement is a win in our book, so we’re excited to see where [John] takes this concept.
OpenCV is an open source library of computer vision algorithms, its power and flexibility made many machine vision projects possible. But even with code highly optimized for maximum performance, we always wish for more. Which is why our ears perk up whenever we hear about a hardware accelerated vision module, and the latest buzz is coming out of the OpenCV AI Kit (OAK) Kickstarter campaign.
There are two vision modules launched with this campaign. The OAK-1 with a single color camera for two dimensional vision applications, and the OAK-D which adds stereo cameras for that third dimension. The onboard brain is a Movidius Myriad X processor which, according to team members who have dug through its datasheet, have been massively underutilized in other products. They believe OAK modules will help the chip fulfill its potential for vision applications, delivering high performance while consuming low power in a small form factor. Reading over the spec sheet, we think it’s fair to call these “Ultimate Myriad X Dev Boards” but we must concede “OpenCV AI Kit” sounds better. It does not provide hardware acceleration for the entire OpenCV library (likely an impossible task) but it does cover the highly demanding subset suitable for Myriad X acceleration.
Since the campaign launched a few weeks ago, some additional information have been released to help assure backers that this project has real substance. It turns out OAK is an evolution of a project we’ve covered almost exactly one year ago that became a real product DepthAI, so at least this is not their first rodeo. It is also encouraging that their invitation to the open hardware community has already borne fruit. Check out this thread discussing OAK for robot vision, where a question was met with an honest “we don’t have expertise there” from the OAK team, but then ArduCam pitched in with their camera module experience to help.
We wish them success for their planned December 2020 delivery. They have already far surpassed their funding goals, they’ve shipped hardware before, and we see a good start to a development community. We look forward to the OAK-1 and OAK-D joining the ranks of other hacking friendly vision modules like OpenMV, JeVois, StereoPi, and AIY Vision.
If you’re playing Texas Hold’em or other card games with a small group, you may get tired of shuffling over and over again. [3dprintedLife] was in just such a position, and realized there were no good automatic card shufflers in his budget. Instead, he elected to build one, and put in some extra functionality to corrupt the game to his whims.
The mechanicals of the machine took much development, as accurately handling and dispensing cards is a challenge, particularly with the loose tolerances of 3D printed parts. After developing a reliable transport mechanism, it was more than capable of shuffling a deck well with some basic commands.
However, the real magic comes from installing a camera and Raspberry Pi running OpenCV. This is capable of reading the value and suit of each card, and then stacking the deck in a particular order to suit the dealer’s wishes. It’s all controlled through a web interface and is capable of creating guaranteed wins in Blackjack and Texas Hold’em. Files are on Github for those eager to delve deeper into how the machine works.
The mechanism does such a beautiful job of shuffling, that your friends may not even notice the ruse. It goes to show that you should always have your wits about you when gambling with the aid of machines. Of course, if you wish only to create havoc, this Lego card machine gun may be more your speed. Video after the break.
[Dirk] shared a fascinating project of his that consists of several different parts coming together in a satisfying whole. It’s all about wanting to do target practice, indoors, using a simple red laser dot instead of any sort of projectile. While it’s possible to practice by flashing a red laser pointer and watching where it lands on a paper target, it’s much more rewarding (and objective) to record the hits in some way. This is what led [Dirk] to create human-powered, battery-free laser guns with software to track and display hits. In the image above, red laser hits on the target are detected and displayed on the screen by the shooter.
Right under the thumb is the pivot point for the lever, and that’s also where a geared stepper motor (used as a generator) is housed. Operating the action cranks the motor.
There are several parts to this project and, sadly, the details are a bit incomplete and somewhat scattered around, so we’ll go through the elements one at a time. The first is the guns themselves, and the star of the show is his 3D printed cowboy rifle design. The rifle paints the target with a momentary red laser dot when the trigger is pressed, but that’s not all. [Dirk] appears to have embedded a stepper motor into the lever action, so that working the lever cranks the motor as a generator and stores the small amount of power in a capacitor. Upon pulling the trigger, the capacitor is dumped into the laser (and into a piezo buzzer for a bit of an audio cue, apparently) with just enough juice to create a momentary flash. We wish [Dirk] had provided more details about this part of his build. There are a few more images here, but if you’d like to replicate [Dirk]’s work it looks like you’ll be on your own to some extent.
As for the target end of things, blipping a red dot onto a paper target and using one’s own eyeballs can do the job in a bare minimum sort of way, but [Dirk] went one further. He used Python and OpenCV with a camera to watch for the red dot, capture it, then push an image of the target (with a mark where the impact was detected) to a Chromecast-enabled screen near the shooter. This offers much better feedback and allows for easier scoring. The GitHub repository for the shot detector and target caster is here, and while it could be used on its own to detect any old laser pointer, it really sings when combined with the 3D printed cowboy rifle that doesn’t need batteries.
Not using projectiles in target practice does have some benefits: it’s silent, it’s easy to do safely, there is no need for a backstop, there are no consumables or cleaning, and there is no need to change or patch targets once they get too many holes. Watch it all in action in the video embedded below.
When you think of robots that were modeled after animals, a brittle star is probably not the first species that comes to mind. Still, this is the animal that inspired [Zach J. Patterson] and his research colleagues from Carnegie Mellon University for their underwater crawling robot PATRICK.
PATRICK is a soft robot made from molded silicone. Each of his five limbs contains several shape memory alloy (SMA) springs which can be contracted through Joule heating thereby causing the limbs to bend. The robot’s control board is sending and receiving commands via Bluetooth Low Energy from a nearby computer. To control PATRICK’s motion the researchers constructed a closed-loop system where an offboard OpenCV based camera system is constantly tracking the robot. As shown in the video below with an average velocity of 1 cm/s, PATRICK’s movement is a bit sluggish but the system is supposedly very robust against uncertainties in the environment.
In the future [Zach J. Patterson et al.] would like to improve their design by giving the robot the ability to grasp objects. Ultimately, also the offboard camera should be replaced with onboard sensors so that PATRICK can navigate autonomously.
