This one is straight out of the Really Bad Ideas™ files, and comes to us from [Marc Radinovic]. His story on this one is that he wants to protect the stuff in his new house, and felt that a face-recognition system with a flame thrower would be the best way to address that. And to somehow make it even better, said system would be built into a ridiculous portrait of everyone’s favorite plutocrat. The guts of the system are pretty much what you’d expect — a camera and a Raspberry Pi running OpenCV and a face recognition library, a butane reservoir and a solenoid valve, an arc lighter as an ignition source, and an Arduino and some completely not sketchy at all wiring to control all pieces. And LCD displays for [Elon]’s eyes, of course.
The system is trained to recognize [Marc]’s face and greets him cheerfully when he’s in view. [Non-Marc] people, however, are treated a bit less accommodatingly, up to and including a face-melting fireball. Effigies of other billionaires got the treatment; strangely, [Marc]’s face-recognition algorithm didn’t even recognize another [Mark] as a human face, which when you think about it is pretty darn funny.
So, certainly not a practical security system, and definitely not something you should build, but it’s pretty good fun anyway. It reminds us a bit of the fire-breathing duck we saw years ago.
Energy costs around the world are going up, whether it’s electricity, natural gas, or gasoline. This is leading to a lot of people looking for ways to decrease their energy use, especially heading into winter in the Northern Hemisphere. As the saying goes, you can’t manage what you can’t measure, so [Steve] has built this system around monitoring the fuel oil level for his home’s furnace.
Fuel oil is an antiquated way of heating, but it’s fairly common in certain parts of the world and involves a large storage tank typically in a home’s basement. Since the technology is so dated, it’s not straightforward to interact with these systems using anything modern. This fuel tank has a level gauge showing its current percentage full. A Raspberry Pi is set up nearby with a small camera module which monitors the gauge, and it runs OpenCV to determine the current fuel level and report its findings.
Since most fuel tanks are hidden in inconvenient locations, it makes checking in on the fuel level a breeze and helps avoid running out of fuel during cold snaps. [Steve] designed this project to be reproducible even if your fuel tank is different than his. You have other options beyond OpenCV as well; this fuel tank uses ultrasonic sensors to measure the fuel depth directly.
Here at Hackaday, we love to see projects re-visited and updated after we’ve covered them on the site. It’s always exciting to see what the creators come up with next, and this Pi-Based Spectrometer project is a great example of that.
[LesWright] found himself with a problem when the new version of Raspberry Pi operating system was released (Bullseye), and it broke some functionality on his original software. Rather than just fix the issues, [Les] chose to rewrite the software more dramatically and has ended up with a much more capable spectrometer that is able to match professional devices costing many times more.
By using multi-wavelength calibration and polynomial regression data, the new version is much more accurate and can now resolve wavelengths down to +/- 1nm.
The whole project is now written in OpenCV, and there’s a nifty new waterfall spectrum display, that will show changes in measured spectra over time.
A low-cost benchtop spectroscope is coupled to a RaspberryPi Camera via a CCTV zoom lens and the whole setup is mounted to a small block of aluminium for thermal and mechanical stability. The spectroscope is pointed at a fluorescent lamp and the user is guided through a calibration routine to tune the software to the hardware.
We’re impressed with the precision [Les] has achieved with his builds, and the write-up is sufficiently detailed to allow others to follow in his footsteps. We’d love to see if readers build one themselves, and what they use them for!
It’s hard to watch [Mark Zuckerberg]’s 2018 Congressional testimony and not come to the conclusion that he is, at a minimum, quite a bit different than the average person. Of course, having built a multibillion-dollar company that drastically changed everything about the way people communicate is pretty solid evidence of that, but the footage at least made a fun test case for this AI truth-detecting algorithm.
Now, we’re not saying that anyone in these videos was lying, and neither is [Fletcher Heisler]. His algorithm, which analyzes video of a person and uses machine vision to pick up cues that might be associated with the stress of untruthfulness, is far from perfect. But as the first video below shows, it is a lot of fun to see it at work. The idea is to capture data like pulse rate, gaze direction, blink rate, mouth posture, and even hand position and use them as a proxy for lying. The second video, from [Fletcher]’s recent DEFCON talk, has much more detail.
The key to all this is finding human faces in a video — a task that seemed to fail suspiciously frequently when [Zuck] was on camera — using OpenCV and MediaPipe’s Face Mesh. The subject’s pulse is detected by watching for subtle changes in the color of a subject’s cheeks as blood flows through them, which we’ve heard about plenty of times but never before seen presented so clearly and executed so simply. Gaze direction, blinking, and lip compression are fairly easy to detect too. [Fletcher] also threw in the FER library for facial expression recognition, to get an idea of the subject’s mood. Together, these cues form a rough estimate of the subject’s truthiness, which [Fletcher] is quick to point out is just for entertainment purposes and totally shouldn’t be used on your colleagues on the next Zoom call.
It seems like most hackers have never played a game without at least wondering how to cheat at it. It’s not that we’re a dishonest lot, at least not as a rule. It’s more that most games hold less challenge for us than does figuring out how to reverse engineer the game’s mechanics. We don’t intend to cheat; it just sort of happens.
Or at least that’s the charitable way to look at such smartphone game cheats as this automated word-search puzzle solver. The game is Wordblitz, which is basically an implementation of classic Boggle along with extra features to release more dopamine and keep you playing. Not one to fall for that trick, [ghettobastler] whipped up a quick X-Y gantry from MDF using a laser cutter, added a stylus in the form of a cotton swab tipped with aluminum foil, and a vision system based on a simple web camera. The bed of the gantry has a capacitive plate so the stylus can operate the phone, along with a frame of ArUco fiducial marker to aid in locating the phone.
A Raspberry Pi handles the machine vision part of the process, which uses OpenCV to estimate the phone’s location and extract the current game tiles. The words in the game field are located by a solver that [ghetto] had previously written; a script then streams G-code to the plotter to peck out the answers at blazing speed, or at least faster than even [Peggy Hill] could manage. See the video below for a sample game being solved.
One word of warning if you choose to build this: [ghettobastler]’s puzzle-solving algorithm is based on a French dictionary, so you’ll have to re-teach it for other languages. But whatever language it’s in, this reminds us a bit of some of the Wordle solvers we’ve seen recently.
Shadowed tool storage — where a tool outline shows at a glance what’s missing from storage — is a really smart way to keep your shop neat. They’re also super important for cases where a tool left behind could be a tragedy. Think, where’s-that-10-mm-socket-while-working-on-a-jet-engine? important. (It’s always the 10-mm socket.)
But just because shadow boards are smart, doesn’t mean they’re easy to make. That’s why [Scott Prince] came up with this semi-automated method for making toolbox shadow boards. The job of tracing around each tool on some sort of suitable material and cutting out the shapes seems straightforward, but the trick comes in organizing the outlines given the space available and the particular collection of tools.
[Scott]’s method starts with capturing images of each individual tool. He used a PiCam and a lightbox housed, strangely enough, in a storage bench; we’d love to hear the full story behind that, but pretty much any digital camera would do for the job. After compensating for distortion with OpenCV, cropping the images, and turning the image into a vector outline of the tool, [Scott] was left with the task of putting the tools into logical groups and laying them out sensibly. After tweaking the tool outlines and adding finger cutouts for easy pickup, [Scott] put his CNC router to work. He chose to use a high-density polyethylene product made by his employer, which looks fantastic, but MDF would work fine too.
When you’re lucky enough to have a dog in your life, you tend to overlook some of the more one-sided aspects of the relationship. While you are severely restrained with regard to where you eliminate your waste, your furry friend is free to roam the yard and dispense his or her nuggets pretty much at will, and fully expect you to follow along on cleanup duty. See what we did there?
And so dog people sometimes rebel at this lopsided power structure, by leaving the cleanup till later — often much, much later, when locating the offending piles can be a bit difficult. So naturally, we now have this poop-shooting laser turret to helpfully guide you through your backyard cleanup sessions. It comes to us from [Caleb Olson], who leveraged his recent poop-posture monitor as the source of data for where exactly in the yard each deposit is located. To point them out, he attached a laser pointer to a cheap robot arm, and used OpenCV to help line up the bright green spot on each poop.
But wait, there’s more. [Caleb]’s code also optimizes his poop patrol route, minimizing the amount of pesky walking he has to do to visit each pile. And, the same pose estimation algorithm that watches the adorable [Twinkie] make her deposits keeps track of which ones [Caleb] stoops by, removing each from the worklist in turn. So now instead of having a dog control his life, he’s got a dog and a computer running the show. Perfect.
We joke, because poop, but really, this is a pretty neat exercise in machine learning. It does seem like the robot arm was bit overkill, though — we’d have thought a simple two-servo turret would have been pretty easy to whip up.