When shooting video, an easy way to get buttery smooth panning and tracking is to use a mechanical device like a rail to literally slide the camera side to side. These range from what is essentially a skateboard to incredible programmable multi-axis industrial robots, a wide variety of which have been visible in the backgrounds of Youtuber’s sets for years. But even the “low end” devices can run hundreds of dollars (all that anodized aluminum doesn’t come cheap!). Edelkrone has been building lust worthy professional (read, pricey) motion setups for a decade. But in the last year they’ve started something pretty unusual; lowering prices with their Ortak series of 3D printed equipment. But this time, you do the printing.
Since the RepRap we’ve been excited about the future of democratized at home manufacturing, but to a large extent that dream hasn’t materialized. Printers are much more useful now than in the early days but you can’t buy a new mug from Starbucks and print it at home. But maybe that’s changing with Edelkrone’s offering.
When you buy an Ortak product you get one thing: all the fasteners and hardware. So the final product is more durable and appears more finished than what would pop out of your Prusa unaided. What about the rest of the device? That’s free. Seriously. Edelkrone freely provides STLs (including print setting recommendations) with detailed step-by-step assembly instructions and videos (sample after the break). Nice hack to avoid piracy, isn’t it?
Why choose the do-it-at-home style product? A significant price reduction of course! The Ortak line currently includes two products, the FlexTILT head you see above, and a skateboard-style slide called the SKATER 3D. Both of these were sold fully finished before making it to the DIY scene. The FlexTILT Head 2 comes in at $149 when you buy it whole. And when the PocketSKATER 2 was for sale, it included a FlexTILT Head and came to $249. Now? Each hardware kit is just $29.
So is this it? Have we hit the artisanal DIY micro-manufactured utopian dream? Not yet, but maybe we’re a little closer. Edelkrone is a real company which is really selling these as products, right there on their website along with everything else. They refer to it as “co-manufacturing” which we think is a clever name, and talk about expanding the program to include electronics. We can’t wait to see how the experiment goes!
When you hear the term “extension tube”, you probably think of something fairly long, right? But when [Loudifier] needed an extension tube to do extreme close-ups with a wide-angle lens on a Canon EF-M camera, it needed to be small…really small. The final 3D printed extension provides an adjustable length between 0 and 10 millimeters.
But it’s not just an extension tube, that would be too easy. According to [Loudifier], the ideal extension distance would be somewhere around 3 mm, but unfortunately the mounting bayonet for an EF-M lens is a little over 5 mm. To get around this, the extension tube also adapts to an EF/EF-S lens, which has a shorter mount and allows bringing it in closer than would be physically possible under otherwise.
[Loudifier] says the addition of electrical connections between the camera and the lens (for functions like auto focus) would be ideal, but the logistics of pulling that off are a bit daunting. For now, the most reasonable upgrades on the horizon are the addition of some colored dots on the outside to help align the camera, adapter, and lens. As the STLs and Fusion design file are released under the Creative Commons, perhaps the community will even take on the challenge of adapting it to other lens types.
When building robots, or indeed other complex mechanical systems, it’s often the case that more and more limit switches, light gates and sensors are amassed as the project evolves. Each addition brings more IO pin usage, cost, potentially new interfacing requirements and accompanying microcontrollers or ADCs. If you don’t have much electronics experience, that’s not ideal. With this in mind, for a Hackaday prize entry [rand3289] is working on FiberGrid, a clever shortcut for interfacing multiple sensors without complex hardware. It doesn’t completely solve the problems above, but it aims to be a cheap, foolproof way to easily add sensors with minimal hardware needed.
The idea is simple: make your sensors from light gates using fiber optics, feed the ends of the plastic fibers into a grid, then film the grid with a camera. After calibrating the software, built with OpenCV, you can “sample” the sensors through a neat abstraction layer. This approach is easier and cheaper than you might think and makes it very easy to add new sensors.
Naturally, it’s not fantastic for sample rates, unless you want to splash out on a fancy high-framerate camera, and even then you likely have to rely on an OS being able to process the frames in time. It’s also not very compact, but fortunately you can connect quite a few sensors to one camera – up to 216 in [rand3289]’s prototype.
Of course, this type of setup is mostly suited to binary sensors/switches where the light path is either blocked or not, but other uses can be devised. For example, rotation sensors made with polarising filters. We’ve even written about optical flex sensors before.
One of the core lessons any physics student will come to realize is that the more you know about physics, the less intuitive it seems. Take the nature of light, for example. Is it a wave? A particle? Both? Neither? Whatever the answer to the question, scientists are at least able to exploit some of its characteristics, like its ability to bend and bounce off of obstacles. This camera, for example, is able to image a room without a direct light-of-sight as a result.
The process works by pointing a camera through an opening in the room and then strobing a laser at the exposed wall. The laser light bounces off of the wall, into the room, off of the objects on the hidden side of the room, and then back to the camera. This concept isn’t new, but the interesting thing that this group has done is lift the curtain on the image processing underpinnings. Before, the process required a research team and often the backing of the university, but this project shows off the technique using just a few lines of code.
This project’s page documents everything extensively, including all of the algorithms used for reconstructing an image of the room. And by the way, it’s not a simple 2D image, but a 3D model that the camera can capture. So there should be some good information for anyone working in the 3D modeling world as well.
Filming in slow-motion has long become a standard feature on the higher end of the smartphone spectrum, and can turn the most trivial physical activity into a majestic action shot to share on social media. It also unveils some little wonders of nature that are otherwise hidden to our eyes: the formation of a lightning flash during a thunderstorm, a hummingbird flapping its wings, or an avocado reaching that perfect moment of ripeness. Altogether, it’s a fun way of recording videos, and as [Robert Elder] shows, something you can do with a few dollars worth of Raspberry Pi equipment at a whopping rate of 660 FPS, if you can live with some limitations.
Taking the classic 24 FPS, this will turn a one-second video into a nearly half-minute long slo-mo-fest. To achieve such a frame rate in the first place, [Robert] uses [Hermann-SW]’s modified version of raspiraw to get raw image data straight from the camera sensor to the Pi’s memory, leaving all the heavy lifting of processing it into an actual video for after all the frames are retrieved. RAM size is of course one limiting factor for recording length, but memory bandwidth is the bigger problem, restricting the resolution to 64×640 pixels on the cheaper $6 camera model he uses. Yes, sixty-four pixels height — but hey, look at that super wide-screen aspect ratio!
[Cole Price] describes himself as a photographer and a space nerd. We’ll give that to him since his web site clearly shows a love of cameras and a love of the NASA programs from the 1960s. [Cole] has painstakingly made replicas of cameras used in the space program including a Hasselblad 500C used on a Mercury flight and another Hasselblad used during Apollo 11. His work is on display in several venues — for example, the 500C is in the Carl Zeiss headquarters building.
[Cole’s] only made a detailed post about 500C and a teaser about the Apollo 11 camera. However, there’s a lot of detail about what NASA — and an RCA technician named [Red Williams] — did to get the camera space-ready.
Taking a selfie before the modern smartphone era was a true endeavor. Flip phones didn’t have forward-facing cameras, and if you want to go really far back to the days of film cameras, you needed to set a timer on your camera and hope, or get a physical remote shutter. You could also try and create a self portrait on an Etch a Sketch, too, but this would take a lot of time and artistic skill. Luckily in the modern world, we can bring some of this old technology into the future and add a robot to create interesting retro selfies – without needing to be an artist.
Once the picture is taken, the ESP32 at the heart of the build handles the image processing and then drawing the image on the Etch a Sketch. The robot needs a black and white image to draw, and an algorithm for doing it without “lifting” the drawing tool, and these tasks stretch the capabilities of such a small processor. It takes some time to work, but in the end the results speak for themselves.
The final project is definitely worth looking for, if not for the interesting ESP32-controlled robot than for the image processing algorithim implementation. The ESP32 is a truly versatile platform, though, and is useful for building almost anything.