Among 3D printer owners, “spaghetti” is the common term for the tangled mess of stringy plastic that’s often the result of a failed print. Fear of their print bed turning into a hot plate of PLA spaghetti is enough to keep many users from leaving their machines operating overnight or while they’re out of the house. Accordingly, we’ve seen a number of methods that allow the human operator to watch their print remotely to make sure everything is progressing smoothly.
But unless you plan on keeping your eyes on your phone the entire time you’re out of the house, there’s still a chance some PETG pasta might sneak its way out. Enter the Spaghetti Detective, an open source project that lets machine learning take over when you can’t sit watching the printer all day. Their system plugs into Octoprint to monitor your print in real-time and pause it if it starts looking particularly stringy. The concept is still under development, but judging by the gallery of results submitted by users, the system seems to have a knack for identifying non-edible noodles.
Once the software comes out of beta it looks like the team is going to try to monetize it by providing hosting and monitoring services for a monthly fee, but as it’s an open source project, you’re also able to run the software on your own machine. Though the documentation notes that the lowly Raspberry Pi doesn’t have quite what it takes to handle the image recognition routines, so you’ll need a proper computer if you want to self-host the service. Could be a good use for that old laptop you’ve got kicking around the lab.
As demonstrated in the video after the break, the system’s “spaghetti confidence” is shown with a simple to understand gauge: green is a good-looking print, and red means the detective is getting a sniff of the stringy stuff. If your print dips into the red too much, Octoprint is commanded to pause the print. The user can then look at the last image from the printer and decide to either cancel the print entirely, or resume if the Spaghetti Detective got a little overzealous.
A Raspberry Pi with a camera is nothing new. But the Pixy2 camera can interface with a variety of microcontrollers and has enough smarts to detect objects, follow lines, or even read barcodes without help from the host computer. [DroneBot Workshop] has a review of the device and he’s very enthused about the camera. You can see the video below.
When you watch the video, you might wonder how much this camera will cost. Turns out it is about $60 which isn’t cheap but for the capabilities it offers it isn’t that much, either. The camera can detect lines, intersections, and barcodes plus any objects you want to train it to recognize. The camera also sports its own light source and dual servo motor drive meant for a pan and tilt mounting arrangement.
The future, if you believe the ad copy, is a world filled with cameras backed by intelligence, neural nets, and computer vision. Despite the hype, this may actually turn out to be true: drones are getting intelligent cameras, self-driving cars are loaded with them, and in any event it makes a great toy.
That’s what makes this Kickstarter so exciting. It’s a camera module, yes, but there are also some smarts behind it. The OpenMV is a MicroPython-powered machine vision camera that gives your project the power of computer vision without the need to haul a laptop or GPU along for the ride.
The OpenMV actually got its start as a Hackaday Prize entry focused on one simple idea. There are cheap camera modules everywhere, so why not attach a processor to that camera that allows for on-board image processing? The first version of the OpenMV could do face detection at 25 fps, color detection at more than 30 fps, and became the basis for hundreds of different robots loaded up with computer vision.
This crowdfunding campaign is financing the latest version of the OpenMV camera, and there are a lot of changes. The camera module is now removable, meaning the OpenMV now supports global shutter and thermal vision in addition to the usual color/rolling shutter sensor. Since this camera has a faster microcontroller, this latest version can support multi-blob color tracking at 80 fps. With the addition of a FLIR Lepton sensor, this camera does thermal sensing, and thanks to a new library, the OpenMV also does number detection with the help of neural networks.
We’ve seen a lot of builds using the OpenMV camera, and it’s getting ot the point where you can’t compete in an autonomous car race without this hardware. This new version has all the bells and whistles, making it one of the best ways we’ve seen to add computer vision to any hardware project.
Getting computers to recognize objects has been a historically difficult problem in computer science, but with the rise of machine learning it is becoming easier to solve. One of the tools that can be put to work in object recognition is an open source library called TensorFlow, which [Evan] aka [Edje Electronics] has put to work for exactly this purpose.
His object recognition software runs on a Raspberry Pi equipped with a webcam, and also makes use of Open CV. [Evan] notes that this opens up a lot of creative low-cost detection applications for the Pi, such as setting up a camera that detects when a pet is waiting at the door to be let inside or outside, counting the number of bees entering and exiting a beehive, or monitoring parking spaces at an office.
This project uses a number of other toolkits as well, including Protobuf. It also makes extensive use of Python scripts, but if you’re comfortable with that and you have an application for computer vision, [Evan]’s tutorial will get you started.
The news is full of self-driving cars and while there is some bad news, most of it is pretty positive. It seems a foregone conclusion that it is just a matter of time before calling for an Uber doesn’t involve another person. But according to a recent article, [Ernst Dickmanns] — a German aerospace engineer — built three autonomous vehicles starting in 1986 and culminating with on-the-road demonstrations in 1994 for Daimler.
It is hard to imagine what had to take place to get a self-driving car in 1986. The article asserts that you need computer analysis of video at 10 frames a second minimum. In the 1980s doing a single frame in 10 minutes was considered an accomplishment. [Dickmanns’] vehicles borrowed tricks from how humans drive. They focused on a small area at any one moment and tried to ignore things that were not relevant.
Even if keeping bees sounds about as wise to you as keeping velociraptors (we all know how that movie went), we have to acknowledge that they are a worthwhile thing to have around. We don’t personally want them around us of course, but we respect those who are willing to keep a hive on their property for the good of the environment. But as it turns out, there are more challenges to keeping bees than not getting stung: you’ve got to keep track of the things too.
Keeping an accurate record of how many bees are coming and going, and when, is a rather tricky problem. Apparently bees don’t like electromagnetic fields, and will flee if they detect them. So putting electronic measuring devices inside of the hive can be an issue. [Mat Kelcey] decided to try counting his bees with computer vision, and so far the results are very promising.
After some training, a Raspberry Pi with a camera can count how many bees are in a given image to within a few percent of the actual number. Getting an accurate count of his bees allows [Mat] to generate fascinating visualizations about his hive’s activity and health. With real-world threats such as colony collapse disorder, this type of hard data can be crucial.
This is a perfect example of a hack which might not pertain to many of us as-is, but still contains a wealth of information which could be applicable to other projects. [Mat] goes into a fantastic amount of detail about the different approaches he tried, what worked, what didn’t, and where he goes from here. So far the only problem he’s having is with the Raspberry Pi: it’s only able to run at one frame per second due to the computational requirements of identifying the bees. But he’s got some ideas to improve the situation.
Depending on which hemisphere of the Earth you’re currently reading this from, summer is finally starting to fight its way to the surface. For the more “green” of our readers, that can mean it’s time to start making plans for summer gardening. But as anyone who’s ever planted something edible can tell you, garden pests such as squirrels are fantastically effective at turning all your hard work into a wasteland. Finding ways to keep them away from your crops can be a full-time job, but luckily it’s a job nobody will mind if automation steals from humans.
[Peter Quinn] writes in to tell us about the elaborate lengths he is going to keep bushy-tailed marauders away from his tomatoes this year. Long term he plans on setting up a non-lethal sentry gun to scare them away, but before he can get to that point he needs to perfect the science of automatically targeting his prey. At the same time, he wants to train the system well enough that it won’t fire on humans or other animals such as cats and birds which might visit his garden.
A Raspberry Pi 3 with a cheap webcam is used to surveil the garden and detect motion. When frames containing motion are detected, they are forwarded to a laptop which has enough horsepower to handle the squirrel detection through Darknet YOLO. [Peter] recognizes this isn’t an ideal architecture for real-time targeting of a sentry turret, but it’s good enough for training the system.
Which incidentally is what [Peter] spends the most time explaining on the project’s Hackaday.io page. From the saga of getting the software environment up and running to determining how many pictures of squirrels in his yard he should provide the software for training, it’s an excellent case study in rolling your own image recognition system. After approximately 18 hours of training, he now has a system which is able to pick squirrels out from the foliage. The next step is hooking up the turret.