It’s been about a year and a half since the Batteroo, formally known as Batteriser, was announced as a crowdfunding project. The premise is a small sleeve that goes around AA and AAA batteries, boosting the voltage to extract more life out of them. [Dave Jones] at EEVblog was one of many people to question the product, which claimed to boost battery life by 800%.
Batteroo did manage to do something many crowdfunding projects can’t: deliver a product. Now that the sleeves are arriving to backers, people are starting to test them in the wild. In fact, there’s an entire thread of tests happening over on EEVblog.
One test being run is a battery powered train, running around a track until the battery dies completely. [Frank Buss] wanted to run this test, but didn’t want to manually count the laps the train made. He whipped up a script in Python and OpenCV to automate the counting.
The script measures laps by setting two zones on the track. When the train enters the first zone, the counter is armed. When it passes through the second zone, the lap is recorded. Each lap time is kept, ensuring good data for comparing the Batteroo against a normal battery.
The script gives a good example for people wanting to play with computer vision. The source is available on Github. As for the Batteroo, we’ll await further test results before passing judgement, but we’re not holding our breath. After all, the train ran half as long when using a Batteroo.
The bad news is that when our robot overlords come to oppress us, they’ll be able to tell how well they’re doing just by reading our facial expressions. The good news? Silly computer-vision-enhanced party games!
[Ricardo] wrote up a quickie demonstration, mostly powered by OpenCV and Microsoft’s Emotion API, that scores your ability to mimic emoticon faces. So when you get shown a devil-with-devilish-grin image, you’re supposed to make the same face convincingly enough to fool a neural network classifier. And hilarity ensues!
For [Jay] and [Ricardo]’s final project for [Dr. Bruce Land]’s ECE4760 course at Cornell, they tackled a problem that is the bane of all machinists. Their project finds the XY zero of a part in a CNC machine using computer vision, vastly reducing the time it take to set up a workpiece and giving us yet another reason to water down the phrase ‘Internet of Things’ by calling this the Internet of CNC Machines.
For the hardware, [Jay] and [Ricardo] used a PIC32 to interface with an Arducam module, a WiFi module, and an inductive sensor for measuring the distance to the workpiece. All of this was brought together on a PCB specifically designed to be single-sided (smart!), and tucked away in an enclosure that can be easily attached to the spindle of a CNC mill. This contraption looks down on a workpiece and uses OpenCV to find the center of a hole in a fixture. When the center is found, the mill is zeroed on its XY axis.
The software is a bit simpler than a device that has OpenCV processing running on a microcontroller. Detecting the center of the bore, for instance, happens on a laptop running a few Python scripts. The mill attachment communicates with the laptop over WiFi, and sends a few images of the downward-facing camera over to the laptop. From there, the laptop detects the center of the bore in the fixture plate and generates some G-code to send over to the mill.
While the device works remarkably well, and is able to center the mill fairly quickly and without a lot of user intervention, there were a few problems. The camera is not perfectly aligned with the axis of the spindle, making the math harder than it should be. Also, the enclosure isn’t rated for being an environment where coolant is sprayed everywhere. Those are small quibbles, and these problems could be fixed simply by designing and printing another enclosure. The device works, though, and really cuts down on the time it takes to zero out a mill.
You can check out the video description of the build below.
[Massimiliano Patacchiola] writes this handy guide on using a histogram intersection algorithm to identify different objects. In this case, lego superheroes. All you need to follow along are eyes, Python, a computer, and a bit of machine learning magic.
He gives a good introduction to the idea. You take a histogram of the colors in a properly cropped and filtered photo of the object you want to identify. You then feed that into a neural network and train it to identify the different superheroes by color. When you feed it a new image later, it will compare the new image’s histogram to its model and output confidences as to which set it belongs.
This is a useful thing to know. While a lot of vision algorithms try to make geometric assertions about the things they see, adding color to the mix can certainly help your friendly robot project recognize friend from foe.
The usefulness of Raspberry Pis seems almost limitless, with new applications being introduced daily and with no end in sight. But, as versatile as they are, it’s no secret that Raspberry Pis are still lacking in pure processing power. So, some serious optimization is needed to squeeze as much power out of the Raspberry Pi as possible when you’re working on processor-intensive projects.
This simplest way to accomplish this optimization, of course, is to simply reduce what’s running down to the essentials. For example, there’s no sense in running a GUI if your project doesn’t even use a display. Another strategy, however, is to ensure that you’re actually using all of the available processing power that the Raspberry Pi offers. In [sagiz’s] case, that meant using Intel’s open source Threading Building Blocks to achieve better parallelism in his OpenCV project.
Not sure where the potatoes were sneaking in, [24Gospel] did what any decent hacker would do: strapped a camera to a Raspberry Pi, hacked a bit on OpenCV, and built himself a potato detection system. Now those pesky Russets can’t get into the house without tripping the tuber alarm.
OK, seriously. [24Gospel] works for a potato farm as a systems/software developer. (How big does a potato farm have to be to require a dedicated software guy?) His system is still a first step, but the goal is to grade the potatoes, record data about size and defects, and even tell different potato types apart. And he’s found decent success so far, especially for the money. We don’t often build projects that need to operate in hostile environments, but we appreciate the nice plastic case and rugged adjustable steel frame that supports the Pi and camera over the sorting bed.
Even more, we applaud the hacker spirit here. [24Gospel] is obviously working in a serious production environment, but still he’s trying out new things in an attempt to make it work better. While it would be impossible to quantify the impact of this kind of on-the-job ingenuity, we bet it’s not insignificant. Why don’t we see more documented workplace hacks around here? Would the unsung heroes please stand up?
What’s going through the mind of those your autonomous vacuum cleaning robots as they traverse a room? There are different ways to find out such as covering the floor with dirt and seeing what remains afterwards (a less desirable approach) or mounting an LED to the top and taking a long exposure photo. [Saulius] decided to do it by videoing his robot with a fisheye lens from near the ceiling and then making a heatmap of the result. Not being satisfied with just a finished photo, he made a video showing the path taken as the room is being traversed, giving us a glimpse of the algorithm itself.
The robot he used was the Vorwerk VR200 which he’d borrowed for testing. In preparation he cleared the room and strategically placed a few obstacles, some of which he knew the robot wouldn’t get between. He started the camera and let the robot do its thing. The resulting video file was then loaded into some quickly written Python code that uses the OpenCV library to do background subtraction, normalizing, grayscaling, and then heatmapping. The individual frames were then rendered into an animated gif and the video which you can see below.