Hackaday Editors Mike Szczys and Elliot Williams get caught up on the most interesting hacks of the past week. On this episode we take a deep dive into radiation-monitor projects, both Geiger tube and scintillator based, as well as LED cube projects that pack pixels onto six PCBs with parts counts reaching into the tens of thousands. In the 3D printing world we want non-planar printing to be the next big thing. Padauk microcontrollers are small, cheap, and do things in really interesting ways if you don’t mind embracing the ecosystem. And what’s the best way to read a water meter with a microcontroller?
Take a look at the links below if you want to follow along, and as always tell us what you think about this episode in the comments!
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Continue reading “Hackaday Podcast 035: LED Cubes Taking Over, Ada Vanquishes C Bugs, Rad Monitoring Is Hot, And 3D Printing Goes Full 3D”
In our info-obsessed culture, hackers are increasingly interested in ways to quantify the world around them. One popular project is to collect data about their home energy or water consumption to try and identify any trends or potential inefficiencies. For safety and potentially legal reasons, this usually has to be done in a minimally invasive way that doesn’t compromise the metering done by the utility provider. As you might expect, that often leads to some creative methods of data collection.
The latest solution comes courtesy of [Keilin Bickar], who’s using the ESP8266 and a serial TTL camera module to read the characters from the LCD of his water meter. With a 3D printed enclosure that doubles as a light source for the camera, the finished device perches on top of the water meter and sends the current reading to HomeAssistant via MQTT without any permanent wiring or mounting.
Of course, the ESP8266 is not a platform we generally see performing optical character recognition. Some clever programming was required to get the Wemos D1 Mini Lite to reliably read the numbers from the meter without having to push the task to a more computationally powerful device such as a Raspberry Pi. The process starts with a 160×120 JPEG image provided by a VC0706 camera module, which is then processed with the JPEGDecoder library. The top and bottom of the image are discarded, and the center band is isolated into blocks that correspond with the position of each digit on the display.
Within each block, the code checks an array of predetermined points to see if the corresponding pixel is black or not. In theory this allows detecting all the digits between 0 and 9, though [Keilin] says there were still the occasional false readings due to inherent instabilities in the camera and mounting. But with a few iterations to the code and the aid of a Python testing program that allowed him to validate the impact of changes to the algorithm, he was able to greatly improve the detection accuracy. He says it also helps that the nature of the data allows for some basic sanity checks; for example the number only ever goes up, and only by a relatively small amount each time.
This method might not allow the per-second sampling required to pull off the impressive (if slightly creepy) water usage data mining we saw recently, but as long as you’re not after very high resolution data this is an elegant and creative way to pull useful data from your existing utility meter.
Every once in a while a project comes along with that magical power to consume your time and attention for many months. When you finally complete it, you feel sorry that you don’t have to do anything more.
What is so special about this Bingo ball reader? It may seem like an ordinary OCR project at first glance; a camera captures the image and OCR software recognizes the number. Simple as that. And it works without problems, like every simple gadget should.
But then again, maybe it’s not that simple. Numbers are scattered all over the ball, so they have to be located first, and the best candidate for reading must be selected. Then, numbers are painted onto a sphere rather than a flat surface, sometimes making them deformed to the point where their shape has to be recovered first. Also, the angle of reading is not fixed but somewhere on a 360° scale. And then we have the glare problem to boot, as Bingo balls are so shiny that every light source reflects as a saturated bright spot.
So, is that all of it? Well, almost. The task is supposed to be performed by an embedded microcontroller, with limited speed and memory, yet the recognition process for one ball has to be fast — 500 ms at worst. But that’s just one part of the process. The project includes the pipelined mechanism which accepts the ball, transports it to be scanned by the OCR and then shot by the public broadcast camera before it gets dumped. And finally, if the reading was not reliable enough, the ball has to be subtly rotated so that the numbers would be repositioned for another reading attempt.
Despite these challenges I did manage to build this system. It’s fast and reliable, and I discovered some very interesting tricks along the way. Take a look at the quick demo video below to get a feel for the speed, and what the system “sees”. Then join me after the break to dive into the details of this interesting embedded build.
Continue reading “Reading Bingo Balls With Microcontrollers”