Resource monitoring solution

Electricity, Gas and Water – three resources that are vital in our daily lives. Monitoring them using modern technology helps with conservation, but the real impact comes when we use the available data to reduce wasteful usage over time. [Sébastien] was rather embarrassed when a problem was detected in his boiler only during its annual inspection. Investigations showed that the problem occurred 4 months earlier, resulting in a net loss of more than 450 cubic meters, equivalent to 3750 liters per day (about 25 baths every day!). Being a self professed geek, living in a modern “connected” home, it rankled him to the core. What resulted was S-Energy – an energy resource monitoring solution (translated) that checks on electricity, gas and water consumption using a Raspberry Pi, an Arduino, some other bits of hardware and some smart software.

[Sébastien] wanted a system that would warn of abnormal consumption and encourage his household folks to consume less. His first hurdle was the meters themselves. All three utilities used pretty old technology, and the meters did not have pulse data output that is commonplace in modern metering. He could have replaced the old meters, but that was going to cost him a lot of money. reflective-power-meter-sensorSo he figured out a way to extract data from the existing meters. For the Electricity meter, he thought of using current clamps, but punted that idea considering them to be suited more for instantaneous readings and prone for significant drift when measuring cumulative consumption. Eventually, he hit upon a pretty neat hack. He took a slot type opto coupler, cut it in half, and used it as a retro-reflective sensor that detected the black band on the spinning disk of the old electro-mechanical meter. Each turn of the disk corresponds to 4 Watt-hours. A little computation, and he’s able to deduce Watt-hours and Amps used. The sensor is hooked up to an Arduino Pro-mini which then sends the data via a nRF24L01+ module to the main circuit located inside his house. The electronics are housed in a small enclosure, and the opto-sensor looks just taped to the meter. He has a nice tip on aligning the infra-red opto-sensor – use a camera to check it (a phone camera can work well).

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Mustachioed Nintendo Virtual Boy Gone Augmented Reality

Some people just want to watch the world burn. Others want to spread peace, joy and mustaches. [Joe Grand] falls into the latter group this time around. His latest creation is Mustache Mayhem, a hack, video game, and art project all rolled into one. This is a bit of a change from deconstructing circuit boards or designing electronic badges, but not completely new for [Joe], who wrote SCSIcide and Ultra SCSIcide for the Atari 2600 back in the early 2000’s.

Mustache Mayhem is built into a Nintendo Virtual Boy housing. The Virtual Boy itself was broken, and unfortunately was beyond repair. [Joe] removed most of the stock electronics and added a BeagleBone Black, Logitech C920 webcam, an LCD screen and some custom electronics. He kept the original audio amplifier, speakers, and controller connector. Angstrom Linux boots into [Joe’s] software, which uses OpenCV to detect faces and overlay mustaches. Gameplay is simple: Point the console at one or more faces. If you see a mustache, press the A button on the controller! The more faces and mustaches on-screen at once, the more points, or “mojo” the player gets. The code is up on Github, and can be built with Xcode targeted to the Mac, or directly on the BeagleBone Black.

[Joe’s] goal for the project was to make a ridiculous game that looks like it could have come out in the 90’s. He also used Mustache Mayhem as a fun way to learn some new skills which will come in handy for more serious projects in the future.

We caught up with [Joe] for a quick interview about his new creation.

How did you come up with the idea for Mustache Mayhem?

blockI was selling a bunch of my video game collection at PRGE (Portland Retro Gaming Expo) a few years ago and had a broken Virtual Boy that no one bought. A friend of mine was at the table and said I had to do something with it. I thought “People wear cosplay and walk around at conventions, so what if I could do something with the Virtual Boy that you could walk around with?” That was the seed.

A few months later, Texas Instruments sent me the original production release of the BeagleBone Black (rev. A5A). Eighteen months after that I actually started the project. The catalyst was to do something for an upcoming Portland, OR art show (Byte Me 4.0), which is an annual event that shows off interactive technology-based artwork. I wrote up a little description and got accepted. I had less than 2 months to actually get things working and it ended up taking about a month of full-time work. It was much more work than I expected for such a silly project. I originally was going to do something along the lines of walking around in a Doom-like perspective and shooting people when their faces were detected.

That would be pretty darn cool. How did you get from Doom to Mustaches? 

I saw a TI BeagleBoard demo called “boothstache” which drew mustaches on faces and tweeted the pictures. I thought that doing something non-violent with mustaches would be more suitable (and funny) to actually show my kids. I also secretly wanted to use this project as a way to experiment with Linux, write some code, and learn about face detection and image processing with OpenCV, which I plan to use for some actual computer security research in the future. Mustache Mayhem turned out to be a super cool project and I’m really happy with it. I sort of feel guilty spending so much time on it, since it’s basically just a one-off prototype, but I just got so obsessed with making it exactly as I wanted.

You mentioned on your website that Mustache was “designed to challenge the paradigms of personal privacy and entertainment.” What exactly did you mean there?

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Face Recognition For Your Next Con

[jwcrawley] is busy planning for the Makevention coming up in Bloomington, Indiana in late August. One problem when working any con is manning the door; it’s a good idea to know how many people are there, and you can’t double count people. Previously, the volunteers used dead trees to estimate how many people have turned up. This year they might go with a more technological solution: face recognition and tracking.

The project is called uWho, and it uses the faceRecognizer class in OpenCV. The purpose of the entire project is to identify who someone is from previous frames. If your face is unknown to the program, your likeness – rather, a few points of data – are added to the database of faces. It’s simple, and according to [jwcrawley], it works.

While this is technically the best way to count how many unique people show up to Makevention, there will be some discussions to see if this solution is appropriate. The program only saves unique data from a face locally, and does nothing online. It’s less evil than whatever Facebook does, but there are obvious privacy implications here.

Link to the Makevention.

Beating Super Hexagon with OpenCV and DLL Injection

Every few months a game comes along which is so addictive, players can’t seem to put it down – no matter how frustrating it may get. Last year one of those games was Super Hexagon. After fighting his way through several levels, [Val] decided that designing a bot to beat the game would be more efficient than doing it himself. Having played a few rounds of Super Hexagon ourselves, we can’t fault him on that front!

At its core, Super Hexagon is a simple game. Walls move from the screen edges toward a ship located near the center of the screen. The player uses the arrow keys to “orbit” the ship around a central shape. Avoid getting crushed by the walls, and you’re golden. However, the entire game board is constantly spinning, expanding, contracting, flashing, and generally doing things to disorient the player while ever more complex wall patterns move in to kill you. In short, Super Hexagaon makes Touhou bullet hell games look like a cakewalk.

The first step in beating the game is to capture the screen. [Val] tried Fraps and VLC, but lags of 2 seconds or more were not going to work. Then [Val] turned to DLL Injection. Super Hexagon calls the OpenGL function glutSwapBuffers() to implement double buffering. Every frame of the game is rendered in the background. Once rendering is complete glutSwapBuffers() is called to swap the buffers, and the process starts over again. [Val] changed the game code such that his own frame capture function would be called instead of glutSwapBuffers(). Once he was done capturing the game’s video buffer, [Val] then called the real glutSwapBuffers() function. It worked perfectly.

Now that he had an image, [Val] used OpenCV to process it. Although game is graphically very noisy, there are only a few colors used at any one time. It didn’t take much work to come up with an algorithm which would create a binary image of the walls and the ship itself.

step5[Val] cast rays from the center of each wall through the center of the screen. The ray which was longest before intersecting another wall would be the best escape route. This simple solution worked, but only for about 40 seconds. At that point, Super Hexagon would start throwing more complex patterns, and the AI would fail. The final solution was to create an accessibility condition which also took into account how much space was available between the various approaching walls. This new version of the AI was able to beat the game.

So was this a more efficient method than grinding through Super Hexagon manually? Since [Val] now knows all about DLL injection and OpenCV, we sure think it was!

Click past the break to see the [Val’s] bot in action!

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A Raspberry Pi Garage Door Opener

We can never seem to get enough garage door hacks around here. [Tanner’s] project is the most recent entry into this category. He’s managed to hook up a Raspberry Pi to his garage door opener. This greatly extends his range to… well anywhere with an Internet connection.

His hack is relatively simple. He started with the garage door opener remote. He removed the momentary switch that was normally used to active the door. He bridged the electrical connection to create a circuit that was always closed. This meant that as long as the remote had power, the switch would be activated. Now all [Tanner] had to do was remove the battery and hook up the power connectors to his Raspberry Pi. Since the remote works on 3.3V and draws little current, he is able to power the remote directly from the Pi. The Pi just has to turn its pin high momentarily to activate the remote.

The ability to toggle the state of your garage door from anywhere in the world also comes with paranoia. [Tanner] wanted to be able to tell if the door is up, down, or stopped somewhere in the middle while he was away from home. He also wanted to use as little equipment as possible. Since he already had an IP camera in the garage, he decided to use computer vision to do the detection.

He printed off two large, black shapes onto ordinary white computer paper. One was taped to the top of the door and one to the bottom. A custom script runs on the Pi that grabs the latest image from the camera and uses OpenCV to detect the shapes. If both shapes are visible, then the script can assume the door is closed. Otherwise, it’s likely open. This makes it easier for [Tanner] to know if the door is opened or closed without having to check the camera himself.

Can’t get enough garage door hacks? Try these on for size. Continue reading “A Raspberry Pi Garage Door Opener”

Selfie-Bots Will Take Your Best Shots For You

Professor [Bruce Land] teaches a microcontroller class at Cornell University, and it seems like this year’s theme was selfie-taking-robots.

First up is a clever mix of technology by [Han, Bihan and Chuan]. What happens when you take an iPhone, three microphones and a microcontroller? The ultimate device in selfie-taking-technology, that’s what — Clap-on! The iPhone is mounted on a few servo motors which allows the bot to direct the camera towards, you guessed it, a clapping noise. On the second clap, the phone takes your picture. Cute.

Next up is a bit more sophisticated — a facial recognition selfie-bot. This little robot can be programmed to track faces and take pictures of you and your friends when your arm is just not long enough. Not only that, you can set all kinds of parameters so you get the perfect picture. It uses OpenCV to crunch the raw data and outputs commands to an ATmega1284 which controls the servo motors that direct the camera. This project was by [Michael and Jennifer] — two fourth year students at Cornell.

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Augmented Reality Pinball

Pinball machines are fascinating pieces of mechanical and electrical engineering, and now [Yair Moshe] and his students at the Israel Institute of Technology has taken the classic game one step further.  Using computer vision and a projector, this group of engineers has created an augmented reality pinball game that takes pinball to a whole new level.

Once the laptop, webcam, and projector are set up, a course is drawn on a whiteboard which the computer “sees” to determine the rules of the game. Any course you can imagine can be drawn on the whiteboard too, with an interesting set of rules that no regular pinball game could take advantage of. Most notably, the ball can change size when it hits certain types of objects, which makes for a very interesting and unconventional style of play.

The player uses their hands to control the flippers as well, but not with buttons. The computer watches the position of the player’s hands and flips the flippers when it sees a hand in the right position. [Yair] and his students recently showed this project off at DLD Tel Aviv and even got [Shimon Perez], former President of Israel, to play some pinball at the conference!