Be On Twitter Without Being On Twitter

Social media can connect us to a vibrant worldwide community, but it is also a huge time sink as it preys on both our need for attention and our insatiable curiosity. Kept on a leash by those constant notification sounds, we can easily look up from our phones to find half a day has gone and we’re behind with our work. [Laura Lytle] has a plan to tackle this problem, her OutBox project involves a single button press machine that posts a picture to Twitter of whatever is put in it. It’s not just another gateway to social media addiction though, she tells us it follows Design For Disuse principles in which it must be powered up and adjusted for each picture, and that it provides no feedback to satisfy the social media craving.

Under the hood of the laser-cut housing reminiscent of an older hobby 3D printer is a Raspberry Pi 3 Model A+ and a webcam, with a ring of LEDs for illumination. On top is the only interface, a small “arm” button to set things up and a big red arcade button to do the business. The software is in Python, and provides glue between resizing the photo, uploading it to a cloud service, and triggering ITTT to do the Tweeting. You can see the whole thing in the video below, and the result is a rather eye-catching device.

Of course, there are other ways to keep yourself off social media.

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Let The Cards Fall Where They May, With A Robotic Rain Man

Finally,  a useful application for machine vision! Forget all that self-driving nonsense and facial recognition stuff – we’ve finally got an AI that can count cards at the blackjack table.

The system that [Edje Electronics] has built, dubbed “Rain Man 2.0” in homage to the classic title character created by [Dustin Hoffman] for the 1988 film, aims to tilt the odds at the blackjack table away from the house by counting cards. He explains one such strategy, a hi-low count, in the video below, which Rain Man 2.0 implements with the help of a webcam and YOLO for real-time object detection. Cards are detected in any orientation based on their suit and rank thanks to an extensive training set of card images, which [Edje] generated synthetically via some trickery with OpenCV. A script automated the process and yielded a rich training set of 50,000 images for YOLO. A Python program implements the trained model into a real-time card counting application.

Rain Man 2.0 is an improvement over [Edje]’s earlier Tensor Flow card counter, but it still has limitations. It can’t count into a six-deck shoe as the fictional [Rain Man] could, at least not yet. And even though cheater’s justice probably isn’t all cattle prods and hammers these days, the hardware needed for this hack is not likely to slip past casino security. So [Edje] has wisely limited its use to practicing his card counting skills. Eventually, he wants to turn Rain Man into a complete AI blackjack player, and explore its potential for other games and to help the visually impaired.

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Yet Another Robotic Rubik’s Solver

The Rubik’s Cube was a smash hit when it came out in 1974, and continues to maintain a following to this day. It can be difficult to solve, but many take up the challenge. The Arduino Rubik’s Solver is a robot that uses electronics and maths to get the job done.

The system consists of computer-based software and a hardware system working in concert to solve the cube. Webcam images are processed on a computer which determines the current state of the cube, and the necessary moves required to solve it. The solving rig is constructed from steel rods, lasercut acrylic, and 3D printed parts, along with an Arduino and six stepper motors. The Arduino receives instructions from the solving computer over USB serial link. These are then used to command the stepper motors to manipulate the cube in the correct fashion.

It’s no speed demon, but the contraption is capable of solving a cube without any problems. Manipulation of the cube is reliable and smooth, and the build is neat and tidy thanks to its carefully designed components. Of course, there are now even Rubik’s Cubes that can solve themselves. Video after the break.

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Raspberry Pi Camera With Smarts — Cloud Or Local?

[Mark West] gave an interesting presentation at last year’s GOTO Copenhagen conference. He shows how he took a simple Raspberry Pi Zero webcam and expanded it with AI. He actually added the intelligent features in two different ways: on in the Amazon cloud and another using the Intel Modvidius NCS USB stick directly connected to the USB. You can see the video below.

Local motion detection uses some open source software. You simply configure it using a text file and it even handles the video streaming. However, at that point, you just have a web camera — not amazing, nor very cost effective. However, you get a lot of false alarms with the motion detection software. A random cat walking past, clouds, trees, or even rain would push [Mark] an email and after 250 alert e-mails a day, [Mark] decided to make something better.

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High-Style Ball Balancing Platform

If IKEA made ball-balancing PID robots, they’d probably look like this one.

This [Johan Link] build isn’t just about style. A look under the hood reveals not the standard, off-the-shelf microcontroller development board you might expect. Instead, [Johan] designed and built his own board with an ATmega32 to run the three servos that control the platform. The entire apparatus is made from a dozen or so 3D-printed parts that interlock to form the base, the platform, and the housing for the USB webcam that’s perched on an aluminum tube. From that vantage point, the camera’s images are analyzed with OpenCV and the center of the ball is located. A PID loop controls the three servos to center the ball on the platform, or razzle-dazzle it a little by moving the ball in a controlled circle. It’s quite a build, and the video below shows it in action.

We’ve seen a few balancing platforms before, but few with such style. This Stewart platform comes close, and this juggling platform gets extra points for closing the control loop with audio feedback. And for juggling, of course.

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Cheap ESP32 Webcam

Looking for a cheap way to keep an eye on something? [Kevin Hester] pointed us to a way to make a WiFi webcam for under $10. This uses one of the many cheap ESP32 dev boards available, along with the Internet of Things platform PlatformIO and a bit of code that creates an RTSP server. This can be accessed by any software that supports this streaming protocol, and a bit of smart routing could put it on the interwebs. [Kevin] claims that the ESP32 camera dev boards he uses can be found for less than $10, but we found that most of them cost about $15. Either way, that’s cheaper than most commercial streaming cameras.

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Open Data Cam Combines Camera, GPU, And Neural Network In An Artisanal DIY Cereal Box

The engineers and product designers at [moovel lab] have created the Open Data Cam – an AI camera platform that can identify and count objects as they move through its field of view – along with an open source guide for making your own.

Step one: get out your ruler and utility knife. In this world of ubiquitous 3D-printers they’ve taken a decidedly low-tech approach to the project’s enclosure: a cut, folded, and zip-tied plastic box, with a cardboard frame inside to hold the electronic bits. It’s “splash proof” and certainly cheap to make, but we’re a little worried about cooling and physical protection for the electronics inside, as they’re not exactly cheap and rugged components.

So what’s inside? An Nvidia Jetson TX2 board, a LiPo battery with some charging circuitry, and a standard webcam. The special sauce, however, is the software, which is available on GitHub. [Moovel lab]’s engineers have put together a nice-looking wifi-accessible mobile UI for marking the areas where you’d like the software to identify and tally objects. The actual object detection and identification tasks are performed by the speedy YOLO neural network, a task the Nvidia board’s GPU is of course well suited for.

As the Open Data Cam’s unblinking glass eye gazes upon our urban environments, it will log its observations in an ancient and mysterious language: CSV. It’s up to you, human, to interpret this information and use it for good.

A summary video and build time lapse are embedded after the break.

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