Review: Vizy Linux-Powered AI Camera

April 11, 2022 by Donald Papp 14 Comments

Vizy is a Linux-based “AI camera” based on the Raspberry Pi 4 that uses machine learning and machine vision to pull off some neat tricks, and has a design centered around hackability. I found it ridiculously simple to get up and running, and it was just as easy to make changes of my own, and start getting ideas.

Person and cat with machine-generated tags identifying them — Out of the box, Vizy is only a couple lines of Python away from being a functional Cat Detector project.

I was running pre-installed examples written in Python within minutes, and editing that very same code in about 30 seconds more. Even better, I did it all without installing a development environment, or even leaving my web browser, for that matter. I have to say, it made for a very hacker-friendly experience.

Vizy comes from the folks at Charmed Labs; this isn’t their first stab at smart cameras, and it shows. They also created the Pixy and Pixy 2 cameras, of which I happen to own several. I have always devoured anything that makes machine vision more accessible and easier to integrate into projects, so when Charmed Labs kindly offered to send me one of their newest devices, I was eager to see what was new.

I found Vizy to be a highly-polished platform with a number of truly useful hardware and software features, and a focus on accessibility and ease of use that I really hope to see more of in future embedded products. Let’s take a closer look.

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Using Statistics Instead Of Sensors

April 3, 2022 by Bryan Cockfield 9 Comments

Statistics often gets a bad rap in mathematics circles for being less than concrete at best, and being downright misleading at worst. While these sentiments might ring true for things like political polling, it hides the fact that statistical methods can be put to good use in engineering systems with fantastic results. [Mark Smith], for example, has been working on an espresso machine which can make the perfect shot of coffee, and turned to one of the tools in the statistics toolbox in order to solve a problem rather than adding another sensor to his complex coffee-brewing machine.

To make espresso, steam is generated which is then forced through finely ground coffee. [Mark] found that his espresso machine was often pouring too much or too little coffee, and in order to improve his machine’s accuracy in this area he turned to the linear regression parameter R², also known as the coefficient of determination. By using a machine learning algorithm tuned to this value, which assesses predictable variation in a data set, a computer can more easily tell when the coffee begins pouring out of the portafilter and into the espresso cup based on the pressure and water flow in the machine itself rather than using some other input such as the weight of the cup.

We have seen in the past how seriously [Mark] takes his coffee-making, and this is another step in a series of improvements he has made to his equipment. In this iteration, he has additionally produced a simulation in JupyterLab to better assist him in modeling the system and making even more accurate predictions. It’s quite a bit more effort than adding sensors, but since his espresso machine already included quite a bit of computing power it’s not too big a leap for him to make.

AI-Generated Sleep Podcast Urges You To Imagine Pleasant Nonsense

March 26, 2022 by Donald Papp 12 Comments

[Stavros Korokithakis] finds the experience of falling asleep to fairy tales soothing, and this has resulted in a fascinating project that indulges this desire by using machine learning to generate mildly incoherent fairy tales and read them aloud. The result is a fantastic sort of automated, machine-generated audible sleep aid. Even the logo is machine-generated!

The Deep Dreams Podcast is entirely machine-generated, including the logo.

The project leverages the natural language generation abilities of OpenAI’s GPT-3 to create fairytale-style content that is just coherent enough to sound natural, but not quite coherent enough to make a sensible plotline. The quasi-lucid, dreamlike result is perfect for urging listeners to imagine pleasant nonsense (thanks to Nathan W Pyle for that term) as they drift off to sleep.

We especially loved reading about the methods and challenges [Stavros] encountered while creating this project. For example, he talks about how there is more to a good-sounding narration than just pointing a text-to-speech engine at a wall of text and mashing “GO”. A good episode has things like strategic pauses, background music, and audio fades. That’s where pydub — a Python library for manipulating audio — came in handy. As for the speech, text-to-speech quality is beyond what it was even just a few years ago (and certainly leaps beyond machine-generated speech in the 80s) but it still took some work to settle on a voice that best suited the content, and the project gradually saw improvement.

Deep Dreams Podcast has a GitLab repository if you want to see the code that drives it all, and you can go to the podcast itself to give it a listen.

Learn All About Writing A Published Technical Book, From Idea To Print

March 19, 2022 by Donald Papp 3 Comments

Ever wondered what, exactly, goes into creating a technical book? If you’d like to know the steps that bring a book from idea to publication, [Sara Robinson] tells all about it as she explains what went into co-authoring O’Reilly’s Machine Learning Design Patterns.

Her post was written in 2020, but don’t let that worry you, because her writeup isn’t about the book itself so much as it is about the whole book-writing process, and her experiences in going through it. (By the way, every O’Reilly book has a distinctive animal on the cover, and we learned from [Sara] that choosing the cover animal is a slightly mysterious process, and is not done by the authors.)

It turns out that there are quite a few steps that need to happen — like proposals and approvals — before the real writing even starts. The book writing itself is a process, and like most processes to which one is new, things start out slow and inefficient before they improve.

[Sara] also talks a bit about burnout, and her advice on dealing with it is as insightful as it is practical: begin by communicating honestly how you are feeling to the people involved.

Over the years I’ve learned that people will very rarely guess how you’re feeling and it’s almost always better to tell them […] I decided to tell my co-authors and my manager that I was burnt out. This went better than expected.

There is a lot of code in the book, and it has its own associated GitHub repository should you wish to check some of it out.

By the way, [Sara] celebrated publication by making a custom cake, which you can see near the bottom of her blog post. This comes as no surprise seeing as she has previously managed to combine machine learning with her love of making cakes!

A Soft Thumb-Sized Vision-Based Touch Sensor

March 12, 2022 by Dave Rowntree 10 Comments

A team from the Max Planck Institute for Intelligent Systems in Germany have developed a novel thumb-shaped touch sensor capable of resolving the force of a contact, as well as its direction, over the whole surface of the structure. Intended for dexterous manipulation systems, the system is constructed from easily sourced components, so should scale up to a larger assemblies without breaking the bank. The first step is to place a soft and compliant outer skin over a rigid metallic skeleton, which is then illuminated internally using structured light techniques. From there, machine learning can be used to estimate the shear and normal force components of the contact with the skin, over the entire surface, by observing how the internal envelope distorts the structured illumination.

The novelty here is the way they combine both photometric stereo processing with other structured light techniques, using only a single camera. The camera image is fed straight into a pre-trained machine learning system (details on this part of the system are unfortunately a bit scarce) which directly outputs an estimate of the contact shape and force distribution, with spatial accuracy reported good to less than 1 mm and force resolution down to 30 millinewtons. By directly estimating normal and shear force components the direction of the contact could be resolved to 5 degrees. The system is so sensitive that it can reportedly detect its own posture by observing the deformation of the skin due its own weight alone!

We’ve not covered all that many optical sensing projects, but here’s one using a linear CIS sensor to turn any TV into a touch screen. And whilst we’re talking about using cameras as sensors, here’s a neat way to use optical fibers to read multiple light-gates with a single camera and OpenCV.

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Weather Station Predicts Air Quality

February 13, 2022 by Bryan Cockfield 7 Comments

Measuring air quality at any particular location isn’t too complicated. Just a sensor or two and a small microcontroller is generally all that’s needed. Predicting the upcoming air quality is a little more complicated, though, since so many factors determine how safe it will be to breathe the air outside. Luckily, though, we don’t need to know all of these factors and their complex interactions in order to predict air quality. We can train a computer to do that for us as [kutluhan_aktar] demonstrates with a machine learning-capable air quality meter.

The build is based around an Arduino Nano 33 BLE which is connected to a small weather station outside. It specifically monitors ozone concentration as a benchmark for overall air quality but also uses an anemometer and a BMP180 precision pressure and temperature sensor to assist in training the algorithm. The weather data is sent over Bluetooth to a Raspberry Pi which is running TensorFlow. Once the neural network was trained, the model was sent back to the Arduino which is now capable of using it to make much more accurate predictions of future air quality.

The build goes into quite a bit of detail on setting up the models, training them, and then using them on the Arduino. It’s an impressive build capped off with a fun 3D-printed case that resembles an old windmill. Using machine learning to help predict the weather is starting to become more commonplace as well, as we have seen before with this weather station that can predict rainfall intensity.

People in meeting, with highlights of detected phones and identities

Machine Learning Detects Distracted Politicians

January 17, 2022 by Donald Papp 39 Comments

[Dries Depoorter] has a knack for highly technical projects with a solid artistic bent to them, and this piece is no exception. The Flemish Scrollers is a software system that watches live streamed sessions of the Flemish government, and uses Python and machine learning to identify and highlight politicians who pull out phones and start scrolling. The results? Pushed out live on Twitter and Instagram, naturally. The project started back in July 2021, and has been dutifully running ever since, so by now we expect that holding one’s phone where the camera can see it is probably considered a rookie mistake.

This project can also be considered a good example of how to properly handle confidence in results depending on the application. In this case, false negatives (a politician is using a phone, but the software doesn’t detect it properly) are much more acceptable than false positives (a member gets incorrectly identified, or is wrongly called-out for using a mobile device when they are not.)

Keras, an open-source software library, is used for the object detection and facial recognition (GitHub repository for Keras is here.) We’ve seen it used in everything from bat detection to automatic trash sorting, so if you’re interested in machine learning applications, give it a peek.