OpenCV Never Forgets a Face

All the cool phones now are doing facial recognition. While that sounds like a big job, you can add face detection and recognition easily to your projects if you can support the OpenCV library. [LinuxHint] has a great tutorial that steps you from the basics of OpenCV to actually acquiring and identifying faces. It is aimed at Ubuntu users, but the code would apply to any OpenCV-supported platform. You can also see a less detailed tutorial to learn more about installing OpenCV on the Pi Zero from [DanishMalhotra].

Of course, any facial recognition system is going to need a camera. The nice thing about the first tutorial is that it assumes you know nothing about OpenCV, so it covers the basics on up to using the face-related libraries.

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Another Defeat of the Intel Management Engine

If you have a computer with an Intel processor that’s newer than about 2007, odds are high that it also contains a mystery software package known as the Intel Management Engine (ME). The ME has complete access to the computer below the operating system and can access a network, the computer’s memory, and many other parts of the computer even when the computer is powered down. If you’re thinking that this seems like an incredible security vulnerability then you’re not alone, and a team at Black Hat Europe 2017 has demonstrated yet another flaw in this black box (PDF), allowing arbitrary code execution and bypassing many of the known ME protections.

[Mark Ermolov] and [Maxim Goryachy] are the two-man team that discovered this exploit, only the second of its kind in the 12 years that the ME has been deployed. Luckily, this exploit can’t be taken advantage of (yet) unless an attacker has physical access to the device. Intel’s firmware upgrades also do not solve the problem because the patches still allow for use of older versions of the ME. [Mark] and [Maxim] speculate in their presentation that this might be fixed on the next version of the ME, but also note that these security vulnerabilities would disappear if Intel would stop shipping processors with the ME.

We won’t hold our breath on Intel doing the right thing by eliminating the ME, though. It’s only a matter of time before someone discovers a zero-day (if they haven’t already, there’s no way to know) which could cripple pretty much every computer built within the last ten years. If you’re OK with using legacy hardware, though, it is possible to eliminate the management engine and have a computer that doesn’t have crippling security vulnerabilities built into it. This post was even written from one. Good luck doing anything more resource-intensive with it, though.

Lisp in 200 Lines

Contrary to popular belief, LISP does not stand for “lots of irritating spurious parenthesis.” However, it is true that people tend to love or hate this venerable programming language. Whichever side of the fence you’re on, many of the ideas it launched decades ago have become staples of other newer languages. How much C code do you think it takes to make a functional LISP system? If you guessed more than 200, you’ll want to go look at this GitHub repository.

Actually, the code isn’t as good as the (sort of) literate programming white paper on the program, but it gives a good overview of how 200 lines of C code can produce a working LISP-like language good enough to create its own eval loop. It does lack memory handling and error detection, so if you really wanted to use it, you’d probably need to spruce it up a bit.

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MeatBagPnP Makes You the Automatic Pick and Place

It’s amazing how hackers are nowadays building increasingly complex hardware with SMD parts as small as grains of sand. Getting multilayer PCB’s and soldering stencils in small quantities for prototyping is easier than ever before. But Pick-and-Place — the process of taking parts and stuffing them on the PCB in preparation for soldering — is elusive, for several reasons. For one, it makes sense only if you plan to do volume production as the cost and time for just setting up the PnP machine for a small run is prohibitive. And a desktop PnP machine isn’t yet as ubiquitous as a 3D printer. Placing parts on the board is one process that still needs to be done manually. Just make sure you don’t sneeze when you’re doing it.

Of course the human is the slow part of this process. [Colin O’Flynn] wrote a python script that he calls MeatBagPnP to ease this bottleneck. It’s designed to look at a row in a parts position file generated from your EDA program and highlight on a render of the board where that part needs to be placed. The human then does what a robotic PnP would have done.

A bar code scanner is not necessary, but using one does make the process a bit quicker. When you scan a code on the part bag, the script highlights the row on the spreadsheet and puts a marker on the first instance of it on the board. After you’ve placed the part, pressing the space bar puts a marker on the next instance of the same value. The script shows it’s done after all parts of the same value are populated and you can then move on to the next part. If you don’t have a bar code scanner handy, you can highlight a row manually and it’ll tell you where to put that part. Check it out in the video below.

Of course, before you use this tool you need some prior preparation. You need a good PNG image of the board (both sides if it is double-sided) scaled so that it is the same dimensions as the target board. The parts position file generated from your EDA tool must use the lower left corner of the board as the origin. You then tell the tool the board dimensions and it scales up everything so that it can put the red markers at the designated XY positions. The script works for single and double-sided boards. For a board with just a few parts, it may not be worth the trouble of doing this, but if you are trying to manually populate a complex board with a lot of parts, using a script like this could make the process a lot less painful.

The project is still fresh and rough around the edges, so if you have comments or feedback to offer, [Colin] is listening.

[Colin]’s name ought to ring a bell — he’s the hacker who built ChipWhisperer which took 2nd Prize at The Hackaday Prize in 2014. The MeatBagPnP project is a result of having worked at building increasingly complex boards manually and trying to make the process easier. In addition to the walk-through of how the script works after the break we’ve embedded his other video from three years back when he was stuffing parts — including BGA’s — the hard way and then reflowing them in a Chinese oven with hacked firmware.

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Tensorflow Tutorial Uses Python

Around the Hackaday secret bunker, we’ve been talking quite a bit about machine learning and neural networks. There’s been a lot of renewed interest in the topic recently because of the success of TensorFlow. If you are adept at Python and remember your high school algebra, you might enjoy [Oliver Holloway’s] tutorial on getting started with Tensorflow in Python.

[Oliver] gives links on how to do the setup with notes on Python versions. Then he shows some basic setup operations. From there, he has the software “learn” how to classify random points that either fall into a circle or don’t. Granted, this is easy enough to do with traditional programming, so it isn’t a great practical example, but it is illustrative for learning purposes.

Given that it is easy to algorithmically decide which points are in the circle and which are not, it is simple to develop training data. It is also easy to look at the result and see how close it is to the actual circle. You’ll see that it takes a lot of slow learning before the result space looks like a circle and not a triangle or some other odd shape.

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Smarter Phones In Your Hacks With TensorFlow Lite

One way to run a compute-intensive neural network on a hack has been to put a decent laptop onboard. But wouldn’t it be great if you could go smaller and cheaper by using a phone instead? If your neural network was written using Google’s TensorFlow framework then you’ve had the option of using TensorFlow Mobile, but it doesn’t use any of the phone’s accelerated hardware, and so it might not have been fast enough.

TensorFlow Lite architecture
TensorFlow Lite architecture

Google has just released a new solution, the developer preview of TensofFlow Lite for iOS and Android and announced plans to support Raspberry Pi 3. On Android, the bottom layer is the Android Neural Networks API which makes use of the phone’s DSP, GPU and/or any other specialized hardware to speed up computations. Failing that, it falls back on the CPU.

Currently, fewer operators are supported than with TensforFlor Mobile, but more will be added. (Most of what you do in TensorFlow is done through operators, or ops. See our introduction to TensorFlow article if you need a refresher on how TensorFlow works.) The Lite version is intended to be the successor to Mobile. As with Mobile, you’d only do inference on the device. That means you’d train the neural network elsewhere, perhaps on a GPU-rich desktop or on a GPU farm over the network, and then make use of the trained network on your device.

What are we envisioning here? How about replacing the MacBook Pro on the self-driving RC cars we’ve talked about with a much smaller, lighter and less power-hungry Android phone? The phone even has a camera and an IMU built-in, though you’d need a way to talk to the rest of the hardware in lieu of GPIO.

You can try out TensorFlow Lite fairly easily by going to their GitHub and downloading a pre-built binary. We suspect that’s what was done to produce the first of the demonstration videos below.

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Learn About Blockchains By Building One

What do we curious Hackaday scribes do when we want to learn about something? First port of call: search the web.

When that something is blockchain technology and we’re looking for an explanation that expands our cursory overview into a more fundamental understanding of the basic principles, there is a problem. It seems that to most people blockchains equate to one thing: cryptocurrencies, and since cryptocurrencies mean MONEY, they then descend into a cultish frenzy surrounded by a little cloud of flying dollar signs. Finding [Daniel van Flymen]’s explanation of the fundamentals of a blockchain in terms of the creation of a simple example chain using Python was thus a breath of fresh air, and provided the required education. Even if he does start the piece by assuming that the reader is yet another cryptocurrency wonk.

We start by creating a simple class to hold all the Python functions, then we are shown a single block. In his example it’s a JSON object, and it contains the payload in the form of a transaction record along with the required proof-of-work and hash. We’re then taken through a very simple proof-of-work algorithm, before being shown how the whole can be implemented as very simple endpoints.

You are not going to launch a cryptocurrency using this code, and indeed that wasn’t our purpose in seeking it out. But if you are curious about the mechanics of a blockchain and are equally tired of evangelists of The Blockchain who claim it will cure all ills but can’t explain it in layman’s terms, then this relatively simple example is for you.

The wrong way to build a blockchain image: Jenny List. #FarmLife.