A glorious thing nowadays is that you needn’t be an AI researcher nor have expensive hardware to leverage machine learning in your projects. Granted, a domain-specific design will net greater benefits in the long run. Yet, until recently, a general-purpose, off-the-shelf solution wasn’t easily consumable by your average developer (that’s me). Nor was such a monster available — by virtue of APIs — to resource-constrained devices. Below, I’ll introduce the reader (that’s you) to API-based object recognition, and how to implement with cheap hardware and . JavaScript The Raspberry Pi Zero W Firstly, you will need an internet-enabled Raspberry Pi. For this project, the most value you’ll get for your money is probably a . Raspberry Pi Zero W Got a different Raspberry Pi? Most RPi boards have a camera interface. A RPi Zero v1.3 (the non-WiFi one with the camera interface) will also need a USB WiFi dongle, Ethernet adapter, or “hat” providing connectivity. The “original” RPi Zero, v1.2, does not have a camera interface, and will not work. While the Zero isn’t fast, it can run Linux, which makes it more capable than your garden-variety microcontroller. As you can see, it huffs & puffs to execute a “useless script”: Node.js $ time node -e 'process.exit()'node -e 'process.exit()' 5.94s user 0.16s system 99% cpu 6.157 total From the above, I’m going to training a on this ARMv6-based single-board computer would be a fool’s errand. But that’s not why you’d buy a Pi Zero W, or build anything with it. This is why: gingerly assume convolutional neural network It’s ten bucks. It’s smaller than a credit card in two out of the three dimensions which count. It’s ten (10) dollars, USD. With some effort and more cheap hardware, it can be . powered via ethernet It exposes GPIO pins. Go nuts. Did I mention it’s $10? What about Brand X single-board computer? The Node.js code leverages the package, which is a wrapper around . So, if it can't run , we can't use it for this tutorial. raspicam raspistill raspistill Once we’ve got an RPi to work with, we’ll need a camera. The Camera A supported module based on OV5647 (“v1”; ) or IMX219 (“v2”; ) will work. There are “official” modules which can run up to $30, but I’ve seen a knockoff “v1” from China around $6 on the low end. to do this; we’ll be taking rather low-resolution photographs. datasheet datasheet You don’t need an 8MP camera These cameras are equipped with lenses. I’ve found that you want to position the camera no less than about 12" (30.48 cm) from the target (another option may be attaching a zoom lens). I’ll leave this as exercise to the reader, but here’s my solution: fixed-focus My jerry-rigged tripod The camera module connects to the RPi via to a socket. A RPi Zero supports a cable of width 11.5mm, but the interfaces expect a width of ~16mm. Adapters and conversion cables exist; one such cable comes with the . flexible flat cable ZIF other official case Building with LEGO? For those attempting to build a custom tripod with LEGO, I note that the dimensions of my “v1” camera module are (in one dimension, anyway) roughly 24mm, which corresponds to a length of 3L, or the length of a . 1 x 5 Technic plates and are helpful here, as well as to secure the module in place. 3623 plate 32124 2711 32028 Now that we have the basic hardware together, let’s get Node.js installed. The Node.js I’m going to assume you’ve got Jessie installed. Theoretically, any distro based upon Debian Jessie should work. Maybe others too, but I haven’t tried them! Raspbian For this project, we’re using Node.js 8 (version 7.x may work with certain command-line flags, but I haven’t tried it). Normally, I’ll grab binaries from . However, they don’t support ARMv6. NodeSource If you are using a RPi 3 (we need ARMv7 or faster), go right ahead and use NodeSource’s distributions, then skip to the next section. But for the Zero, you have several options, two of which I can recommend: Manually install a tarball ; as a superuser, untar the archive and extract it over or , from nodejs.org /usr /usr/local or My preferred method: install via . As a (e.g. ), follow the instructions on the site and in the terminal to install NVM. Then, run: This will install the latest version of Node.js 8 under your home directory, then enable it. Run to test your install. Node Version Manager normal user pi $ nvm install 8 node -v The next piece of the puzzle is an API key. The Cloud This project uses IBM’s (hereafter “WVR”). It’s available from within IBM’s PaaS, Bluemix ( ). Watson Visual Recognition wiki You may use an existing Bluemix login, or sign up . Once you’re logged in, from , create a service instance; name it whatever you like. here the same page After it’s ready, you’ll land on the dashboard for the instance. Here, you can find your API key: Click “Service credentials”. Click “View credentials” under “Actions”. Copy the API key and paste it somewhere safe (like a password manager app) to keep it handy. Armed with our API key, let’s take a short detour into concepts. I promise this won’t hurt. The Concepts You’ll need to know this stuff or you will be arrested by the police. The Class The most important concept you need to understand is the “class”. In fact, the picture on the WVR site illustrates this well: An example of “classes” In the picture above, we have five (5) classes: Green: the subject of the image is green Leaf: the subject of the image contains a leaf Plant stem: The subject contains a plant stem Herb: the subject of the image is in the “herb” category of plants Basil: the subject is specifically a basil herb It’s important to note that a class may be as narrow or broad as you wish. For example, there are shades of the color “green” — but only one plant named “basil”! many While WVR has some pre-existing classes which work out-of-the-box, our aim is to . create our own custom classes To do this, we will need to create a . classifier The Classifier A “classifier” can be thought of as a logical of classes. For example, say you had four friends and family you wanted to be able to recognize the faces of. Each individual could correspond to a “class”: collection Uncle Snimm Aunt Butters Sister Clammy Bill The classifier would be “faces of friends & family”, or something of that nature. Perhaps you would add another class to this classifier which was only “family” — you could re-use the same images. In addition to this, WVR allows have a special class within your classifier representing . For example, you could put images of random strangers (or your enemies) in this “negative” class. This helps the underlying network avoid false positives. single images which are not in the classifier If you don’t have any enemies to use for this project, I can provide a few pointers on how to acquire them. I’ll save that for a future post. More use-cases of classifiers include: By limiting the scope of the classes to which WVR compares an image, we increase the likelihood of a good match Similarly If we know our picture won’t be in classifier , then we don’t need to classify using classifier X X Limiting scope will increase performance (though I don’t know by how much — seems logical, however!) So, how do we create classes and classifiers? The Training Regimen When we create a class, we give WVR an archive (a file) of images. These images are of class members. Once this archive is uploaded, the process begins. Training is a process of "learning" in "machine learning". Depending on the number of images in your archive(s), this can take a little while (on the order of minutes for just a paucity of images). .zip positive examples training Remember, you can also supply your new classifier an additional, single archive of negative examples. .zip In other words, in WVR, the action of a classifier implies _training_it as well. creating Now, for the payoff. Once we have trained a classifier, we get to classify images! The Classification is the action of providing WVR to a classifier, and receiving information about how well each image might “belong” to its classes. Classification one or more images For each image, WVR will give you zero or more classes with a corresponding fraction between 0 and 1. This fractional number represents , not . Then, for some classifiers, a confidence for class of could imply “member of class ”, but for others it could disqualify an image completely. confidence accuracy X 0.6 X If WVR’s confidence drops below a certain threshold, it won’t return a number at all. This threshold is configurable; the default is 0.5. If you’re only using 10–50 images, you may want to drop it to 0.3–0.4. Let’s recap the four terms we need to know: : A set of images having a common attribute which we intend to recognize Class : A logical collection of classes Classifier : Using WVR to decide which class(es) an arbitrary image could “belong” to, by reporting a confidence level Classification : In WVR, we train a classifier; we provide images to the service which we will then use for classification Training What classifiers will create? Wait — before you answer — let me rain on your parade. I’ll tell you what I wanted to do until reality sunk in. you Gather ‘round and weepe, while I bid mine own tale of woe! The Tale of Woe I like LEGOs. Inspired by Jacques Mattheij’s , I wanted to see if I could easily spin up an accurate classifier for different categories of LEGO pieces. For example, could I recognize “plates”: LEGO sorting project A LEGO “plate” versus “bricks”? A LEGO “brick” Could I do this? No. Of course not. The long answer: Once I had a working PoC of my tool (see below), I took many, pictures of LEGO bricks, plates, etc. They looked something like this: many A blurry image of a red 3666 plate on a background of white paper But the classification worked poorly. I tried a lot of different things, such as removing color information, changing backgrounds: A greyscale image of a red 3623 plate on a background of corrugated cardboard Or fiddling with the color temperature: I don’t know what the hell I did here. But it’s a red 4477 plate. Soul-crushing, abject failure. Every. Time. One thing I keep was a lower resolution — high resolution images will not necessarily net better results! In fact, often the opposite: a higher-resolution image will potentially contain an , resulting in . did unnecessary level of detail extra useless information Like usual, I pondered on “useless information”. Look at the previous image. Its resolution is 428x290; multiply and we get pixels. If we rotate it slightly, then crop down to the relevant information, we get the image below: 124120 That’s 20x202 or pixels. So: 4040 4040 / 124120 = ~0.03250.0325 * 100 = ~3.25 That means a bit over 3% of the photos I was taking contained relevant information. It follows that 97% of each photo was . useless, wasteful trashpixels Remember, the RPi cameras are fixed-focus. If I had a better camera or and/or macro lens, I probably could have made this work. Alas! LEGOs were too small. I needed something larger; something with fewer important details. My eyes darted around the room. What would be a good size for a picture taken about 12" away? Maybe kitchen utensils? Cups? That seems boring. Regrets? What do I have a lot of… (I realize you can’t answer this)? Maybe you have a few of these around: Your friendly neighborhood AC adapter Wall Warts! If you’re into hobby electronics, you might actually . I have …a few extras. collect wall warts Some of my wall warts in a fish-eye style You may not have, say, 20 or 30 of these handy (without having to, you know, unplug stuff). But do. If you can put aside your envy, you’ll notice the signal-to-noise ratio improves dramatically: I A blurry picture of a wall wart The images are still a bit blurry, but it doesn’t matter — we’re not trying to read the fine print. Also, scavening similar-sized objects for a “negative example” class was almost enjoyable: This is not an advertisement for Scotch double-sided tape, though it is good tape. I settled on a resolution of 640x480, and chose to discard color information. See the end of this post for links to my class archives, if you’d like to try them yourself! Given wall warts are usually black, maybe I would have better results if I kept the color data??? I can offer some general advice for taking your own snapshots: Keep the signal-to-noise ratio high; don’t include unnecessary pixels! Color temperature, shadows, lighting — the less consistent, the more images you’ll need. Don’t worry too much about blurriness ( this ain’t) OCR Consider different placements and angles of your objects 50 images per class or more. WVR’s lower limit is 10 per, but 50 is recommended as the absolute minimum! Even a “low” confidence level can work in practice. Adjust your threshold; as long as the network is when you expect it to be, then you’re doing fine! more confident To help me: Take all these pictures, Put them in the correct buckets, Archive them, and Upload them to Watson, I ended up writing a tool. That tool is called . No, really. puddlenuts Introducing puddlenuts is what I wrote to ease the insufferable process of . puddlenuts taking hundreds of pictures Don’t freak. You don’t need to take them all at once! You can always add more images to a class later. This is called retraining. can help with this. puddlenuts At this point, you should have your RPi configured, with Node.js installed and camera connected. If you don’t, what is wrong with you? On your RPi, install , then go mow the lawn while you wait: puddlenuts # this may require `sudo` if you aren't using NVM$ npm install --global puddlenuts- [ ] # ... time passes ...+ puddlenuts@0.2.4added 245 packages in 488.451s isn't a library; it's a command-line tool. What can it do? puddlenuts $ puddlenuts --helpCommands: classify [..classifier] Classify an image against one or more classifiers by a snapshot or existingimage. Default is to run against all classifiers. shoot <classifier> <classes..> Take snapshots to train classifier with two (2) or more positive example classes, OR one (1) or more positive example classes, and one (1) negative example class (see "-n") train <classifier> Train Watson with existing .zip archivesIO --color Enable color output, if available [boolean] [default: true] --loglevel Logging level [choices: "error", "warn", "info", "debug", "silly"] [default: "info"] --debug Shortcut for '--loglevel debug' [boolean] [default: false]Watson --api-key Set PUDDLENUTS_API_KEY env var instead! [string] [required]Options: --help Show help [boolean] We want to take photos, so is the command we want. shoot Shoot Here’s the dirt on : shoot $ puddlenuts shoot --helppuddlenuts shoot <classifier> <classes..>Camera control --raspistill, -r Options for raspistill in dot notation (e.g. "-r.width 640 -r.height 480") [default: {"width":640,"height":480,"quality":100,"timeout":1}] --limit, -l Limit to this many snapshots per class [number] [default: 50] --delay, -d Delay between snapshots in ms [number] [default: 3000 (3s)] --class-delay, -D Delay between classes in ms [number] [default: 10000 (10s)] --trigger, -t Set trigger interrupt on this GPIO pin (RPi only) [number] [default: No trigger]Watson --api-key Set PUDDLENUTS_API_KEY env var instead! [string] [required] --retrain Retrain classifier (if exists) [boolean] [default: false] --dry-run Don't actually upload anything [boolean] [default: false]Class --negative, -n Include negative example class in training (will be final class) [boolean] [default: false]IO --color Enable color output, if available [boolean] [default: true] --loglevel Logging level [choices: "error", "warn", "info", "debug", "silly"] [default: "info"] --debug Shortcut for '--loglevel debug' [boolean] [default: false]Options: --help Show help [boolean]Examples: blueface/bin/puddlenuts.js shoot Take snapshots to train or dogs poodles -n --retrain retrain the "dogs" classifier, with a positive example set of "poodles" and a negative example set (i.e. non-dogs); upload to Watson blueface/bin/puddlenuts.js shoot Take snapshots to train (do fish catfish swordfish --dry-run not retrain if "fish" exists") the "fish" classifier with positive examples of "catfish" and "swordfish"; don't upload The “camera control” options will allow you granular control over , which is the official command-line interface for the RPi cam. This is how you can change the resolution, fiddle w/ color correction, silly effects, etc. raspistill These options also allow you to define and . After each picture is taken, there’s a short pause. I found a delay ( ) of less than three (3) seconds between pictures isn't quite enough time to comfortably switch an object out for another, or readjust, so this is the default. how many pictures to take how quickly to take them --delay Since you tell to take snaps for multiple classes, you can also tell it how long to pause between switching from the last picture of one class to the first picture of the next. I was taking a bit longer to get setup when the class changed (e.g., swapping my pile of wall warts for a pile of random, non-wall-wart objects)--this defaults to ten (10) seconds. puddlenuts Finally, will limit each class to exactly the number of images you provide it (minimum 10). --limit The option allows you to wire a switch to one of the RPi's GPIOs. If the GPIO is "high", snaps will be taken (with specified delays). But if it's "low", will pause until you flip the switch back "high" again. Neat! --trigger puddlenuts I realize this first example might get me some unintended search engine traffic, but here we go: $ puddlenuts shoot dogs poodles --negative --retrain But what the above command will do, in gory detail, is: Take 50 pictures of “poodles”, with a 3s delay between each Pause 10s Take 50 pictures of “not dogs”, with a 3s delay between each Create archives for each set of 50 .zip If the “dogs” classifier doesn’t exist, it gets created If the “poodles” class doesn’t exist, it gets created/trained If the “poodles” class exist, the 50 images are used for more training does If the “negative examples” (“not dogs”) class doesn’t exist, it gets created/trained If the “negative examples” class exist, the 50 images are used for more training does You’ll also see plenty of beautiful console output while this is happening. There’s certainly room for improvement here; try it out and . let me know what could be easier Here’s a time-lapse of me taking a bunch of pictures using puddlenuts. Train Execute for more information, as I realize it's silly to copy and paste the output here. puddlenuts train --help The command allows you create (or retrain) classes using existing archives. . train .zip It doesn't take pictures For example, if you have to cobble together several “shoot” runs (use to create files w/o uploading; see log output for their location), or need to collect some images via other means, you should use . puddlenuts shoot --dry-run .zip puddlenuts train Classify This is the “fun” command — it will take a picture and attempt to classify it against the classifier(s) you provide. If you provide a classifier, the image will be compared against _all_classifiers. Watson provides a “default” classifier, which may be of use — give it a shot and see. don’t Two more options of note: You can also tell to just upload a file (via the option) instead of take a picture. puddlenuts classify --input <path/to/file> You can specify the confidence threshold with . You don't want to set this to or , as the former will give you way too much information, and the latter will give you . --threshold <number between 0 and 1 inclusive> probably 0 1 diddly squat What this command provides is a pretty-printed data structure with the classification information. This is an unwieldy tree, and I wasn’t sure how to better distill and/or represent it. So you just get a dump. You must admit, it’s really all you deserve. Regardless, please if you have a better idea. let me know For the conclusion, let’s stop. Conclusion A novice consumer of ML API’s may trip up or become frustrated when a system doesn’t do what you expect. You must remember that bringing this kind of power down to “our” level will come with caveats. There are limitations in what these shrinkwrapped solutions can offer, but with some persistence, I believe these technologies are widely applicable. It’s my hope you learn from my mistakes (and I hope I learn from them as well). All things considered, it’s to get started with this stuff. And cheaper. It’s trivial (JavaScript) to do (computer vision) with ($10 computers). way easier than I would have expected more less My prediction is this trend will continue. In a future post, I’ll explain how to do nearly everything using almost nothing. Addendum Below are links to the images I used for my “wall warts” classifier. There are only two classes: (wall warts) Positive examples (direct download) (not wall warts) Negative examples (direct download) And here’s my associated with a I gave on this subject at the meetup in Portland, Oregon, on August 22 2017. slide deck talk JavaScript & the Internet of Things This post originally appeared on boneskull.com , September 12, 2017.
