| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
cribbed from http://pastebin.com/xgzeAmBn
Templates to remind you of the options and formatting for the different types of objects you might want to document using YARD.
Ever wanted to delete all your likes/favorites from Twitter but only found broken/expensive tools? You are in the right place.
setInterval(() => {
for (const d of document.querySelectorAll('div[data-testid="unlike"]')) {
d.click()
}| /* bling.js */ | |
| window.$ = document.querySelectorAll.bind(document); | |
| Node.prototype.on = window.on = function (name, fn) { | |
| this.addEventListener(name, fn); | |
| } | |
| NodeList.prototype.__proto__ = Array.prototype; |
| <?xml version="1.0" encoding="UTF-8" ?> | |
| <!DOCTYPE plist PUBLIC "-//Apple Computer/DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd"> | |
| <plist version="1.0"> | |
| <dict> | |
| <key>Label</key> | |
| <string>org.scottlowe.pf.plist</string> | |
| <key>Program</key> | |
| <string>/sbin/pfctl</string> | |
| <key>ProgramArguments</key> | |
| <array> |
| <script src="http://cdnjs.cloudflare.com/ajax/libs/processing.js/1.4.1/processing-api.min.js"></script><html> | |
| <!-- | |
| Created using jsbin.com | |
| Source can be edited via http://jsbin.com/pdfjs-helloworld-v2/8598/edit | |
| --> | |
| <body> | |
| <canvas id="the-canvas" style="border:1px solid black"></canvas> | |
| <input id='pdf' type='file'/> | |
| <!-- Use latest PDF.js build from Github --> |
| // ~/docker/config.json | |
| { | |
| "credsStore": "desktop", | |
| "credHelpers": { | |
| "docker.pkg.github.com": "gh", | |
| "ghcr.io": "gh" | |
| } | |
| } |
Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.
The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:
####### 1. A low-resolution photo of road signs
| // Based on: https://stackoverflow.com/a/46814952/283851 | |
| // Based on: https://gist.github.com/mindplay-dk/72f47c1a570e870a375bd3dbcb9328fb | |
| /** | |
| * Create a Base64 Image URL, with rotation applied to compensate for EXIF orientation, if needed. | |
| * | |
| * Optionally resize to a smaller maximum width - to improve performance for larger image thumbnails. | |
| */ | |
| export function getImageUrl(file: File, maxWidth: number|undefined): Promise<string> { | |
| return readOrientation(file).then(orientation => { |
| # Creates a "dummy" network interface | |
| # we'll configure this interface with a link-local address | |
| # See: https://www.freedesktop.org/software/systemd/man/systemd.netdev.html | |
| ## | |
| [NetDev] | |
| Name=dummy0 | |
| Kind=dummy |
