chipx86

#!/usr/bin/env python
#
# Building a tar file chunk-by-chunk.
#
# This is a quick bit of sample code for streaming data to a tar file,
# building it piece-by-piece. The tarfile is built on-the-fly and streamed
# back out. This is useful for web applications that need to dynamically
# build a tar file without swamping the server.
import os
import sys

dotob

# Start `coffee d3-server.coffee`
# Then visit http://localhost:1337/
# originally from: https://gist.github.com/Caged/6407459
d3 = require('d3')
http = require('http')
jsdom = require('jsdom')

http.createServer((req, res) ->
  # Chrome automatically sends a requests for favicons
  # Looks like https://code.google.com/p/chromium/issues/detail?id=39402 isn't

random-person-001

#
# Plot some data as a filled chord graph!
#  The data labels will be '[label1] donated $[qty] to [label2].
#
# You should have plotly installed and set up for this to work.
#
# By Riley, Feb 2017, but almost all of it stolen from examples
# at https://plot.ly/python/filled-chord-diagram/
#

iandees

I've been interested in computer vision for a long time, but I haven't had any free time to make any progress until this holiday season. Over Christmas and the New Years I experimented with various methodologies in OpenCV to detect road signs and other objects of interest to OpenStreetMap. After some failed experiments with thresholding and feature detection, the excellent /r/computervision suggested using the dlib C++ module because it has more consistently-good documentation and the pre-built tools are faster.

After a day or two figuring out how to compile the examples, I finally made some progress:

Compiling dlib C++ on a Mac with Homebrew

1. Clone dlib from Github to your local machine: