| // Based off example code from Hal Robertson | |
| // https://github.com/halrobertson/test-restify-passport-facebook | |
| // See discussion: https://groups.google.com/forum/?fromgroups#!topic/passportjs/zCz0nXB_gao | |
| var restify = require('restify') | |
| // config vars | |
| var FB_LOGIN_PATH = '/api/facebook_login' | |
| var FB_CALLBACK_PATH = '/api/facebook_callback' | |
| var FB_APPID = '<<YOUR APPID HERE>>' |
Use magit-ediff or 'e' on an unmerged item to resolve merge conflicts with ediff. Magit will set up an ediff with three buffers A, B and C. A and B are the original (conflicting) files, and C is the conflicted merge.
Use 'n'/'p' to move to the next/previous conflict, use 'a'/'b' to choose which changes (those in a A or B) should be the ones to keep in the merged file.
You can always just switch to buffer C and edit what the merged version should look like.
WHEREAS, the City of Philadelphia is committed to creating a high level of openness and transparency in government; and
WHEREAS, the three principles of transparency, participation, and collaboration form the cornerstone of an open government; and
WHEREAS, the City’s participation as a founding and vital partner in the open data consortium has provided a model for transparency on which the City should continue to build; and
WHEREAS, more City data sets should be published and made available via an Open Data Portal which will provide access to information and a mechanism for public feedback and participation; and
git add HISTORY.md
git commit -m "Changelog for upcoming release 0.1.1."
bumpversion patch
| d3.select("#save").on("click", function(){ | |
| var html = d3.select("svg") | |
| .attr("version", 1.1) | |
| .attr("xmlns", "http://www.w3.org/2000/svg") | |
| .node().parentNode.innerHTML; | |
| //console.log(html); | |
| var imgsrc = 'data:image/svg+xml;base64,'+ btoa(html); | |
| var img = '<img src="'+imgsrc+'">'; | |
| d3.select("#svgdataurl").html(img); |
This is my default career advice for people starting out in geo/GIS, especially remote sensing, adapted from a response to a letter in 2013.
I'm currently about to start a Geography degree at the University of [Redacted] at [Redacted] with a focus in GIS, and I've been finding that I have an interest in working with imagery. Obviously I should take Remote Sensing and other similar classes, but I'm the type of person who likes to self learn as well. So my question is this: What recommendations would you give to a student who is interested in working with imagery? Are there any self study paths that you could recommend?
I learned on my own and on the job, and there are a lot of important topics in GIS that I don’t know anything about, so I can’t give comprehensive advice. I haven’t arrived anywhere; I’m just ten minutes ahead in the convoy we’re both in. Take these recommendations critically.
Find interesting people. You’ll learn a lot more from a great professor (or mentor, or friend, or conference) o
This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.
The script is here:
#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"
| require([ | |
| 'matchers/Topics', | |
| 'dojo/topic' | |
| ], function ( | |
| Topics, | |
| dojoTopic | |
| ) { |
| license: gpl-3.0 |
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:
dlib from Github to your local machine: