ryangomba

# Copyright (c) 2012, Ryan Gomba
# All rights reserved.
# 
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# 
# 1. Redistributions of source code must retain the above copyright notice, this
#    list of conditions and the following disclaimer. 
# 2. Redistributions in binary form must reproduce the above copyright notice,
#    this list of conditions and the following disclaimer in the documentation

markcoleman

.directive("scroll", function ($window) {
    return function (scope, element, attrs) {

        function getScrollOffsets(w) {

            // Use the specified window or the current window if no argument 
            w = w || window;

            // This works for all browsers except IE versions 8 and before
            if (w.pageXOffset != null) return {

broady

/* Copyright 2013 Google Inc.
   Licensed under Apache 2.0: http://www.apache.org/licenses/LICENSE-2.0.html */

package com.example.latlnginterpolation;

import android.animation.ObjectAnimator;
import android.animation.TypeEvaluator;
import android.animation.ValueAnimator;
import android.annotation.TargetApi;
import android.os.Build;

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:

MateoV

//identify disconnected major roads
var turf = require('turf');
var tilebelt = require('tilebelt');
var flatten = require('geojson-flatten');

module.exports = function(tileLayers, tile, done){
  var bbox = tilebelt.tileToBBOX(tile);
  var minDistance = 50/5280; // 50 ft in miles
  var disconnects = turf.featurecollection([]);
  var caps = [];

MateoV

var turf = require('turf'),
  flatten = require('geojson-flatten'),
  normalize = require('geojson-normalize'),
  tilebelt = require('tilebelt');

module.exports = function(tileLayers, tile, done) {
  // concat feature classes and normalize data
  var tigerRoads = normalize(tileLayers.tiger.tiger20062014);
  var osmData = normalize(tileLayers.osmdata.migeojson);