hawkeye64

/* this is a partial gist */
const Promise = require('bluebird')
const axios = require('axios')
const Camera = require('onvif').Cam

class OnvifManager {
  //...
  this.cameras = {}
  //...
  

hawkeye64

Previous gist: https://gist.github.com/hawkeye64/a30e43c8301626f7687b985b5e7b04bd

Axios couldn't handle working with Axis camera authentication because it uses Realm Digest. I have since changed my code to use the 'request' package.

NOTE: It is very *important* to have request do `'sendImmediately': false` which means that request won't send the authentication until the server (camera) responds in the header with *www-authenticate*, then request will send the authorization at that time.

  commandFetchSnapshot (id) {
    let self = this
    return new Promise((resolve, reject) => {
      let camera = self.cameras[id]

hawkeye64

mkdir sql
cd sql
sudo -u postgres pg_dumpall > backup.sql
sudo service postgresql stop
sudo apt remove postgresql
sudo apt remove postgresql-9.6 postgresql-client-9.6 postgresql-client-common  postgresql-common postgresql-contrib postgresql-contrib-9.6 postgresql-plpython3-9.6
sudo sh -c 'echo "deb http://apt.postgresql.org/pub/repos/apt/ xenial-pgdg main" > /etc/apt/sources.list.d/pgdg.list'
wget --quiet -O - https://www.postgresql.org/media/keys/ACCC4CF8.asc | sudo apt-key add -
sudo apt update
sudo apt upgrade

hawkeye64

const commandLineUsage = require('command-line-usage')

const sections = [
  {
    header: 'classify',
    content: 'Classifies an image using machine learning from passed in image path.'
  },
  {
    header: 'Options',
    optionList: [

hawkeye64

const fs = require('fs')
const path = require('path')
const commandLineArgs = require('command-line-args')

/**
 * Returns true if the passed in object is empty
 * @param {Object} obj 
 */
const isEmptyObject = (obj) => {
  return JSON.stringify(obj) === JSON.stringify({})

hawkeye64

// OpenCV
const cv = require('opencv4nodejs')

// initialize model from prototxt and modelFile
let net
if (dataFile === 'coco300' || dataFile === 'coco512') {
  net = cv.readNetFromCaffe(prototxt, modelFile)
}

// read the image

hawkeye64

/**
 * Predicts classifications based on passed in image
 * @param {Object} img The image to use for predictions
 */
const predict = (img) => {
  // white is the better padding color
  const white = new cv.Vec(255, 255, 255)

  // resize to model size
  const theImage = img.resizeToMax(modelData.size, modelData.size).padToSquare(white)

hawkeye64

/**
 * Extracts results from a network OutputBob
 * @param {Object} outputBlob The outputBlob returned from net.forward()
 * @param {Object} img The image used for classification
 */
const extractResultsCoco = (outputBlob, img) => {
  return Array(outputBlob.rows).fill(0)
    .map((res, i) => {
      // get class index
      const classIndex = outputBlob.at(i, 1);

hawkeye64

/**
 * Generate a random color
 */
const getRandomColor = () => new cv.Vec(Math.random() * 255, Math.random() * 255, Math.random() * 255);

/**
 * Returns a function that, for each prediction, draws a rect area with rndom color
 * @param {Arry} predictions Array of predictions
 */
const makeDrawClassDetections = (predictions) => (drawImg, getColor, thickness = 2) => {

hawkeye64

find . -type f -name "*Color*.jpg" -exec classify --image "{}" --confidence 30 \;