hawkeye64

  created: function () {
    this.$root.$on('expand-tree', this.expandTree)
  },

  beforeDestroy: function () {
    this.$root.$off('expand-tree', this.expandTree)
  },

hawkeye64

    <q-tree
      ref="folders"
      :nodes="rootDir"
      label-key="label"
      node-key="nodeKey"
      accordion
      default-expand-all
      :selected.sync="selected"
      @lazy-load="lazyLoad"
    />

hawkeye64

    expandTree: function (absolutePath) {
      // get parts for the path
      let parts = absolutePath.split(path.sep)
      let path2 = ''
      let lastNodeKey

      // iterate through the path parts.
      // This code will get the node. If the node is not found,
      // it forces lazy-load by programmatically expanding
      // the parent node.

hawkeye64

const exec = require('child_process').exec
const fs = require('fs')
const path = require('path')

function getWindowsDrives (callback) {
  if (!callback) {
    throw new Error('getWindowsDrives called with no callback')
  }
  if (process.platform !== 'win32') {
    throw new Error('getWindowsDrives called but process.plaform !== \'win32\'')

hawkeye64

const path = require('path')
const fs = require('fs')

/**
 * Generator function that lists all files in a folder recursively
 * in a synchronous fashion
 *
 * @param {String} folder - folder to start with
 * @param {Number} recurseLevel - number of times to recurse folders
 * @returns {IterableIterator<String>}

hawkeye64

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

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

/**
 * 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);

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/**
 * 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

// 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