ml5 ImageClassification_MultipleImages Trial (X)

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// Copyright (c) 2018 ml5
// 
// This software is released under the MIT License.
// https://opensource.org/licenses/MIT
​
/* ===
ml5 Example
Multiple Image classification using MobileNet and p5.js
=== */
​
// Initialize the Image Classifier method using MobileNet
const classifier = ml5.imageClassifier('MobileNet');
​
let img;
let currentIndex = 0;
let allImages = [];
let display = true;
let displayTime = 750;
let predictions = [];
​
function appendImages() {
  for (i = 0; i < data.images.length; i++) {
    imgPath = data.images[i];
    allImages.push(imgPath);
  }
}
​
function preload() {
  data = loadJSON('https://github.com/ml5js/ml5-examples/blob/master/p5js/ImageClassification_MultipleImages/assets/data.json');
}
​
function drawNextImage() {
  img.attribute('src', allImages[currentIndex], imageReady);
}
​
function setup() {
  noCanvas();
  appendImages();
  img = createImg(allImages[0], imageReady);
}
​
// When the image has been loaded,
// get a prediction for that image
function imageReady() {
  classifier.predict(img, gotResult);
}
​
function savePredictions() {
  predictionsJSON = {
    "predictions": predictions
  }
  saveJSON(predictionsJSON, 'predictions.json');
}
​
function removeImage() {
  currentIndex++;
  if (currentIndex <= allImages.length - 1) {
    drawNextImage();
  }
  else {
    savePredictions();
  }
}
​
// When we get the results
function gotResult(results) {
  information = {
    "name": allImages[currentIndex],
    "result": results,
  }
  predictions.push(information);
  console.log(results);
  if (display) {
    // The results are in an array ordered by probability.
    print(results[0].className);
    print(results[0].probability);
    //select('#result').html(results[0].className);
    //select('#probability').html(nf(results[0].probability, 0, 2));
    // Can be changed with the displayTime variable.
    setTimeout(removeImage, displayTime);
  }
  else {
    removeImage();
  }
}