ml5 Image Classification Example to P5 (X)

// Copyright (c) 2018 ml5

// 

// This software is released under the MIT License.

// https://opensource.org/licenses/MIT

​

/* ===

ml5 Example

Image classification using MobileNet and p5.js

=== */

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// Initialize the Image Classifier method with MobileNet

const classifier = ml5.imageClassifier('MobileNet');

​

// A variable to hold the image we want to classify

let img;

​

function setup() {

  imageMode(CENTER);

  createCanvas(windowWidth, windowHeight);

  // Load the image

  //img = createImg('bird.jpg', imageReady);

  //img.size(400, 400);

  img = loadImage('bird.jpg', imageReady); // run callback imageReady when finishing loading

}

​

// When the image has been loaded,

// get a prediction for that image

function imageReady() {

  image(img, width/2, height/2, 400, 400); // display the image

  classifier.predict(img, gotResult);

  // You can also specify the amount of classes you want

  // classifier.predict(img, 10, gotResult);

}

​

// A function to run when we get the results

function gotResult(results) {

  // The results are in an array ordered by probability.

  console.log(results);

  //print("The MobileNet model labeled this as " + results[0].className + " with a confidence of " + results[0].probability);

  textAlign(CENTER);

  text("The MobileNet model labeled this as " + results[0].className + "with a confidence of " + results[0].probability, width/2, height-height/3);

  //select('#result').html(results[0].className);

  //select('#probability').html(nf(results[0].probability, 0, 2));

}