//

// This software is released under the MIT License.

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

​

/* ===

ml5 Example

Webcam Image Classification using MobileNet and p5.js

This example uses a callback pattern to create the classifier

=== */

​

let classifier;

let video;

let currentPrediction = "";

​

function setup() {

  createCanvas(640, 480);

  // Create a video element

  video = createCapture(VIDEO);

  video.size(640, 480);

  video.hide();

  // Initialize the Image Classifier method with MobileNet and the video as the second argument

  classifier = ml5.imageClassifier('MobileNet', video, modelReady);

}

​

function draw() {

  image(video, 0, 0);

  //background(255,0,0);

​

  if (currentPrediction === "iPod") {

    background(45, 250, 100);

    textSize(40);

    text("KEANU IS BEAUTIFUL", 10, height / 2);

  }

}

​

function modelReady() {

  // Change the status of the model once its ready

  select('#status').html('Model Loaded');

  // Call the classifyVideo function to start classifying the video

  classifyVideo();

}

​

// Get a prediction for the current video frame

function classifyVideo() {

  classifier.predict(gotResult);

}

​

// When we get a result

function gotResult(err, results) {

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

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

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

​

  currentPrediction = results[0].className;

​

  if (results[0].probability > 0.3) {

    console.log("sure");

  } else {

    console.log("unsure");

  }

​

  classifyVideo();

}