//

// 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 myResults

let resultLabel

let myColor;

​

​

// Create a new p5.speech object

// You can also control the Language, Rate, Pitch and Volumn of the voice

// Read more at http://ability.nyu.edu/p5.js-speech/

const myVoice = new p5.Speech();

​

function setup() {

  noCanvas();

  // Create a camera input

  video = createCapture(VIDEO);

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

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

}

​

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.classify(gotResult);

}

​

// When we get a result

function gotResult(err, results) {

  myResults = results

  resultLabel = results[0].label

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

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

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

  console.log(myResults)

  classifyVideo();

}

​

function draw(){

}

​

function keyTyped(){

  if (key === 'a') {

    if (resultLabel == "hair spray"){

  myVoice.speak(`Is that a ${myColor}${resultLabel}?`);

}

}

}

​

​

​

//punching bag, punch bag, punching ball, punchball