Bildstatistik - Bitte und Danke

let classifier;

​

​

let label = 'listening...';

let confidence;

​

let bitte = 12;

let danke = 8;

​

let img, img_bitte, img_danke;

​

// Teachable Machine model URL:

let soundModel = 'https://teachablemachine.withgoogle.com/models/G_4j_LZ_n/';

​

​

function preload() {

  // Load the model

  classifier = ml5.soundClassifier(soundModel + 'model.json');

  img = loadImage("Sprechen_Zeichenfläche 1.png"); // Gespräch

  img_bitte = loadImage("Bitte_Negativ.png"); // Bitte

  img_danke = loadImage("Danke_Negativ.png"); // Danke

}

​

function setup() {

  createCanvas(windowWidth, windowHeight);

  // Start classifying

  // The sound model will continuously listen to the microphone

  classifier.classify(gotResult);

}

​

function draw() {

  background(255);

  imageMode(CENTER); 

  let icon_size = 80;

  // colums

  let colums = 5;

  push();

  translate(width/2 - colums*icon_size - icon_size, height/4);

  for (i=1; i<=danke; i++) {

    image(img_danke, ((i-1)%colums)*icon_size, 0, icon_size, icon_size);

    if (i%colums == 0)

      translate(0, icon_size); // next row

  }

  pop();

  push();

  translate(width/2 + icon_size, height/4);

  for (i=1; i<=bitte; i++) {

    image(img_bitte, ((i-1)%colums)*icon_size, 0, icon_size, icon_size);

    if (i%colums == 0)

      translate(0, icon_size); // next row

  }

​

​

}

​

​

// The model recognizing a sound will trigger this event

function gotResult(error, results) {

  if (error) {

    console.error(error);

    return;

  }

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

  // console.log(results[0]);

  label = results[0].label;

  confidence = nf(results[0].confidence, 0, 2);

  //print(label + " / " + confidence);

  if (label.indexOf("bitte") > -1 && confidence > 0.8) {

    bitte += 1;

  }

  else if (label.indexOf("danke") > -1 && confidence > 0.8) {

    danke += 1;

  }

}