//

// 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;

let detector

let detections=[]

​

​

// 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 preload(){

  detector = ml5.objectDetector('cocossd');

}

​

function gotDetections(error, results) {

  if (error) {

    console.error(error);

  }

  detections = results;

  detector.detect(video, gotDetections);

}

​

​

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

  detector.detect(video, gotDetections);

}

​

​

​

function draw(){

  for (let i = 0; i < detections.length; i++) {

    let object = detections[i];

    stroke(0, 255, 0);

    strokeWeight(4);

    noFill();

    rect(object.x, object.y, object.width, object.height);

    ellipse(object.x + object.width/2, object.y + object.height/2, 10, 10)

    noStroke();

    fill(255);

    textSize(24);

    text(object.label, object.x + 10, object.y + 24);

  }

}

​

function keyTyped(){

  if (key === 'a') {

    if (resultLabel == "hair spray"){

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

}

}

}

​

​

​

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

​

​

​

​

// 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();

// }