//

// This software is released under the MIT License.

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

​

/* ===

ml5 Example

Webcam Image Classification using a pre-trained customized model and p5.js

This example uses p5 preload function to create the classifier

=== */

​

let classifier;

let imageModelURL = 'https://teachablemachine.withgoogle.com/models/OnMttmbI4/' ;

​

let camera;

​

let count = 0;

​

// Video

let video;

let flippedVideo;

// To store the classification

let label = "";

​

​

function preload() {

  camera = loadImage("camera.png");

  classifier = ml5.imageClassifier(imageModelURL + 'model.json');

}

​

function setup() {

  createCanvas(608, 342);

  // Create the video

  video = createCapture(VIDEO);

  video.size(288, 224);

​

  flippedVideo = ml5.flipImage(video)

  video.hide();

  // Start classifying

  classifyVideo();

}

​

function draw() {

  background(0);

  image(flippedVideo, 68, 85);

  image(camera, 0, 0, 608, 342);

  fill(255);

  textSize(14);

  textAlign(CENTER);

  if(label == "Penny"){

    text('lil penny mamita', 210, 114);

  } else if(label == "Penny & Boom"){

    text('penny & boom', 216, 114);

  } else {

    text('penny & koob', 216, 114);}

​

}

​

// Get a prediction for the current video frame

function classifyVideo() {

  flippedVideo = ml5.flipImage(video)

  classifier.classify(flippedVideo, gotResult);

}

​

// function classifyVideo() {

//   flippedVideo = ml5.flipImage(video)

​

//   for (let i = 0; i <= 100; i++) {

//       counter = i;

//       console.log(`Counter: ${counter}`);

​

//       if (counter === 100) {

//          classifier.classify(flippedVideo, gotResult);

//         }

//     }

// }

​

​

// When we get a result

function gotResult(error, results) {

  // If there is an error

  if (error) {

    console.error(error);

    return;

  }

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

  // console.log(results[0]);

  label = results[0].label;

  // Classifiy again!

  classifyVideo();

}