//

// 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

=== */

​

// Classifier Variable

let classifier;

// Model URL

let imageModelURL = 'https://teachablemachine.withgoogle.com/models/NZuEU76pl/' ;

​

// Video

let video;

let flippedVideo;

// To store the classification

let label = "";

​

//image variables

let adder;

let crow;

let deer;

let hare;

let toad;

let cat;

let book;

​

// Load the model first

function preload() {

  adder = loadImage("adder.png");

  crow = loadImage("crow.png");

  deer = loadImage("deer.png");

  hare = loadImage("hare.png");

  toad = loadImage("toad.png");

  cat = loadImage("cat.png");

  book = loadImage("book.png")

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

}

​

function setup() {

  createCanvas(400, 400);

  let readButton = createButton("consult grimoire");

  readButton.position(150, 420);

  readButton.mousePressed(classifyVideo);

  textSize(18);

  // Create the video

  video = createCapture(VIDEO);

  video.size(400, 400);

  video.hide();

​

  flippedVideo = ml5.flipImage(video)

  // Start classifying

  //classifyVideo();

}

​

function draw() {

  background(0);

​

  // Draw the video

  //image(flippedVideo, 0, 0);

​

  if(label == "HARE"){

    image(hare, 0, 0, 400, 400);

  }else if(label == "ADDER"){

    image(adder, 0, 0, 400, 400);

  }else if(label =="DEER"){

    image(deer, 0, 0, 400, 400);

  }else if(label == "CROW"){

    image(crow, 0, 0, 400, 400);

  }else if(label == "CAT"){

    image(cat, 0, 0, 400, 400);

  }else if (label == "TOAD"){

    image(toad, 0, 0, 400, 400);

  }else {

    image(book, 0, 0, 400, 400);

  }

  // Draw the label

​

 // Draw the label

  fill(153, 102, 0);

  textFont('Garamond');

  textSize(40);

  textAlign(CENTER);

  textStyle(ITALIC);

  text(label, width / 2, height - 70);}

​

// Get a prediction for the current video frame

function classifyVideo() {

  flippedVideo = ml5.flipImage(video)

  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();

}