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// Classifier Variable
let classifier;
// Model URL
let imageModelURL = 'https://teachablemachine.withgoogle.com/models/CtEKOSQJU/';
let dobut;
let question;
let yes;
// Video
let video;
let flippedVideo;
// To store the classification
let label = "";
let questionFade = 0;
let yesFade = 0;
// Load the model first
function preload() {
classifier = ml5.imageClassifier(imageModelURL + 'model.json');
dobut = loadImage('dobut.png');
question = loadImage('question.png')
yes = loadImage('Thumbs_Up.png')
}
function setup() {
createCanvas(16000,9000);
// Create the video
video = createCapture(VIDEO);
video.size(width,height-35);
video.hide();
flippedVideo = ml5.flipImage(video)
// Start classifying
classifyVideo();
}
function draw() {
background(0);
// Draw the video
tint(255);
image(flippedVideo, 0, 0);
console.log(label);
if (label == 'background'){
noLoop();
}
if (label == 'Question'){
loop();
questionFade = 255 ;
}
questionFade -= 15 ;
if (questionFade > 0){
loop();
tint(255 , questionFade);
image(question,-10 ,-10 ,0,0);
image(dobut, 400, 277, 0, 0);
// Draw the label
fill(255);
textSize(40);
textAlign(CENTER);
text("I Have A Dobut!", width / 2, height - 4);
}
if (label == 'Yes'){
yesFade = 255 ;
}
yesFade -= 15 ;
if (yesFade > 0){
tint(255 , yesFade);
image(yes, 0, 0);
// Draw the label
fill(255);
textSize(40);
textAlign(CENTER);
text("Yes", width / 2, height - 4);
}
}
// 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();
}