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ml5.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta http-equiv="X-UA-Compatible" content="ie=edge" />
<title>Document</title>
</head>
<body>
<div>Teachable Machine Image Model - p5.js and ml5.js</div>
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.9.0/p5.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.9.0/addons/p5.dom.min.js"></script>
<script src="https://unpkg.com/[email protected]/dist/ml5.min.js"></script>
<script type="text/javascript">
// Model URL
let imageModelURL =
'https://teachablemachine.withgoogle.com/models/0LXis1Fj/';
// Classifier Variable
let classifier;
// Video
let video;
let flippedVideo;
// To store the classification
let label = '';
// Load the model first
function preload() {
classifier = ml5.imageClassifier(imageModelURL + 'model.json');
}
function setup() {
createCanvas(320, 270);
// Create the video
video = createCapture(VIDEO);
video.size(320, 240);
video.hide();
flippedVideo = ml5.flipImage(video);
// Start classifying
classifyVideo();
}
function draw() {
background(0);
// Draw the video
image(flippedVideo, 0, 0);
// Draw the label
fill(255);
textSize(32);
textAlign(CENTER);
text(label, 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();
}
</script>
</body>
</html>