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index.html
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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<title></title>
<meta name="description" content="">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" href="./style.css">
<title>The first official website for trading K-pop Photocards in Vietnam</title>
</head>
<body>
<h1>SEVENTEEN</h1>
<!--
0 SEVENTEEN_attacca_album photocards
1 SEVENTEEN_heng garæ_album photocards
2 SEVENTEEN_caratland 2021_trading card
3 SEVENTEEN_haru tour_trading card
4 SEVENTEEN_incomplete_2021_trading card
5 Others
-->
<div class="btn-container">
<button type="button" onclick="init()">CLICK HERE TO SCAN K-POP PHOTOCARD</button>
</div>
<div id="webcam-container"></div>
<div id="label-container"></div>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/[email protected]/dist/teachablemachine-image.min.js"></script>
<script type="text/javascript">
// Specify the directory of the trained ML model
const URL = "./models/";
let model, webcam, labelContainer, maxPredictions;
// Load the image model and setup the webcam
async function init() {
const modelURL = URL + "model.json";
const metadataURL = URL + "metadata.json";
// load the model and metadata
// Refer to tmImage.loadFromFiles() in the API to support files from a file picker
// or files from your local hard drive
// Note: the pose library adds "tmImage" object to your window (window.tmImage)
model = await tmImage.load(modelURL, metadataURL);
maxPredictions = model.getTotalClasses();
// Convenience function to setup a webcam
const flip = true; // whether to flip the webcam
webcam = new tmImage.Webcam(200, 200, flip); // width, height, flip
await webcam.setup(); // request access to the webcam
await webcam.play();
window.requestAnimationFrame(loop);
// append elements to the DOM
document.getElementById("webcam-container").appendChild(webcam.canvas);
labelContainer = document.getElementById("label-container");
for (let i = 0; i < maxPredictions; i++) { // and class labels
labelContainer.appendChild(document.createElement("div"));
}
}
async function loop() {
webcam.update(); // update the webcam frame
await predict();
window.requestAnimationFrame(loop);
}
// run the webcam image through the image model
async function predict() {
// predict can take in an image, video or canvas html element
const prediction = await model.predict(webcam.canvas);
for (let i = 0; i < maxPredictions; i++) {
const classPrediction =
prediction[i].className + ": " + prediction[i].probability.toFixed(2);
labelContainer.childNodes[i].innerHTML = classPrediction;
}
}
</script>
<script src="" async defer></script>
</body>
</html>