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index.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">
<title>Mask Detector</title>
<link rel="stylesheet" href="./public/style.css">
</head>
<body>
<div class="header">
<h1>Portaria Inteligente</h1>
<p class="legend">Detector de máscaras faciais, caso não esteja usando uma máscara, sua entrada será bloqueada.</p>
</div>
<div class="wrap">
<div class="container">
<button id="init" type="button" onclick="init()">Start</button>
<div id="webcam-container"></div>
<div id="label-container"></div>
<!-- <form id="form" action="/posts" method="POST"> -->
<button id="ask" onclick="sub()" hidden>Pedir entrada</button>
<!-- </form> -->
</div>
<div class="result">
<h1 id='main'></h1>
<img id="padlock" src="" alt="status" hidden/>
</div>
</div>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/image@0.8/dist/teachablemachine-image.min.js"></script>
<script type="text/javascript">
// More API functions here:
// https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image
// the link to your model provided by Teachable Machine export panel
const URL = "https://teachablemachine.withgoogle.com/models/9rL9C4JLA/";
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"));
}
//show hidden button / hide button
document.getElementById("ask").hidden = false
document.getElementById("init").hidden = true
}
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>
async function sub() {
const nomask = await document.getElementById("label-container").firstElementChild.innerHTML
const withmask = await document.getElementById("label-container").lastElementChild.innerHTML
var no = await [];
var com = [];
for(i=7; i<nomask.length; i++) {
no = no + nomask[i]
}
for(i=9; i<withmask.length; i++) {
com = com + withmask[i]
}
if(no > com) {
document.getElementById("main").textContent = 'Entrada bloqueada.'
document.getElementById("padlock").src = "./public/locked.jpg"
document.getElementById("padlock").hidden = false
// document.getElementById("form").action = "/posts/block"
}
else {
document.getElementById("main").textContent = 'Entrada liberada.'
document.getElementById("padlock").src = "./public/unlocked.jpg"
document.getElementById("padlock").hidden = false
// document.getElementById("form").action = "/posts/pass"
}
}
</script>
<script>
</script>
</body>
</html>