index.ejs
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<!DOCTYPE html>
<html>
<head>
<title><%= title %></title>
<!-- Bootstrap -->
<!-- 합쳐지고 최소화된 최신 CSS -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.2/css/bootstrap.min.css">
<link rel='stylesheet' href='/stylesheets/style.css' />
</head>
<body>
<!-- header -->
<header>
<p class="header_name">MWD</p>
</header>
<!-- main contents -->
<div class="container" style="margin-top: 30px">
<div class="box1">
<div id="webcam-container"></div>
<div id="label-container" ></div>
</div>
<div class="box2">
<div id="check_image">
<img class="alert_image" src="/images/1.jpg">
</div>
<!-- 인풋 폼 나중에 합치기 -->
<div class="col-lg-6">
<form>
<legend>방문자 인적 사항</legend>
<p>이름: <input class="form-control" type='text' name='name' placeholder="고길동">
</p>
<p>전화번호: <input class="form-control" type='text' name='number' placeholder="010-1234-1234." ></input></p>
<p>신분:
<select name = "id" class="form-control">
<option value = "재학생">재학생</option>
<option value = "휴학생">휴학생</option>
<option value = "직원">직원</option>
<option value = "외부인">외부인</option>
</select>
</p>
<p>
<input type = "reset" style="float:right; margin-left:5px; background-image: linear-gradient(to top, #fbc2eb 0%, #a6c1ee 100%); border: 0;" class="btn btn-warning"value = "다시입력"/>
<input type = "submit" style="float:right; background-image: linear-gradient(120deg, #a1c4fd 0%, #c2e9fb 100%); border: 0; " class="btn btn-success" value = "제출"/>
</p>
</form>
</div>
<!-- 인풋 폼 종료 -->
</div>
</div>
<span id = "check_audio">
<!--초기값으로 '시작'이라는 음성을 사용 -->
<!--auto play 기능을 위해 iframe 사용 -->
<iframe src="/audios/4.mp3" allow="autoplay" id="audio" style="display:none"></iframe>
</span>
<!-- footer -->
<footer>
<p>Mask-Wearing Discriminate Program Using Teachable Machine 2.0</p>
<p>Using teachable machine 2.0 provided by Google, a model was created that learned the type of mask wearing.</p>
<p>2017103978김태영 20174015이재호</p>
<p>contact us <a href="http://khuhub.khu.ac.kr/MWD/2020-02-OSS-TermProject">khuhub.khu.ac.kr/MWD/2020-02-OSS-TermProject</a></p>
</footer>
<!--https://teachablemachine.withgoogle.com/ 를 통해 학습시키고 얻은 스켈레톤 코드 -->
<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 src="https://ajax.googleapis.com/ajax/libs/jquery/3.2.1/jquery.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/2QtWMcVET/";
let model, webcam, labelContainer, maxPredictions;
//predic이전 값을 기억해주는 전역변수. -999로 초기화.
var last_result_predict=-999;
// Load the image model and setup the webcam
window.onload = 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(550, 550, 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);
//나중에 모두 하고나면 이부분은 항목별 일치확률을 보여주는 것으로 제거해도 무방.
for (let i = 0; i < maxPredictions; i++) {
const classPrediction =
prediction[i].className + ": " + prediction[i].probability.toFixed(2);
labelContainer.childNodes[i].innerHTML = classPrediction;
}
//ajax로 요청을 보낼 값 초기값 -1
var predict_id = -1;
//해당 항목의 일치확률이 몇프로 이상이어야 해당 항목이라 판단할지 결정하는 변수
var check_probablity=0.95;
//0: 완벽 1: 안씀 2: 턱스크 3: 입스크
if(prediction[0].probability.toFixed(2)>check_probablity){
predict_id=0;
}
else if(prediction[1].probability.toFixed(2)>check_probablity){
predict_id=1;
}
else if(prediction[2].probability.toFixed(2)>check_probablity){
predict_id=2;
}
else if(prediction[3].probability.toFixed(2)>check_probablity){
predict_id=3;
}
//이전 결과와 다를떄만 ajax요청
//last_result_predict는 전역변수. 초기값 -999로 설정되어있다.
//이것 없으면 계속 요청이 보내진다.
if(last_result_predict!=predict_id){
//ajax로 서버에 그 id에 해당하는 이미지의 주소와 음성파일을 달라고 요청한다.
if(predict_id > 0){
//last_result_predict값을 지금 결과로 나온 predict로 초기화해준다.
last_result_predict = predict_id;
$(function() {
$.ajax({
type: 'GET',
datatype:'json',
url: '/data?id='+predict_id,
success: function(result) {
console.log(result);
//받아온 json데이터를 처리한다
process_json(result);
}
});
})
}
}
}
//JSON 데이터 처리
function process_json(json_data){
var images = json_data.image;
var strDOM = "";
var audio = json_data.audio;
var audioName = "";
//이미지 태그 생성
strDOM += '<img class="alert_image" src="'+images+'">';
//오디오 태그 생성
audioName += '<iframe src="' + audio + '" allow="autoplay" id="audio" style="display:none"></iframe>';
//#cehck_image div의 이미지 교체
$('#check_image').html(strDOM);
//#check_audio div의 오디오 교체
$('#check_audio').html(audioName);
}
</script>
</body>
</html>