index.html
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<!DOCTYPE html>
<html>
<head>
<link rel="stylesheet" href="style.css">
<meta charset="utf-8">
<link rel="shortcut icon" href="/favicon.ico" type="image/x-icon">
<link rel="icon" href="/favicon.ico" type="image/x-icon">
<title>겨울왕국 캐릭터 닮은꼴</title>
<meta name="keywords" content="엘사, 안나, 크리스토프, 한스, 울라프, 스벤, 브루니">
<link href="https://fonts.googleapis.com/css2?family=Jua&display=swap" rel="stylesheet">
<script type="text/javascript" src="//s7.addthis.com/js/300/addthis_widget.js#pubid=ra-5e64b3727e862b06"></script>
</head>
<body>
<script class="jsbin" src="https://ajax.googleapis.com/ajax/libs/jquery/1/jquery.min.js"></script>
<h1 contenteditable data-heading="Frozen">
Frozen
</h1>
<div class="file-upload">
<h2>
나와 닮은 겨울왕국 캐릭터는?
</h2>
<div class="image-upload-wrap">
<input class="file-upload-input" type='file' onchange="readURL(this);" accept="image/*" />
<div class="drag-text">
<h3>이곳에 얼굴 사진을 업로드 하세요!</h3>
<img src="olaf.png" align="center">
</div>
</div>
<div class="file-upload-content">
<img class="file-upload-image" id="face-image" src="#" alt="your image" />
<div id="loading" class="animated bounce">
<div class="spinner-border" role="status">
<span class="sr-only">Loading...</span>
</div>
<p class="text-center">AI가 당신과 닮은 캐릭터를 분석중입니다.</p>
</div>
<p class="result-message"></p>
<div id="label-container" class="d-flex flex-column justify-content-around"></div>
<div id="addThis" class="addthis_inline_share_toolbox_6lz1"></div>
<div class="pt-3 image-title-wrap">
<button type="button" onclick="removeUpload()" class="remove-image">Remove
<span class="image-title">Uploaded Image</span>
</button>
</div>
</div>
</div>
<div id="webcam-container"></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>
<footer class="footer pt-5 container d-flex justify-content-center">
</footer>
<svg class="svg-snowscene" xmlns="http://www.w3.org/2000/svg">
<circle class="snow" />
<circle class="snow" />
<circle class="snow" />
<circle class="snow" />
<circle class="snow" />
<circle class="snow" />
<circle class="snow" />
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<circle class="snow" />
<circle class="snow" />
<circle class="snow" />
<circle class="snow" />
<circle class="snow" />
</svg>
<script>
function readURL(input) {
if (input.files && input.files[0]) {
var reader = new FileReader();
reader.onload = function(e) {
$('.image-upload-wrap').hide();
$('.file-upload-image').attr('src', e.target.result);
$('.file-upload-content').show();
$('.image-title').html(input.files[0].name);
};
reader.readAsDataURL(input.files[0]);
init().then(function() {
predict();
$('#loading').hide();
});
} else {
removeUpload();
}
}
function removeUpload() {
$('.file-upload-input').replaceWith($('.file-upload-input').clone());
$('.file-upload-content').hide();
$('.image-upload-wrap').show();
}
$('.image-upload-wrap').bind('dragover', function() {
$('.image-upload-wrap').addClass('image-dropping');
});
$('.image-upload-wrap').bind('dragleave', function() {
$('.image-upload-wrap').removeClass('image-dropping');
});
</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/wXBDodJuF/";
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";
model = await tmImage.load(modelURL, metadataURL);
maxPredictions = model.getTotalClasses();
labelContainer = document.getElementById("label-container");
for (let i = 0; i < maxPredictions; i++) {
var element = document.createElement("div")
element.classList.add("d-flex");
labelContainer.appendChild(element);
}
}
// run the webcam image through the image model
async function predict() {
var image = document.getElementById("face-image")
const prediction = await model.predict(image, false);
prediction.sort((a, b) => parseFloat(b.probability) - parseFloat(a.probability));
var resultMessage, resultExplain;
switch (prediction[0].className) {
case "엘사":
resultMessage = "엘사"
resultExplain = "당신은 얼음공주 엘사!"
break;
case "안나":
resultMessage = "안나"
resultExplain = "당신은 밝고 명랑한 안나!"
break;
case "크리스토프":
resultMessage = "크리스토프"
resultExplain = "당신은 코가 크고 듬직한 크리스토프!"
break;
case "한스":
resultMessage = "한스"
resultExplain = "당신은 잘생겼지만 찌질한 한스!"
break;
case "울라프":
resultMessage = "울라프"
resultExplain = "헐~ 당신은 눈사람?! 최강 귀요미 울라프!"
break;
case "스벤":
resultMessage = "스벤"
resultExplain = "헐~ 당신은 순록?! 크리스토프의 영원한 친구"
break;
case "브루니":
resultMessage = "브루니"
resultExplain = "헐~ 당신은 도마뱀?! 정말 귀여운 브루니!"
break;
case "트롤":
resultMessage = "트롤"
resultExplain = "헐~ 당신은 트롤?! 2등신 요정!"
break;
default:
resultMessage = "알수없음"
resultExplain = ""
}
var title = "<div class='" + prediction[0].className + "-frozen-title'>" + resultMessage + "</div>"
var explain = "<div class='frozen-explain pt-2'>" + resultExplain + "</div>"
$('.result-message').html(title + explain);
var barWidth;
for (let i = 0; i < maxPredictions; i++) {
if (prediction[i].probability.toFixed(2) > 0.1) {
barWidth = Math.round(prediction[i].probability.toFixed(2) * 100) + "%";
} else if (prediction[i].probability.toFixed(2) >= 0.01) {
barWidth = "4%"
} else {
barWidth = "2%"
}
var labelTitle;
switch (prediction[i].className) {
case "엘사":
labelTitle = "엘사"
break;
case "안나":
labelTitle = "안나"
break;
case "크리스토프":
labelTitle = "크리스토프"
break;
case "한스":
labelTitle = "한스"
break;
case "울라프":
labelTitle = "울라프"
break;
case "스벤":
labelTitle = "스벤"
break;
case "브루니":
labelTitle = "브루니"
break;
case "트롤":
labelTitle = "트롤"
break;
default:
labelTitle = "알수없음"
}
var label = "<div class='frozen-label d-flex align-items-center'>" + labelTitle + "</div>"
var bar = "<div class='bar-container position-relative container'><div class='" + prediction[i].className + "-box'></div><div class='d-flex justify-content-center align-items-center " + prediction[i].className + "-bar' style='width: " + barWidth + "'><span class='d-block percent-text'>" + Math.round(prediction[i].probability.toFixed(2) * 100) + "%</span></div></div>"
labelContainer.childNodes[i].innerHTML = label + bar;
console.log(prediction);
}
}
</script>
<script>
function getRandom(min, max) {
return Math.random() * (max - min) + min;
}
(function letItSnow() {
var snowflakes = document.querySelectorAll('.snow');
for (var i = 0; i < snowflakes.length; i++) {
var snowflake = snowflakes[i];
snowflake.setAttribute('cx', getRandom(1, 100) + '%');
snowflake.setAttribute('cy', '-' + getRandom(1, 100));
snowflake.setAttribute('r', getRandom(1, 3));
}
})();
</script>
</body>
</html>