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coco.html
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<!DOCTYPE html>
<html>
<head>
<title>PoseNet - Coco Images Demo</title>
<style type='text/css'>
h3,
h4,
p {
margin: 10px 0;
}
#main ul {
margin: 10px 0;
padding: 0;
}
#main li {
list-style-type: none;
display: inline-block;
}
#results>div {
display: inline-block;
width: 513px
}
.footer {
position: fixed;
left: 0;
bottom: 0;
width: 100%;
color: black;
}
.footer-text {
max-width: 600px;
text-align: center;
margin: auto;
}
</style>
</head>
<body>
<div id="loading">
</div>
<div id='main'>
<div id="status"></div>
<div id='results' style='display:none'>
<div id="multi">
<h4>Multiple Poses Estimation</h4>
<canvas></canvas>
</div>
<div id="single">
<h4>Single Pose Estimation</h4>
<canvas></canvas>
</div>
</div>
</div>
<p id="textp">
</p>
<div class="footer">
<div class="footer-text">
<p>
PoseNet runs with either a
<strong>single-pose</strong> or
<strong>multi-pose</strong> detection algorithm. The single person pose detector is faster and more accurate but requires
only one subject present in the image.
<br>
<br> The
<strong>output stride</strong> and
<strong>input image resolution</strong> have the largest effects on accuracy/speed. A
<i>higher</i> output stride results in lower accuracy but higher speed. A
<i>higher</i> input image resolution results in higher accuracy but lower speed.
</p>
</div>
<img id='img2' src='whatsapp-image-2019-05-13-at-1.jpg' style='display: none;'/>
<!-- <img id='img2' src='planknew2(1).jpg' style='display: none;'/> -->
</div>
<script src="coco.js"></script>
</body>
</html>