-
Notifications
You must be signed in to change notification settings - Fork 34
/
Copy pathindex.html
67 lines (60 loc) · 2.26 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
<!DOCTYPE html>
<html>
<head>
<title>PoseNet - Camera Feed Demo</title>
<style>
.footer {
position: fixed;
left: 0;
bottom: 0;
width: 100%;
color: black;
}
.footer-text {
max-width: 600px;
text-align: center;
margin: auto;
}
@media only screen and (max-width: 600px) {
.footer-text, .dg {
display: none;
}
}
</style>
<meta name="viewport" content="width=device-width, initial-scale=1">
</head>
<body>
<div id="loading">
Loading the model...
</div>
<div id='main' style='display:none'>
<video id="video" playsinline style=" -moz-transform: scaleX(-1);
-o-transform: scaleX(-1);
-webkit-transform: scaleX(-1);
transform: scaleX(-1);
display: none;
">
</video>
<canvas id="output" />
</div>
<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>image scale factor</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> image scale factor results in higher accuracy but lower speed.
<br>
<br> This is a Pure Javascript implementation of: <a href="https://github.com/tensorflow/tfjs-models/tree/master/posenet" target="_blank" rel="noopener">PoseNet</a>.
<br>
<br> Thank you <a href="https://js.tensorflow.org" target="_blank" rel="noopener">TensorFlow.js</a> for your flexible and intuitive APIs.
</p>
</div>
</div>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/build/dat.gui.js"></script>
<script src="https://unpkg.com/@tensorflow/tfjs"></script>
<script src="https://unpkg.com/@tensorflow-models/posenet"></script>
<script src="demo_util.js"></script>
<script src="stats.min.js "></script>
<script src="camera.js"></script>
</body>
</html>