index.html
5.26 KB
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
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Web Attendance System</title>
<style>
#container {
margin: 0px auto;
width: 640px;
height: 480px;
border: 10px #333 solid;
}
#videoInput {
background-color: #666;
}
#canvasOutput {
background-color: #666;
}
</style>
<script type='text/javascript' src="{{url_for('static', filename='js/opencv.js')}}"></script>
<script type='text/javascript' src="{{url_for('static', filename='js/utils.js')}}"></script>
<script type='text/javascript' src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
<script type='text/javascript'>
function load_cascade()
{
let faceCascadeFile = 'haarcascade_frontalface_default.xml'
let faceCascadeURL = 'static/js/haarcascade_frontalface_default.xml'
let utils = new Utils('errorMessage');
utils.createFileFromUrl(faceCascadeFile, faceCascadeURL, () => {
main()
});
}
function main()
{
let video = document.getElementById("videoInput");
let canvasOutput = document.getElementById("canvasOutput");
let canvasContext = canvasOutput.getContext('2d');
if (navigator.mediaDevices.getUserMedia){
navigator.mediaDevices.getUserMedia({ video: true })
.then(function (stream) {
video.srcObject = stream;
}).catch(function (err0r) {
console.log("Something went wrong!");
streaming = false;
});
}
let src = new cv.Mat(video.height, video.width, cv.CV_8UC4);
let dst = new cv.Mat(video.height, video.width, cv.CV_8UC4);
let gray = new cv.Mat();
let cap = new cv.VideoCapture(video);
let faces = new cv.RectVector();
let classifier = new cv.CascadeClassifier();
class Tracker{
constructor(){
this.arr = new Array();
}
register = function(x, y, width, height) {
var x_center = (x + width) / 2;
var y_center = (y + height) / 2;
var now = Date.now()
this.arr = this.arr.filter(ent => now - ent.time < 300);
for (const prop in this.arr){
var prop_x_center = (this.arr[prop].x + this.arr[prop].width) / 2;
var prop_y_center = (this.arr[prop].y + this.arr[prop].height) / 2;
if (Math.abs(x_center - prop_x_center) < 10 && Math.abs(y_center - prop_y_center) < 10){
this.arr[prop].x = x;
this.arr[prop].y = y;
this.arr[prop].width = width;
this.arr[prop].height = height;
this.arr[prop].time = now;
return false;
}
}
var ent = {x: x, y: y, width: width, height: height, time: now}
this.arr.push(ent)
return true;
}
};
var tracker = new Tracker();
var streaming = true;
classifier.load('haarcascade_frontalface_default.xml');
const FPS = 30;
function processVideo() {
try {
if (!streaming) {
// clean and stop.
src.delete();
dst.delete();
gray.delete();
faces.delete();
classifier.delete();
return;
}
let begin = Date.now();
// start processing.
cap.read(src);
src.copyTo(dst);
cv.cvtColor(dst, gray, cv.COLOR_RGBA2GRAY, 0);
// detect faces.
let msize = new cv.Size(120, 120);
classifier.detectMultiScale(gray, faces, 1.1, 3, 0, msize);
// draw faces.
for (let i = 0; i < faces.size(); ++i) {
let face = faces.get(i);
let point1 = new cv.Point(face.x, face.y);
let point2 = new cv.Point(face.x + face.width, face.y + face.height);
cv.rectangle(dst, point1, point2, [255, 0, 0, 255]);
let cropped = new cv.Mat();
let rect = new cv.Rect(face.x, face.y, face.width, face.height);
cropped = src.roi(rect);
let tempCanvas = document.createElement("canvas");
cv.imshow(tempCanvas,cropped);
if (tracker.register(face.x, face.y, face.width, face.height, Date.now())){
let b64encoded = tempCanvas.toDataURL("image/jpeg", 1.0);
b64encoded = b64encoded.replace('data:image/jpeg;base64,', '')
$.ajax({
type: "POST",
url: "/verify",
dataType: "json",
data: {'image':b64encoded},
success: function(data){
if (data.status == "success"){
var newDiv = document.createElement("div");
var newContent = document.createTextNode("출석");
newDiv.appendChild(newContent);
document.body.appendChild(newDiv);
}
else{
var newDiv = document.createElement("div");
var newContent = document.createTextNode("실패");
newDiv.appendChild(newContent);
document.body.appendChild(newDiv);
}
}
});
}
}
cv.imshow('canvasOutput', dst);
// schedule the next one.
let delay = 1000/FPS - (Date.now() - begin);
setTimeout(processVideo, delay);
} catch (err) {
console.log(err);
}
}
setTimeout(processVideo, 0);
}
</script>
</head>
<body onload="cv['onRuntimeInitialized']=()=>{ load_cascade() }">
<div id="container">
<video autoplay="true" id="videoInput" width=640 height=480 style="display: none;"></video>
<canvas id="canvasOutput" width=640 height=480></canvas>
</div>
</body>
</html>