index.html
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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Web Attendance System</title>
<style>
#container {
margin: 0px auto;
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 init()
{
let video = document.getElementById('videoInput');
let container = document.getElementById('container');
let canvasOutput = document.getElementById("canvasOutput");
if (navigator.mediaDevices.getUserMedia){
navigator.mediaDevices.getUserMedia({ video: true })
.then(function (stream) {
video.srcObject = stream;
video.addEventListener('canplay', () => {
video.width = video.videoWidth;
video.height = video.videoHeight;
container.style.width = video.videoWidth + 'px';
container.style.height = video.videoHeight + 'px';
canvasOutput.width = video.videoWidth;
canvasOutput.height = video.videoHeight;
load_cascade();
});
}).catch(function (err0r) {
console.log("Something went wrong!");
streaming = false;
});
}
}
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');
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);
cv.flip(src, src, 1);
src.copyTo(dst);
// cv.cvtColor(dst, gray, cv.COLOR_RGBA2GRAY, 0);
// detect faces.
let msize = new cv.Size(video.width / 4, video.height / 4);
classifier.detectMultiScale(dst, faces, 1.1, 3, 0, msize);
// draw faces.
console.log('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], 8);
let cropped = new cv.Mat();
let rect = new cv.Rect(Math.max(face.x-22, 0), Math.max(face.y-44, 0), Math.min(face.width+44, src.cols), Math.min(face.height+66, src.rows));
cropped = src.roi(rect);
let tempCanvas = document.createElement("canvas");
cv.imshow(tempCanvas,cropped);
console.log('b64encode');
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,', '')
console.log('ajax post');
$.ajax({
type: "POST",
url: "/verify",
dataType: "json",
data: {'image':b64encoded},
success: function(data){
if (data.status == "attend"){
var newDiv = document.createElement("div");
var newContent = document.createTextNode('[' + data.student_id + '/' + data.student_name + ']' + "출석");
newDiv.appendChild(newContent);
document.body.appendChild(newDiv);
}
else if (data.status == "already"){
var newDiv = document.createElement("div");
var newContent = document.createTextNode('[' + data.student_id + '/' + data.student_name + ']' + "이미 출석처리 되었습니다.");
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);
}
}
});
}
}
// to do resize preview
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']=()=>{ init(); };">
<div id="container">
<video autoplay="true" id="videoInput" style="display: none; object-fit: cover;"></video>
<canvas id="canvasOutput"></canvas>
</div>
</body>
</html>