index.html
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<!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 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!");
});
}
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();
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);
let b64encoded = tempCanvas.toDataURL("image/jpeg", 1.0);
$.ajax({
type: "POST",
url: "{{url_for('verify')}}",
dataType: "json",
data: {'image':b64encoded, 'data':'testestest'}
});
}
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 id="hidden_container" style="display: none;"><div>
</div>
</body>
</html>