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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Face Detection Camera Example</title>
<link href="js_example_style.css" rel="stylesheet" type="text/css" />
</head>
<body>
<h2>Face Detection Camera Example</h2>
<p>
Click <b>Start/Stop</b> button to start or stop the camera capture.<br>
The <b>videoInput</b> is a <video> element used as face detector input.
The <b>canvasOutput</b> is a <canvas> element used as face detector output.<br>
The code of <textarea> will be executed when video is started.
You can modify the code to investigate more.
</p>
<div>
<div class="control"><button id="startAndStop" disabled>Start</button></div>
<textarea class="code" rows="29" cols="80" id="codeEditor" spellcheck="false">
</textarea>
</div>
<p class="err" id="errorMessage"></p>
<div>
<table cellpadding="0" cellspacing="0" width="0" border="0">
<tr>
<td>
<video id="videoInput" width=320 height=240></video>
</td>
<td>
<canvas id="canvasOutput" width=320 height=240></canvas>
</td>
<td></td>
<td></td>
</tr>
<tr>
<td>
<div class="caption">videoInput</div>
</td>
<td>
<div class="caption">canvasOutput</div>
</td>
<td></td>
<td></td>
</tr>
</table>
</div>
<script src="https://webrtc.github.io/adapter/adapter-5.0.4.js" type="text/javascript"></script>
<script src="utils.js" type="text/javascript"></script>
<script id="codeSnippet" type="text/code-snippet">
let video = document.getElementById('videoInput');
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();
// load pre-trained classifiers
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.
classifier.detectMultiScale(gray, faces, 1.1, 3, 0);
// 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]);
}
cv.imshow('canvasOutput', dst);
// schedule the next one.
let delay = 1000/FPS - (Date.now() - begin);
setTimeout(processVideo, delay);
} catch (err) {
utils.printError(err);
}
};
// schedule the first one.
setTimeout(processVideo, 0);
</script>
<script type="text/javascript">
let utils = new Utils('errorMessage');
utils.loadCode('codeSnippet', 'codeEditor');
let streaming = false;
let videoInput = document.getElementById('videoInput');
let startAndStop = document.getElementById('startAndStop');
let canvasOutput = document.getElementById('canvasOutput');
let canvasContext = canvasOutput.getContext('2d');
startAndStop.addEventListener('click', () => {
if (!streaming) {
utils.clearError();
utils.startCamera('qvga', onVideoStarted, 'videoInput');
} else {
utils.stopCamera();
onVideoStopped();
}
});
function onVideoStarted() {
streaming = true;
startAndStop.innerText = 'Stop';
videoInput.width = videoInput.videoWidth;
videoInput.height = videoInput.videoHeight;
utils.executeCode('codeEditor');
}
function onVideoStopped() {
streaming = false;
canvasContext.clearRect(0, 0, canvasOutput.width, canvasOutput.height);
startAndStop.innerText = 'Start';
}
utils.loadOpenCv(() => {
let faceCascadeFile = 'haarcascade_frontalface_default.xml';
utils.createFileFromUrl(faceCascadeFile, faceCascadeFile, () => {
startAndStop.removeAttribute('disabled');
});
});
</script>
</body>
</html>
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