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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>MeanShift Example</title>
<link href="js_example_style.css" rel="stylesheet" type="text/css" />
</head>
<body>
<h2>MeanShift Example</h2>
<p>
Click <b>Start/Stop</b> button to start or stop the video.<br>
The <b>videoInput</b> is a <video> element used as meanShift input.
The <b>canvasOutput</b> is a <canvas> element used as meanShift 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="100" 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" muted loop></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 cap = new cv.VideoCapture(video);
// take first frame of the video
let frame = new cv.Mat(video.height, video.width, cv.CV_8UC4);
cap.read(frame);
// hardcode the initial location of window
let trackWindow = new cv.Rect(150, 60, 63, 125);
// set up the ROI for tracking
let roi = frame.roi(trackWindow);
let hsvRoi = new cv.Mat();
cv.cvtColor(roi, hsvRoi, cv.COLOR_RGBA2RGB);
cv.cvtColor(hsvRoi, hsvRoi, cv.COLOR_RGB2HSV);
let mask = new cv.Mat();
let lowScalar = new cv.Scalar(30, 30, 0);
let highScalar = new cv.Scalar(180, 180, 180);
let low = new cv.Mat(hsvRoi.rows, hsvRoi.cols, hsvRoi.type(), lowScalar);
let high = new cv.Mat(hsvRoi.rows, hsvRoi.cols, hsvRoi.type(), highScalar);
cv.inRange(hsvRoi, low, high, mask);
let roiHist = new cv.Mat();
let hsvRoiVec = new cv.MatVector();
hsvRoiVec.push_back(hsvRoi);
cv.calcHist(hsvRoiVec, [0], mask, roiHist, [180], [0, 180]);
cv.normalize(roiHist, roiHist, 0, 255, cv.NORM_MINMAX);
// delete useless mats.
roi.delete(); hsvRoi.delete(); mask.delete(); low.delete(); high.delete(); hsvRoiVec.delete();
// Setup the termination criteria, either 10 iteration or move by at least 1 pt
let termCrit = new cv.TermCriteria(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1);
let hsv = new cv.Mat(video.height, video.width, cv.CV_8UC3);
let dst = new cv.Mat();
let hsvVec = new cv.MatVector();
hsvVec.push_back(hsv);
const FPS = 30;
function processVideo() {
try {
if (!streaming) {
// clean and stop.
frame.delete(); dst.delete(); hsvVec.delete(); roiHist.delete(); hsv.delete();
return;
}
let begin = Date.now();
// start processing.
cap.read(frame);
cv.cvtColor(frame, hsv, cv.COLOR_RGBA2RGB);
cv.cvtColor(hsv, hsv, cv.COLOR_RGB2HSV);
cv.calcBackProject(hsvVec, [0], roiHist, dst, [0, 180], 1);
// Apply meanshift to get the new location
// and it also returns number of iterations meanShift took to converge,
// which is useless in this demo.
[, trackWindow] = cv.meanShift(dst, trackWindow, termCrit);
// Draw it on image
let [x, y, w, h] = [trackWindow.x, trackWindow.y, trackWindow.width, trackWindow.height];
cv.rectangle(frame, new cv.Point(x, y), new cv.Point(x+w, y+h), [255, 0, 0, 255], 2);
cv.imshow('canvasOutput', frame);
// 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();
videoInput.play().then(() => {
onVideoStarted();
});
} else {
videoInput.pause();
videoInput.currentTime = 0;
onVideoStopped();
}
});
function onVideoStarted() {
streaming = true;
startAndStop.innerText = 'Stop';
videoInput.height = videoInput.width * (videoInput.videoHeight / videoInput.videoWidth);
utils.executeCode('codeEditor');
}
function onVideoStopped() {
streaming = false;
canvasContext.clearRect(0, 0, canvasOutput.width, canvasOutput.height);
startAndStop.innerText = 'Start';
}
utils.loadOpenCv(() => {
videoInput.addEventListener('canplay', () => {
startAndStop.removeAttribute('disabled');
});
videoInput.src = 'cup.mp4';
});
</script>
</body>
</html>
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