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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<title>MeanShift Example</title>
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<link href="js_example_style.css" rel="stylesheet" type="text/css" />
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</head>
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<body>
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<h2>MeanShift Example</h2>
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<p>
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Click <b>Start/Stop</b> button to start or stop the video.<br>
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The <b>videoInput</b> is a <video> element used as meanShift input.
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The <b>canvasOutput</b> is a <canvas> element used as meanShift output.<br>
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The code of <textarea> will be executed when video is started.
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You can modify the code to investigate more.
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</p>
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<div>
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<div class="control"><button id="startAndStop" disabled>Start</button></div>
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<textarea class="code" rows="29" cols="100" id="codeEditor" spellcheck="false">
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</textarea>
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</div>
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<p class="err" id="errorMessage"></p>
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<div>
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<table cellpadding="0" cellspacing="0" width="0" border="0">
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<tr>
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<td>
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<video id="videoInput" width="320" height="240" muted loop></video>
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</td>
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<td>
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<canvas id="canvasOutput" width="320" height="240" ></canvas>
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</td>
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<td></td>
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<td></td>
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</tr>
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<tr>
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<td>
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<div class="caption">videoInput</div>
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</td>
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<td>
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<div class="caption">canvasOutput</div>
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</td>
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<td></td>
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<td></td>
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</tr>
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</table>
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</div>
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<script src="https://webrtc.github.io/adapter/adapter-5.0.4.js" type="text/javascript"></script>
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<script src="utils.js" type="text/javascript"></script>
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<script id="codeSnippet" type="text/code-snippet">
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let video = document.getElementById('videoInput');
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let cap = new cv.VideoCapture(video);
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// take first frame of the video
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let frame = new cv.Mat(video.height, video.width, cv.CV_8UC4);
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cap.read(frame);
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// hardcode the initial location of window
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let trackWindow = new cv.Rect(150, 60, 63, 125);
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// set up the ROI for tracking
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let roi = frame.roi(trackWindow);
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let hsvRoi = new cv.Mat();
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cv.cvtColor(roi, hsvRoi, cv.COLOR_RGBA2RGB);
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cv.cvtColor(hsvRoi, hsvRoi, cv.COLOR_RGB2HSV);
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let mask = new cv.Mat();
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let lowScalar = new cv.Scalar(30, 30, 0);
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let highScalar = new cv.Scalar(180, 180, 180);
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let low = new cv.Mat(hsvRoi.rows, hsvRoi.cols, hsvRoi.type(), lowScalar);
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let high = new cv.Mat(hsvRoi.rows, hsvRoi.cols, hsvRoi.type(), highScalar);
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cv.inRange(hsvRoi, low, high, mask);
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let roiHist = new cv.Mat();
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let hsvRoiVec = new cv.MatVector();
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hsvRoiVec.push_back(hsvRoi);
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cv.calcHist(hsvRoiVec, [0], mask, roiHist, [180], [0, 180]);
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cv.normalize(roiHist, roiHist, 0, 255, cv.NORM_MINMAX);
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// delete useless mats.
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roi.delete(); hsvRoi.delete(); mask.delete(); low.delete(); high.delete(); hsvRoiVec.delete();
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// Setup the termination criteria, either 10 iteration or move by at least 1 pt
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let termCrit = new cv.TermCriteria(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1);
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let hsv = new cv.Mat(video.height, video.width, cv.CV_8UC3);
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let dst = new cv.Mat();
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let hsvVec = new cv.MatVector();
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hsvVec.push_back(hsv);
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const FPS = 30;
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function processVideo() {
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try {
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if (!streaming) {
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// clean and stop.
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frame.delete(); dst.delete(); hsvVec.delete(); roiHist.delete(); hsv.delete();
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return;
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}
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let begin = Date.now();
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// start processing.
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cap.read(frame);
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cv.cvtColor(frame, hsv, cv.COLOR_RGBA2RGB);
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cv.cvtColor(hsv, hsv, cv.COLOR_RGB2HSV);
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cv.calcBackProject(hsvVec, [0], roiHist, dst, [0, 180], 1);
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// Apply meanshift to get the new location
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// and it also returns number of iterations meanShift took to converge,
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// which is useless in this demo.
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[, trackWindow] = cv.meanShift(dst, trackWindow, termCrit);
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// Draw it on image
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let [x, y, w, h] = [trackWindow.x, trackWindow.y, trackWindow.width, trackWindow.height];
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cv.rectangle(frame, new cv.Point(x, y), new cv.Point(x+w, y+h), [255, 0, 0, 255], 2);
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cv.imshow('canvasOutput', frame);
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// schedule the next one.
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let delay = 1000/FPS - (Date.now() - begin);
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setTimeout(processVideo, delay);
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} catch (err) {
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utils.printError(err);
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}
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};
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// schedule the first one.
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setTimeout(processVideo, 0);
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</script>
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<script type="text/javascript">
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let utils = new Utils('errorMessage');
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utils.loadCode('codeSnippet', 'codeEditor');
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let streaming = false;
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let videoInput = document.getElementById('videoInput');
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let startAndStop = document.getElementById('startAndStop');
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let canvasOutput = document.getElementById('canvasOutput');
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let canvasContext = canvasOutput.getContext('2d');
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startAndStop.addEventListener('click', () => {
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if (!streaming) {
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utils.clearError();
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videoInput.play().then(() => {
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onVideoStarted();
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});
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} else {
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videoInput.pause();
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videoInput.currentTime = 0;
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onVideoStopped();
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}
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});
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function onVideoStarted() {
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streaming = true;
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startAndStop.innerText = 'Stop';
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videoInput.height = videoInput.width * (videoInput.videoHeight / videoInput.videoWidth);
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utils.executeCode('codeEditor');
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}
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function onVideoStopped() {
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streaming = false;
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canvasContext.clearRect(0, 0, canvasOutput.width, canvasOutput.height);
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startAndStop.innerText = 'Start';
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}
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utils.loadOpenCv(() => {
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videoInput.addEventListener('canplay', () => {
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startAndStop.removeAttribute('disabled');
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});
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videoInput.src = 'cup.mp4';
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});
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</script>
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</body>
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</html>
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