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Merge pull request #3712 from kaingwade:clean_haarcascades_jsbindings

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Fix broken js build after moving HaarCascades to contrib
pull/3729/head
Alexander Smorkalov 11 months ago committed by GitHub
parent 140086338a 5ce3b61cfe
commit fe06856b0c
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  1. 100
      modules/xobjdetect/tutorials/js_tutorials/js_assets/js_face_detection.html
  2. 142
      modules/xobjdetect/tutorials/js_tutorials/js_assets/js_face_detection_camera.html

100
modules/xobjdetect/tutorials/js_tutorials/js_assets/js_face_detection.html
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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Face Detection Example</title>
<link href="js_example_style.css" rel="stylesheet" type="text/css" />
</head>
<body>
<h2>Face Detection Example</h2>
<p>
&lt;canvas&gt; elements named <b>canvasInput</b> and <b>canvasOutput</b> have been prepared.<br>
Click <b>Try it</b> button to see the result. You can choose another image.<br>
You can change the code in the &lt;textarea&gt; to investigate more.
</p>
<div>
<div class="control"><button id="tryIt" disabled>Try it</button></div>
<textarea class="code" rows="9" cols="100" id="codeEditor" spellcheck="false">
</textarea>
<p class="err" id="errorMessage"></p>
</div>
<div>
<table cellpadding="0" cellspacing="0" width="0" border="0">
<tr>
<td>
<canvas id="canvasInput"></canvas>
</td>
<td>
<canvas id="canvasOutput"></canvas>
</td>
</tr>
<tr>
<td>
<div class="caption">canvasInput <input type="file" id="fileInput" name="file" accept="image/*" /></div>
</td>
<td>
<div class="caption">canvasOutput</div>
</td>
</tr>
</table>
</div>
<script src="utils.js" type="text/javascript"></script>
<script id="codeSnippet" type="text/code-snippet">
let src = cv.imread('canvasInput');
let gray = new cv.Mat();
cv.cvtColor(src, gray, cv.COLOR_RGBA2GRAY, 0);
let faces = new cv.RectVector();
let eyes = new cv.RectVector();
let faceCascade = new cv.CascadeClassifier();
let eyeCascade = new cv.CascadeClassifier();
// load pre-trained classifiers
faceCascade.load('haarcascade_frontalface_default.xml');
eyeCascade.load('haarcascade_eye.xml');
// detect faces
let msize = new cv.Size(0, 0);
faceCascade.detectMultiScale(gray, faces, 1.1, 3, 0, msize, msize);
for (let i = 0; i < faces.size(); ++i) {
let roiGray = gray.roi(faces.get(i));
let roiSrc = src.roi(faces.get(i));
let point1 = new cv.Point(faces.get(i).x, faces.get(i).y);
let point2 = new cv.Point(faces.get(i).x + faces.get(i).width,
faces.get(i).y + faces.get(i).height);
cv.rectangle(src, point1, point2, [255, 0, 0, 255]);
// detect eyes in face ROI
eyeCascade.detectMultiScale(roiGray, eyes);
for (let j = 0; j < eyes.size(); ++j) {
let point1 = new cv.Point(eyes.get(j).x, eyes.get(j).y);
let point2 = new cv.Point(eyes.get(j).x + eyes.get(j).width,
eyes.get(j).y + eyes.get(j).height);
cv.rectangle(roiSrc, point1, point2, [0, 0, 255, 255]);
}
roiGray.delete(); roiSrc.delete();
}
cv.imshow('canvasOutput', src);
src.delete(); gray.delete(); faceCascade.delete();
eyeCascade.delete(); faces.delete(); eyes.delete();
</script>
<script type="text/javascript">
let utils = new Utils('errorMessage');
utils.loadCode('codeSnippet', 'codeEditor');
utils.loadImageToCanvas('lena.jpg', 'canvasInput');
utils.addFileInputHandler('fileInput', 'canvasInput');
let tryIt = document.getElementById('tryIt');
tryIt.addEventListener('click', () => {
utils.executeCode('codeEditor');
});
utils.loadOpenCv(() => {
let eyeCascadeFile = 'haarcascade_eye.xml';
utils.createFileFromUrl(eyeCascadeFile, eyeCascadeFile, () => {
let faceCascadeFile = 'haarcascade_frontalface_default.xml';
utils.createFileFromUrl(faceCascadeFile, faceCascadeFile, () => {
tryIt.removeAttribute('disabled');
});
});
});
</script>
</body>
</html>

142
modules/xobjdetect/tutorials/js_tutorials/js_assets/js_face_detection_camera.html
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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 &lt;video&gt; element used as face detector input.
The <b>canvasOutput</b> is a &lt;canvas&gt; element used as face detector output.<br>
The code of &lt;textarea&gt; 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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