Symisc Systems
6 years ago
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GitHub
parent
4a14b23692
commit
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<!DOCTYPE html>
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<head>
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<meta charset="utf-8">
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<meta http-equiv="X-UA-Compatible" content="IE=edge">
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<title>WebAssembly Real-Time Face Detection</title>
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<meta name="viewport" content="width=device-width, initial-scale=1">
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</head>
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<style>
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body {
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font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Roboto", "Oxygen", "Ubuntu", "Cantarell", "Fira Sans", "Droid Sans", "Helvetica Neue", sans-serif;
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}
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</style>
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<body>
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<div align="center">
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<h1>WebAssembly Real-Time Face Detection <small>via <a href="https://sod.pixlab.io" target="_blank">SOD RealNets</a></small></h1>
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<button onclick="face_detect()">Start Face Detection</button><hr/>
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<canvas id="face-canvas" style="border: 1px solid black;"></canvas>
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<video id="face-video" autoplay playsinline hidden="true"></video>
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</div>
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<!-- This will load the RealNet WebAssembly face model and run its main.
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The model must be downloaded from https://pixlab.io/downloads
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Once downloaded, just put it on the directory where this HTML
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file `usage.html` reside.
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For chrome users, you must test the model on actual Web server,
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whether served locally (i.e http://127.0.0.1) or remotely.
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This is due to the fact that chrome does not allow WebAssembly
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modules to be loaded directly from the file system (
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Edge and Firefox does not have such issue).
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-->
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<script src="facemodel.js"></script>
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<script>
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function face_detect(){
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var grayBuf = null; /* Working buffer holding the grayscale conversion of the current video frame */
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var recs = null; /* Each detected face coordinates (top, left, width & height) is recorded in this array */
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let vidSz = {width: 640, height: 480 , facingMode: "user" };
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if (document.body.clientWidth < 600 ){
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vidSz = true;
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}
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navigator.mediaDevices.getUserMedia({
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audio: false,
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video: vidSz
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}).then(function(mediaStream) {
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/* Code taken from https://faceio.net JavaScript Widget */
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let video = document.getElementById('face-video');
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let vidCanvas = document.getElementById('face-canvas');
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let vidctx = vidCanvas.getContext('2d');
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video.srcObject = mediaStream;
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video.onloadedmetadata = function(e) {
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vidCanvas.width = video.videoWidth;
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vidCanvas.height = video.videoHeight;
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video.play();
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}
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video.addEventListener('play', function () {
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let fps = 1000 / 25; /* Render at 25 fps. Feel free to increase to 30 if you are on a PC/Mac or Modern iPhone */
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let loop = function() {
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if (!video.paused && !video.ended) {
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vidctx.drawImage(video, 0, 0);
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if( grayBuf === null ){
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grayBuf = _realnet_alloc_gray_image_buffer(640, 480);
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recs = new Float32Array(Module.HEAPU8.buffer, _realnet_alloc_face_result_array(), _realnet_face_max_detection());
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}else{
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let imgData = vidctx.getImageData(0,0, vidCanvas.width, vidCanvas.height);
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let gray = new Uint8ClampedArray( Module.HEAPU8.buffer, grayBuf, imgData.width * imgData.height);
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for (let i = 0; i < imgData.data.length ; i += 4) {
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gray[i >> 2] = (imgData.data[i] * 306 + imgData.data[i + 1] * 601 + imgData.data[i + 2] * 117) >> 10;
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}
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let faces = _realnet_face_detect(grayBuf, imgData.width, imgData.height, 11.0, recs.byteOffset);
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/* faces now hold the total number of detected faces and the recs[] array hold their coordinates */
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for(let i = 0; i < faces; i += 5 ){
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/* Draw a rectangle on each detected face */
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vidctx.beginPath();
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vidctx.strokeStyle = "#32cd32";
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vidctx.strokeRect(recs[i + 0],recs[i + 1],recs[i + 2],recs[i + 3]);
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/* recs[i + 4] hold the score (i.e. threshold) of the current face */
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console.log("Face_" + i + ": Score: " + recs[i + 4]);
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}
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}
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setTimeout(loop, fps);
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}else{
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console.log("Video stream stopped");
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}
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}
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/* Start painting on the canvas */
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loop();
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});
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}).catch(function(err){
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console.log("Access not granted", err);
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});
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}
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</script>
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</body>
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</html>
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