mirror of
https://github.com/opencv/opencv.git
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206 lines
6.8 KiB
HTML
206 lines
6.8 KiB
HTML
<!DOCTYPE html>
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<html>
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<head>
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<script async src="../../opencv.js" type="text/javascript"></script>
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<script src="../../utils.js" type="text/javascript"></script>
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<script type='text/javascript'>
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var netDet = undefined, netRecogn = undefined;
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var persons = {};
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//! [Run face detection model]
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function detectFaces(img) {
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var blob = cv.blobFromImage(img, 1, {width: 128, height: 96}, [104, 177, 123, 0], false, false);
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netDet.setInput(blob);
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var out = netDet.forward();
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var faces = [];
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for (var i = 0, n = out.data32F.length; i < n; i += 7) {
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var confidence = out.data32F[i + 2];
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var left = out.data32F[i + 3] * img.cols;
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var top = out.data32F[i + 4] * img.rows;
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var right = out.data32F[i + 5] * img.cols;
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var bottom = out.data32F[i + 6] * img.rows;
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left = Math.min(Math.max(0, left), img.cols - 1);
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right = Math.min(Math.max(0, right), img.cols - 1);
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bottom = Math.min(Math.max(0, bottom), img.rows - 1);
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top = Math.min(Math.max(0, top), img.rows - 1);
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if (confidence > 0.5 && left < right && top < bottom) {
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faces.push({x: left, y: top, width: right - left, height: bottom - top})
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}
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}
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blob.delete();
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out.delete();
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return faces;
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};
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//! [Run face detection model]
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//! [Get 128 floating points feature vector]
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function face2vec(face) {
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var blob = cv.blobFromImage(face, 1.0 / 255, {width: 96, height: 96}, [0, 0, 0, 0], true, false)
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netRecogn.setInput(blob);
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var vec = netRecogn.forward();
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blob.delete();
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return vec;
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};
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//! [Get 128 floating points feature vector]
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//! [Recognize]
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function recognize(face) {
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var vec = face2vec(face);
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var bestMatchName = 'unknown';
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var bestMatchScore = 0.5; // Actually, the minimum is -1 but we use it as a threshold.
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for (name in persons) {
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var personVec = persons[name];
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var score = vec.dot(personVec);
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if (score > bestMatchScore) {
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bestMatchScore = score;
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bestMatchName = name;
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}
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}
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vec.delete();
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return bestMatchName;
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};
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//! [Recognize]
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function loadModels(callback) {
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var utils = new Utils('');
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var proto = 'https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt';
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var weights = 'https://github.com/opencv/opencv_3rdparty/raw/19512576c112aa2c7b6328cb0e8d589a4a90a26d/res10_300x300_ssd_iter_140000_fp16.caffemodel';
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var recognModel = 'https://raw.githubusercontent.com/pyannote/pyannote-data/master/openface.nn4.small2.v1.t7';
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utils.createFileFromUrl('face_detector.prototxt', proto, () => {
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document.getElementById('status').innerHTML = 'Downloading face_detector.caffemodel';
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utils.createFileFromUrl('face_detector.caffemodel', weights, () => {
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document.getElementById('status').innerHTML = 'Downloading OpenFace model';
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utils.createFileFromUrl('face_recognition.t7', recognModel, () => {
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document.getElementById('status').innerHTML = '';
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netDet = cv.readNetFromCaffe('face_detector.prototxt', 'face_detector.caffemodel');
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netRecogn = cv.readNetFromTorch('face_recognition.t7');
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callback();
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});
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});
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});
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};
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function main() {
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// Create a camera object.
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var output = document.getElementById('output');
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var camera = document.createElement("video");
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camera.setAttribute("width", output.width);
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camera.setAttribute("height", output.height);
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// Get a permission from user to use a camera.
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navigator.mediaDevices.getUserMedia({video: true, audio: false})
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.then(function(stream) {
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camera.srcObject = stream;
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camera.onloadedmetadata = function(e) {
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camera.play();
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};
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});
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//! [Open a camera stream]
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var cap = new cv.VideoCapture(camera);
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var frame = new cv.Mat(camera.height, camera.width, cv.CV_8UC4);
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var frameBGR = new cv.Mat(camera.height, camera.width, cv.CV_8UC3);
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//! [Open a camera stream]
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//! [Add a person]
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document.getElementById('addPersonButton').onclick = function() {
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var rects = detectFaces(frameBGR);
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if (rects.length > 0) {
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var face = frameBGR.roi(rects[0]);
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var name = prompt('Say your name:');
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var cell = document.getElementById("targetNames").insertCell(0);
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cell.innerHTML = name;
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persons[name] = face2vec(face).clone();
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var canvas = document.createElement("canvas");
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canvas.setAttribute("width", 96);
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canvas.setAttribute("height", 96);
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var cell = document.getElementById("targetImgs").insertCell(0);
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cell.appendChild(canvas);
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var faceResized = new cv.Mat(canvas.height, canvas.width, cv.CV_8UC3);
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cv.resize(face, faceResized, {width: canvas.width, height: canvas.height});
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cv.cvtColor(faceResized, faceResized, cv.COLOR_BGR2RGB);
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cv.imshow(canvas, faceResized);
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faceResized.delete();
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}
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};
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//! [Add a person]
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//! [Define frames processing]
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var isRunning = false;
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const FPS = 30; // Target number of frames processed per second.
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function captureFrame() {
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var begin = Date.now();
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cap.read(frame); // Read a frame from camera
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cv.cvtColor(frame, frameBGR, cv.COLOR_RGBA2BGR);
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var faces = detectFaces(frameBGR);
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faces.forEach(function(rect) {
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cv.rectangle(frame, {x: rect.x, y: rect.y}, {x: rect.x + rect.width, y: rect.y + rect.height}, [0, 255, 0, 255]);
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var face = frameBGR.roi(rect);
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var name = recognize(face);
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cv.putText(frame, name, {x: rect.x, y: rect.y}, cv.FONT_HERSHEY_SIMPLEX, 1.0, [0, 255, 0, 255]);
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});
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cv.imshow(output, frame);
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// Loop this function.
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if (isRunning) {
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var delay = 1000 / FPS - (Date.now() - begin);
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setTimeout(captureFrame, delay);
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}
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};
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//! [Define frames processing]
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document.getElementById('startStopButton').onclick = function toggle() {
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if (isRunning) {
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isRunning = false;
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document.getElementById('startStopButton').innerHTML = 'Start';
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document.getElementById('addPersonButton').disabled = true;
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} else {
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function run() {
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isRunning = true;
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captureFrame();
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document.getElementById('startStopButton').innerHTML = 'Stop';
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document.getElementById('startStopButton').disabled = false;
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document.getElementById('addPersonButton').disabled = false;
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}
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if (netDet == undefined || netRecogn == undefined) {
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document.getElementById('startStopButton').disabled = true;
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loadModels(run); // Load models and run a pipeline;
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} else {
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run();
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}
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}
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};
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document.getElementById('startStopButton').disabled = false;
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};
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</script>
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</head>
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<body onload="main()">
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<button id="startStopButton" type="button" disabled="true">Start</button>
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<div id="status"></div>
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<canvas id="output" width=640 height=480 style="max-width: 100%"></canvas>
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<table>
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<tr id="targetImgs"></tr>
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<tr id="targetNames"></tr>
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</table>
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<button id="addPersonButton" type="button" disabled="true">Add a person</button>
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</body>
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</html>
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