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<!DOCTYPE html>
< html >
< 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 >
< script type = 'text/javascript' >
var netDet = undefined, netRecogn = undefined;
var persons = {};
//! [Run face detection model]
function detectFaces(img) {
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var blob = cv.blobFromImage(img, 1, {width: 192, height: 144}, [104, 117, 123, 0], false, false);
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netDet.setInput(blob);
var out = netDet.forward();
var faces = [];
for (var i = 0, n = out.data32F.length; i < n ; i + = 7 ) {
var confidence = out.data32F[i + 2];
var left = out.data32F[i + 3] * img.cols;
var top = out.data32F[i + 4] * img.rows;
var right = out.data32F[i + 5] * img.cols;
var bottom = out.data32F[i + 6] * img.rows;
left = Math.min(Math.max(0, left), img.cols - 1);
right = Math.min(Math.max(0, right), img.cols - 1);
bottom = Math.min(Math.max(0, bottom), img.rows - 1);
top = Math.min(Math.max(0, top), img.rows - 1);
if (confidence > 0.5 & & left < right & & top < bottom ) {
faces.push({x: left, y: top, width: right - left, height: bottom - top})
}
}
blob.delete();
out.delete();
return faces;
};
//! [Run face detection model]
//! [Get 128 floating points feature vector]
function face2vec(face) {
var blob = cv.blobFromImage(face, 1.0 / 255, {width: 96, height: 96}, [0, 0, 0, 0], true, false)
netRecogn.setInput(blob);
var vec = netRecogn.forward();
blob.delete();
return vec;
};
//! [Get 128 floating points feature vector]
//! [Recognize]
function recognize(face) {
var vec = face2vec(face);
var bestMatchName = 'unknown';
var bestMatchScore = 0.5; // Actually, the minimum is -1 but we use it as a threshold.
for (name in persons) {
var personVec = persons[name];
var score = vec.dot(personVec);
if (score > bestMatchScore) {
bestMatchScore = score;
bestMatchName = name;
}
}
vec.delete();
return bestMatchName;
};
//! [Recognize]
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_lowres.prototxt';
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var weights = 'https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/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';
utils.createFileFromUrl('face_detector.prototxt', proto, () => {
document.getElementById('status').innerHTML = 'Downloading face_detector.caffemodel';
utils.createFileFromUrl('face_detector.caffemodel', weights, () => {
document.getElementById('status').innerHTML = 'Downloading OpenFace model';
utils.createFileFromUrl('face_recognition.t7', recognModel, () => {
document.getElementById('status').innerHTML = '';
netDet = cv.readNetFromCaffe('face_detector.prototxt', 'face_detector.caffemodel');
netRecogn = cv.readNetFromTorch('face_recognition.t7');
callback();
});
});
});
};
function main() {
// Create a camera object.
var output = document.getElementById('output');
var camera = document.createElement("video");
camera.setAttribute("width", output.width);
camera.setAttribute("height", output.height);
// Get a permission from user to use a camera.
navigator.mediaDevices.getUserMedia({video: true, audio: false})
.then(function(stream) {
camera.srcObject = stream;
camera.onloadedmetadata = function(e) {
camera.play();
};
});
//! [Open a camera stream]
var cap = new cv.VideoCapture(camera);
var frame = new cv.Mat(camera.height, camera.width, cv.CV_8UC4);
var frameBGR = new cv.Mat(camera.height, camera.width, cv.CV_8UC3);
//! [Open a camera stream]
//! [Add a person]
document.getElementById('addPersonButton').onclick = function() {
var rects = detectFaces(frameBGR);
if (rects.length > 0) {
var face = frameBGR.roi(rects[0]);
var name = prompt('Say your name:');
var cell = document.getElementById("targetNames").insertCell(0);
cell.innerHTML = name;
persons[name] = face2vec(face).clone();
var canvas = document.createElement("canvas");
canvas.setAttribute("width", 96);
canvas.setAttribute("height", 96);
var cell = document.getElementById("targetImgs").insertCell(0);
cell.appendChild(canvas);
var faceResized = new cv.Mat(canvas.height, canvas.width, cv.CV_8UC3);
cv.resize(face, faceResized, {width: canvas.width, height: canvas.height});
cv.cvtColor(faceResized, faceResized, cv.COLOR_BGR2RGB);
cv.imshow(canvas, faceResized);
faceResized.delete();
}
};
//! [Add a person]
//! [Define frames processing]
var isRunning = false;
const FPS = 30; // Target number of frames processed per second.
function captureFrame() {
var begin = Date.now();
cap.read(frame); // Read a frame from camera
cv.cvtColor(frame, frameBGR, cv.COLOR_RGBA2BGR);
var faces = detectFaces(frameBGR);
faces.forEach(function(rect) {
cv.rectangle(frame, {x: rect.x, y: rect.y}, {x: rect.x + rect.width, y: rect.y + rect.height}, [0, 255, 0, 255]);
var face = frameBGR.roi(rect);
var name = recognize(face);
cv.putText(frame, name, {x: rect.x, y: rect.y}, cv.FONT_HERSHEY_SIMPLEX, 1.0, [0, 255, 0, 255]);
});
cv.imshow(output, frame);
// Loop this function.
if (isRunning) {
var delay = 1000 / FPS - (Date.now() - begin);
setTimeout(captureFrame, delay);
}
};
//! [Define frames processing]
document.getElementById('startStopButton').onclick = function toggle() {
if (isRunning) {
isRunning = false;
document.getElementById('startStopButton').innerHTML = 'Start';
document.getElementById('addPersonButton').disabled = true;
} else {
function run() {
isRunning = true;
captureFrame();
document.getElementById('startStopButton').innerHTML = 'Stop';
document.getElementById('startStopButton').disabled = false;
document.getElementById('addPersonButton').disabled = false;
}
if (netDet == undefined || netRecogn == undefined) {
document.getElementById('startStopButton').disabled = true;
loadModels(run); // Load models and run a pipeline;
} else {
run();
}
}
};
document.getElementById('startStopButton').disabled = false;
};
< / script >
< / head >
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< body onload = "cv['onRuntimeInitialized']=()=>{ main() }" >
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< button id = "startStopButton" type = "button" disabled = "true" > Start< / button >
< div id = "status" > < / div >
< canvas id = "output" width = 640 height = 480 style = "max-width: 100%" > < / canvas >
< table >
< tr id = "targetImgs" > < / tr >
< tr id = "targetNames" > < / tr >
< / table >
< button id = "addPersonButton" type = "button" disabled = "true" > Add a person< / button >
< / body >
< / html >
