mirror of
https://github.com/opencv/opencv.git
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230 lines
8.3 KiB
HTML
230 lines
8.3 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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netDet.setInputSize(new cv.Size(img.cols, img.rows));
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var out = new cv.Mat();
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netDet.detect(img, out);
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var faces = [];
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for (var i = 0, n = out.data32F.length; i < n; i += 15) {
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var left = out.data32F[i];
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var top = out.data32F[i + 1];
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var right = (out.data32F[i] + out.data32F[i + 2]);
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var bottom = (out.data32F[i + 1] + out.data32F[i + 3]);
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left = Math.min(Math.max(0, left), img.cols - 1);
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top = Math.min(Math.max(0, top), img.rows - 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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if (left < right && top < bottom) {
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faces.push({
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x: left,
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y: top,
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width: right - left,
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height: bottom - top,
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x1: out.data32F[i + 4] < 0 || out.data32F[i + 4] > img.cols - 1 ? -1 : out.data32F[i + 4],
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y1: out.data32F[i + 5] < 0 || out.data32F[i + 5] > img.rows - 1 ? -1 : out.data32F[i + 5],
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x2: out.data32F[i + 6] < 0 || out.data32F[i + 6] > img.cols - 1 ? -1 : out.data32F[i + 6],
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y2: out.data32F[i + 7] < 0 || out.data32F[i + 7] > img.rows - 1 ? -1 : out.data32F[i + 7],
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x3: out.data32F[i + 8] < 0 || out.data32F[i + 8] > img.cols - 1 ? -1 : out.data32F[i + 8],
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y3: out.data32F[i + 9] < 0 || out.data32F[i + 9] > img.rows - 1 ? -1 : out.data32F[i + 9],
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x4: out.data32F[i + 10] < 0 || out.data32F[i + 10] > img.cols - 1 ? -1 : out.data32F[i + 10],
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y4: out.data32F[i + 11] < 0 || out.data32F[i + 11] > img.rows - 1 ? -1 : out.data32F[i + 11],
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x5: out.data32F[i + 12] < 0 || out.data32F[i + 12] > img.cols - 1 ? -1 : out.data32F[i + 12],
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y5: out.data32F[i + 13] < 0 || out.data32F[i + 13] > img.rows - 1 ? -1 : out.data32F[i + 13],
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confidence: out.data32F[i + 14]
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})
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}
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}
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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, {width: 112, height: 112}, [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 = 30; // Threshold for face recognition.
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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 detectModel = 'https://media.githubusercontent.com/media/opencv/opencv_zoo/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx';
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var recognModel = 'https://media.githubusercontent.com/media/opencv/opencv_zoo/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx';
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document.getElementById('status').innerHTML = 'Downloading YuNet model';
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utils.createFileFromUrl('face_detection_yunet_2023mar.onnx', detectModel, () => {
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document.getElementById('status').innerHTML = 'Downloading OpenFace model';
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utils.createFileFromUrl('face_recognition_sface_2021dec.onnx', recognModel, () => {
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document.getElementById('status').innerHTML = '';
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netDet = new cv.FaceDetectorYN("face_detection_yunet_2023mar.onnx", "", new cv.Size(320, 320), 0.9, 0.3, 5000);
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netRecogn = cv.readNet('face_recognition_sface_2021dec.onnx');
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callback();
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});
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});
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};
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function main() {
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if(!cv.FaceDetectorYN){
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alert(`Error: This sample require OpenCV.js built with FaceDetectorYN. Please rebuild it with FaceDetectorYN or use the latest version of OpenCV.js.`);
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return;
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}
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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", 112);
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canvas.setAttribute("height", 112);
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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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if(rect.x1>0 && rect.y1>0)
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cv.circle(frame, {x: rect.x1, y: rect.y1}, 2, [255, 0, 0, 255], 2)
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if(rect.x2>0 && rect.y2>0)
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cv.circle(frame, {x: rect.x2, y: rect.y2}, 2, [0, 0, 255, 255], 2)
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if(rect.x3>0 && rect.y3>0)
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cv.circle(frame, {x: rect.x3, y: rect.y3}, 2, [0, 255, 0, 255], 2)
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if(rect.x4>0 && rect.y4>0)
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cv.circle(frame, {x: rect.x4, y: rect.y4}, 2, [255, 0, 255, 255], 2)
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if(rect.x5>0 && rect.y5>0)
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cv.circle(frame, {x: rect.x5, y: rect.y5}, 2, [0, 255, 255, 255], 2)
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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="cv['onRuntimeInitialized']=()=>{ 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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