diff --git a/samples/dnn/js_face_recognition.html b/samples/dnn/js_face_recognition.html
index 5893a5cf13..3b4ed390d7 100644
--- a/samples/dnn/js_face_recognition.html
+++ b/samples/dnn/js_face_recognition.html
@@ -12,27 +12,40 @@ var persons = {};
//! [Run face detection model]
function detectFaces(img) {
- var blob = cv.blobFromImage(img, 1, {width: 192, height: 144}, [104, 117, 123, 0], false, false);
- netDet.setInput(blob);
- var out = netDet.forward();
-
+ netDet.setInputSize(new cv.Size(img.cols, img.rows));
+ var out = new cv.Mat();
+ netDet.detect(img, out);
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;
+ for (var i = 0, n = out.data32F.length; i < n; i += 15) {
+ var left = out.data32F[i];
+ var top = out.data32F[i + 1];
+ var right = (out.data32F[i] + out.data32F[i + 2]);
+ var bottom = (out.data32F[i + 1] + out.data32F[i + 3]);
left = Math.min(Math.max(0, left), img.cols - 1);
+ top = Math.min(Math.max(0, top), img.rows - 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})
+ if (left < right && top < bottom) {
+ faces.push({
+ x: left,
+ y: top,
+ width: right - left,
+ height: bottom - top,
+ x1: out.data32F[i + 4] < 0 || out.data32F[i + 4] > img.cols - 1 ? -1 : out.data32F[i + 4],
+ y1: out.data32F[i + 5] < 0 || out.data32F[i + 5] > img.rows - 1 ? -1 : out.data32F[i + 5],
+ x2: out.data32F[i + 6] < 0 || out.data32F[i + 6] > img.cols - 1 ? -1 : out.data32F[i + 6],
+ y2: out.data32F[i + 7] < 0 || out.data32F[i + 7] > img.rows - 1 ? -1 : out.data32F[i + 7],
+ x3: out.data32F[i + 8] < 0 || out.data32F[i + 8] > img.cols - 1 ? -1 : out.data32F[i + 8],
+ y3: out.data32F[i + 9] < 0 || out.data32F[i + 9] > img.rows - 1 ? -1 : out.data32F[i + 9],
+ x4: out.data32F[i + 10] < 0 || out.data32F[i + 10] > img.cols - 1 ? -1 : out.data32F[i + 10],
+ y4: out.data32F[i + 11] < 0 || out.data32F[i + 11] > img.rows - 1 ? -1 : out.data32F[i + 11],
+ x5: out.data32F[i + 12] < 0 || out.data32F[i + 12] > img.cols - 1 ? -1 : out.data32F[i + 12],
+ y5: out.data32F[i + 13] < 0 || out.data32F[i + 13] > img.rows - 1 ? -1 : out.data32F[i + 13],
+ confidence: out.data32F[i + 14]
+ })
}
}
- blob.delete();
out.delete();
return faces;
};
@@ -53,7 +66,7 @@ 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.
+ var bestMatchScore = 30; // Threshold for face recognition.
for (name in persons) {
var personVec = persons[name];
var score = vec.dot(personVec);
@@ -69,24 +82,25 @@ function recognize(face) {
function loadModels(callback) {
var utils = new Utils('');
- var proto = 'https://raw.githubusercontent.com/opencv/opencv/4.x/samples/dnn/face_detector/deploy_lowres.prototxt';
- var weights = 'https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel';
+ var detectModel = 'https://media.githubusercontent.com/media/opencv/opencv_zoo/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx';
var recognModel = 'https://media.githubusercontent.com/media/opencv/opencv_zoo/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx';
- 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_sface_2021dec.onnx', recognModel, () => {
- document.getElementById('status').innerHTML = '';
- netDet = cv.readNetFromCaffe('face_detector.prototxt', 'face_detector.caffemodel');
- netRecogn = cv.readNet('face_recognition_sface_2021dec.onnx');
- callback();
- });
+ document.getElementById('status').innerHTML = 'Downloading YuNet model';
+ utils.createFileFromUrl('face_detection_yunet_2023mar.onnx', detectModel, () => {
+ document.getElementById('status').innerHTML = 'Downloading OpenFace model';
+ utils.createFileFromUrl('face_recognition_sface_2021dec.onnx', recognModel, () => {
+ document.getElementById('status').innerHTML = '';
+ netDet = new cv.FaceDetectorYN("face_detection_yunet_2023mar.onnx", "", new cv.Size(320, 320), 0.9, 0.3, 5000);
+ netRecogn = cv.readNet('face_recognition_sface_2021dec.onnx');
+ callback();
});
});
};
function main() {
+ if(!cv.FaceDetectorYN){
+ alert(`Error: This sample require OpenCV.js built with FaceDetectorYN. Please rebuild it with FaceDetectorYN or use the latest version of OpenCV.js.`);
+ return;
+ }
// Create a camera object.
var output = document.getElementById('output');
var camera = document.createElement("video");
@@ -146,6 +160,16 @@ function main() {
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]);
+ if(rect.x1>0 && rect.y1>0)
+ cv.circle(frame, {x: rect.x1, y: rect.y1}, 2, [255, 0, 0, 255], 2)
+ if(rect.x2>0 && rect.y2>0)
+ cv.circle(frame, {x: rect.x2, y: rect.y2}, 2, [0, 0, 255, 255], 2)
+ if(rect.x3>0 && rect.y3>0)
+ cv.circle(frame, {x: rect.x3, y: rect.y3}, 2, [0, 255, 0, 255], 2)
+ if(rect.x4>0 && rect.y4>0)
+ cv.circle(frame, {x: rect.x4, y: rect.y4}, 2, [255, 0, 255, 255], 2)
+ if(rect.x5>0 && rect.y5>0)
+ cv.circle(frame, {x: rect.x5, y: rect.y5}, 2, [0, 255, 255, 255], 2)
var face = frameBGR.roi(rect);
var name = recognize(face);