Thanks to visit codestin.com
Credit goes to github.com

Skip to content

Replace opencv_face_detector with YuNet in js_face_recognition sample #25468

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Apr 22, 2024
Merged

Replace opencv_face_detector with YuNet in js_face_recognition sample #25468

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
78 changes: 51 additions & 27 deletions samples/dnn/js_face_recognition.html
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,27 +12,40 @@

//! [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;
};
Expand All @@ -53,7 +66,7 @@
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);
Expand All @@ -69,24 +82,25 @@

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");
Expand Down Expand Up @@ -146,6 +160,16 @@
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);
Expand Down