-
-
Notifications
You must be signed in to change notification settings - Fork 56k
/
Copy pathjs_face_recognition.html
229 lines (201 loc) · 8.31 KB
/
js_face_recognition.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
<!DOCTYPE html>
<html>
<head>
<script async src="../../opencv.js" type="text/javascript"></script>
<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) {
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 += 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);
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]
})
}
}
out.delete();
return faces;
};
//! [Run face detection model]
//! [Get 128 floating points feature vector]
function face2vec(face) {
var blob = cv.blobFromImage(face, 1.0, {width: 112, height: 112}, [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 = 30; // Threshold for face recognition.
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) {
var utils = new Utils('');
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';
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");
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", 112);
canvas.setAttribute("height", 112);
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]);
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);
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>
<body onload="cv['onRuntimeInitialized']=()=>{ main() }">
<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>