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human
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updated emotion models

pull/50/head
Vladimir Mandic 2020-11-05 15:38:09 -05:00
parent 8ae7b8b47b
commit 09756545cc
29 changed files with 5376 additions and 5203 deletions
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14
config.js
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@ -56,7 +56,7 @@ export default {
skipFrames: 10, // how many frames to go without re-running the face bounding box detector, only used for video inputs skipFrames: 10, // how many frames to go without re-running the face bounding box detector, only used for video inputs
// if model is running st 25 FPS, we can re-use existing bounding box for updated face mesh analysis // if model is running st 25 FPS, we can re-use existing bounding box for updated face mesh analysis
// as face probably hasn't moved much in short time (10 * 1/25 = 0.25 sec) // as face probably hasn't moved much in short time (10 * 1/25 = 0.25 sec)
minConfidence: 0.5, // threshold for discarding a prediction minConfidence: 0.3, // threshold for discarding a prediction
iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much in non-maximum suppression iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much in non-maximum suppression
scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score in non-maximum suppression scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score in non-maximum suppression
}, },
@ -74,21 +74,21 @@ export default {
age: { age: {
enabled: true, enabled: true,
modelPath: '../models/ssrnet-age-imdb.json', // can be 'imdb' or 'wiki' modelPath: '../models/ssrnet-age-imdb.json', // can be 'imdb' or 'wiki'
// which determines training set for model // which determines training set for model
inputSize: 64, // fixed value inputSize: 64, // fixed value
skipFrames: 10, // how many frames to go without re-running the detector, only used for video inputs skipFrames: 10, // how many frames to go without re-running the detector, only used for video inputs
}, },
gender: { gender: {
enabled: true, enabled: true,
minConfidence: 0.8, // threshold for discarding a prediction minConfidence: 0.3, // threshold for discarding a prediction
modelPath: '../models/ssrnet-gender-imdb.json', modelPath: '../models/ssrnet-gender-imdb.json',
}, },
emotion: { emotion: {
enabled: true, enabled: true,
inputSize: 64, // fixed value inputSize: 64, // fixed value, 64 for 'mini' and 'lage', 48 for 'cnn'
minConfidence: 0.5, // threshold for discarding a prediction minConfidence: 0.3, // threshold for discarding a prediction
skipFrames: 10, // how many frames to go without re-running the detector skipFrames: 10, // how many frames to go without re-running the detector
modelPath: '../models/emotion.json', modelPath: '../models/emotion-large.json', // can be 'mini', 'large' or 'cnn'
}, },
}, },
body: { body: {
@ -106,7 +106,7 @@ export default {
skipFrames: 10, // how many frames to go without re-running the hand bounding box detector, only used for video inputs skipFrames: 10, // how many frames to go without re-running the hand bounding box detector, only used for video inputs
// if model is running st 25 FPS, we can re-use existing bounding box for updated hand skeleton analysis // if model is running st 25 FPS, we can re-use existing bounding box for updated hand skeleton analysis
// as the hand probably hasn't moved much in short time (10 * 1/25 = 0.25 sec) // as the hand probably hasn't moved much in short time (10 * 1/25 = 0.25 sec)
minConfidence: 0.5, // threshold for discarding a prediction minConfidence: 0.3, // threshold for discarding a prediction
iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much in non-maximum suppression iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much in non-maximum suppression
scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score in non-maximum suppression scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score in non-maximum suppression
enlargeFactor: 1.65, // empiric tuning as skeleton prediction prefers hand box with some whitespace enlargeFactor: 1.65, // empiric tuning as skeleton prediction prefers hand box with some whitespace

70
demo/browser.js
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@ -10,7 +10,7 @@ const ui = {
baseBackground: 'rgba(50, 50, 50, 1)', // 'grey' baseBackground: 'rgba(50, 50, 50, 1)', // 'grey'
baseLabel: 'rgba(173, 216, 230, 0.9)', // 'lightblue' with dark alpha channel baseLabel: 'rgba(173, 216, 230, 0.9)', // 'lightblue' with dark alpha channel
baseFontProto: 'small-caps {size} "Segoe UI"', baseFontProto: 'small-caps {size} "Segoe UI"',
baseLineWidth: 16, baseLineWidth: 12,
baseLineHeightProto: 2, baseLineHeightProto: 2,
columns: 2, columns: 2,
busy: false, busy: false,
@ -55,15 +55,15 @@ const config = {
videoOptimized: true, videoOptimized: true,
face: { face: {
enabled: true, enabled: true,
detector: { maxFaces: 10, skipFrames: 10, minConfidence: 0.5, iouThreshold: 0.3, scoreThreshold: 0.5 }, detector: { maxFaces: 10, skipFrames: 10, minConfidence: 0.3, iouThreshold: 0.3, scoreThreshold: 0.5 },
mesh: { enabled: true }, mesh: { enabled: true },
iris: { enabled: true }, iris: { enabled: true },
age: { enabled: true, skipFrames: 10 }, age: { enabled: true, skipFrames: 10 },
gender: { enabled: true }, gender: { enabled: true },
emotion: { enabled: true, minConfidence: 0.5, useGrayscale: true }, emotion: { enabled: true, minConfidence: 0.3, useGrayscale: true },
}, },
body: { enabled: true, maxDetections: 10, scoreThreshold: 0.5, nmsRadius: 20 }, body: { enabled: true, maxDetections: 10, scoreThreshold: 0.5, nmsRadius: 20 },
hand: { enabled: true, skipFrames: 10, minConfidence: 0.5, iouThreshold: 0.3, scoreThreshold: 0.5 }, hand: { enabled: true, skipFrames: 10, minConfidence: 0.3, iouThreshold: 0.3, scoreThreshold: 0.5 },
gesture: { enabled: true }, gesture: { enabled: true },
}; };
@ -149,7 +149,7 @@ async function setupCamera() {
const output = document.getElementById('log'); const output = document.getElementById('log');
const live = video.srcObject ? ((video.srcObject.getVideoTracks()[0].readyState === 'live') && (video.readyState > 2) && (!video.paused)) : false; const live = video.srcObject ? ((video.srcObject.getVideoTracks()[0].readyState === 'live') && (video.readyState > 2) && (!video.paused)) : false;
let msg = ''; let msg = '';
status('starting camera'); status('setting up camera');
// setup webcam. note that navigator.mediaDevices requires that page is accessed via https // setup webcam. note that navigator.mediaDevices requires that page is accessed via https
if (!navigator.mediaDevices) { if (!navigator.mediaDevices) {
msg = 'camera access not supported'; msg = 'camera access not supported';
@ -179,9 +179,7 @@ async function setupCamera() {
else return null; else return null;
const track = stream.getVideoTracks()[0]; const track = stream.getVideoTracks()[0];
const settings = track.getSettings(); const settings = track.getSettings();
log('camera constraints:', constraints, 'window:', { width: window.innerWidth, height: window.innerHeight }); log('camera constraints:', constraints, 'window:', { width: window.innerWidth, height: window.innerHeight }, 'settings:', settings, 'track:', track);
log('camera settings:', settings);
log('camera track:', track);
camera = { name: track.label, width: settings.width, height: settings.height, facing: settings.facingMode === 'user' ? 'front' : 'back' }; camera = { name: track.label, width: settings.width, height: settings.height, facing: settings.facingMode === 'user' ? 'front' : 'back' };
return new Promise((resolve) => { return new Promise((resolve) => {
video.onloadeddata = async () => { video.onloadeddata = async () => {
@ -193,6 +191,7 @@ async function setupCamera() {
ui.busy = false; ui.busy = false;
// do once more because onresize events can be delayed or skipped // do once more because onresize events can be delayed or skipped
// if (video.width > window.innerWidth) await setupCamera(); // if (video.width > window.innerWidth) await setupCamera();
status('');
resolve(video); resolve(video);
}; };
}); });
@ -222,32 +221,29 @@ function webWorker(input, image, canvas) {
// main processing function when input is webcam, can use direct invocation or web worker // main processing function when input is webcam, can use direct invocation or web worker
function runHumanDetect(input, canvas) { function runHumanDetect(input, canvas) {
timeStamp = performance.now(); timeStamp = performance.now();
// perform detect if live video or not video at all // if live video
if (input.srcObject) { const live = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);
// if video not ready, just redo if (!live) {
const live = (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused); // if we want to continue and camera not ready, retry in 0.5sec, else just give up
if (!live) { if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500);
if (!input.paused) { else log(`camera not ready: track state: ${input.srcObject?.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);
log(`video not ready: state: ${input.srcObject.getVideoTracks()[0].readyState} stream state: ${input.readyState}`); return;
setTimeout(() => runHumanDetect(input, canvas), 500); }
} status('');
return; if (ui.useWorker) {
} // get image data from video as we cannot send html objects to webworker
if (ui.useWorker) { const offscreen = new OffscreenCanvas(canvas.width, canvas.height);
// get image data from video as we cannot send html objects to webworker const ctx = offscreen.getContext('2d');
const offscreen = new OffscreenCanvas(canvas.width, canvas.height); ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);
const ctx = offscreen.getContext('2d'); const data = ctx.getImageData(0, 0, canvas.width, canvas.height);
ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height); // perform detection in worker
const data = ctx.getImageData(0, 0, canvas.width, canvas.height); webWorker(input, data, canvas);
// perform detection in worker } else {
webWorker(input, data, canvas); human.detect(input, config).then((result) => {
} else { if (result.error) log(result.error);
human.detect(input, config).then((result) => { else drawResults(input, result, canvas);
if (result.error) log(result.error); if (config.profile) log('profile data:', human.profile());
else drawResults(input, result, canvas); });
if (config.profile) log('profile data:', human.profile());
});
}
} }
} }
@ -286,7 +282,8 @@ async function detectVideo() {
document.getElementById('canvas').style.display = 'block'; document.getElementById('canvas').style.display = 'block';
const video = document.getElementById('video'); const video = document.getElementById('video');
const canvas = document.getElementById('canvas'); const canvas = document.getElementById('canvas');
ui.baseFont = ui.baseFontProto.replace(/{size}/, '1.3rem'); const size = 12 + Math.trunc(window.innerWidth / 400);
ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);
ui.baseLineHeight = ui.baseLineHeightProto; ui.baseLineHeight = ui.baseLineHeightProto;
if ((video.srcObject !== null) && !video.paused) { if ((video.srcObject !== null) && !video.paused) {
document.getElementById('play').style.display = 'block'; document.getElementById('play').style.display = 'block';
@ -305,7 +302,8 @@ async function detectVideo() {
async function detectSampleImages() { async function detectSampleImages() {
document.getElementById('play').style.display = 'none'; document.getElementById('play').style.display = 'none';
config.videoOptimized = false; config.videoOptimized = false;
ui.baseFont = ui.baseFontProto.replace(/{size}/, `${1.3 * ui.columns}rem`); const size = Math.trunc(ui.columns * 25600 / window.innerWidth);
ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);
ui.baseLineHeight = ui.baseLineHeightProto * ui.columns; ui.baseLineHeight = ui.baseLineHeightProto * ui.columns;
document.getElementById('canvas').style.display = 'none'; document.getElementById('canvas').style.display = 'none';
document.getElementById('samples-container').style.display = 'block'; document.getElementById('samples-container').style.display = 'block';

5
demo/draw.js
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@ -30,7 +30,10 @@ async function drawFace(result, canvas, ui, triangulation) {
if (face.agConfidence) labels.push(`${Math.trunc(100 * face.agConfidence)}% ${face.gender || ''}`); if (face.agConfidence) labels.push(`${Math.trunc(100 * face.agConfidence)}% ${face.gender || ''}`);
if (face.age) labels.push(`age: ${face.age || ''}`); if (face.age) labels.push(`age: ${face.age || ''}`);
if (face.iris) labels.push(`iris: ${face.iris}`); if (face.iris) labels.push(`iris: ${face.iris}`);
if (face.emotion && face.emotion[0]) labels.push(`${Math.trunc(100 * face.emotion[0].score)}% ${face.emotion[0].emotion}`); if (face.emotion && face.emotion.length > 0) {
const emotion = face.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
labels.push(emotion.join(' '));
}
ctx.fillStyle = ui.baseLabel; ctx.fillStyle = ui.baseLabel;
for (const i in labels) ctx.fillText(labels[i], face.box[0] + 8, face.box[1] + 24 + ((i + 1) * ui.baseLineHeight)); for (const i in labels) ctx.fillText(labels[i], face.box[0] + 8, face.box[1] + 24 + ((i + 1) * ui.baseLineHeight));
ctx.stroke(); ctx.stroke();

2
demo/index.html
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@ -35,7 +35,7 @@
.log { position: fixed; bottom: 0; margin: 0.4rem; font-size: 0.9rem; } .log { position: fixed; bottom: 0; margin: 0.4rem; font-size: 0.9rem; }
.samples-container { display: flex; flex-wrap: wrap; } .samples-container { display: flex; flex-wrap: wrap; }
.video { display: none; } .video { display: none; }
.canvas { margin: 0 auto; height: 100%; } .canvas { margin: 0 auto; height: 100vh; }
.loader { width: 300px; height: 300px; border: 3px solid transparent; border-radius: 50%; border-top: 4px solid #f15e41; animation: spin 4s linear infinite; position: absolute; top: 30%; left: 50%; margin-left: -150px; z-index: 15; } .loader { width: 300px; height: 300px; border: 3px solid transparent; border-radius: 50%; border-top: 4px solid #f15e41; animation: spin 4s linear infinite; position: absolute; top: 30%; left: 50%; margin-left: -150px; z-index: 15; }
.loader::before, .loader::after { content: ""; position: absolute; top: 6px; bottom: 6px; left: 6px; right: 6px; border-radius: 50%; border: 4px solid transparent; } .loader::before, .loader::after { content: ""; position: absolute; top: 6px; bottom: 6px; left: 6px; right: 6px; border-radius: 50%; border: 4px solid transparent; }
.loader::before { border-top-color: #bad375; animation: 3s spin linear infinite; } .loader::before { border-top-color: #bad375; animation: 3s spin linear infinite; }

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@ -1,7 +1,7 @@
{ {
"inputs": { "inputs": {
"demo/browser.js": { "demo/browser.js": {
"bytes": 17704, "bytes": 17853,
"imports": [ "imports": [
{ {
"path": "dist/human.esm.js" "path": "dist/human.esm.js"
@ -15,7 +15,7 @@
] ]
}, },
"demo/draw.js": { "demo/draw.js": {
"bytes": 7493, "bytes": 7553,
"imports": [] "imports": []
}, },
"demo/menu.js": { "demo/menu.js": {
@ -23,7 +23,7 @@
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"dist/human.esm.js": { "dist/human.esm.js": {
"bytes": 1274776, "bytes": 1275145,
"imports": [] "imports": []
} }
}, },
@ -31,28 +31,28 @@
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"imports": [], "imports": [],
"inputs": {}, "inputs": {},
"bytes": 5520670 "bytes": 5522703
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} }
}, },
"bytes": 1704999 "bytes": 1705669
} }
} }
} }

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@ -1,7 +1,7 @@
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"package.json": { "package.json": {
@ -9,7 +9,7 @@
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"imports": [ "imports": [
{ {
"path": "src/profile.js" "path": "src/profile.js"
@ -278,7 +278,7 @@
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"imports": [ "imports": [
{ {
"path": "src/profile.js" "path": "src/profile.js"
@ -290,7 +290,7 @@
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@ -323,10 +323,10 @@
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@ -389,7 +389,7 @@
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@ -401,7 +401,7 @@
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@ -1,7 +1,7 @@
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@ -153,7 +153,7 @@
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@ -468,7 +468,7 @@
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@ -499,7 +499,7 @@
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@ -655,7 +655,7 @@
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@ -153,7 +153,7 @@
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@ -468,7 +468,7 @@
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@ -499,7 +499,7 @@
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2
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} }
] ]

26
src/emotion/emotion.js
View File

@ -5,7 +5,11 @@ const annotations = ['angry', 'discust', 'fear', 'happy', 'sad', 'surpise', 'neu
const models = {}; const models = {};
let last = []; let last = [];
let frame = Number.MAX_SAFE_INTEGER; let frame = Number.MAX_SAFE_INTEGER;
const multiplier = 1.5;
// tuning values
const zoom = [0, 0]; // 0..1 meaning 0%..100%
const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale
const scale = 1; // score multiplication factor
async function load(config) { async function load(config) {
if (!models.emotion) models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath); if (!models.emotion) models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);
@ -18,17 +22,25 @@ async function predict(image, config) {
return last; return last;
} }
frame = 0; frame = 0;
const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false); const box = [[
(image.shape[1] * zoom[0]) / image.shape[1],
(image.shape[2] * zoom[1]) / image.shape[2],
(image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],
(image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],
]];
const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);
// const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);
const [red, green, blue] = tf.split(resize, 3, 3); const [red, green, blue] = tf.split(resize, 3, 3);
resize.dispose(); resize.dispose();
// weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html
const redNorm = tf.mul(red, [0.2989]); const redNorm = tf.mul(red, rgb[0]);
const greenNorm = tf.mul(green, [0.5870]); const greenNorm = tf.mul(green, rgb[1]);
const blueNorm = tf.mul(blue, [0.1140]); const blueNorm = tf.mul(blue, rgb[2]);
red.dispose(); red.dispose();
green.dispose(); green.dispose();
blue.dispose(); blue.dispose();
const grayscale = tf.addN([redNorm, greenNorm, blueNorm]); const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));
redNorm.dispose(); redNorm.dispose();
greenNorm.dispose(); greenNorm.dispose();
blueNorm.dispose(); blueNorm.dispose();
@ -36,7 +48,7 @@ async function predict(image, config) {
if (config.face.emotion.enabled) { if (config.face.emotion.enabled) {
let data; let data;
if (!config.profile) { if (!config.profile) {
const emotionT = await models.emotion.predict(grayscale); const emotionT = await models.emotion.predict(normalize);
data = emotionT.dataSync(); data = emotionT.dataSync();
tf.dispose(emotionT); tf.dispose(emotionT);
} else { } else {
@ -46,7 +58,7 @@ async function predict(image, config) {
profile.run('emotion', profileData); profile.run('emotion', profileData);
} }
for (let i = 0; i < data.length; i++) { for (let i = 0; i < data.length; i++) {
if (multiplier * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * multiplier * data[i]) / 100), emotion: annotations[i] }); if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });
} }
obj.sort((a, b) => b.score - a.score); obj.sort((a, b) => b.score - a.score);
} }

12
src/ssrnet/ssrnet.js
View File

@ -5,6 +5,9 @@ const models = {};
let last = { age: 0, gender: '' }; let last = { age: 0, gender: '' };
let frame = Number.MAX_SAFE_INTEGER; let frame = Number.MAX_SAFE_INTEGER;
// tuning values
const zoom = [0, 0]; // 0..1 meaning 0%..100%
async function loadAge(config) { async function loadAge(config) {
if (!models.age) models.age = await tf.loadGraphModel(config.face.age.modelPath); if (!models.age) models.age = await tf.loadGraphModel(config.face.age.modelPath);
return models.age; return models.age;
@ -21,7 +24,14 @@ async function predict(image, config) {
return last; return last;
} }
frame = 0; frame = 0;
const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false); const box = [[
(image.shape[1] * zoom[0]) / image.shape[1],
(image.shape[2] * zoom[1]) / image.shape[2],
(image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],
(image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],
]];
const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);
// const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);
const enhance = tf.mul(resize, [255.0]); const enhance = tf.mul(resize, [255.0]);
tf.dispose(resize); tf.dispose(resize);