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human
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model tuning

pull/50/head
Vladimir Mandic 2020-11-06 15:35:58 -05:00
parent db85fdb895
commit 3b9338d47b
23 changed files with 313 additions and 136 deletions
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22
config.js
View File

@ -56,9 +56,9 @@ export default {
skipFrames: 15, // how many frames to go without re-running the face bounding box detector, only used for video inputs skipFrames: 15, // 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: 0.1, // threshold for discarding a prediction
iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much in non-maximum suppression iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much in non-maximum suppression (0.1 means drop if overlap 10%)
scoreThreshold: 0.8, // threshold for deciding when to remove boxes based on score in non-maximum suppression scoreThreshold: 0.1, // threshold for deciding when to remove boxes based on score in non-maximum suppression, this is applied on detection objects only and before minConfidence
}, },
mesh: { mesh: {
enabled: true, enabled: true,
@ -73,20 +73,22 @@ export default {
}, },
age: { age: {
enabled: true, enabled: true,
modelPath: '../models/ssrnet-age-imdb.json', // can be 'imdb' or 'wiki' modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'
// which determines training set for model // which determines training set for model
inputSize: 64, // fixed value inputSize: 64, // fixed value
skipFrames: 15, // how many frames to go without re-running the detector, only used for video inputs skipFrames: 15, // how many frames to go without re-running the detector, only used for video inputs
}, },
gender: { gender: {
enabled: true, enabled: true,
minConfidence: 0.5, // threshold for discarding a prediction minConfidence: 0.1, // threshold for discarding a prediction
modelPath: '../models/ssrnet-gender-imdb.json', modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'
inputSize: 64, // fixed value
skipFrames: 15, // how many frames to go without re-running the detector, only used for video inputs
}, },
emotion: { emotion: {
enabled: true, enabled: true,
inputSize: 64, // fixed value inputSize: 64, // fixed value
minConfidence: 0.5, // threshold for discarding a prediction minConfidence: 0.2, // threshold for discarding a prediction
skipFrames: 15, // how many frames to go without re-running the detector skipFrames: 15, // how many frames to go without re-running the detector
modelPath: '../models/emotion-large.json', // can be 'mini', 'large' modelPath: '../models/emotion-large.json', // can be 'mini', 'large'
}, },
@ -106,9 +108,9 @@ export default {
skipFrames: 15, // how many frames to go without re-running the hand bounding box detector, only used for video inputs skipFrames: 15, // 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: 0.1, // threshold for discarding a prediction
iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much in non-maximum suppression iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in non-maximum suppression
scoreThreshold: 0.8, // threshold for deciding when to remove boxes based on score in non-maximum suppression scoreThreshold: 0.1, // 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
maxHands: 10, // maximum number of hands detected in the input, should be set to the minimum number for performance maxHands: 10, // maximum number of hands detected in the input, should be set to the minimum number for performance
detector: { detector: {

5
demo/browser.js
View File

@ -69,7 +69,7 @@ function drawResults(input, result, canvas) {
// console.log(result.performance); // console.log(result.performance);
// eslint-disable-next-line no-use-before-define // eslint-disable-next-line no-use-before-define
requestAnimationFrame(() => runHumanDetect(input, canvas)); // immediate loop before we even draw results if (input.srcObject) requestAnimationFrame(() => runHumanDetect(input, canvas)); // immediate loop before we even draw results
// draw fps chart // draw fps chart
menu.updateChart('FPS', fps); menu.updateChart('FPS', fps);
@ -187,7 +187,7 @@ function runHumanDetect(input, canvas) {
timeStamp = performance.now(); timeStamp = performance.now();
// if live video // if live video
const live = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused); const live = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);
if (!live) { if (!live && input.srcObject) {
// if we want to continue and camera not ready, retry in 0.5sec, else just give up // if we want to continue and camera not ready, retry in 0.5sec, else just give up
if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500); if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500);
else log(`camera not ready: track state: ${input.srcObject?.getVideoTracks()[0].readyState} stream state: ${input.readyState}`); else log(`camera not ready: track state: ${input.srcObject?.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);
@ -317,6 +317,7 @@ function setupMenu() {
}); });
menu.addRange('Min Confidence', human.config.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => { menu.addRange('Min Confidence', human.config.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => {
human.config.face.detector.minConfidence = parseFloat(val); human.config.face.detector.minConfidence = parseFloat(val);
human.config.face.gender.minConfidence = parseFloat(val);
human.config.face.emotion.minConfidence = parseFloat(val); human.config.face.emotion.minConfidence = parseFloat(val);
human.config.hand.minConfidence = parseFloat(val); human.config.hand.minConfidence = parseFloat(val);
}); });

2
demo/menu.js
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@ -213,7 +213,7 @@ class Menu {
el.innerHTML = `<input class="menu-range" type="range" id="${this.newID}" min="${min}" max="${max}" step="${step}" value="${object[variable]}">${title}`; el.innerHTML = `<input class="menu-range" type="range" id="${this.newID}" min="${min}" max="${max}" step="${step}" value="${object[variable]}">${title}`;
this.container.appendChild(el); this.container.appendChild(el);
el.addEventListener('change', (evt) => { el.addEventListener('change', (evt) => {
object[variable] = evt.target.value; object[variable] = parseInt(evt.target.value) === parseFloat(evt.target.value) ? parseInt(evt.target.value) : parseFloat(evt.target.value);
evt.target.setAttribute('value', evt.target.value); evt.target.setAttribute('value', evt.target.value);
if (callback) callback(evt.target.value); if (callback) callback(evt.target.value);
}); });

92
dist/demo-browser-index.js vendored
View File

@ -67088,20 +67088,18 @@ var require_blazeface = __commonJS((exports) => {
this.blazeFaceModel = model; this.blazeFaceModel = model;
this.width = config.detector.inputSize; this.width = config.detector.inputSize;
this.height = config.detector.inputSize; this.height = config.detector.inputSize;
this.maxFaces = config.detector.maxFaces;
this.anchorsData = generateAnchors(config.detector.inputSize); this.anchorsData = generateAnchors(config.detector.inputSize);
this.anchors = tf2.tensor2d(this.anchorsData); this.anchors = tf2.tensor2d(this.anchorsData);
this.inputSize = tf2.tensor1d([this.width, this.height]); this.inputSize = tf2.tensor1d([this.width, this.height]);
this.iouThreshold = config.detector.iouThreshold; this.config = config;
this.scaleFaces = 0.8; this.scaleFaces = 0.8;
this.scoreThreshold = config.detector.scoreThreshold;
} }
async getBoundingBoxes(inputImage) { async getBoundingBoxes(inputImage) {
if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return null; return null;
const [detectedOutputs, boxes, scores] = tf2.tidy(() => { const [detectedOutputs, boxes, scores] = tf2.tidy(() => {
const resizedImage = inputImage.resizeBilinear([this.width, this.height]); const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
const normalizedImage = tf2.mul(tf2.sub(resizedImage.div(255), 0.5), 2); const normalizedImage = tf2.sub(resizedImage.div(127.5), 1);
const batchedPrediction = this.blazeFaceModel.predict(normalizedImage); const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
let prediction; let prediction;
if (Array.isArray(batchedPrediction)) { if (Array.isArray(batchedPrediction)) {
@ -67115,10 +67113,10 @@ var require_blazeface = __commonJS((exports) => {
} }
const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize); const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);
const logits = tf2.slice(prediction, [0, 0], [-1, 1]); const logits = tf2.slice(prediction, [0, 0], [-1, 1]);
const scoresOut = tf2.sigmoid(logits).squeeze(); const scoresOut = logits.squeeze();
return [prediction, decodedBounds, scoresOut]; return [prediction, decodedBounds, scoresOut];
}); });
const boxIndicesTensor = await tf2.image.nonMaxSuppressionAsync(boxes, scores, this.maxFaces, this.iouThreshold, this.scoreThreshold); const boxIndicesTensor = await tf2.image.nonMaxSuppressionAsync(boxes, scores, this.config.detector.maxFaces, this.config.detector.iouThreshold, this.config.detector.scoreThreshold);
const boxIndices = boxIndicesTensor.arraySync(); const boxIndices = boxIndicesTensor.arraySync();
boxIndicesTensor.dispose(); boxIndicesTensor.dispose();
const boundingBoxesMap = boxIndices.map((boxIndex) => tf2.slice(boxes, [boxIndex, 0], [1, -1])); const boundingBoxesMap = boxIndices.map((boxIndex) => tf2.slice(boxes, [boxIndex, 0], [1, -1]));
@ -70877,7 +70875,7 @@ var require_profile = __commonJS((exports) => {
exports.run = profile2; exports.run = profile2;
exports.data = profileData; exports.data = profileData;
}); });
var require_ssrnet = __commonJS((exports) => { var require_age = __commonJS((exports) => {
const tf2 = require_tf_node(); const tf2 = require_tf_node();
const profile2 = require_profile(); const profile2 = require_profile();
const models = {}; const models = {};
@ -70929,20 +70927,23 @@ var require_ssrnet = __commonJS((exports) => {
exports.predict = predict; exports.predict = predict;
exports.load = load; exports.load = load;
}); });
var require_ssrnet2 = __commonJS((exports) => { var require_gender = __commonJS((exports) => {
const tf2 = require_tf_node(); const tf2 = require_tf_node();
const profile2 = require_profile(); const profile2 = require_profile();
const models = {}; const models = {};
let last = {gender: ""}; let last = {gender: ""};
let frame = Number.MAX_SAFE_INTEGER; let frame = Number.MAX_SAFE_INTEGER;
let alternative = false;
const zoom = [0, 0]; const zoom = [0, 0];
const rgb = [0.2989, 0.587, 0.114];
async function load(config) { async function load(config) {
if (!models.gender) if (!models.gender)
models.gender = await tf2.loadGraphModel(config.face.gender.modelPath); models.gender = await tf2.loadGraphModel(config.face.gender.modelPath);
alternative = models.gender.inputs[0].shape[3] === 1;
return models.gender; return models.gender;
} }
async function predict(image2, config) { async function predict(image2, config) {
if (frame < config.face.age.skipFrames && last.gender !== "") { if (frame < config.face.gender.skipFrames && last.gender !== "") {
frame += 1; frame += 1;
return last; return last;
} }
@ -70954,8 +70955,20 @@ var require_ssrnet2 = __commonJS((exports) => {
(image2.shape[1] - image2.shape[1] * zoom[0]) / image2.shape[1], (image2.shape[1] - image2.shape[1] * zoom[0]) / image2.shape[1],
(image2.shape[2] - image2.shape[2] * zoom[1]) / image2.shape[2] (image2.shape[2] - image2.shape[2] * zoom[1]) / image2.shape[2]
]]; ]];
const resize = tf2.image.cropAndResize(image2, box, [0], [config.face.age.inputSize, config.face.age.inputSize]); const resize = tf2.image.cropAndResize(image2, box, [0], [config.face.gender.inputSize, config.face.gender.inputSize]);
const enhance = tf2.mul(resize, [255]); let enhance;
if (alternative) {
enhance = tf2.tidy(() => {
const [red, green, blue] = tf2.split(resize, 3, 3);
const redNorm = tf2.mul(red, rgb[0]);
const greenNorm = tf2.mul(green, rgb[1]);
const blueNorm = tf2.mul(blue, rgb[2]);
const grayscale = tf2.addN([redNorm, greenNorm, blueNorm]);
return grayscale.sub(0.5).mul(2);
});
} else {
enhance = tf2.mul(resize, [255]);
}
tf2.dispose(resize); tf2.dispose(resize);
let genderT; let genderT;
const obj = {}; const obj = {};
@ -70971,10 +70984,18 @@ var require_ssrnet2 = __commonJS((exports) => {
enhance.dispose(); enhance.dispose();
if (genderT) { if (genderT) {
const data = genderT.dataSync(); const data = genderT.dataSync();
const confidence = Math.trunc(Math.abs(1.9 * 100 * (data[0] - 0.5))) / 100; if (alternative) {
if (confidence > config.face.gender.minConfidence) { const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;
obj.gender = data[0] <= 0.5 ? "female" : "male"; if (confidence > config.face.gender.minConfidence) {
obj.confidence = confidence; obj.gender = data[0] > data[1] ? "female" : "male";
obj.confidence = confidence;
}
} else {
const confidence = Math.trunc(200 * Math.abs(data[0] - 0.5)) / 100;
if (confidence > config.face.gender.minConfidence) {
obj.gender = data[0] <= 0.5 ? "female" : "male";
obj.confidence = confidence;
}
} }
} }
genderT.dispose(); genderT.dispose();
@ -90659,9 +90680,9 @@ var require_config = __commonJS((exports) => {
inputSize: 256, inputSize: 256,
maxFaces: 10, maxFaces: 10,
skipFrames: 15, skipFrames: 15,
minConfidence: 0.5, minConfidence: 0.1,
iouThreshold: 0.3, iouThreshold: 0.1,
scoreThreshold: 0.8 scoreThreshold: 0.1
}, },
mesh: { mesh: {
enabled: true, enabled: true,
@ -90676,19 +90697,21 @@ var require_config = __commonJS((exports) => {
}, },
age: { age: {
enabled: true, enabled: true,
modelPath: "../models/ssrnet-age-imdb.json", modelPath: "../models/age-ssrnet-imdb.json",
inputSize: 64, inputSize: 64,
skipFrames: 15 skipFrames: 15
}, },
gender: { gender: {
enabled: true, enabled: true,
minConfidence: 0.5, minConfidence: 0.1,
modelPath: "../models/ssrnet-gender-imdb.json" modelPath: "../models/gender-ssrnet-imdb.json",
inputSize: 64,
skipFrames: 15
}, },
emotion: { emotion: {
enabled: true, enabled: true,
inputSize: 64, inputSize: 64,
minConfidence: 0.5, minConfidence: 0.2,
skipFrames: 15, skipFrames: 15,
modelPath: "../models/emotion-large.json" modelPath: "../models/emotion-large.json"
} }
@ -90706,9 +90729,9 @@ var require_config = __commonJS((exports) => {
enabled: true, enabled: true,
inputSize: 256, inputSize: 256,
skipFrames: 15, skipFrames: 15,
minConfidence: 0.5, minConfidence: 0.1,
iouThreshold: 0.3, iouThreshold: 0.2,
scoreThreshold: 0.8, scoreThreshold: 0.1,
enlargeFactor: 1.65, enlargeFactor: 1.65,
maxHands: 10, maxHands: 10,
detector: { detector: {
@ -90791,8 +90814,8 @@ var require_package = __commonJS((exports, module) => {
}); });
const tf = require_tf_node(); const tf = require_tf_node();
const facemesh = require_facemesh(); const facemesh = require_facemesh();
const age = require_ssrnet(); const age = require_age();
const gender = require_ssrnet2(); const gender = require_gender();
const emotion = require_emotion(); const emotion = require_emotion();
const posenet = require_posenet(); const posenet = require_posenet();
const handpose = require_handpose(); const handpose = require_handpose();
@ -90802,7 +90825,7 @@ const profile = require_profile();
const defaults = require_config().default; const defaults = require_config().default;
const app = require_package(); const app = require_package();
const override = { const override = {
face: {detector: {skipFrames: 0}, age: {skipFrames: 0}, emotion: {skipFrames: 0}}, face: {detector: {skipFrames: 0}, age: {skipFrames: 0}, gender: {skipFrames: 0}, emotion: {skipFrames: 0}},
hand: {skipFrames: 0} hand: {skipFrames: 0}
}; };
const now = () => { const now = () => {
@ -90831,7 +90854,6 @@ class Human {
constructor() { constructor() {
this.tf = tf; this.tf = tf;
this.version = app.version; this.version = app.version;
this.defaults = defaults;
this.config = defaults; this.config = defaults;
this.fx = null; this.fx = null;
this.state = "idle"; this.state = "idle";
@ -90894,7 +90916,7 @@ class Human {
this.state = "load"; this.state = "load";
const timeStamp2 = now(); const timeStamp2 = now();
if (userConfig) if (userConfig)
this.config = mergeDeep(defaults, userConfig); this.config = mergeDeep(this.config, userConfig);
if (this.firstRun) { if (this.firstRun) {
this.checkBackend(true); this.checkBackend(true);
this.log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`); this.log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);
@ -91045,7 +91067,7 @@ class Human {
async detect(input, userConfig = {}) { async detect(input, userConfig = {}) {
this.state = "config"; this.state = "config";
let timeStamp2; let timeStamp2;
this.config = mergeDeep(defaults, userConfig); this.config = mergeDeep(this.config, userConfig);
if (!this.config.videoOptimized) if (!this.config.videoOptimized)
this.config = mergeDeep(this.config, override); this.config = mergeDeep(this.config, override);
this.state = "check"; this.state = "check";
@ -91532,7 +91554,7 @@ class Menu {
el.innerHTML = `<input class="menu-range" type="range" id="${this.newID}" min="${min}" max="${max}" step="${step}" value="${object[variable]}">${title}`; el.innerHTML = `<input class="menu-range" type="range" id="${this.newID}" min="${min}" max="${max}" step="${step}" value="${object[variable]}">${title}`;
this.container.appendChild(el); this.container.appendChild(el);
el.addEventListener("change", (evt) => { el.addEventListener("change", (evt) => {
object[variable] = evt.target.value; object[variable] = parseInt(evt.target.value) === parseFloat(evt.target.value) ? parseInt(evt.target.value) : parseFloat(evt.target.value);
evt.target.setAttribute("value", evt.target.value); evt.target.setAttribute("value", evt.target.value);
if (callback) if (callback)
callback(evt.target.value); callback(evt.target.value);
@ -91673,7 +91695,8 @@ function drawResults(input, result, canvas) {
fps.push(1e3 / (performance.now() - timeStamp)); fps.push(1e3 / (performance.now() - timeStamp));
if (fps.length > ui.maxFrames) if (fps.length > ui.maxFrames)
fps.shift(); fps.shift();
requestAnimationFrame(() => runHumanDetect(input, canvas)); if (input.srcObject)
requestAnimationFrame(() => runHumanDetect(input, canvas));
menu2.updateChart("FPS", fps); menu2.updateChart("FPS", fps);
const ctx = canvas.getContext("2d"); const ctx = canvas.getContext("2d");
ctx.fillStyle = ui.baseBackground; ctx.fillStyle = ui.baseBackground;
@ -91787,7 +91810,7 @@ function runHumanDetect(input, canvas) {
var _a; var _a;
timeStamp = performance.now(); timeStamp = performance.now();
const live = input.srcObject && input.srcObject.getVideoTracks()[0].readyState === "live" && input.readyState > 2 && !input.paused; const live = input.srcObject && input.srcObject.getVideoTracks()[0].readyState === "live" && input.readyState > 2 && !input.paused;
if (!live) { if (!live && input.srcObject) {
if (input.srcObject.getVideoTracks()[0].readyState === "live" && input.readyState <= 2) if (input.srcObject.getVideoTracks()[0].readyState === "live" && input.readyState <= 2)
setTimeout(() => runHumanDetect(input, canvas), 500); setTimeout(() => runHumanDetect(input, canvas), 500);
else else
@ -91911,6 +91934,7 @@ function setupMenu() {
}); });
menu2.addRange("Min Confidence", human.config.face.detector, "minConfidence", 0, 1, 0.05, (val) => { menu2.addRange("Min Confidence", human.config.face.detector, "minConfidence", 0, 1, 0.05, (val) => {
human.config.face.detector.minConfidence = parseFloat(val); human.config.face.detector.minConfidence = parseFloat(val);
human.config.face.gender.minConfidence = parseFloat(val);
human.config.face.emotion.minConfidence = parseFloat(val); human.config.face.emotion.minConfidence = parseFloat(val);
human.config.hand.minConfidence = parseFloat(val); human.config.hand.minConfidence = parseFloat(val);
}); });

6
dist/demo-browser-index.js.map vendored
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16
dist/demo-browser-index.json vendored
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@ -1,7 +1,7 @@
{ {
"inputs": { "inputs": {
"demo/browser.js": { "demo/browser.js": {
"bytes": 17412, "bytes": 17514,
"imports": [ "imports": [
{ {
"path": "dist/human.esm.js" "path": "dist/human.esm.js"
@ -19,11 +19,11 @@
"imports": [] "imports": []
}, },
"demo/menu.js": { "demo/menu.js": {
"bytes": 12357, "bytes": 12460,
"imports": [] "imports": []
}, },
"dist/human.esm.js": { "dist/human.esm.js": {
"bytes": 3196136, "bytes": 3196946,
"imports": [] "imports": []
} }
}, },
@ -31,25 +31,25 @@
"dist/demo-browser-index.js.map": { "dist/demo-browser-index.js.map": {
"imports": [], "imports": [],
"inputs": {}, "inputs": {},
"bytes": 5557260 "bytes": 5559544
}, },
"dist/demo-browser-index.js": { "dist/demo-browser-index.js": {
"imports": [], "imports": [],
"inputs": { "inputs": {
"dist/human.esm.js": { "dist/human.esm.js": {
"bytesInOutput": 3193996 "bytesInOutput": 3194809
}, },
"demo/draw.js": { "demo/draw.js": {
"bytesInOutput": 7453 "bytesInOutput": 7453
}, },
"demo/menu.js": { "demo/menu.js": {
"bytesInOutput": 12359 "bytesInOutput": 12462
}, },
"demo/browser.js": { "demo/browser.js": {
"bytesInOutput": 15694 "bytesInOutput": 15800
} }
}, },
"bytes": 3229624 "bytes": 3230646
} }
} }
} }

88
dist/human.esm.js vendored
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@ -67127,20 +67127,18 @@ var require_blazeface = __commonJS((exports) => {
this.blazeFaceModel = model; this.blazeFaceModel = model;
this.width = config.detector.inputSize; this.width = config.detector.inputSize;
this.height = config.detector.inputSize; this.height = config.detector.inputSize;
this.maxFaces = config.detector.maxFaces;
this.anchorsData = generateAnchors(config.detector.inputSize); this.anchorsData = generateAnchors(config.detector.inputSize);
this.anchors = tf2.tensor2d(this.anchorsData); this.anchors = tf2.tensor2d(this.anchorsData);
this.inputSize = tf2.tensor1d([this.width, this.height]); this.inputSize = tf2.tensor1d([this.width, this.height]);
this.iouThreshold = config.detector.iouThreshold; this.config = config;
this.scaleFaces = 0.8; this.scaleFaces = 0.8;
this.scoreThreshold = config.detector.scoreThreshold;
} }
async getBoundingBoxes(inputImage) { async getBoundingBoxes(inputImage) {
if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) if (!inputImage || inputImage.isDisposedInternal || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
return null; return null;
const [detectedOutputs, boxes, scores] = tf2.tidy(() => { const [detectedOutputs, boxes, scores] = tf2.tidy(() => {
const resizedImage = inputImage.resizeBilinear([this.width, this.height]); const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
const normalizedImage = tf2.mul(tf2.sub(resizedImage.div(255), 0.5), 2); const normalizedImage = tf2.sub(resizedImage.div(127.5), 1);
const batchedPrediction = this.blazeFaceModel.predict(normalizedImage); const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
let prediction; let prediction;
if (Array.isArray(batchedPrediction)) { if (Array.isArray(batchedPrediction)) {
@ -67154,10 +67152,10 @@ var require_blazeface = __commonJS((exports) => {
} }
const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize); const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);
const logits = tf2.slice(prediction, [0, 0], [-1, 1]); const logits = tf2.slice(prediction, [0, 0], [-1, 1]);
const scoresOut = tf2.sigmoid(logits).squeeze(); const scoresOut = logits.squeeze();
return [prediction, decodedBounds, scoresOut]; return [prediction, decodedBounds, scoresOut];
}); });
const boxIndicesTensor = await tf2.image.nonMaxSuppressionAsync(boxes, scores, this.maxFaces, this.iouThreshold, this.scoreThreshold); const boxIndicesTensor = await tf2.image.nonMaxSuppressionAsync(boxes, scores, this.config.detector.maxFaces, this.config.detector.iouThreshold, this.config.detector.scoreThreshold);
const boxIndices = boxIndicesTensor.arraySync(); const boxIndices = boxIndicesTensor.arraySync();
boxIndicesTensor.dispose(); boxIndicesTensor.dispose();
const boundingBoxesMap = boxIndices.map((boxIndex) => tf2.slice(boxes, [boxIndex, 0], [1, -1])); const boundingBoxesMap = boxIndices.map((boxIndex) => tf2.slice(boxes, [boxIndex, 0], [1, -1]));
@ -70933,8 +70931,8 @@ var require_profile = __commonJS((exports) => {
exports.data = profileData; exports.data = profileData;
}); });
// src/age/ssrnet.js // src/age/age.js
var require_ssrnet = __commonJS((exports) => { var require_age = __commonJS((exports) => {
const tf2 = require_tf_node(); const tf2 = require_tf_node();
const profile2 = require_profile(); const profile2 = require_profile();
const models = {}; const models = {};
@ -70987,21 +70985,24 @@ var require_ssrnet = __commonJS((exports) => {
exports.load = load; exports.load = load;
}); });
// src/gender/ssrnet.js // src/gender/gender.js
var require_ssrnet2 = __commonJS((exports) => { var require_gender = __commonJS((exports) => {
const tf2 = require_tf_node(); const tf2 = require_tf_node();
const profile2 = require_profile(); const profile2 = require_profile();
const models = {}; const models = {};
let last = {gender: ""}; let last = {gender: ""};
let frame = Number.MAX_SAFE_INTEGER; let frame = Number.MAX_SAFE_INTEGER;
let alternative = false;
const zoom = [0, 0]; const zoom = [0, 0];
const rgb = [0.2989, 0.587, 0.114];
async function load(config) { async function load(config) {
if (!models.gender) if (!models.gender)
models.gender = await tf2.loadGraphModel(config.face.gender.modelPath); models.gender = await tf2.loadGraphModel(config.face.gender.modelPath);
alternative = models.gender.inputs[0].shape[3] === 1;
return models.gender; return models.gender;
} }
async function predict(image2, config) { async function predict(image2, config) {
if (frame < config.face.age.skipFrames && last.gender !== "") { if (frame < config.face.gender.skipFrames && last.gender !== "") {
frame += 1; frame += 1;
return last; return last;
} }
@ -71013,8 +71014,20 @@ var require_ssrnet2 = __commonJS((exports) => {
(image2.shape[1] - image2.shape[1] * zoom[0]) / image2.shape[1], (image2.shape[1] - image2.shape[1] * zoom[0]) / image2.shape[1],
(image2.shape[2] - image2.shape[2] * zoom[1]) / image2.shape[2] (image2.shape[2] - image2.shape[2] * zoom[1]) / image2.shape[2]
]]; ]];
const resize = tf2.image.cropAndResize(image2, box, [0], [config.face.age.inputSize, config.face.age.inputSize]); const resize = tf2.image.cropAndResize(image2, box, [0], [config.face.gender.inputSize, config.face.gender.inputSize]);
const enhance = tf2.mul(resize, [255]); let enhance;
if (alternative) {
enhance = tf2.tidy(() => {
const [red, green, blue] = tf2.split(resize, 3, 3);
const redNorm = tf2.mul(red, rgb[0]);
const greenNorm = tf2.mul(green, rgb[1]);
const blueNorm = tf2.mul(blue, rgb[2]);
const grayscale = tf2.addN([redNorm, greenNorm, blueNorm]);
return grayscale.sub(0.5).mul(2);
});
} else {
enhance = tf2.mul(resize, [255]);
}
tf2.dispose(resize); tf2.dispose(resize);
let genderT; let genderT;
const obj = {}; const obj = {};
@ -71030,10 +71043,18 @@ var require_ssrnet2 = __commonJS((exports) => {
enhance.dispose(); enhance.dispose();
if (genderT) { if (genderT) {
const data = genderT.dataSync(); const data = genderT.dataSync();
const confidence = Math.trunc(Math.abs(1.9 * 100 * (data[0] - 0.5))) / 100; if (alternative) {
if (confidence > config.face.gender.minConfidence) { const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;
obj.gender = data[0] <= 0.5 ? "female" : "male"; if (confidence > config.face.gender.minConfidence) {
obj.confidence = confidence; obj.gender = data[0] > data[1] ? "female" : "male";
obj.confidence = confidence;
}
} else {
const confidence = Math.trunc(200 * Math.abs(data[0] - 0.5)) / 100;
if (confidence > config.face.gender.minConfidence) {
obj.gender = data[0] <= 0.5 ? "female" : "male";
obj.confidence = confidence;
}
} }
} }
genderT.dispose(); genderT.dispose();
@ -90762,9 +90783,9 @@ var require_config = __commonJS((exports) => {
inputSize: 256, inputSize: 256,
maxFaces: 10, maxFaces: 10,
skipFrames: 15, skipFrames: 15,
minConfidence: 0.5, minConfidence: 0.1,
iouThreshold: 0.3, iouThreshold: 0.1,
scoreThreshold: 0.8 scoreThreshold: 0.1
}, },
mesh: { mesh: {
enabled: true, enabled: true,
@ -90779,19 +90800,21 @@ var require_config = __commonJS((exports) => {
}, },
age: { age: {
enabled: true, enabled: true,
modelPath: "../models/ssrnet-age-imdb.json", modelPath: "../models/age-ssrnet-imdb.json",
inputSize: 64, inputSize: 64,
skipFrames: 15 skipFrames: 15
}, },
gender: { gender: {
enabled: true, enabled: true,
minConfidence: 0.5, minConfidence: 0.1,
modelPath: "../models/ssrnet-gender-imdb.json" modelPath: "../models/gender-ssrnet-imdb.json",
inputSize: 64,
skipFrames: 15
}, },
emotion: { emotion: {
enabled: true, enabled: true,
inputSize: 64, inputSize: 64,
minConfidence: 0.5, minConfidence: 0.2,
skipFrames: 15, skipFrames: 15,
modelPath: "../models/emotion-large.json" modelPath: "../models/emotion-large.json"
} }
@ -90809,9 +90832,9 @@ var require_config = __commonJS((exports) => {
enabled: true, enabled: true,
inputSize: 256, inputSize: 256,
skipFrames: 15, skipFrames: 15,
minConfidence: 0.5, minConfidence: 0.1,
iouThreshold: 0.3, iouThreshold: 0.2,
scoreThreshold: 0.8, scoreThreshold: 0.1,
enlargeFactor: 1.65, enlargeFactor: 1.65,
maxHands: 10, maxHands: 10,
detector: { detector: {
@ -90898,8 +90921,8 @@ var require_package = __commonJS((exports, module) => {
// src/human.js // src/human.js
const tf = require_tf_node(); const tf = require_tf_node();
const facemesh = require_facemesh(); const facemesh = require_facemesh();
const age = require_ssrnet(); const age = require_age();
const gender = require_ssrnet2(); const gender = require_gender();
const emotion = require_emotion(); const emotion = require_emotion();
const posenet = require_posenet(); const posenet = require_posenet();
const handpose = require_handpose(); const handpose = require_handpose();
@ -90909,7 +90932,7 @@ const profile = require_profile();
const defaults = require_config().default; const defaults = require_config().default;
const app = require_package(); const app = require_package();
const override = { const override = {
face: {detector: {skipFrames: 0}, age: {skipFrames: 0}, emotion: {skipFrames: 0}}, face: {detector: {skipFrames: 0}, age: {skipFrames: 0}, gender: {skipFrames: 0}, emotion: {skipFrames: 0}},
hand: {skipFrames: 0} hand: {skipFrames: 0}
}; };
const now = () => { const now = () => {
@ -90938,7 +90961,6 @@ class Human {
constructor() { constructor() {
this.tf = tf; this.tf = tf;
this.version = app.version; this.version = app.version;
this.defaults = defaults;
this.config = defaults; this.config = defaults;
this.fx = null; this.fx = null;
this.state = "idle"; this.state = "idle";
@ -91001,7 +91023,7 @@ class Human {
this.state = "load"; this.state = "load";
const timeStamp = now(); const timeStamp = now();
if (userConfig) if (userConfig)
this.config = mergeDeep(defaults, userConfig); this.config = mergeDeep(this.config, userConfig);
if (this.firstRun) { if (this.firstRun) {
this.checkBackend(true); this.checkBackend(true);
this.log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`); this.log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);
@ -91152,7 +91174,7 @@ class Human {
async detect(input, userConfig = {}) { async detect(input, userConfig = {}) {
this.state = "config"; this.state = "config";
let timeStamp; let timeStamp;
this.config = mergeDeep(defaults, userConfig); this.config = mergeDeep(this.config, userConfig);
if (!this.config.videoOptimized) if (!this.config.videoOptimized)
this.config = mergeDeep(this.config, override); this.config = mergeDeep(this.config, override);
this.state = "check"; this.state = "check";

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34
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@ -1,7 +1,7 @@
{ {
"inputs": { "inputs": {
"config.js": { "config.js": {
"bytes": 7319, "bytes": 7664,
"imports": [] "imports": []
}, },
"node_modules/@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.node.js": { "node_modules/@tensorflow/tfjs-backend-cpu/dist/tf-backend-cpu.node.js": {
@ -152,7 +152,7 @@
"bytes": 3389, "bytes": 3389,
"imports": [] "imports": []
}, },
"src/age/ssrnet.js": { "src/age/age.js": {
"bytes": 1766, "bytes": 1766,
"imports": [ "imports": [
{ {
@ -288,7 +288,7 @@
] ]
}, },
"src/face/blazeface.js": { "src/face/blazeface.js": {
"bytes": 6991, "bytes": 7096,
"imports": [ "imports": [
{ {
"path": "node_modules/@tensorflow/tfjs/dist/tf.node.js" "path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
@ -359,8 +359,8 @@
"bytes": 19592, "bytes": 19592,
"imports": [] "imports": []
}, },
"src/gender/ssrnet.js": { "src/gender/gender.js": {
"bytes": 2015, "bytes": 3042,
"imports": [ "imports": [
{ {
"path": "node_modules/@tensorflow/tfjs/dist/tf.node.js" "path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
@ -433,7 +433,7 @@
"imports": [] "imports": []
}, },
"src/human.js": { "src/human.js": {
"bytes": 14051, "bytes": 14049,
"imports": [ "imports": [
{ {
"path": "node_modules/@tensorflow/tfjs/dist/tf.node.js" "path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
@ -442,10 +442,10 @@
"path": "src/face/facemesh.js" "path": "src/face/facemesh.js"
}, },
{ {
"path": "src/age/ssrnet.js" "path": "src/age/age.js"
}, },
{ {
"path": "src/gender/ssrnet.js" "path": "src/gender/gender.js"
}, },
{ {
"path": "src/emotion/emotion.js" "path": "src/emotion/emotion.js"
@ -513,7 +513,7 @@
"dist/human.esm.js.map": { "dist/human.esm.js.map": {
"imports": [], "imports": [],
"inputs": {}, "inputs": {},
"bytes": 5607938 "bytes": 5609921
}, },
"dist/human.esm.js": { "dist/human.esm.js": {
"imports": [], "imports": [],
@ -576,7 +576,7 @@
"bytesInOutput": 3025 "bytesInOutput": 3025
}, },
"src/face/blazeface.js": { "src/face/blazeface.js": {
"bytesInOutput": 7123 "bytesInOutput": 7010
}, },
"src/face/keypoints.js": { "src/face/keypoints.js": {
"bytesInOutput": 2768 "bytesInOutput": 2768
@ -602,11 +602,11 @@
"src/profile.js": { "src/profile.js": {
"bytesInOutput": 1092 "bytesInOutput": 1092
}, },
"src/age/ssrnet.js": { "src/age/age.js": {
"bytesInOutput": 1747 "bytesInOutput": 1744
}, },
"src/gender/ssrnet.js": { "src/gender/gender.js": {
"bytesInOutput": 2007 "bytesInOutput": 2892
}, },
"src/emotion/emotion.js": { "src/emotion/emotion.js": {
"bytesInOutput": 2612 "bytesInOutput": 2612
@ -672,19 +672,19 @@
"bytesInOutput": 4482 "bytesInOutput": 4482
}, },
"config.js": { "config.js": {
"bytesInOutput": 2230 "bytesInOutput": 2277
}, },
"package.json": { "package.json": {
"bytesInOutput": 3533 "bytesInOutput": 3533
}, },
"src/human.js": { "src/human.js": {
"bytesInOutput": 11852 "bytesInOutput": 11849
}, },
"src/human.js": { "src/human.js": {
"bytesInOutput": 0 "bytesInOutput": 0
} }
}, },
"bytes": 3196136 "bytes": 3196946
} }
} }
} }

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@ -0,0 +1,105 @@
{
"format": "graph-model",
"generatedBy": "2.3.1",
"convertedBy": "TensorFlow.js Converter v2.7.0",
"userDefinedMetadata":
{
"signature":
{
"inputs": {"input_1:0":{"name":"input_1:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"-1"},{"size":"64"},{"size":"64"},{"size":"1"}]}}},
"outputs": {"Identity:0":{"name":"Identity:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"-1"},{"size":"2"}]}}}
}
},
"modelTopology":
{
"node":
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src/face/blazeface.js
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@ -69,22 +69,20 @@ class BlazeFaceModel {
this.blazeFaceModel = model; this.blazeFaceModel = model;
this.width = config.detector.inputSize; this.width = config.detector.inputSize;
this.height = config.detector.inputSize; this.height = config.detector.inputSize;
this.maxFaces = config.detector.maxFaces;
this.anchorsData = generateAnchors(config.detector.inputSize); this.anchorsData = generateAnchors(config.detector.inputSize);
this.anchors = tf.tensor2d(this.anchorsData); this.anchors = tf.tensor2d(this.anchorsData);
this.inputSize = tf.tensor1d([this.width, this.height]); this.inputSize = tf.tensor1d([this.width, this.height]);
this.iouThreshold = config.detector.iouThreshold; this.config = config;
this.scaleFaces = 0.8; this.scaleFaces = 0.8;
this.scoreThreshold = config.detector.scoreThreshold;
} }
// toto blazeface leaks two tensors per run
async getBoundingBoxes(inputImage) { async getBoundingBoxes(inputImage) {
// sanity check on input // sanity check on input
if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null; if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;
const [detectedOutputs, boxes, scores] = tf.tidy(() => { const [detectedOutputs, boxes, scores] = tf.tidy(() => {
const resizedImage = inputImage.resizeBilinear([this.width, this.height]); const resizedImage = inputImage.resizeBilinear([this.width, this.height]);
const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2); // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);
const normalizedImage = tf.sub(resizedImage.div(127.5), 1);
const batchedPrediction = this.blazeFaceModel.predict(normalizedImage); const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);
let prediction; let prediction;
// are we using tfhub or pinto converted model? // are we using tfhub or pinto converted model?
@ -99,10 +97,12 @@ class BlazeFaceModel {
} }
const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize); const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);
const logits = tf.slice(prediction, [0, 0], [-1, 1]); const logits = tf.slice(prediction, [0, 0], [-1, 1]);
const scoresOut = tf.sigmoid(logits).squeeze(); // const scoresOut = tf.sigmoid(logits).squeeze();
const scoresOut = logits.squeeze();
return [prediction, decodedBounds, scoresOut]; return [prediction, decodedBounds, scoresOut];
}); });
const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.maxFaces, this.iouThreshold, this.scoreThreshold); // activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh')
const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.detector.maxFaces, this.config.detector.iouThreshold, this.config.detector.scoreThreshold);
const boxIndices = boxIndicesTensor.arraySync(); const boxIndices = boxIndicesTensor.arraySync();
boxIndicesTensor.dispose(); boxIndicesTensor.dispose();
const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1])); const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));

39
src/gender/ssrnet.js → src/gender/gender.js
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@ -4,17 +4,20 @@ const profile = require('../profile.js');
const models = {}; const models = {};
let last = { gender: '' }; let last = { gender: '' };
let frame = Number.MAX_SAFE_INTEGER; let frame = Number.MAX_SAFE_INTEGER;
let alternative = false;
// tuning values // tuning values
const zoom = [0, 0]; // 0..1 meaning 0%..100% 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
async function load(config) { async function load(config) {
if (!models.gender) models.gender = await tf.loadGraphModel(config.face.gender.modelPath); if (!models.gender) models.gender = await tf.loadGraphModel(config.face.gender.modelPath);
alternative = models.gender.inputs[0].shape[3] === 1;
return models.gender; return models.gender;
} }
async function predict(image, config) { async function predict(image, config) {
if ((frame < config.face.age.skipFrames) && last.gender !== '') { if ((frame < config.face.gender.skipFrames) && last.gender !== '') {
frame += 1; frame += 1;
return last; return last;
} }
@ -26,9 +29,21 @@ async function predict(image, config) {
(image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1], (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],
(image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2], (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.cropAndResize(image, box, [0], [config.face.gender.inputSize, config.face.gender.inputSize]);
let enhance;
if (alternative) {
enhance = tf.tidy(() => {
const [red, green, blue] = tf.split(resize, 3, 3);
const redNorm = tf.mul(red, rgb[0]);
const greenNorm = tf.mul(green, rgb[1]);
const blueNorm = tf.mul(blue, rgb[2]);
const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
return grayscale.sub(0.5).mul(2);
});
} else {
enhance = tf.mul(resize, [255.0]);
}
// const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false); // const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);
const enhance = tf.mul(resize, [255.0]);
tf.dispose(resize); tf.dispose(resize);
let genderT; let genderT;
@ -46,10 +61,20 @@ async function predict(image, config) {
if (genderT) { if (genderT) {
const data = genderT.dataSync(); const data = genderT.dataSync();
const confidence = Math.trunc(Math.abs(1.9 * 100 * (data[0] - 0.5))) / 100; if (alternative) {
if (confidence > config.face.gender.minConfidence) { // returns two values 0..1, bigger one is prediction
obj.gender = data[0] <= 0.5 ? 'female' : 'male'; const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;
obj.confidence = confidence; if (confidence > config.face.gender.minConfidence) {
obj.gender = data[0] > data[1] ? 'female' : 'male';
obj.confidence = confidence;
}
} else {
// returns one value 0..1, .5 is prediction threshold
const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;
if (confidence > config.face.gender.minConfidence) {
obj.gender = data[0] <= 0.5 ? 'female' : 'male';
obj.confidence = confidence;
}
} }
} }
genderT.dispose(); genderT.dispose();

12
src/human.js
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@ -1,7 +1,7 @@
const tf = require('@tensorflow/tfjs'); const tf = require('@tensorflow/tfjs');
const facemesh = require('./face/facemesh.js'); const facemesh = require('./face/facemesh.js');
const age = require('./age/ssrnet.js'); const age = require('./age/age.js');
const gender = require('./gender/ssrnet.js'); const gender = require('./gender/gender.js');
const emotion = require('./emotion/emotion.js'); const emotion = require('./emotion/emotion.js');
const posenet = require('./body/posenet.js'); const posenet = require('./body/posenet.js');
const handpose = require('./hand/handpose.js'); const handpose = require('./hand/handpose.js');
@ -13,8 +13,7 @@ const app = require('../package.json');
// static config override for non-video detection // static config override for non-video detection
const override = { const override = {
face: { detector: { skipFrames: 0 }, age: { skipFrames: 0 }, emotion: { skipFrames: 0 } }, face: { detector: { skipFrames: 0 }, age: { skipFrames: 0 }, gender: { skipFrames: 0 }, emotion: { skipFrames: 0 } }, hand: { skipFrames: 0 },
hand: { skipFrames: 0 },
}; };
// helper function: gets elapsed time on both browser and nodejs // helper function: gets elapsed time on both browser and nodejs
@ -46,7 +45,6 @@ class Human {
constructor() { constructor() {
this.tf = tf; this.tf = tf;
this.version = app.version; this.version = app.version;
this.defaults = defaults;
this.config = defaults; this.config = defaults;
this.fx = null; this.fx = null;
this.state = 'idle'; this.state = 'idle';
@ -114,7 +112,7 @@ class Human {
async load(userConfig) { async load(userConfig) {
this.state = 'load'; this.state = 'load';
const timeStamp = now(); const timeStamp = now();
if (userConfig) this.config = mergeDeep(defaults, userConfig); if (userConfig) this.config = mergeDeep(this.config, userConfig);
if (this.firstRun) { if (this.firstRun) {
this.checkBackend(true); this.checkBackend(true);
@ -300,7 +298,7 @@ class Human {
let timeStamp; let timeStamp;
// update configuration // update configuration
this.config = mergeDeep(defaults, userConfig); this.config = mergeDeep(this.config, userConfig);
if (!this.config.videoOptimized) this.config = mergeDeep(this.config, override); if (!this.config.videoOptimized) this.config = mergeDeep(this.config, override);
// sanity checks // sanity checks