mirror of https://github.com/vladmandic/human
improved caching and warmup
Vladimir Mandic
parent
4cf71ea08e
commit
cebd075047
12
config.js
12
config.js
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@ -26,7 +26,9 @@ export default {
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// must be disabled for images
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// basically this skips object box boundary detection for every n frames
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// while maintaining in-box detection since objects cannot move that fast
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warmup: 'full', // what to use for human.warmup(), can be 'none', 'face', 'full'
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// warmup pre-initializes all models for faster inference but can take
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// significant time on startup
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filter: {
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enabled: true, // enable image pre-processing filters
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width: 0, // resize input width
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@ -69,7 +71,7 @@ export default {
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// false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees
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maxFaces: 10, // maximum number of faces detected in the input
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// should be set to the minimum number for performance
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skipFrames: 20, // how many frames to go without re-running the face bounding box detector
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skipFrames: 11, // how many frames to go without re-running the face bounding box detector
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// only used for video inputs
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// e.g., if model is running st 25 FPS, we can re-use existing bounding
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// box for updated face analysis as the head probably hasn't moved much
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@ -99,7 +101,7 @@ export default {
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modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'
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// which determines training set for model
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inputSize: 64, // fixed value
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skipFrames: 41, // how many frames to go without re-running the detector
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skipFrames: 31, // how many frames to go without re-running the detector
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// only used for video inputs
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},
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@ -108,7 +110,7 @@ export default {
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minConfidence: 0.1, // threshold for discarding a prediction
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modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'
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inputSize: 64, // fixed value
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skipFrames: 42, // how many frames to go without re-running the detector
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skipFrames: 41, // how many frames to go without re-running the detector
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// only used for video inputs
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},
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@ -143,7 +145,7 @@ export default {
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rotation: false, // use best-guess rotated hand image or just box with rotation as-is
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// false means higher performance, but incorrect finger mapping if hand is inverted
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inputSize: 256, // fixed value
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skipFrames: 19, // how many frames to go without re-running the hand bounding box detector
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skipFrames: 12, // how many frames to go without re-running the hand bounding box detector
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// only used for video inputs
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// e.g., if model is running st 25 FPS, we can re-use existing bounding
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// box for updated hand skeleton analysis as the hand probably
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@ -37,7 +37,6 @@ const ui = {
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console: true,
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maxFPSframes: 10,
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modelsPreload: true,
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modelsWarmup: true,
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menuWidth: 0,
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menuHeight: 0,
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camera: {},
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@ -518,7 +517,7 @@ async function main() {
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status('loading');
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await human.load(userConfig); // this is not required, just pre-loads all models
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}
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if (ui.modelsWarmup && !ui.useWorker) {
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if (!ui.useWorker) {
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status('initializing');
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await human.warmup(userConfig); // this is not required, just pre-warms all models for faster initial inference
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}
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@ -1,7 +1,7 @@
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{
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"inputs": {
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"dist/human.esm.js": {
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"bytes": 1786155,
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"bytes": 1830186,
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"imports": []
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},
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"demo/draw.js": {
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"imports": []
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},
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"demo/browser.js": {
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"imports": [
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{
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"path": "dist/human.esm.js"
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@ -38,14 +38,14 @@
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"dist/demo-browser-index.js.map": {
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"imports": [],
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"inputs": {},
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"bytes": 2747800
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},
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"dist/demo-browser-index.js": {
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"imports": [],
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"exports": [],
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"inputs": {
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"dist/human.esm.js": {
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"bytesInOutput": 1778915
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"bytesInOutput": 1822946
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},
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"demo/draw.js": {
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}
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@ -36,7 +36,7 @@
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"imports": []
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},
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"src/face/facepipeline.js": {
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"imports": [
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{
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"path": "dist/tfjs.esm.js"
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]
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},
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"src/age/age.js": {
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"bytes": 1943,
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"bytes": 2017,
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"imports": [
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{
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"path": "src/log.js"
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@ -98,7 +98,7 @@
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]
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},
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"src/gender/gender.js": {
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"bytes": 3384,
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"bytes": 2886,
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"imports": [
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{
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"path": "src/log.js"
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@ -112,7 +112,7 @@
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]
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},
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"src/emotion/emotion.js": {
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"bytes": 2981,
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"imports": [
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{
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"path": "src/log.js"
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@ -264,7 +264,7 @@
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]
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},
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"src/hand/handdetector.js": {
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"bytes": 4252,
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"imports": [
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{
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"path": "dist/tfjs.esm.js"
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@ -279,7 +279,7 @@
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"imports": []
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},
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"src/hand/handpipeline.js": {
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"bytes": 7923,
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"imports": [
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"path": "dist/tfjs.esm.js"
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@ -342,7 +342,7 @@
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]
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},
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"config.js": {
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"bytes": 8990,
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"imports": []
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},
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"src/sample.js": {
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"imports": []
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},
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"src/human.js": {
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"bytes": 16524,
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"imports": [
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{
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"path": "src/log.js"
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"dist/human.esm.js.map": {
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"imports": [],
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"inputs": {},
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"bytesInOutput": 30817
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"src/face/facepipeline.js": {
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"bytesInOutput": 9323
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"bytesInOutput": 9314
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"src/face/facemesh.js": {
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"bytesInOutput": 846
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},
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"src/age/age.js": {
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"bytesInOutput": 1112
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},
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"src/gender/gender.js": {
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"bytesInOutput": 1858
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"bytesInOutput": 1915
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},
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"src/emotion/emotion.js": {
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"bytesInOutput": 1738
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"src/embedding/embedding.js": {
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"src/hand/handdetector.js": {
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"src/hand/handpipeline.js": {
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"src/hand/anchors.js": {
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"src/human.js": {
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"src/hand/box.js": {
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"imports": []
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},
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"src/face/facepipeline.js": {
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"path": "dist/tfjs.esm.js"
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"src/age/age.js": {
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"bytes": 1943,
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"bytes": 2017,
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"imports": [
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{
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"path": "src/log.js"
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"src/gender/gender.js": {
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"imports": [
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"path": "src/log.js"
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"src/emotion/emotion.js": {
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"bytes": 2981,
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"imports": [
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{
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"path": "src/log.js"
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"src/hand/handdetector.js": {
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"bytes": 4252,
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"imports": [
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{
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"path": "dist/tfjs.esm.js"
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"imports": []
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"src/hand/handpipeline.js": {
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"config.js": {
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"bytes": 8990,
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"src/sample.js": {
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"imports": []
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"src/human.js": {
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"bytes": 16524,
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"imports": [
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{
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"path": "src/log.js"
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"dist/human.js.map": {
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"imports": [],
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"inputs": {},
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"src/face/facepipeline.js": {
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"bytesInOutput": 9323
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"src/face/facemesh.js": {
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"src/age/age.js": {
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"src/gender/gender.js": {
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},
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"src/emotion/emotion.js": {
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"src/embedding/embedding.js": {
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"package.json": {
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}
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}
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const models = {};
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let last = { age: 0 };
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let frame = Number.MAX_SAFE_INTEGER;
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let skipped = Number.MAX_SAFE_INTEGER;
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async function load(config) {
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if (!models.age) {
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async function predict(image, config) {
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if (!models.age) return null;
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if ((frame < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {
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frame += 1;
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if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {
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skipped++;
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return last;
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}
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frame = 0;
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if (config.videoOptimized) skipped = 0;
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else skipped = Number.MAX_SAFE_INTEGER;
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return new Promise(async (resolve) => {
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/*
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const zoom = [0, 0]; // 0..1 meaning 0%..100%
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const annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surpise', 'neutral'];
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const models = {};
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let last = [];
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let frame = Number.MAX_SAFE_INTEGER;
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let skipped = Number.MAX_SAFE_INTEGER;
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// tuning values
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const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale
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async function predict(image, config) {
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if (!models.emotion) return null;
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if ((frame < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {
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frame += 1;
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if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {
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skipped++;
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return last;
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}
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frame = 0;
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if (config.videoOptimized) skipped = 0;
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else skipped = Number.MAX_SAFE_INTEGER;
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return new Promise(async (resolve) => {
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/*
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const zoom = [0, 0]; // 0..1 meaning 0%..100%
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this.meshHeight = config.face.mesh.inputSize;
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this.irisSize = config.face.iris.inputSize;
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this.irisEnlarge = 2.3;
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this.skipped = 1000;
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this.skipped = 0;
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this.detectedFaces = 0;
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}
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}
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||||
|
||||
async predict(input, config) {
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||||
this.skipped++;
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||||
let useFreshBox = false;
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||||
// run new detector every skipFrames unless we only want box to start with
|
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let detector;
|
||||
if ((this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {
|
||||
if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {
|
||||
detector = await this.boundingBoxDetector.getBoundingBoxes(input);
|
||||
// don't reset on test image
|
||||
if ((input.shape[1] !== 255) && (input.shape[2] !== 255)) this.skipped = 0;
|
||||
this.skipped = 0;
|
||||
}
|
||||
if (config.videoOptimized) this.skipped++;
|
||||
|
||||
// if detector result count doesn't match current working set, use it to reset current working set
|
||||
if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {
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|
|
@ -172,7 +171,7 @@ class Pipeline {
|
|||
});
|
||||
}
|
||||
|
||||
// log(this.skipped, config.face.detector.skipFrames, this.detectedFaces, config.face.detector.maxFaces, detector?.boxes.length, this.storedBoxes.length);
|
||||
// log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);
|
||||
let results = tf.tidy(() => this.storedBoxes.map((box, i) => {
|
||||
// The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).
|
||||
let face;
|
||||
|
|
@ -207,7 +206,6 @@ class Pipeline {
|
|||
const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.
|
||||
const confidenceVal = confidence.dataSync()[0];
|
||||
if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit
|
||||
|
||||
const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);
|
||||
let rawCoords = coordsReshaped.arraySync();
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ import * as profile from '../profile.js';
|
|||
|
||||
const models = {};
|
||||
let last = { gender: '' };
|
||||
let frame = Number.MAX_SAFE_INTEGER;
|
||||
let skipped = Number.MAX_SAFE_INTEGER;
|
||||
let alternative = false;
|
||||
|
||||
// tuning values
|
||||
|
|
@ -21,22 +21,13 @@ async function load(config) {
|
|||
|
||||
async function predict(image, config) {
|
||||
if (!models.gender) return null;
|
||||
if ((frame < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {
|
||||
frame += 1;
|
||||
if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {
|
||||
skipped++;
|
||||
return last;
|
||||
}
|
||||
frame = 0;
|
||||
if (config.videoOptimized) skipped = 0;
|
||||
else skipped = Number.MAX_SAFE_INTEGER;
|
||||
return new Promise(async (resolve) => {
|
||||
/*
|
||||
const zoom = [0, 0]; // 0..1 meaning 0%..100%
|
||||
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.gender.inputSize, config.face.gender.inputSize]);
|
||||
*/
|
||||
const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);
|
||||
let enhance;
|
||||
if (alternative) {
|
||||
|
|
@ -51,7 +42,6 @@ async function predict(image, config) {
|
|||
} else {
|
||||
enhance = tf.mul(resize, [255.0]);
|
||||
}
|
||||
// const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);
|
||||
tf.dispose(resize);
|
||||
|
||||
let genderT;
|
||||
|
|
|
|||
|
|
@ -81,8 +81,8 @@ class HandDetector {
|
|||
const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));
|
||||
const predictions = await this.getBoxes(image, config);
|
||||
image.dispose();
|
||||
if (!predictions || predictions.length === 0) return null;
|
||||
const hands = [];
|
||||
if (!predictions || predictions.length === 0) return hands;
|
||||
for (const prediction of predictions) {
|
||||
const boxes = prediction.box.dataSync();
|
||||
const startPoint = boxes.slice(0, 2);
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ class HandPipeline {
|
|||
this.landmarkDetector = landmarkDetector;
|
||||
this.inputSize = inputSize;
|
||||
this.storedBoxes = [];
|
||||
this.skipped = 1000;
|
||||
this.skipped = 0;
|
||||
this.detectedHands = 0;
|
||||
}
|
||||
|
||||
|
|
@ -84,16 +84,15 @@ class HandPipeline {
|
|||
}
|
||||
|
||||
async estimateHands(image, config) {
|
||||
this.skipped++;
|
||||
let useFreshBox = false;
|
||||
|
||||
// run new detector every skipFrames unless we only want box to start with
|
||||
let boxes;
|
||||
if ((this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {
|
||||
if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {
|
||||
boxes = await this.handDetector.estimateHandBounds(image, config);
|
||||
// don't reset on test image
|
||||
if ((image.shape[1] !== 255) && (image.shape[2] !== 255)) this.skipped = 0;
|
||||
this.skipped = 0;
|
||||
}
|
||||
if (config.videoOptimized) this.skipped++;
|
||||
|
||||
// if detector result count doesn't match current working set, use it to reset current working set
|
||||
if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {
|
||||
|
|
@ -103,7 +102,7 @@ class HandPipeline {
|
|||
if (this.storedBoxes.length > 0) useFreshBox = true;
|
||||
}
|
||||
const hands = [];
|
||||
// log(`skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);
|
||||
// log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);
|
||||
|
||||
// go through working set of boxes
|
||||
for (let i = 0; i < this.storedBoxes.length; i++) {
|
||||
|
|
|
|||
36
src/human.js
36
src/human.js
|
|
@ -418,26 +418,42 @@ class Human {
|
|||
|
||||
async warmup(userConfig) {
|
||||
if (userConfig) this.config = mergeDeep(this.config, userConfig);
|
||||
const width = 256;
|
||||
const height = 256;
|
||||
return new Promise((resolve) => {
|
||||
const video = this.config.videoOptimized;
|
||||
this.config.videoOptimized = false;
|
||||
return new Promise((resolve) => {
|
||||
const img = new Image(width, height);
|
||||
let src;
|
||||
let size;
|
||||
switch (this.config.warmup) {
|
||||
case 'face':
|
||||
size = 256;
|
||||
src = sample.face;
|
||||
break;
|
||||
case 'full':
|
||||
size = 1200;
|
||||
src = sample.body;
|
||||
break;
|
||||
default:
|
||||
size = 0;
|
||||
src = null;
|
||||
}
|
||||
const img = new Image(size, size);
|
||||
img.onload = () => {
|
||||
const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(width, height) : document.createElement('canvas');
|
||||
canvas.width = width;
|
||||
canvas.height = height;
|
||||
const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');
|
||||
canvas.width = size;
|
||||
canvas.height = size;
|
||||
const ctx = canvas.getContext('2d');
|
||||
ctx.drawImage(img, 0, 0);
|
||||
const data = ctx.getImageData(0, 0, width, height);
|
||||
const data = ctx.getImageData(0, 0, size, size);
|
||||
const t0 = now();
|
||||
this.detect(data, config).then((warmup) => {
|
||||
log('Warmup', warmup);
|
||||
const t1 = now();
|
||||
log('Warmup', this.config.warmup, (t1 - t0), warmup);
|
||||
this.config.videoOptimized = video;
|
||||
resolve(warmup);
|
||||
});
|
||||
};
|
||||
img.src = sample.face;
|
||||
if (src) img.src = src;
|
||||
else resolve(null);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
|
|
|||
2
wiki
2
wiki
|
|
@ -1 +1 @@
|
|||
Subproject commit 785bde4caa1a29d8bfe82a4ae987ffde1d9a0a73
|
||||
Subproject commit c4c8b30f6bf211ee267cf1884aaff9725f594631
|
||||
Loading…
Reference in New Issue