mirror of https://github.com/vladmandic/human
updated typings
Vladimir Mandic
parent
6fb133b55e
commit
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// @ts-nocheck
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/* eslint-disable max-len */
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// based on: https://github.com/munrocket/gl-bench
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// @ts-nocheck
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let instance = 0;
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let CSScreated = false;
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{
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"inputs": {
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"dist/human.esm.js": {
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"bytes": 1343005,
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"bytes": 1342808,
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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/menu.js": {
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"imports": []
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},
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"bytes": 10797,
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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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"exports": [],
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"inputs": {},
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"bytes": 2018414
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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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"demo/draw.js": {
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"imports": []
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},
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"src/tfjs/backend.ts": {
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"bytes": 2168,
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"imports": [
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{
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"path": "src/log.ts",
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]
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},
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"src/blazeface/blazeface.ts": {
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"imports": [
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{
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"path": "src/log.ts",
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]
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},
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"src/blazeface/util.ts": {
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"bytes": 2777,
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"imports": []
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},
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"src/blazeface/coords.ts": {
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"imports": []
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},
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"src/blazeface/facepipeline.ts": {
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]
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},
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"src/blazeface/facemesh.ts": {
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"bytes": 2935,
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"imports": [
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{
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"path": "src/log.ts",
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},
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"src/faceboxes/faceboxes.ts": {
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"imports": [
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{
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"path": "src/log.ts",
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"src/age/age.ts": {
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"imports": [
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{
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"path": "src/log.ts",
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]
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"src/gender/gender.ts": {
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"imports": [
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"path": "src/log.ts",
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"src/embedding/embedding.ts": {
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"imports": [
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{
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"path": "src/log.ts",
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]
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},
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"src/posenet/decoders.ts": {
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"bytes": 1943,
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"bytes": 1958,
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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/posenet/decodeMultiple.ts": {
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"bytes": 2259,
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"bytes": 2301,
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"imports": [
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{
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"path": "src/posenet/buildParts.ts",
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]
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},
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"src/handpose/handdetector.ts": {
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"imports": [
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"path": "dist/tfjs.esm.js",
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]
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},
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"src/handpose/util.ts": {
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"imports": []
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"src/handpose/handpipeline.ts": {
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"path": "dist/tfjs.esm.js",
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"imports": []
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"path": "src/log.ts",
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]
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},
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"bytes": 4265,
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"bytes": 4497,
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"imports": []
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"src/imagefx.js": {
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"imports": []
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"bytes": 2385,
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"imports": []
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"path": "src/log.ts",
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"dist/human.esm.js": {
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},
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"src/tfjs/backend.ts": {
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"bytes": 6983,
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"path": "src/log.ts",
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"path": "dist/tfjs.esm.js",
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"path": "dist/tfjs.esm.js",
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},
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{
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"path": "src/blazeface/box.js",
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"path": "src/blazeface/box.ts",
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"kind": "import-statement"
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},
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{
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"path": "src/blazeface/util.js",
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"path": "src/blazeface/util.ts",
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"kind": "import-statement"
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"path": "src/blazeface/coords.js",
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{
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"path": "src/log.ts",
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"path": "src/blazeface/coords.ts",
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"kind": "import-statement"
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"path": "src/log.ts",
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"kind": "import-statement"
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},
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{
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"path": "src/blazeface/blazeface.js",
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"path": "src/blazeface/blazeface.ts",
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"kind": "import-statement"
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},
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{
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"path": "src/blazeface/facepipeline.js",
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"path": "src/blazeface/facepipeline.ts",
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},
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{
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"path": "src/blazeface/coords.js",
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"path": "src/blazeface/coords.ts",
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||||
"path": "src/log.ts",
|
||||
|
|
@ -227,7 +227,7 @@
|
|||
]
|
||||
},
|
||||
"src/posenet/decoders.ts": {
|
||||
"bytes": 1943,
|
||||
"bytes": 1958,
|
||||
"imports": [
|
||||
{
|
||||
"path": "dist/tfjs.esm.js",
|
||||
|
|
@ -257,7 +257,7 @@
|
|||
]
|
||||
},
|
||||
"src/posenet/decodeMultiple.ts": {
|
||||
"bytes": 2259,
|
||||
"bytes": 2301,
|
||||
"imports": [
|
||||
{
|
||||
"path": "src/posenet/buildParts.ts",
|
||||
|
|
@ -321,7 +321,7 @@
|
|||
]
|
||||
},
|
||||
"src/handpose/handdetector.ts": {
|
||||
"bytes": 3627,
|
||||
"bytes": 3701,
|
||||
"imports": [
|
||||
{
|
||||
"path": "dist/tfjs.esm.js",
|
||||
|
|
@ -334,11 +334,11 @@
|
|||
]
|
||||
},
|
||||
"src/handpose/util.ts": {
|
||||
"bytes": 2254,
|
||||
"bytes": 2286,
|
||||
"imports": []
|
||||
},
|
||||
"src/handpose/handpipeline.ts": {
|
||||
"bytes": 7344,
|
||||
"bytes": 7279,
|
||||
"imports": [
|
||||
{
|
||||
"path": "dist/tfjs.esm.js",
|
||||
|
|
@ -359,7 +359,7 @@
|
|||
"imports": []
|
||||
},
|
||||
"src/handpose/handpose.ts": {
|
||||
"bytes": 2529,
|
||||
"bytes": 2565,
|
||||
"imports": [
|
||||
{
|
||||
"path": "src/log.ts",
|
||||
|
|
@ -384,7 +384,7 @@
|
|||
]
|
||||
},
|
||||
"src/gesture/gesture.ts": {
|
||||
"bytes": 4265,
|
||||
"bytes": 4497,
|
||||
"imports": []
|
||||
},
|
||||
"src/imagefx.js": {
|
||||
|
|
@ -392,7 +392,7 @@
|
|||
"imports": []
|
||||
},
|
||||
"src/image.ts": {
|
||||
"bytes": 5851,
|
||||
"bytes": 5867,
|
||||
"imports": [
|
||||
{
|
||||
"path": "src/log.ts",
|
||||
|
|
@ -417,11 +417,11 @@
|
|||
"imports": []
|
||||
},
|
||||
"package.json": {
|
||||
"bytes": 2344,
|
||||
"bytes": 2385,
|
||||
"imports": []
|
||||
},
|
||||
"src/human.ts": {
|
||||
"bytes": 19393,
|
||||
"bytes": 19619,
|
||||
"imports": [
|
||||
{
|
||||
"path": "src/log.ts",
|
||||
|
|
@ -499,7 +499,7 @@
|
|||
"imports": [],
|
||||
"exports": [],
|
||||
"inputs": {},
|
||||
"bytes": 1923057
|
||||
"bytes": 1923340
|
||||
},
|
||||
"dist/human.ts": {
|
||||
"imports": [],
|
||||
|
|
@ -515,7 +515,7 @@
|
|||
"bytesInOutput": 11016
|
||||
},
|
||||
"src/human.ts": {
|
||||
"bytesInOutput": 10259
|
||||
"bytesInOutput": 10331
|
||||
},
|
||||
"src/log.ts": {
|
||||
"bytesInOutput": 252
|
||||
|
|
@ -539,7 +539,7 @@
|
|||
"bytesInOutput": 28983
|
||||
},
|
||||
"src/blazeface/facepipeline.ts": {
|
||||
"bytesInOutput": 5046
|
||||
"bytesInOutput": 5040
|
||||
},
|
||||
"src/faceboxes/faceboxes.ts": {
|
||||
"bytesInOutput": 1549
|
||||
|
|
@ -548,16 +548,16 @@
|
|||
"bytesInOutput": 606
|
||||
},
|
||||
"src/age/age.ts": {
|
||||
"bytesInOutput": 826
|
||||
"bytesInOutput": 775
|
||||
},
|
||||
"src/gender/gender.ts": {
|
||||
"bytesInOutput": 1309
|
||||
"bytesInOutput": 1237
|
||||
},
|
||||
"src/emotion/emotion.ts": {
|
||||
"bytesInOutput": 1243
|
||||
"bytesInOutput": 1180
|
||||
},
|
||||
"src/embedding/embedding.ts": {
|
||||
"bytesInOutput": 802
|
||||
"bytesInOutput": 725
|
||||
},
|
||||
"src/posenet/posenet.ts": {
|
||||
"bytesInOutput": 1007
|
||||
|
|
@ -620,7 +620,7 @@
|
|||
"bytesInOutput": 16
|
||||
}
|
||||
},
|
||||
"bytes": 1343047
|
||||
"bytes": 1342850
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -2527,9 +2527,9 @@
|
|||
"dev": true
|
||||
},
|
||||
"typescript": {
|
||||
"version": "4.1.3",
|
||||
"resolved": "https://registry.npmjs.org/typescript/-/typescript-4.1.3.tgz",
|
||||
"integrity": "sha512-B3ZIOf1IKeH2ixgHhj6la6xdwR9QrLC5d1VKeCSY4tvkqhF2eqd9O7txNlS0PO3GrBAFIdr3L1ndNwteUbZLYg==",
|
||||
"version": "4.2.0-dev.20210208",
|
||||
"resolved": "https://registry.npmjs.org/typescript/-/typescript-4.2.0-dev.20210208.tgz",
|
||||
"integrity": "sha512-zZD4z9MS8iy9dPxwBeEcQDemi13rfdAk7oCyAaiMQXsw1rcdmdsNGjpaQ8OZiY3I5Y/vaoDCu7yBB1ZaNSemLA==",
|
||||
"dev": true
|
||||
},
|
||||
"uri-js": {
|
||||
|
|
|
|||
|
|
@ -46,7 +46,7 @@
|
|||
"seedrandom": "^3.0.5",
|
||||
"simple-git": "^2.34.2",
|
||||
"tslib": "^2.1.0",
|
||||
"typescript": "^4.1.3"
|
||||
"typescript": "^4.2.0-dev.20210208"
|
||||
},
|
||||
"scripts": {
|
||||
"start": "node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js",
|
||||
|
|
|
|||
122
server/build.js
122
server/build.js
|
|
@ -1,8 +1,9 @@
|
|||
#!/usr/bin/env -S node --trace-warnings
|
||||
|
||||
const fs = require('fs');
|
||||
const esbuild = require('esbuild');
|
||||
const log = require('@vladmandic/pilogger');
|
||||
const esbuild = require('esbuild');
|
||||
const ts = require('typescript');
|
||||
|
||||
// keeps esbuild service instance cached
|
||||
let es;
|
||||
|
|
@ -15,6 +16,29 @@ const banner = `
|
|||
*/
|
||||
`;
|
||||
|
||||
// tsc configuration for building types only
|
||||
const tsconfig = {
|
||||
noEmitOnError: false,
|
||||
target: ts.ScriptTarget.ES2018,
|
||||
module: ts.ModuleKind.ES2020,
|
||||
outDir: 'types/',
|
||||
declaration: true,
|
||||
emitDeclarationOnly: true,
|
||||
emitDecoratorMetadata: true,
|
||||
experimentalDecorators: true,
|
||||
skipLibCheck: true,
|
||||
importHelpers: true,
|
||||
noImplicitAny: false,
|
||||
preserveConstEnums: true,
|
||||
strictNullChecks: true,
|
||||
baseUrl: './',
|
||||
typeRoots: ['node_modules/@types'],
|
||||
paths: {
|
||||
tslib: ['node_modules/tslib/tslib.d.ts'],
|
||||
'@tensorflow/tfjs-node/dist/io/file_system': ['node_modules/@tensorflow/tfjs-node/dist/io/file_system.js'],
|
||||
},
|
||||
};
|
||||
|
||||
// common configuration
|
||||
const common = {
|
||||
banner,
|
||||
|
|
@ -29,42 +53,6 @@ const common = {
|
|||
};
|
||||
|
||||
const targets = {
|
||||
node: {
|
||||
tfjs: {
|
||||
platform: 'node',
|
||||
format: 'cjs',
|
||||
metafile: 'dist/tfjs.esm.json',
|
||||
entryPoints: ['src/tfjs/tf-node.ts'],
|
||||
outfile: 'dist/tfjs.esm.js',
|
||||
external: ['@tensorflow'],
|
||||
},
|
||||
node: {
|
||||
platform: 'node',
|
||||
format: 'cjs',
|
||||
metafile: 'dist/human.node.json',
|
||||
entryPoints: ['src/human.ts'],
|
||||
outfile: 'dist/human.node.js',
|
||||
external: ['@tensorflow'],
|
||||
},
|
||||
},
|
||||
nodeGPU: {
|
||||
tfjs: {
|
||||
platform: 'node',
|
||||
format: 'cjs',
|
||||
metafile: 'dist/tfjs.esm.json',
|
||||
entryPoints: ['src/tfjs/tf-node-gpu.ts'],
|
||||
outfile: 'dist/tfjs.esm.js',
|
||||
external: ['@tensorflow'],
|
||||
},
|
||||
node: {
|
||||
platform: 'node',
|
||||
format: 'cjs',
|
||||
metafile: 'dist/human.node.json',
|
||||
entryPoints: ['src/human.ts'],
|
||||
outfile: 'dist/human.node-gpu.js',
|
||||
external: ['@tensorflow'],
|
||||
},
|
||||
},
|
||||
browserNoBundle: {
|
||||
tfjs: {
|
||||
platform: 'browser',
|
||||
|
|
@ -118,6 +106,42 @@ const targets = {
|
|||
external: ['fs', 'buffer', 'util'],
|
||||
},
|
||||
},
|
||||
node: {
|
||||
tfjs: {
|
||||
platform: 'node',
|
||||
format: 'cjs',
|
||||
metafile: 'dist/tfjs.esm.json',
|
||||
entryPoints: ['src/tfjs/tf-node.ts'],
|
||||
outfile: 'dist/tfjs.esm.js',
|
||||
external: ['@tensorflow'],
|
||||
},
|
||||
node: {
|
||||
platform: 'node',
|
||||
format: 'cjs',
|
||||
metafile: 'dist/human.node.json',
|
||||
entryPoints: ['src/human.ts'],
|
||||
outfile: 'dist/human.node.js',
|
||||
external: ['@tensorflow'],
|
||||
},
|
||||
},
|
||||
nodeGPU: {
|
||||
tfjs: {
|
||||
platform: 'node',
|
||||
format: 'cjs',
|
||||
metafile: 'dist/tfjs.esm.json',
|
||||
entryPoints: ['src/tfjs/tf-node-gpu.ts'],
|
||||
outfile: 'dist/tfjs.esm.js',
|
||||
external: ['@tensorflow'],
|
||||
},
|
||||
node: {
|
||||
platform: 'node',
|
||||
format: 'cjs',
|
||||
metafile: 'dist/human.node.json',
|
||||
entryPoints: ['src/human.ts'],
|
||||
outfile: 'dist/human.node-gpu.js',
|
||||
external: ['@tensorflow'],
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
async function getStats(metafile) {
|
||||
|
|
@ -147,6 +171,28 @@ async function getStats(metafile) {
|
|||
return stats;
|
||||
}
|
||||
|
||||
// rebuild typings
|
||||
function compile(fileNames, options) {
|
||||
log.info('Compile:', fileNames);
|
||||
const program = ts.createProgram(fileNames, options);
|
||||
const emit = program.emit();
|
||||
const diag = ts
|
||||
.getPreEmitDiagnostics(program)
|
||||
.concat(emit.diagnostics);
|
||||
for (const info of diag) {
|
||||
// @ts-ignore
|
||||
const msg = info.messageText.messageText || info.messageText;
|
||||
if (msg.includes('package.json')) continue;
|
||||
if (msg.includes('Expected 0 arguments, but got 1')) continue;
|
||||
if (info.file) {
|
||||
const pos = info.file.getLineAndCharacterOfPosition(info.start || 0);
|
||||
log.error(`TSC: ${info.file.fileName} [${pos.line + 1},${pos.character + 1}]:`, msg);
|
||||
} else {
|
||||
log.error('TSC:', msg);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// rebuild on file change
|
||||
async function build(f, msg) {
|
||||
if (busy) {
|
||||
|
|
@ -169,6 +215,8 @@ async function build(f, msg) {
|
|||
log.state(`Build for: ${targetGroupName} type: ${targetName}:`, stats);
|
||||
}
|
||||
}
|
||||
// generate typings
|
||||
compile(targets.browserBundle.esm.entryPoints, tsconfig);
|
||||
if (require.main === module) process.exit(0);
|
||||
} catch (err) {
|
||||
// catch errors and print where it occured
|
||||
|
|
|
|||
|
|
@ -2,20 +2,20 @@ import { log } from '../log';
|
|||
import * as tf from '../../dist/tfjs.esm.js';
|
||||
import * as profile from '../profile.js';
|
||||
|
||||
const models = { age: null };
|
||||
let model;
|
||||
let last = { age: 0 };
|
||||
let skipped = Number.MAX_SAFE_INTEGER;
|
||||
|
||||
export async function load(config) {
|
||||
if (!models.age) {
|
||||
models.age = await tf.loadGraphModel(config.face.age.modelPath);
|
||||
if (!model) {
|
||||
model = await tf.loadGraphModel(config.face.age.modelPath);
|
||||
log(`load model: ${config.face.age.modelPath.match(/\/(.*)\./)[1]}`);
|
||||
}
|
||||
return models.age;
|
||||
return model;
|
||||
}
|
||||
|
||||
export async function predict(image, config) {
|
||||
if (!models.age) return null;
|
||||
if (!model) return null;
|
||||
if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {
|
||||
skipped++;
|
||||
return last;
|
||||
|
|
@ -38,13 +38,12 @@ export async function predict(image, config) {
|
|||
tf.dispose(resize);
|
||||
|
||||
let ageT;
|
||||
const obj = { age: undefined };
|
||||
const obj = { age: 0 };
|
||||
|
||||
if (models.age) {
|
||||
if (!config.profile) {
|
||||
if (config.face.age.enabled) ageT = await models.age.predict(enhance);
|
||||
if (config.face.age.enabled) ageT = await model.predict(enhance);
|
||||
} else {
|
||||
const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};
|
||||
const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};
|
||||
ageT = profileAge.result.clone();
|
||||
profileAge.result.dispose();
|
||||
profile.run('age', profileAge);
|
||||
|
|
@ -58,7 +57,6 @@ export async function predict(image, config) {
|
|||
ageT.dispose();
|
||||
|
||||
last = obj;
|
||||
}
|
||||
resolve(obj);
|
||||
});
|
||||
}
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ const NUM_LANDMARKS = 6;
|
|||
|
||||
function generateAnchors(inputSize) {
|
||||
const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };
|
||||
const anchors = [];
|
||||
const anchors: Array<[number, number]> = [];
|
||||
for (let i = 0; i < spec.strides.length; i++) {
|
||||
const stride = spec.strides[i];
|
||||
const gridRows = Math.floor((inputSize + stride - 1) / stride);
|
||||
|
|
@ -121,7 +121,7 @@ export class BlazeFaceModel {
|
|||
});
|
||||
|
||||
const scoresVal = scores.dataSync();
|
||||
const annotatedBoxes = [];
|
||||
const annotatedBoxes: Array<{ box: any, landmarks: any, anchor: any, confidence: number }> = [];
|
||||
for (let i = 0; i < boundingBoxes.length; i++) {
|
||||
const boxIndex = boxIndices[i];
|
||||
const confidence = scoresVal[boxIndex];
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ export class MediaPipeFaceMesh {
|
|||
|
||||
async estimateFaces(input, config) {
|
||||
const predictions = await this.facePipeline.predict(input, config);
|
||||
const results = [];
|
||||
const results: Array<{}> = [];
|
||||
for (const prediction of (predictions || [])) {
|
||||
if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing
|
||||
const mesh = prediction.coords ? prediction.coords.arraySync() : null;
|
||||
|
|
@ -33,15 +33,7 @@ export class MediaPipeFaceMesh {
|
|||
Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],
|
||||
Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],
|
||||
] : 0;
|
||||
results.push({
|
||||
confidence: prediction.confidence || 0,
|
||||
box,
|
||||
mesh,
|
||||
boxRaw,
|
||||
meshRaw,
|
||||
annotations,
|
||||
image: prediction.image ? tf.clone(prediction.image) : null,
|
||||
});
|
||||
results.push({ confidence: prediction.confidence || 0, box, mesh, boxRaw, meshRaw, annotations, image: prediction.image ? tf.clone(prediction.image) : null });
|
||||
if (prediction.coords) prediction.coords.dispose();
|
||||
if (prediction.image) prediction.image.dispose();
|
||||
}
|
||||
|
|
@ -51,6 +43,7 @@ export class MediaPipeFaceMesh {
|
|||
|
||||
let faceModels = [null, null, null];
|
||||
export async function load(config) {
|
||||
// @ts-ignore
|
||||
faceModels = await Promise.all([
|
||||
(!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,
|
||||
(!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,
|
||||
|
|
|
|||
|
|
@ -26,8 +26,8 @@ function replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {
|
|||
for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {
|
||||
const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];
|
||||
const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];
|
||||
const shouldReplaceAllKeys = keys === null;
|
||||
if (shouldReplaceAllKeys || keys.includes(key)) {
|
||||
// @ts-ignore
|
||||
if (!keys || keys.includes(key)) {
|
||||
for (let j = 0; j < indices.length; j++) {
|
||||
const index = indices[j];
|
||||
rawCoords[originalIndices[j]] = [
|
||||
|
|
@ -108,15 +108,13 @@ export class Pipeline {
|
|||
|
||||
// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.
|
||||
getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {
|
||||
const eyeRawCoords = [];
|
||||
const eyeRawCoords: Array<any[]> = [];
|
||||
for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {
|
||||
const x = eyeData[i * 3];
|
||||
const y = eyeData[i * 3 + 1];
|
||||
const z = eyeData[i * 3 + 2];
|
||||
eyeRawCoords.push([
|
||||
(flip
|
||||
? (1 - (x / this.irisSize))
|
||||
: (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],
|
||||
(flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],
|
||||
(y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,
|
||||
]);
|
||||
}
|
||||
|
|
@ -237,8 +235,10 @@ export class Pipeline {
|
|||
replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');
|
||||
// If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.
|
||||
} else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.
|
||||
// @ts-ignore
|
||||
replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);
|
||||
} else { // User is looking towards the left.
|
||||
// @ts-ignore
|
||||
replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);
|
||||
}
|
||||
const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@ export function dot(v1, v2) {
|
|||
}
|
||||
|
||||
export function getColumnFrom2DArr(arr, columnIndex) {
|
||||
const column = [];
|
||||
const column: Array<number> = [];
|
||||
for (let i = 0; i < arr.length; i++) {
|
||||
column.push(arr[i][columnIndex]);
|
||||
}
|
||||
|
|
@ -42,7 +42,7 @@ export function getColumnFrom2DArr(arr, columnIndex) {
|
|||
}
|
||||
|
||||
export function multiplyTransformMatrices(mat1, mat2) {
|
||||
const product = [];
|
||||
const product: Array<number[]> = [];
|
||||
const size = mat1.length;
|
||||
for (let row = 0; row < size; row++) {
|
||||
product.push([]);
|
||||
|
|
|
|||
|
|
@ -5,14 +5,14 @@ import * as profile from '../profile.js';
|
|||
// based on https://github.com/sirius-ai/MobileFaceNet_TF
|
||||
// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip
|
||||
|
||||
const models = { embedding: null };
|
||||
let model;
|
||||
|
||||
export async function load(config) {
|
||||
if (!models.embedding) {
|
||||
models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);
|
||||
if (!model) {
|
||||
model = await tf.loadGraphModel(config.face.embedding.modelPath);
|
||||
log(`load model: ${config.face.embedding.modelPath.match(/\/(.*)\./)[1]}`);
|
||||
}
|
||||
return models.embedding;
|
||||
return model;
|
||||
}
|
||||
|
||||
export function simmilarity(embedding1, embedding2) {
|
||||
|
|
@ -25,18 +25,18 @@ export function simmilarity(embedding1, embedding2) {
|
|||
}
|
||||
|
||||
export async function predict(image, config) {
|
||||
if (!models.embedding) return null;
|
||||
if (!model) return null;
|
||||
return new Promise(async (resolve) => {
|
||||
const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);
|
||||
// const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???
|
||||
let data = [];
|
||||
let data: Array<[]> = [];
|
||||
if (config.face.embedding.enabled) {
|
||||
if (!config.profile) {
|
||||
const embeddingT = await models.embedding.predict({ img_inputs: resize });
|
||||
const embeddingT = await model.predict({ img_inputs: resize });
|
||||
data = [...embeddingT.dataSync()]; // convert object array to standard array
|
||||
tf.dispose(embeddingT);
|
||||
} else {
|
||||
const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));
|
||||
const profileData = await tf.profile(() => model.predict({ img_inputs: resize }));
|
||||
data = [...profileData.result.dataSync()];
|
||||
profileData.result.dispose();
|
||||
profile.run('emotion', profileData);
|
||||
|
|
|
|||
|
|
@ -3,8 +3,8 @@ import * as tf from '../../dist/tfjs.esm.js';
|
|||
import * as profile from '../profile.js';
|
||||
|
||||
const annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];
|
||||
const models = { emotion: null };
|
||||
let last = [];
|
||||
let model;
|
||||
let last: Array<{ score: number, emotion: string }> = [];
|
||||
let skipped = Number.MAX_SAFE_INTEGER;
|
||||
|
||||
// tuning values
|
||||
|
|
@ -12,15 +12,15 @@ const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when
|
|||
const scale = 1; // score multiplication factor
|
||||
|
||||
export async function load(config) {
|
||||
if (!models.emotion) {
|
||||
models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);
|
||||
if (!model) {
|
||||
model = await tf.loadGraphModel(config.face.emotion.modelPath);
|
||||
log(`load model: ${config.face.emotion.modelPath.match(/\/(.*)\./)[1]}`);
|
||||
}
|
||||
return models.emotion;
|
||||
return model;
|
||||
}
|
||||
|
||||
export async function predict(image, config) {
|
||||
if (!models.emotion) return null;
|
||||
if (!model) return null;
|
||||
if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {
|
||||
skipped++;
|
||||
return last;
|
||||
|
|
@ -54,15 +54,15 @@ export async function predict(image, config) {
|
|||
blueNorm.dispose();
|
||||
const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));
|
||||
grayscale.dispose();
|
||||
const obj = [];
|
||||
const obj: Array<{ score: number, emotion: string }> = [];
|
||||
if (config.face.emotion.enabled) {
|
||||
let data;
|
||||
if (!config.profile) {
|
||||
const emotionT = await models.emotion.predict(normalize);
|
||||
const emotionT = await model.predict(normalize);
|
||||
data = emotionT.dataSync();
|
||||
tf.dispose(emotionT);
|
||||
} else {
|
||||
const profileData = await tf.profile(() => models.emotion.predict(normalize));
|
||||
const profileData = await tf.profile(() => model.predict(normalize));
|
||||
data = profileData.result.dataSync();
|
||||
profileData.result.dispose();
|
||||
profile.run('emotion', profileData);
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ export class FaceBoxes {
|
|||
|
||||
async estimateFaces(input, config) {
|
||||
if (config) this.config = config;
|
||||
const results = [];
|
||||
const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];
|
||||
const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);
|
||||
const castT = resizeT.toInt();
|
||||
let scores;
|
||||
|
|
@ -51,15 +51,8 @@ export class FaceBoxes {
|
|||
const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);
|
||||
const image = resized.div([255]);
|
||||
resized.dispose();
|
||||
results.push({
|
||||
confidence: scores[i],
|
||||
box,
|
||||
boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null,
|
||||
image,
|
||||
// mesh,
|
||||
// meshRaw,
|
||||
// annotations,
|
||||
});
|
||||
results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image, });
|
||||
// add mesh, meshRaw, annotations,
|
||||
}
|
||||
}
|
||||
return results;
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ import { log } from '../log';
|
|||
import * as tf from '../../dist/tfjs.esm.js';
|
||||
import * as profile from '../profile.js';
|
||||
|
||||
const models = { gender: null };
|
||||
let model;
|
||||
let last = { gender: '' };
|
||||
let skipped = Number.MAX_SAFE_INTEGER;
|
||||
let alternative = false;
|
||||
|
|
@ -11,16 +11,16 @@ let alternative = false;
|
|||
const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale
|
||||
|
||||
export async function load(config) {
|
||||
if (!models.gender) {
|
||||
models.gender = await tf.loadGraphModel(config.face.gender.modelPath);
|
||||
alternative = models.gender.inputs[0].shape[3] === 1;
|
||||
if (!model) {
|
||||
model = await tf.loadGraphModel(config.face.gender.modelPath);
|
||||
alternative = model.inputs[0].shape[3] === 1;
|
||||
log(`load model: ${config.face.gender.modelPath.match(/\/(.*)\./)[1]}`);
|
||||
}
|
||||
return models.gender;
|
||||
return model;
|
||||
}
|
||||
|
||||
export async function predict(image, config) {
|
||||
if (!models.gender) return null;
|
||||
if (!model) return null;
|
||||
if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {
|
||||
skipped++;
|
||||
return last;
|
||||
|
|
@ -45,12 +45,12 @@ export async function predict(image, config) {
|
|||
tf.dispose(resize);
|
||||
|
||||
let genderT;
|
||||
const obj = { gender: undefined, confidence: undefined };
|
||||
const obj = { gender: '', confidence: 0 };
|
||||
|
||||
if (!config.profile) {
|
||||
if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);
|
||||
if (config.face.gender.enabled) genderT = await model.predict(enhance);
|
||||
} else {
|
||||
const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};
|
||||
const profileGender = config.face.gender.enabled ? await tf.profile(() => model.predict(enhance)) : {};
|
||||
genderT = profileGender.result.clone();
|
||||
profileGender.result.dispose();
|
||||
profile.run('gender', profileGender);
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
export const body = (res) => {
|
||||
if (!res) return [];
|
||||
const gestures = [];
|
||||
const gestures: Array<{ body: number, gesture: string }> = [];
|
||||
for (let i = 0; i < res.length; i++) {
|
||||
// raising hands
|
||||
const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));
|
||||
|
|
@ -20,7 +20,7 @@ export const body = (res) => {
|
|||
|
||||
export const face = (res) => {
|
||||
if (!res) return [];
|
||||
const gestures = [];
|
||||
const gestures: Array<{ face: number, gesture: string }> = [];
|
||||
for (let i = 0; i < res.length; i++) {
|
||||
if (res[i].mesh && res[i].mesh.length > 0) {
|
||||
const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];
|
||||
|
|
@ -41,7 +41,7 @@ export const face = (res) => {
|
|||
|
||||
export const iris = (res) => {
|
||||
if (!res) return [];
|
||||
const gestures = [];
|
||||
const gestures: Array<{ iris: number, gesture: string }> = [];
|
||||
for (let i = 0; i < res.length; i++) {
|
||||
if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;
|
||||
const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];
|
||||
|
|
@ -60,10 +60,11 @@ export const iris = (res) => {
|
|||
|
||||
export const hand = (res) => {
|
||||
if (!res) return [];
|
||||
const gestures = [];
|
||||
const gestures: Array<{ hand: number, gesture: string }> = [];
|
||||
for (let i = 0; i < res.length; i++) {
|
||||
const fingers = [];
|
||||
const fingers: Array<{ name: string, position: number }> = [];
|
||||
for (const [finger, pos] of Object.entries(res[i]['annotations'])) {
|
||||
// @ts-ignore
|
||||
if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger
|
||||
}
|
||||
if (fingers && fingers.length > 0) {
|
||||
|
|
|
|||
|
|
@ -49,7 +49,7 @@ export class HandDetector {
|
|||
|
||||
scoresT.dispose();
|
||||
filteredT.dispose();
|
||||
const hands = [];
|
||||
const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];
|
||||
for (const index of filtered) {
|
||||
if (scores[index] >= config.hand.minConfidence) {
|
||||
const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);
|
||||
|
|
@ -70,7 +70,7 @@ export 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();
|
||||
const hands = [];
|
||||
const hands: Array<{}> = [];
|
||||
if (!predictions || predictions.length === 0) return hands;
|
||||
for (const prediction of predictions) {
|
||||
const boxes = prediction.box.dataSync();
|
||||
|
|
|
|||
|
|
@ -91,7 +91,7 @@ export class HandPipeline {
|
|||
// for (const possible of boxes) this.storedBoxes.push(possible);
|
||||
if (this.storedBoxes.length > 0) useFreshBox = true;
|
||||
}
|
||||
const hands = [];
|
||||
const hands: Array<{}> = [];
|
||||
// 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
|
||||
|
|
@ -124,10 +124,7 @@ export class HandPipeline {
|
|||
const result = {
|
||||
landmarks: coords,
|
||||
confidence,
|
||||
box: {
|
||||
topLeft: nextBoundingBox.startPoint,
|
||||
bottomRight: nextBoundingBox.endPoint,
|
||||
},
|
||||
box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },
|
||||
};
|
||||
hands.push(result);
|
||||
} else {
|
||||
|
|
@ -139,10 +136,7 @@ export class HandPipeline {
|
|||
const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);
|
||||
const result = {
|
||||
confidence: currentBox.confidence,
|
||||
box: {
|
||||
topLeft: enlarged.startPoint,
|
||||
bottomRight: enlarged.endPoint,
|
||||
},
|
||||
box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
|
||||
};
|
||||
hands.push(result);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ export class HandPose {
|
|||
async estimateHands(input, config) {
|
||||
const predictions = await this.handPipeline.estimateHands(input, config);
|
||||
if (!predictions) return [];
|
||||
const hands = [];
|
||||
const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];
|
||||
for (const prediction of predictions) {
|
||||
const annotations = {};
|
||||
if (prediction.landmarks) {
|
||||
|
|
@ -43,12 +43,7 @@ export class HandPose {
|
|||
Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],
|
||||
Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],
|
||||
] : 0;
|
||||
hands.push({
|
||||
confidence: prediction.confidence,
|
||||
box,
|
||||
landmarks: prediction.landmarks,
|
||||
annotations,
|
||||
});
|
||||
hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });
|
||||
}
|
||||
return hands;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ export function dot(v1, v2) {
|
|||
}
|
||||
|
||||
export function getColumnFrom2DArr(arr, columnIndex) {
|
||||
const column = [];
|
||||
const column: Array<number> = [];
|
||||
for (let i = 0; i < arr.length; i++) {
|
||||
column.push(arr[i][columnIndex]);
|
||||
}
|
||||
|
|
@ -26,7 +26,7 @@ export function getColumnFrom2DArr(arr, columnIndex) {
|
|||
}
|
||||
|
||||
export function multiplyTransformMatrices(mat1, mat2) {
|
||||
const product = [];
|
||||
const product: Array<number[]> = [];
|
||||
const size = mat1.length;
|
||||
for (let row = 0; row < size; row++) {
|
||||
product.push([]);
|
||||
|
|
|
|||
11
src/human.ts
11
src/human.ts
|
|
@ -236,7 +236,7 @@ class Human {
|
|||
let genderRes;
|
||||
let emotionRes;
|
||||
let embeddingRes;
|
||||
const faceRes = [];
|
||||
const faceRes: Array<{ confidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];
|
||||
this.state = 'run:face';
|
||||
timeStamp = now();
|
||||
const faces = await this.models.face?.estimateFaces(input, this.config);
|
||||
|
|
@ -349,8 +349,8 @@ class Human {
|
|||
this.state = 'image';
|
||||
this.config = mergeDeep(this.config, userConfig);
|
||||
const process = image.process(input, this.config);
|
||||
process.tensor.dispose();
|
||||
return process.canvas;
|
||||
process?.tensor?.dispose();
|
||||
return process?.canvas;
|
||||
}
|
||||
|
||||
// main detect function
|
||||
|
|
@ -445,6 +445,7 @@ class Human {
|
|||
let gestureRes = [];
|
||||
if (this.config.gesture.enabled) {
|
||||
timeStamp = now();
|
||||
// @ts-ignore
|
||||
gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];
|
||||
if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);
|
||||
else if (this.perf.gesture) delete this.perf.gesture;
|
||||
|
|
@ -495,8 +496,8 @@ class Human {
|
|||
canvas.width = size;
|
||||
canvas.height = size;
|
||||
const ctx = canvas.getContext('2d');
|
||||
ctx.drawImage(img, 0, 0);
|
||||
const data = ctx.getImageData(0, 0, size, size);
|
||||
ctx?.drawImage(img, 0, 0);
|
||||
const data = ctx?.getImageData(0, 0, size, size);
|
||||
this.detect(data, this.config).then((res) => resolve(res));
|
||||
};
|
||||
if (src) img.src = src;
|
||||
|
|
|
|||
|
|
@ -1,3 +1,5 @@
|
|||
// @ts-nocheck
|
||||
|
||||
import { log } from './log';
|
||||
import * as tf from '../dist/tfjs.esm.js';
|
||||
import * as fxImage from './imagefx';
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ function getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {
|
|||
}
|
||||
|
||||
export function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {
|
||||
const poses = [];
|
||||
const poses: Array<{ keypoints: any, score: number }> = [];
|
||||
const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);
|
||||
const squaredNmsRadius = config.body.nmsRadius ^ 2;
|
||||
// Generate at most maxDetections object instances per image in decreasing root part score order.
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ function getOffsetPoint(y, x, keypoint, offsetsBuffer) {
|
|||
}
|
||||
|
||||
export function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {
|
||||
const result = [];
|
||||
const result: Array<number> = [];
|
||||
for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {
|
||||
const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();
|
||||
const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();
|
||||
|
|
|
|||
|
|
@ -24,12 +24,14 @@ export function register() {
|
|||
if (!tf.findBackend(config.name)) {
|
||||
log('backend registration:', config.name);
|
||||
try {
|
||||
// @ts-ignore
|
||||
config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');
|
||||
} catch (err) {
|
||||
log('error: cannot create canvas:', err);
|
||||
return;
|
||||
}
|
||||
try {
|
||||
// @ts-ignore
|
||||
config.gl = config.canvas.getContext('webgl2', config.webGLattr);
|
||||
} catch (err) {
|
||||
log('error: cannot get WebGL2 context:', err);
|
||||
|
|
@ -60,6 +62,7 @@ export function register() {
|
|||
}
|
||||
try {
|
||||
tf.ENV.set('WEBGL_VERSION', 2);
|
||||
// @ts-ignore
|
||||
tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));
|
||||
tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);
|
||||
tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);
|
||||
|
|
|
|||
|
|
@ -17,9 +17,9 @@
|
|||
"resolveJsonModule": true,
|
||||
"skipLibCheck": true,
|
||||
"sourceMap": false,
|
||||
"strictNullChecks": false,
|
||||
"strictNullChecks": true,
|
||||
"allowJs": true
|
||||
},
|
||||
"formatCodeOptions": { "indentSize": 2, "tabSize": 2 },
|
||||
"include": ["src/*", "demo/*"],
|
||||
"include": ["src/*", "src/***/*", "demo/*"],
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,2 @@
|
|||
export declare function load(config: any): Promise<any>;
|
||||
export declare function predict(image: any, config: any): Promise<unknown>;
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
export declare const disposeBox: (box: any) => void;
|
||||
export declare class BlazeFaceModel {
|
||||
blazeFaceModel: any;
|
||||
width: number;
|
||||
height: number;
|
||||
anchorsData: any;
|
||||
anchors: any;
|
||||
inputSize: number;
|
||||
config: any;
|
||||
scaleFaces: number;
|
||||
constructor(model: any, config: any);
|
||||
getBoundingBoxes(inputImage: any): Promise<{
|
||||
boxes: {
|
||||
box: any;
|
||||
landmarks: any;
|
||||
anchor: any;
|
||||
confidence: number;
|
||||
}[];
|
||||
scaleFactor: number[];
|
||||
} | null>;
|
||||
}
|
||||
export declare function load(config: any): Promise<BlazeFaceModel>;
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
export declare function scaleBoxCoordinates(box: any, factor: any): {
|
||||
startPoint: number[];
|
||||
endPoint: number[];
|
||||
};
|
||||
export declare function getBoxSize(box: any): number[];
|
||||
export declare function getBoxCenter(box: any): any[];
|
||||
export declare function cutBoxFromImageAndResize(box: any, image: any, cropSize: any): any;
|
||||
export declare function enlargeBox(box: any, factor?: number): {
|
||||
startPoint: number[];
|
||||
endPoint: any[];
|
||||
landmarks: any;
|
||||
};
|
||||
export declare function squarifyBox(box: any): {
|
||||
startPoint: number[];
|
||||
endPoint: any[];
|
||||
landmarks: any;
|
||||
};
|
||||
|
|
@ -0,0 +1,49 @@
|
|||
export declare const MESH_ANNOTATIONS: {
|
||||
silhouette: number[];
|
||||
lipsUpperOuter: number[];
|
||||
lipsLowerOuter: number[];
|
||||
lipsUpperInner: number[];
|
||||
lipsLowerInner: number[];
|
||||
rightEyeUpper0: number[];
|
||||
rightEyeLower0: number[];
|
||||
rightEyeUpper1: number[];
|
||||
rightEyeLower1: number[];
|
||||
rightEyeUpper2: number[];
|
||||
rightEyeLower2: number[];
|
||||
rightEyeLower3: number[];
|
||||
rightEyebrowUpper: number[];
|
||||
rightEyebrowLower: number[];
|
||||
rightEyeIris: number[];
|
||||
leftEyeUpper0: number[];
|
||||
leftEyeLower0: number[];
|
||||
leftEyeUpper1: number[];
|
||||
leftEyeLower1: number[];
|
||||
leftEyeUpper2: number[];
|
||||
leftEyeLower2: number[];
|
||||
leftEyeLower3: number[];
|
||||
leftEyebrowUpper: number[];
|
||||
leftEyebrowLower: number[];
|
||||
leftEyeIris: number[];
|
||||
midwayBetweenEyes: number[];
|
||||
noseTip: number[];
|
||||
noseBottom: number[];
|
||||
noseRightCorner: number[];
|
||||
noseLeftCorner: number[];
|
||||
rightCheek: number[];
|
||||
leftCheek: number[];
|
||||
};
|
||||
export declare const MESH_TO_IRIS_INDICES_MAP: {
|
||||
key: string;
|
||||
indices: number[];
|
||||
}[];
|
||||
export declare const UV468: number[][];
|
||||
export declare const TRI468: number[];
|
||||
export declare const TRI68: number[];
|
||||
export declare const TRI33: number[];
|
||||
export declare const TRI7: number[];
|
||||
export declare const VTX68: number[];
|
||||
export declare const VTX33: number[];
|
||||
export declare const VTX7: number[];
|
||||
export declare const UV68: number[][];
|
||||
export declare const UV33: number[][];
|
||||
export declare const UV7: number[][];
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
export declare class MediaPipeFaceMesh {
|
||||
facePipeline: any;
|
||||
config: any;
|
||||
constructor(blazeFace: any, blazeMeshModel: any, irisModel: any, config: any);
|
||||
estimateFaces(input: any, config: any): Promise<{}[]>;
|
||||
}
|
||||
export declare function load(config: any): Promise<MediaPipeFaceMesh>;
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
export declare class Pipeline {
|
||||
storedBoxes: any;
|
||||
runsWithoutFaceDetector: number;
|
||||
boundingBoxDetector: any;
|
||||
meshDetector: any;
|
||||
irisModel: any;
|
||||
meshWidth: number;
|
||||
meshHeight: number;
|
||||
irisSize: number;
|
||||
irisEnlarge: number;
|
||||
skipped: number;
|
||||
detectedFaces: number;
|
||||
constructor(boundingBoxDetector: any, meshDetector: any, irisModel: any, config: any);
|
||||
transformRawCoords(rawCoords: any, box: any, angle: any, rotationMatrix: any): any;
|
||||
getLeftToRightEyeDepthDifference(rawCoords: any): number;
|
||||
getEyeBox(rawCoords: any, face: any, eyeInnerCornerIndex: any, eyeOuterCornerIndex: any, flip?: boolean): {
|
||||
box: {
|
||||
startPoint: number[];
|
||||
endPoint: any[];
|
||||
landmarks: any;
|
||||
};
|
||||
boxSize: number[];
|
||||
crop: any;
|
||||
};
|
||||
getEyeCoords(eyeData: any, eyeBox: any, eyeBoxSize: any, flip?: boolean): {
|
||||
rawCoords: any[][];
|
||||
iris: any[][];
|
||||
};
|
||||
getAdjustedIrisCoords(rawCoords: any, irisCoords: any, direction: any): any;
|
||||
predict(input: any, config: any): Promise<any>;
|
||||
calculateLandmarksBoundingBox(landmarks: any): {
|
||||
startPoint: number[];
|
||||
endPoint: number[];
|
||||
landmarks: any;
|
||||
};
|
||||
}
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
export declare const IDENTITY_MATRIX: number[][];
|
||||
/**
|
||||
* Normalizes the provided angle to the range -pi to pi.
|
||||
* @param angle The angle in radians to be normalized.
|
||||
*/
|
||||
export declare function normalizeRadians(angle: any): number;
|
||||
/**
|
||||
* Computes the angle of rotation between two anchor points.
|
||||
* @param point1 First anchor point
|
||||
* @param point2 Second anchor point
|
||||
*/
|
||||
export declare function computeRotation(point1: any, point2: any): number;
|
||||
export declare function radToDegrees(rad: any): number;
|
||||
export declare function buildTranslationMatrix(x: any, y: any): any[][];
|
||||
export declare function dot(v1: any, v2: any): number;
|
||||
export declare function getColumnFrom2DArr(arr: any, columnIndex: any): number[];
|
||||
export declare function multiplyTransformMatrices(mat1: any, mat2: any): number[][];
|
||||
export declare function buildRotationMatrix(rotation: any, center: any): number[][];
|
||||
export declare function invertTransformMatrix(matrix: any): any[][];
|
||||
export declare function rotatePoint(homogeneousCoordinate: any, rotationMatrix: any): number[];
|
||||
export declare function xyDistanceBetweenPoints(a: any, b: any): number;
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
export declare function load(config: any): Promise<any>;
|
||||
export declare function simmilarity(embedding1: any, embedding2: any): number;
|
||||
export declare function predict(image: any, config: any): Promise<unknown>;
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
export declare function load(config: any): Promise<any>;
|
||||
export declare function predict(image: any, config: any): Promise<unknown>;
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
export declare class FaceBoxes {
|
||||
enlarge: number;
|
||||
model: any;
|
||||
config: any;
|
||||
constructor(model: any, config: any);
|
||||
estimateFaces(input: any, config: any): Promise<{
|
||||
confidence: number;
|
||||
box: any;
|
||||
boxRaw: any;
|
||||
image: any;
|
||||
}[]>;
|
||||
}
|
||||
export declare function load(config: any): Promise<FaceBoxes>;
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
export declare function load(config: any): Promise<any>;
|
||||
export declare function predict(image: any, config: any): Promise<unknown>;
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
export declare const body: (res: any) => {
|
||||
body: number;
|
||||
gesture: string;
|
||||
}[];
|
||||
export declare const face: (res: any) => {
|
||||
face: number;
|
||||
gesture: string;
|
||||
}[];
|
||||
export declare const iris: (res: any) => {
|
||||
iris: number;
|
||||
gesture: string;
|
||||
}[];
|
||||
export declare const hand: (res: any) => {
|
||||
hand: number;
|
||||
gesture: string;
|
||||
}[];
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
export declare const anchors: {
|
||||
w: number;
|
||||
h: number;
|
||||
x_center: number;
|
||||
y_center: number;
|
||||
}[];
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
export declare function getBoxSize(box: any): number[];
|
||||
export declare function getBoxCenter(box: any): any[];
|
||||
export declare function cutBoxFromImageAndResize(box: any, image: any, cropSize: any): any;
|
||||
export declare function scaleBoxCoordinates(box: any, factor: any): {
|
||||
startPoint: number[];
|
||||
endPoint: number[];
|
||||
palmLandmarks: any;
|
||||
confidence: any;
|
||||
};
|
||||
export declare function enlargeBox(box: any, factor?: number): {
|
||||
startPoint: number[];
|
||||
endPoint: any[];
|
||||
palmLandmarks: any;
|
||||
};
|
||||
export declare function squarifyBox(box: any): {
|
||||
startPoint: number[];
|
||||
endPoint: any[];
|
||||
palmLandmarks: any;
|
||||
};
|
||||
export declare function shiftBox(box: any, shiftFactor: any): {
|
||||
startPoint: any[];
|
||||
endPoint: any[];
|
||||
palmLandmarks: any;
|
||||
};
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
export declare class HandDetector {
|
||||
model: any;
|
||||
anchors: any;
|
||||
anchorsTensor: any;
|
||||
inputSizeTensor: any;
|
||||
doubleInputSizeTensor: any;
|
||||
constructor(model: any, inputSize: any, anchorsAnnotated: any);
|
||||
normalizeBoxes(boxes: any): any;
|
||||
normalizeLandmarks(rawPalmLandmarks: any, index: any): any;
|
||||
getBoxes(input: any, config: any): Promise<{
|
||||
box: any;
|
||||
palmLandmarks: any;
|
||||
confidence: number;
|
||||
}[]>;
|
||||
estimateHandBounds(input: any, config: any): Promise<{}[]>;
|
||||
}
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
export declare class HandPipeline {
|
||||
handDetector: any;
|
||||
landmarkDetector: any;
|
||||
inputSize: number;
|
||||
storedBoxes: any;
|
||||
skipped: number;
|
||||
detectedHands: number;
|
||||
constructor(handDetector: any, landmarkDetector: any, inputSize: any);
|
||||
getBoxForPalmLandmarks(palmLandmarks: any, rotationMatrix: any): {
|
||||
startPoint: number[];
|
||||
endPoint: any[];
|
||||
palmLandmarks: any;
|
||||
};
|
||||
getBoxForHandLandmarks(landmarks: any): {
|
||||
startPoint: number[];
|
||||
endPoint: any[];
|
||||
palmLandmarks: any;
|
||||
};
|
||||
transformRawCoords(rawCoords: any, box2: any, angle: any, rotationMatrix: any): any;
|
||||
estimateHands(image: any, config: any): Promise<{}[]>;
|
||||
calculateLandmarksBoundingBox(landmarks: any): {
|
||||
startPoint: number[];
|
||||
endPoint: number[];
|
||||
};
|
||||
}
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
export declare class HandPose {
|
||||
handPipeline: any;
|
||||
constructor(handPipeline: any);
|
||||
static getAnnotations(): {
|
||||
thumb: number[];
|
||||
indexFinger: number[];
|
||||
middleFinger: number[];
|
||||
ringFinger: number[];
|
||||
pinky: number[];
|
||||
palmBase: number[];
|
||||
};
|
||||
estimateHands(input: any, config: any): Promise<{
|
||||
confidence: number;
|
||||
box: any;
|
||||
landmarks: any;
|
||||
annotations: any;
|
||||
}[]>;
|
||||
}
|
||||
export declare function load(config: any): Promise<HandPose>;
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
export declare function normalizeRadians(angle: any): number;
|
||||
export declare function computeRotation(point1: any, point2: any): number;
|
||||
export declare const buildTranslationMatrix: (x: any, y: any) => any[][];
|
||||
export declare function dot(v1: any, v2: any): number;
|
||||
export declare function getColumnFrom2DArr(arr: any, columnIndex: any): number[];
|
||||
export declare function multiplyTransformMatrices(mat1: any, mat2: any): number[][];
|
||||
export declare function buildRotationMatrix(rotation: any, center: any): number[][];
|
||||
export declare function invertTransformMatrix(matrix: any): any[][];
|
||||
export declare function rotatePoint(homogeneousCoordinate: any, rotationMatrix: any): number[];
|
||||
|
|
@ -0,0 +1,47 @@
|
|||
declare class Human {
|
||||
tf: any;
|
||||
version: string;
|
||||
config: any;
|
||||
fx: any;
|
||||
state: string;
|
||||
numTensors: number;
|
||||
analyzeMemoryLeaks: boolean;
|
||||
checkSanity: boolean;
|
||||
firstRun: boolean;
|
||||
perf: any;
|
||||
models: any;
|
||||
facemesh: any;
|
||||
age: any;
|
||||
gender: any;
|
||||
emotion: any;
|
||||
body: any;
|
||||
hand: any;
|
||||
constructor(userConfig?: {});
|
||||
profile(): {};
|
||||
analyze(...msg: any[]): void;
|
||||
sanity(input: any): "input is not defined" | "input must be a tensor" | "backend not loaded" | null;
|
||||
simmilarity(embedding1: any, embedding2: any): number;
|
||||
load(userConfig?: null): Promise<void>;
|
||||
checkBackend(force?: boolean): Promise<void>;
|
||||
detectFace(input: any): Promise<{
|
||||
confidence: number;
|
||||
box: any;
|
||||
mesh: any;
|
||||
meshRaw: any;
|
||||
boxRaw: any;
|
||||
annotations: any;
|
||||
age: number;
|
||||
gender: string;
|
||||
genderConfidence: number;
|
||||
emotion: string;
|
||||
embedding: any;
|
||||
iris: number;
|
||||
}[]>;
|
||||
image(input: any, userConfig?: {}): Promise<null | undefined>;
|
||||
detect(input: any, userConfig?: {}): Promise<unknown>;
|
||||
warmupBitmap(): Promise<any>;
|
||||
warmupCanvas(): Promise<unknown>;
|
||||
warmupNode(): Promise<unknown>;
|
||||
warmup(userConfig: any): Promise<any>;
|
||||
}
|
||||
export { Human as default };
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
export declare function process(input: any, config: any): {
|
||||
tensor: any;
|
||||
canvas: null;
|
||||
} | null;
|
||||
|
|
@ -0,0 +1 @@
|
|||
export declare function log(...msg: any[]): void;
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
import * as heapSort from './heapSort';
|
||||
export declare function buildPartWithScoreQueue(scoreThreshold: any, localMaximumRadius: any, scores: any): heapSort.MaxHeap;
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
export declare function decodeMultiplePoses(scoresBuffer: any, offsetsBuffer: any, displacementsFwdBuffer: any, displacementsBwdBuffer: any, config: any): {
|
||||
keypoints: any;
|
||||
score: number;
|
||||
}[];
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
export declare function decodePose(root: any, scores: any, offsets: any, outputStride: any, displacementsFwd: any, displacementsBwd: any): any[];
|
||||
export declare function decodeSinglePose(heatmapScores: any, offsets: any, config: any): Promise<{
|
||||
keypoints: {
|
||||
position: {
|
||||
y: any;
|
||||
x: any;
|
||||
};
|
||||
part: string;
|
||||
score: number;
|
||||
}[];
|
||||
score: number;
|
||||
}>;
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
export declare function getPointsConfidence(heatmapScores: any, heatMapCoords: any): Float32Array;
|
||||
export declare function getOffsetVectors(heatMapCoordsBuffer: any, offsetsBuffer: any): any;
|
||||
export declare function getOffsetPoints(heatMapCoordsBuffer: any, outputStride: any, offsetsBuffer: any): any;
|
||||
export declare function argmax2d(inputs: any): any;
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
export declare class MaxHeap {
|
||||
priorityQueue: any;
|
||||
numberOfElements: number;
|
||||
getElementValue: any;
|
||||
constructor(maxSize: any, getElementValue: any);
|
||||
enqueue(x: any): void;
|
||||
dequeue(): any;
|
||||
empty(): boolean;
|
||||
size(): number;
|
||||
all(): any;
|
||||
max(): any;
|
||||
swim(k: any): void;
|
||||
sink(k: any): void;
|
||||
getValueAt(i: any): any;
|
||||
less(i: any, j: any): boolean;
|
||||
exchange(i: any, j: any): void;
|
||||
}
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
export declare const partNames: string[];
|
||||
export declare const NUM_KEYPOINTS: any;
|
||||
export declare const partIds: any;
|
||||
export declare const connectedPartIndices: any[][];
|
||||
export declare const poseChain: string[][];
|
||||
export declare const partChannels: string[];
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
export declare class BaseModel {
|
||||
model: any;
|
||||
constructor(model: any);
|
||||
predict(input: any, config: any): any;
|
||||
dispose(): void;
|
||||
}
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
export declare class PoseNet {
|
||||
baseModel: any;
|
||||
constructor(model: any);
|
||||
estimatePoses(input: any, config: any): Promise<unknown>;
|
||||
dispose(): void;
|
||||
}
|
||||
export declare function load(config: any): Promise<PoseNet>;
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
export declare function eitherPointDoesntMeetConfidence(a: any, b: any, minConfidence: any): boolean;
|
||||
export declare function getAdjacentKeyPoints(keypoints: any, minConfidence: any): any[];
|
||||
export declare function getBoundingBox(keypoints: any): any;
|
||||
export declare function getBoundingBoxPoints(keypoints: any): {
|
||||
x: any;
|
||||
y: any;
|
||||
}[];
|
||||
export declare function toTensorBuffers3D(tensors: any): Promise<[unknown, unknown, unknown, unknown, unknown, unknown, unknown, unknown, unknown, unknown]>;
|
||||
export declare function scalePose(pose: any, scaleY: any, scaleX: any): {
|
||||
score: any;
|
||||
keypoints: any;
|
||||
};
|
||||
export declare function resizeTo(image: any, [targetH, targetW]: [any, any]): any;
|
||||
export declare function scaleAndFlipPoses(poses: any, [height, width]: [any, any], [inputResolutionHeight, inputResolutionWidth]: [any, any]): any;
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
export declare function getOffsetPoint(y: any, x: any, keypoint: any, offsets: any): {
|
||||
y: any;
|
||||
x: any;
|
||||
};
|
||||
export declare function getImageCoords(part: any, outputStride: any, offsets: any): {
|
||||
x: any;
|
||||
y: any;
|
||||
};
|
||||
export declare function fillArray(element: any, size: any): any[];
|
||||
export declare function clamp(a: any, min: any, max: any): any;
|
||||
export declare function squaredDistance(y1: any, x1: any, y2: any, x2: any): number;
|
||||
export declare function addVectors(a: any, b: any): {
|
||||
x: any;
|
||||
y: any;
|
||||
};
|
||||
export declare function clampVector(a: any, min: any, max: any): {
|
||||
y: any;
|
||||
x: any;
|
||||
};
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
export declare const data: {};
|
||||
export declare function run(name: any, raw: any): void;
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,19 @@
|
|||
export declare const config: {
|
||||
name: string;
|
||||
priority: number;
|
||||
canvas: null;
|
||||
gl: null;
|
||||
width: number;
|
||||
height: number;
|
||||
webGLattr: {
|
||||
alpha: boolean;
|
||||
antialias: boolean;
|
||||
premultipliedAlpha: boolean;
|
||||
preserveDrawingBuffer: boolean;
|
||||
depth: boolean;
|
||||
stencil: boolean;
|
||||
failIfMajorPerformanceCaveat: boolean;
|
||||
desynchronized: boolean;
|
||||
};
|
||||
};
|
||||
export declare function register(): void;
|
||||
2
wiki
2
wiki
|
|
@ -1 +1 @@
|
|||
Subproject commit d88043901df1d671a0897275bae8d0f537b84ff5
|
||||
Subproject commit d365c4e487508181811ea7ff1a26d682ef6896b7
|
||||
Loading…
Reference in New Issue