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human
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updated typings

pull/70/head
Vladimir Mandic 2021-02-08 12:47:38 -05:00
parent 6fb133b55e
commit 7fd677681c
76 changed files with 1188 additions and 716 deletions
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1
demo/gl-bench.js
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@ -1,4 +1,3 @@
// @ts-nocheck
/* eslint-disable max-len */ /* eslint-disable max-len */
// based on: https://github.com/munrocket/gl-bench // based on: https://github.com/munrocket/gl-bench

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demo/menu.js
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// @ts-nocheck
let instance = 0; let instance = 0;
let CSScreated = false; let CSScreated = false;

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dist/demo-browser-index.json vendored
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@ -9,11 +9,11 @@
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} }

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@ -73,7 +73,7 @@
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@ -538,10 +538,10 @@
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dist/human.node.js vendored
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dist/human.node.json vendored
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@ -444,7 +436,7 @@
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}, },
{ {
"path": "src/blazeface/facemesh.js", "path": "src/blazeface/facemesh.ts",
"kind": "import-statement" "kind": "import-statement"
}, },
{ {
@ -468,11 +460,11 @@
"kind": "import-statement" "kind": "import-statement"
}, },
{ {
"path": "src/posenet/posenet.js", "path": "src/posenet/posenet.ts",
"kind": "import-statement" "kind": "import-statement"
}, },
{ {
"path": "src/handpose/handpose.js", "path": "src/handpose/handpose.ts",
"kind": "import-statement" "kind": "import-statement"
}, },
{ {
@ -507,77 +499,26 @@
"imports": [], "imports": [],
"exports": [], "exports": [],
"inputs": {}, "inputs": {},
"bytes": 712944 "bytes": 706125
}, },
"dist/human.node-gpu.js": { "dist/human.node-gpu.js": {
"imports": [], "imports": [],
"exports": [], "exports": [],
"inputs": { "inputs": {
"dist/tfjs.esm.js": { "dist/tfjs.esm.js": {
"bytesInOutput": 550 "bytesInOutput": 551
}, },
"src/blazeface/blazeface.js": { "src/blazeface/facemesh.ts": {
"bytesInOutput": 3077 "bytesInOutput": 1476
}, },
"src/blazeface/box.js": { "src/posenet/keypoints.ts": {
"bytesInOutput": 998 "bytesInOutput": 1677
}, },
"src/blazeface/util.js": { "src/imagefx.js": {
"bytesInOutput": 1173 "bytesInOutput": 11012
},
"src/blazeface/coords.js": {
"bytesInOutput": 30504
},
"src/blazeface/facepipeline.js": {
"bytesInOutput": 5593
},
"src/blazeface/facemesh.js": {
"bytesInOutput": 1574
},
"src/posenet/modelBase.js": {
"bytesInOutput": 688
},
"src/posenet/heapSort.js": {
"bytesInOutput": 1047
},
"src/posenet/buildParts.js": {
"bytesInOutput": 523
},
"src/posenet/keypoints.js": {
"bytesInOutput": 1624
},
"src/posenet/vectors.js": {
"bytesInOutput": 615
},
"src/posenet/decoders.js": {
"bytesInOutput": 925
},
"src/posenet/decodePose.js": {
"bytesInOutput": 1465
},
"src/posenet/decodeMultiple.js": {
"bytesInOutput": 694
},
"src/posenet/util.js": {
"bytesInOutput": 1051
},
"src/posenet/posenet.js": {
"bytesInOutput": 1223
},
"src/handpose/handdetector.js": {
"bytesInOutput": 1844
},
"src/handpose/handpipeline.js": {
"bytesInOutput": 2503
},
"src/handpose/anchors.js": {
"bytesInOutput": 127005
},
"src/handpose/handpose.js": {
"bytesInOutput": 1397
}, },
"src/human.ts": { "src/human.ts": {
"bytesInOutput": 10355 "bytesInOutput": 10462
}, },
"src/log.ts": { "src/log.ts": {
"bytesInOutput": 251 "bytesInOutput": 251
@ -585,38 +526,89 @@
"src/tfjs/backend.ts": { "src/tfjs/backend.ts": {
"bytesInOutput": 1304 "bytesInOutput": 1304
}, },
"src/blazeface/blazeface.ts": {
"bytesInOutput": 2329
},
"src/blazeface/facepipeline.ts": {
"bytesInOutput": 5089
},
"src/blazeface/box.ts": {
"bytesInOutput": 854
},
"src/blazeface/util.ts": {
"bytesInOutput": 848
},
"src/blazeface/coords.ts": {
"bytesInOutput": 28973
},
"src/faceboxes/faceboxes.ts": { "src/faceboxes/faceboxes.ts": {
"bytesInOutput": 1592 "bytesInOutput": 1586
}, },
"src/profile.ts": { "src/profile.ts": {
"bytesInOutput": 604 "bytesInOutput": 604
}, },
"src/age/age.ts": { "src/age/age.ts": {
"bytesInOutput": 828 "bytesInOutput": 822
}, },
"src/gender/gender.ts": { "src/gender/gender.ts": {
"bytesInOutput": 1337 "bytesInOutput": 1310
}, },
"src/emotion/emotion.ts": { "src/emotion/emotion.ts": {
"bytesInOutput": 1265 "bytesInOutput": 1247
}, },
"src/embedding/embedding.ts": { "src/embedding/embedding.ts": {
"bytesInOutput": 851 "bytesInOutput": 769
}, },
"src/handpose/box.js": { "src/posenet/posenet.ts": {
"bytesInOutput": 1030
},
"src/posenet/modelBase.ts": {
"bytesInOutput": 656
},
"src/posenet/heapSort.ts": {
"bytesInOutput": 1017
},
"src/posenet/buildParts.ts": {
"bytesInOutput": 454
},
"src/posenet/decodePose.ts": {
"bytesInOutput": 1282
},
"src/posenet/vectors.ts": {
"bytesInOutput": 345
},
"src/posenet/decoders.ts": {
"bytesInOutput": 823
},
"src/posenet/decodeMultiple.ts": {
"bytesInOutput": 553
},
"src/posenet/util.ts": {
"bytesInOutput": 352
},
"src/handpose/handpose.ts": {
"bytesInOutput": 1304
},
"src/handpose/handdetector.ts": {
"bytesInOutput": 1813
},
"src/handpose/box.ts": {
"bytesInOutput": 958 "bytesInOutput": 958
}, },
"src/handpose/util.js": { "src/handpose/handpipeline.ts": {
"bytesInOutput": 812 "bytesInOutput": 2470
},
"src/handpose/util.ts": {
"bytesInOutput": 806
},
"src/handpose/anchors.ts": {
"bytesInOutput": 126985
}, },
"src/gesture/gesture.ts": { "src/gesture/gesture.ts": {
"bytesInOutput": 2391 "bytesInOutput": 2391
}, },
"src/image.ts": { "src/image.ts": {
"bytesInOutput": 2434 "bytesInOutput": 2455
},
"src/imagefx.js": {
"bytesInOutput": 11045
}, },
"config.js": { "config.js": {
"bytesInOutput": 1426 "bytesInOutput": 1426
@ -628,7 +620,7 @@
"bytesInOutput": 16 "bytesInOutput": 16
} }
}, },
"bytes": 279609 "bytes": 274367
} }
} }
} }

190
dist/human.ts vendored
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4
dist/human.ts.map vendored
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56
dist/human.tson vendored
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@ -9,7 +9,7 @@
"imports": [] "imports": []
}, },
"src/tfjs/backend.ts": { "src/tfjs/backend.ts": {
"bytes": 2168, "bytes": 2228,
"imports": [ "imports": [
{ {
"path": "src/log.ts", "path": "src/log.ts",
@ -22,7 +22,7 @@
] ]
}, },
"src/blazeface/blazeface.ts": { "src/blazeface/blazeface.ts": {
"bytes": 6024, "bytes": 6119,
"imports": [ "imports": [
{ {
"path": "src/log.ts", "path": "src/log.ts",
@ -44,7 +44,7 @@
] ]
}, },
"src/blazeface/util.ts": { "src/blazeface/util.ts": {
"bytes": 2777, "bytes": 2809,
"imports": [] "imports": []
}, },
"src/blazeface/coords.ts": { "src/blazeface/coords.ts": {
@ -52,7 +52,7 @@
"imports": [] "imports": []
}, },
"src/blazeface/facepipeline.ts": { "src/blazeface/facepipeline.ts": {
"bytes": 14254, "bytes": 14251,
"imports": [ "imports": [
{ {
"path": "dist/tfjs.esm.js", "path": "dist/tfjs.esm.js",
@ -73,7 +73,7 @@
] ]
}, },
"src/blazeface/facemesh.ts": { "src/blazeface/facemesh.ts": {
"bytes": 2935, "bytes": 2899,
"imports": [ "imports": [
{ {
"path": "src/log.ts", "path": "src/log.ts",
@ -107,7 +107,7 @@
] ]
}, },
"src/faceboxes/faceboxes.ts": { "src/faceboxes/faceboxes.ts": {
"bytes": 2852, "bytes": 2846,
"imports": [ "imports": [
{ {
"path": "src/log.ts", "path": "src/log.ts",
@ -124,7 +124,7 @@
] ]
}, },
"src/age/age.ts": { "src/age/age.ts": {
"bytes": 2064, "bytes": 1949,
"imports": [ "imports": [
{ {
"path": "src/log.ts", "path": "src/log.ts",
@ -141,7 +141,7 @@
] ]
}, },
"src/gender/gender.ts": { "src/gender/gender.ts": {
"bytes": 2904, "bytes": 2811,
"imports": [ "imports": [
{ {
"path": "src/log.ts", "path": "src/log.ts",
@ -158,7 +158,7 @@
] ]
}, },
"src/emotion/emotion.ts": { "src/emotion/emotion.ts": {
"bytes": 3033, "bytes": 3042,
"imports": [ "imports": [
{ {
"path": "src/log.ts", "path": "src/log.ts",
@ -175,7 +175,7 @@
] ]
}, },
"src/embedding/embedding.ts": { "src/embedding/embedding.ts": {
"bytes": 1992, "bytes": 1912,
"imports": [ "imports": [
{ {
"path": "src/log.ts", "path": "src/log.ts",
@ -227,7 +227,7 @@
] ]
}, },
"src/posenet/decoders.ts": { "src/posenet/decoders.ts": {
"bytes": 1943, "bytes": 1958,
"imports": [ "imports": [
{ {
"path": "dist/tfjs.esm.js", "path": "dist/tfjs.esm.js",
@ -257,7 +257,7 @@
] ]
}, },
"src/posenet/decodeMultiple.ts": { "src/posenet/decodeMultiple.ts": {
"bytes": 2259, "bytes": 2301,
"imports": [ "imports": [
{ {
"path": "src/posenet/buildParts.ts", "path": "src/posenet/buildParts.ts",
@ -321,7 +321,7 @@
] ]
}, },
"src/handpose/handdetector.ts": { "src/handpose/handdetector.ts": {
"bytes": 3627, "bytes": 3701,
"imports": [ "imports": [
{ {
"path": "dist/tfjs.esm.js", "path": "dist/tfjs.esm.js",
@ -334,11 +334,11 @@
] ]
}, },
"src/handpose/util.ts": { "src/handpose/util.ts": {
"bytes": 2254, "bytes": 2286,
"imports": [] "imports": []
}, },
"src/handpose/handpipeline.ts": { "src/handpose/handpipeline.ts": {
"bytes": 7344, "bytes": 7279,
"imports": [ "imports": [
{ {
"path": "dist/tfjs.esm.js", "path": "dist/tfjs.esm.js",
@ -359,7 +359,7 @@
"imports": [] "imports": []
}, },
"src/handpose/handpose.ts": { "src/handpose/handpose.ts": {
"bytes": 2529, "bytes": 2565,
"imports": [ "imports": [
{ {
"path": "src/log.ts", "path": "src/log.ts",
@ -384,7 +384,7 @@
] ]
}, },
"src/gesture/gesture.ts": { "src/gesture/gesture.ts": {
"bytes": 4265, "bytes": 4497,
"imports": [] "imports": []
}, },
"src/imagefx.js": { "src/imagefx.js": {
@ -392,7 +392,7 @@
"imports": [] "imports": []
}, },
"src/image.ts": { "src/image.ts": {
"bytes": 5851, "bytes": 5867,
"imports": [ "imports": [
{ {
"path": "src/log.ts", "path": "src/log.ts",
@ -417,11 +417,11 @@
"imports": [] "imports": []
}, },
"package.json": { "package.json": {
"bytes": 2344, "bytes": 2385,
"imports": [] "imports": []
}, },
"src/human.ts": { "src/human.ts": {
"bytes": 19393, "bytes": 19619,
"imports": [ "imports": [
{ {
"path": "src/log.ts", "path": "src/log.ts",
@ -499,7 +499,7 @@
"imports": [], "imports": [],
"exports": [], "exports": [],
"inputs": {}, "inputs": {},
"bytes": 1923057 "bytes": 1923340
}, },
"dist/human.ts": { "dist/human.ts": {
"imports": [], "imports": [],
@ -515,7 +515,7 @@
"bytesInOutput": 11016 "bytesInOutput": 11016
}, },
"src/human.ts": { "src/human.ts": {
"bytesInOutput": 10259 "bytesInOutput": 10331
}, },
"src/log.ts": { "src/log.ts": {
"bytesInOutput": 252 "bytesInOutput": 252
@ -539,7 +539,7 @@
"bytesInOutput": 28983 "bytesInOutput": 28983
}, },
"src/blazeface/facepipeline.ts": { "src/blazeface/facepipeline.ts": {
"bytesInOutput": 5046 "bytesInOutput": 5040
}, },
"src/faceboxes/faceboxes.ts": { "src/faceboxes/faceboxes.ts": {
"bytesInOutput": 1549 "bytesInOutput": 1549
@ -548,16 +548,16 @@
"bytesInOutput": 606 "bytesInOutput": 606
}, },
"src/age/age.ts": { "src/age/age.ts": {
"bytesInOutput": 826 "bytesInOutput": 775
}, },
"src/gender/gender.ts": { "src/gender/gender.ts": {
"bytesInOutput": 1309 "bytesInOutput": 1237
}, },
"src/emotion/emotion.ts": { "src/emotion/emotion.ts": {
"bytesInOutput": 1243 "bytesInOutput": 1180
}, },
"src/embedding/embedding.ts": { "src/embedding/embedding.ts": {
"bytesInOutput": 802 "bytesInOutput": 725
}, },
"src/posenet/posenet.ts": { "src/posenet/posenet.ts": {
"bytesInOutput": 1007 "bytesInOutput": 1007
@ -620,7 +620,7 @@
"bytesInOutput": 16 "bytesInOutput": 16
} }
}, },
"bytes": 1343047 "bytes": 1342850
} }
} }
} }

6
package-lock.json generated
View File

@ -2527,9 +2527,9 @@
"dev": true "dev": true
}, },
"typescript": { "typescript": {
"version": "4.1.3", "version": "4.2.0-dev.20210208",
"resolved": "https://registry.npmjs.org/typescript/-/typescript-4.1.3.tgz", "resolved": "https://registry.npmjs.org/typescript/-/typescript-4.2.0-dev.20210208.tgz",
"integrity": "sha512-B3ZIOf1IKeH2ixgHhj6la6xdwR9QrLC5d1VKeCSY4tvkqhF2eqd9O7txNlS0PO3GrBAFIdr3L1ndNwteUbZLYg==", "integrity": "sha512-zZD4z9MS8iy9dPxwBeEcQDemi13rfdAk7oCyAaiMQXsw1rcdmdsNGjpaQ8OZiY3I5Y/vaoDCu7yBB1ZaNSemLA==",
"dev": true "dev": true
}, },
"uri-js": { "uri-js": {

2
package.json
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@ -46,7 +46,7 @@
"seedrandom": "^3.0.5", "seedrandom": "^3.0.5",
"simple-git": "^2.34.2", "simple-git": "^2.34.2",
"tslib": "^2.1.0", "tslib": "^2.1.0",
"typescript": "^4.1.3" "typescript": "^4.2.0-dev.20210208"
}, },
"scripts": { "scripts": {
"start": "node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js", "start": "node --trace-warnings --unhandled-rejections=strict --trace-uncaught --no-deprecation src/node.js",

122
server/build.js
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@ -1,8 +1,9 @@
#!/usr/bin/env -S node --trace-warnings #!/usr/bin/env -S node --trace-warnings
const fs = require('fs'); const fs = require('fs');
const esbuild = require('esbuild');
const log = require('@vladmandic/pilogger'); const log = require('@vladmandic/pilogger');
const esbuild = require('esbuild');
const ts = require('typescript');
// keeps esbuild service instance cached // keeps esbuild service instance cached
let es; 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 // common configuration
const common = { const common = {
banner, banner,
@ -29,42 +53,6 @@ const common = {
}; };
const targets = { 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: { browserNoBundle: {
tfjs: { tfjs: {
platform: 'browser', platform: 'browser',
@ -118,6 +106,42 @@ const targets = {
external: ['fs', 'buffer', 'util'], 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) { async function getStats(metafile) {
@ -147,6 +171,28 @@ async function getStats(metafile) {
return stats; 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 // rebuild on file change
async function build(f, msg) { async function build(f, msg) {
if (busy) { if (busy) {
@ -169,6 +215,8 @@ async function build(f, msg) {
log.state(`Build for: ${targetGroupName} type: ${targetName}:`, stats); log.state(`Build for: ${targetGroupName} type: ${targetName}:`, stats);
} }
} }
// generate typings
compile(targets.browserBundle.esm.entryPoints, tsconfig);
if (require.main === module) process.exit(0); if (require.main === module) process.exit(0);
} catch (err) { } catch (err) {
// catch errors and print where it occured // catch errors and print where it occured

46
src/age/age.ts
View File

@ -2,20 +2,20 @@ import { log } from '../log';
import * as tf from '../../dist/tfjs.esm.js'; import * as tf from '../../dist/tfjs.esm.js';
import * as profile from '../profile.js'; import * as profile from '../profile.js';
const models = { age: null }; let model;
let last = { age: 0 }; let last = { age: 0 };
let skipped = Number.MAX_SAFE_INTEGER; let skipped = Number.MAX_SAFE_INTEGER;
export async function load(config) { export async function load(config) {
if (!models.age) { if (!model) {
models.age = await tf.loadGraphModel(config.face.age.modelPath); model = await tf.loadGraphModel(config.face.age.modelPath);
log(`load model: ${config.face.age.modelPath.match(/\/(.*)\./)[1]}`); log(`load model: ${config.face.age.modelPath.match(/\/(.*)\./)[1]}`);
} }
return models.age; return model;
} }
export async function predict(image, config) { 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)) { if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {
skipped++; skipped++;
return last; return last;
@ -38,27 +38,25 @@ export async function predict(image, config) {
tf.dispose(resize); tf.dispose(resize);
let ageT; let ageT;
const obj = { age: undefined }; const obj = { age: 0 };
if (models.age) { if (!config.profile) {
if (!config.profile) { if (config.face.age.enabled) ageT = await model.predict(enhance);
if (config.face.age.enabled) ageT = await models.age.predict(enhance); } else {
} else { const profileAge = config.face.age.enabled ? await tf.profile(() => model.predict(enhance)) : {};
const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {}; ageT = profileAge.result.clone();
ageT = profileAge.result.clone(); profileAge.result.dispose();
profileAge.result.dispose(); profile.run('age', profileAge);
profile.run('age', profileAge);
}
enhance.dispose();
if (ageT) {
const data = ageT.dataSync();
obj.age = Math.trunc(10 * data[0]) / 10;
}
ageT.dispose();
last = obj;
} }
enhance.dispose();
if (ageT) {
const data = ageT.dataSync();
obj.age = Math.trunc(10 * data[0]) / 10;
}
ageT.dispose();
last = obj;
resolve(obj); resolve(obj);
}); });
} }

4
src/blazeface/blazeface.ts
View File

@ -5,7 +5,7 @@ const NUM_LANDMARKS = 6;
function generateAnchors(inputSize) { function generateAnchors(inputSize) {
const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; 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++) { for (let i = 0; i < spec.strides.length; i++) {
const stride = spec.strides[i]; const stride = spec.strides[i];
const gridRows = Math.floor((inputSize + stride - 1) / stride); const gridRows = Math.floor((inputSize + stride - 1) / stride);
@ -121,7 +121,7 @@ export class BlazeFaceModel {
}); });
const scoresVal = scores.dataSync(); 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++) { for (let i = 0; i < boundingBoxes.length; i++) {
const boxIndex = boxIndices[i]; const boxIndex = boxIndices[i];
const confidence = scoresVal[boxIndex]; const confidence = scoresVal[boxIndex];

13
src/blazeface/facemesh.ts
View File

@ -15,7 +15,7 @@ export class MediaPipeFaceMesh {
async estimateFaces(input, config) { async estimateFaces(input, config) {
const predictions = await this.facePipeline.predict(input, config); const predictions = await this.facePipeline.predict(input, config);
const results = []; const results: Array<{}> = [];
for (const prediction of (predictions || [])) { for (const prediction of (predictions || [])) {
if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing 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; 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[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],
Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1], Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],
] : 0; ] : 0;
results.push({ results.push({ confidence: prediction.confidence || 0, box, mesh, boxRaw, meshRaw, annotations, image: prediction.image ? tf.clone(prediction.image) : null });
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.coords) prediction.coords.dispose();
if (prediction.image) prediction.image.dispose(); if (prediction.image) prediction.image.dispose();
} }
@ -51,6 +43,7 @@ export class MediaPipeFaceMesh {
let faceModels = [null, null, null]; let faceModels = [null, null, null];
export async function load(config) { export async function load(config) {
// @ts-ignore
faceModels = await Promise.all([ faceModels = await Promise.all([
(!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null, (!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, (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,

12
src/blazeface/facepipeline.ts
View File

@ -26,8 +26,8 @@ function replaceRawCoordinates(rawCoords, newCoords, prefix, keys = null) {
for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) { for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {
const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i]; const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];
const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`]; const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];
const shouldReplaceAllKeys = keys === null; // @ts-ignore
if (shouldReplaceAllKeys || keys.includes(key)) { if (!keys || keys.includes(key)) {
for (let j = 0; j < indices.length; j++) { for (let j = 0; j < indices.length; j++) {
const index = indices[j]; const index = indices[j];
rawCoords[originalIndices[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. // 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) { getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {
const eyeRawCoords = []; const eyeRawCoords: Array<any[]> = [];
for (let i = 0; i < IRIS_NUM_COORDINATES; i++) { for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {
const x = eyeData[i * 3]; const x = eyeData[i * 3];
const y = eyeData[i * 3 + 1]; const y = eyeData[i * 3 + 1];
const z = eyeData[i * 3 + 2]; const z = eyeData[i * 3 + 2];
eyeRawCoords.push([ eyeRawCoords.push([
(flip (flip ? (1 - (x / this.irisSize)) : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],
? (1 - (x / this.irisSize))
: (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],
(y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z, (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,
]); ]);
} }
@ -237,8 +235,10 @@ export class Pipeline {
replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right'); 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. // 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. } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.
// @ts-ignore
replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']); replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);
} else { // User is looking towards the left. } else { // User is looking towards the left.
// @ts-ignore
replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']); replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);
} }
const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left'); const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');

4
src/blazeface/util.ts
View File

@ -34,7 +34,7 @@ export function dot(v1, v2) {
} }
export function getColumnFrom2DArr(arr, columnIndex) { export function getColumnFrom2DArr(arr, columnIndex) {
const column = []; const column: Array<number> = [];
for (let i = 0; i < arr.length; i++) { for (let i = 0; i < arr.length; i++) {
column.push(arr[i][columnIndex]); column.push(arr[i][columnIndex]);
} }
@ -42,7 +42,7 @@ export function getColumnFrom2DArr(arr, columnIndex) {
} }
export function multiplyTransformMatrices(mat1, mat2) { export function multiplyTransformMatrices(mat1, mat2) {
const product = []; const product: Array<number[]> = [];
const size = mat1.length; const size = mat1.length;
for (let row = 0; row < size; row++) { for (let row = 0; row < size; row++) {
product.push([]); product.push([]);

16
src/embedding/embedding.ts
View File

@ -5,14 +5,14 @@ import * as profile from '../profile.js';
// based on https://github.com/sirius-ai/MobileFaceNet_TF // 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 // 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) { export async function load(config) {
if (!models.embedding) { if (!model) {
models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath); model = await tf.loadGraphModel(config.face.embedding.modelPath);
log(`load model: ${config.face.embedding.modelPath.match(/\/(.*)\./)[1]}`); log(`load model: ${config.face.embedding.modelPath.match(/\/(.*)\./)[1]}`);
} }
return models.embedding; return model;
} }
export function simmilarity(embedding1, embedding2) { export function simmilarity(embedding1, embedding2) {
@ -25,18 +25,18 @@ export function simmilarity(embedding1, embedding2) {
} }
export async function predict(image, config) { export async function predict(image, config) {
if (!models.embedding) return null; if (!model) return null;
return new Promise(async (resolve) => { return new Promise(async (resolve) => {
const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false); 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 ??? // 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.face.embedding.enabled) {
if (!config.profile) { 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 data = [...embeddingT.dataSync()]; // convert object array to standard array
tf.dispose(embeddingT); tf.dispose(embeddingT);
} else { } 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()]; data = [...profileData.result.dataSync()];
profileData.result.dispose(); profileData.result.dispose();
profile.run('emotion', profileData); profile.run('emotion', profileData);

18
src/emotion/emotion.ts
View File

@ -3,8 +3,8 @@ import * as tf from '../../dist/tfjs.esm.js';
import * as profile from '../profile.js'; import * as profile from '../profile.js';
const annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']; const annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];
const models = { emotion: null }; let model;
let last = []; let last: Array<{ score: number, emotion: string }> = [];
let skipped = Number.MAX_SAFE_INTEGER; let skipped = Number.MAX_SAFE_INTEGER;
// tuning values // 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 const scale = 1; // score multiplication factor
export async function load(config) { export async function load(config) {
if (!models.emotion) { if (!model) {
models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath); model = await tf.loadGraphModel(config.face.emotion.modelPath);
log(`load model: ${config.face.emotion.modelPath.match(/\/(.*)\./)[1]}`); log(`load model: ${config.face.emotion.modelPath.match(/\/(.*)\./)[1]}`);
} }
return models.emotion; return model;
} }
export async function predict(image, config) { 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)) { if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {
skipped++; skipped++;
return last; return last;
@ -54,15 +54,15 @@ export async function predict(image, config) {
blueNorm.dispose(); blueNorm.dispose();
const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2)); const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));
grayscale.dispose(); grayscale.dispose();
const obj = []; const obj: Array<{ score: number, emotion: string }> = [];
if (config.face.emotion.enabled) { if (config.face.emotion.enabled) {
let data; let data;
if (!config.profile) { if (!config.profile) {
const emotionT = await models.emotion.predict(normalize); const emotionT = await model.predict(normalize);
data = emotionT.dataSync(); data = emotionT.dataSync();
tf.dispose(emotionT); tf.dispose(emotionT);
} else { } else {
const profileData = await tf.profile(() => models.emotion.predict(normalize)); const profileData = await tf.profile(() => model.predict(normalize));
data = profileData.result.dataSync(); data = profileData.result.dataSync();
profileData.result.dispose(); profileData.result.dispose();
profile.run('emotion', profileData); profile.run('emotion', profileData);

13
src/faceboxes/faceboxes.ts
View File

@ -15,7 +15,7 @@ export class FaceBoxes {
async estimateFaces(input, config) { async estimateFaces(input, config) {
if (config) this.config = 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 resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);
const castT = resizeT.toInt(); const castT = resizeT.toInt();
let scores; 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 resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);
const image = resized.div([255]); const image = resized.div([255]);
resized.dispose(); resized.dispose();
results.push({ results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image, });
confidence: scores[i], // add mesh, meshRaw, annotations,
box,
boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null,
image,
// mesh,
// meshRaw,
// annotations,
});
} }
} }
return results; return results;

18
src/gender/gender.ts
View File

@ -2,7 +2,7 @@ import { log } from '../log';
import * as tf from '../../dist/tfjs.esm.js'; import * as tf from '../../dist/tfjs.esm.js';
import * as profile from '../profile.js'; import * as profile from '../profile.js';
const models = { gender: null }; let model;
let last = { gender: '' }; let last = { gender: '' };
let skipped = Number.MAX_SAFE_INTEGER; let skipped = Number.MAX_SAFE_INTEGER;
let alternative = false; 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 const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale
export async function load(config) { export async function load(config) {
if (!models.gender) { if (!model) {
models.gender = await tf.loadGraphModel(config.face.gender.modelPath); model = await tf.loadGraphModel(config.face.gender.modelPath);
alternative = models.gender.inputs[0].shape[3] === 1; alternative = model.inputs[0].shape[3] === 1;
log(`load model: ${config.face.gender.modelPath.match(/\/(.*)\./)[1]}`); log(`load model: ${config.face.gender.modelPath.match(/\/(.*)\./)[1]}`);
} }
return models.gender; return model;
} }
export async function predict(image, config) { 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 !== '') { if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {
skipped++; skipped++;
return last; return last;
@ -45,12 +45,12 @@ export async function predict(image, config) {
tf.dispose(resize); tf.dispose(resize);
let genderT; let genderT;
const obj = { gender: undefined, confidence: undefined }; const obj = { gender: '', confidence: 0 };
if (!config.profile) { 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 { } 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(); genderT = profileGender.result.clone();
profileGender.result.dispose(); profileGender.result.dispose();
profile.run('gender', profileGender); profile.run('gender', profileGender);

11
src/gesture/gesture.ts
View File

@ -1,6 +1,6 @@
export const body = (res) => { export const body = (res) => {
if (!res) return []; if (!res) return [];
const gestures = []; const gestures: Array<{ body: number, gesture: string }> = [];
for (let i = 0; i < res.length; i++) { for (let i = 0; i < res.length; i++) {
// raising hands // raising hands
const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist')); const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));
@ -20,7 +20,7 @@ export const body = (res) => {
export const face = (res) => { export const face = (res) => {
if (!res) return []; if (!res) return [];
const gestures = []; const gestures: Array<{ face: number, gesture: string }> = [];
for (let i = 0; i < res.length; i++) { for (let i = 0; i < res.length; i++) {
if (res[i].mesh && res[i].mesh.length > 0) { if (res[i].mesh && res[i].mesh.length > 0) {
const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2]; const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];
@ -41,7 +41,7 @@ export const face = (res) => {
export const iris = (res) => { export const iris = (res) => {
if (!res) return []; if (!res) return [];
const gestures = []; const gestures: Array<{ iris: number, gesture: string }> = [];
for (let i = 0; i < res.length; i++) { for (let i = 0; i < res.length; i++) {
if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue; 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]; 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) => { export const hand = (res) => {
if (!res) return []; if (!res) return [];
const gestures = []; const gestures: Array<{ hand: number, gesture: string }> = [];
for (let i = 0; i < res.length; i++) { 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'])) { 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 (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger
} }
if (fingers && fingers.length > 0) { if (fingers && fingers.length > 0) {

4
src/handpose/handdetector.ts
View File

@ -49,7 +49,7 @@ export class HandDetector {
scoresT.dispose(); scoresT.dispose();
filteredT.dispose(); filteredT.dispose();
const hands = []; const hands: Array<{ box: any, palmLandmarks: any, confidence: number }> = [];
for (const index of filtered) { for (const index of filtered) {
if (scores[index] >= config.hand.minConfidence) { if (scores[index] >= config.hand.minConfidence) {
const matchingBox = tf.slice(boxes, [index, 0], [1, -1]); 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 image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));
const predictions = await this.getBoxes(image, config); const predictions = await this.getBoxes(image, config);
image.dispose(); image.dispose();
const hands = []; const hands: Array<{}> = [];
if (!predictions || predictions.length === 0) return hands; if (!predictions || predictions.length === 0) return hands;
for (const prediction of predictions) { for (const prediction of predictions) {
const boxes = prediction.box.dataSync(); const boxes = prediction.box.dataSync();

12
src/handpose/handpipeline.ts
View File

@ -91,7 +91,7 @@ export class HandPipeline {
// for (const possible of boxes) this.storedBoxes.push(possible); // for (const possible of boxes) this.storedBoxes.push(possible);
if (this.storedBoxes.length > 0) useFreshBox = true; 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}`); // 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 // go through working set of boxes
@ -124,10 +124,7 @@ export class HandPipeline {
const result = { const result = {
landmarks: coords, landmarks: coords,
confidence, confidence,
box: { box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },
topLeft: nextBoundingBox.startPoint,
bottomRight: nextBoundingBox.endPoint,
},
}; };
hands.push(result); hands.push(result);
} else { } else {
@ -139,10 +136,7 @@ export class HandPipeline {
const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR); const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);
const result = { const result = {
confidence: currentBox.confidence, confidence: currentBox.confidence,
box: { box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
topLeft: enlarged.startPoint,
bottomRight: enlarged.endPoint,
},
}; };
hands.push(result); hands.push(result);
} }

9
src/handpose/handpose.ts
View File

@ -29,7 +29,7 @@ export class HandPose {
async estimateHands(input, config) { async estimateHands(input, config) {
const predictions = await this.handPipeline.estimateHands(input, config); const predictions = await this.handPipeline.estimateHands(input, config);
if (!predictions) return []; if (!predictions) return [];
const hands = []; const hands: Array<{ confidence: number, box: any, landmarks: any, annotations: any }> = [];
for (const prediction of predictions) { for (const prediction of predictions) {
const annotations = {}; const annotations = {};
if (prediction.landmarks) { 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[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],
Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1], Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],
] : 0; ] : 0;
hands.push({ hands.push({ confidence: prediction.confidence, box, landmarks: prediction.landmarks, annotations });
confidence: prediction.confidence,
box,
landmarks: prediction.landmarks,
annotations,
});
} }
return hands; return hands;
} }

4
src/handpose/util.ts
View File

@ -18,7 +18,7 @@ export function dot(v1, v2) {
} }
export function getColumnFrom2DArr(arr, columnIndex) { export function getColumnFrom2DArr(arr, columnIndex) {
const column = []; const column: Array<number> = [];
for (let i = 0; i < arr.length; i++) { for (let i = 0; i < arr.length; i++) {
column.push(arr[i][columnIndex]); column.push(arr[i][columnIndex]);
} }
@ -26,7 +26,7 @@ export function getColumnFrom2DArr(arr, columnIndex) {
} }
export function multiplyTransformMatrices(mat1, mat2) { export function multiplyTransformMatrices(mat1, mat2) {
const product = []; const product: Array<number[]> = [];
const size = mat1.length; const size = mat1.length;
for (let row = 0; row < size; row++) { for (let row = 0; row < size; row++) {
product.push([]); product.push([]);

11
src/human.ts
View File

@ -236,7 +236,7 @@ class Human {
let genderRes; let genderRes;
let emotionRes; let emotionRes;
let embeddingRes; 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'; this.state = 'run:face';
timeStamp = now(); timeStamp = now();
const faces = await this.models.face?.estimateFaces(input, this.config); const faces = await this.models.face?.estimateFaces(input, this.config);
@ -349,8 +349,8 @@ class Human {
this.state = 'image'; this.state = 'image';
this.config = mergeDeep(this.config, userConfig); this.config = mergeDeep(this.config, userConfig);
const process = image.process(input, this.config); const process = image.process(input, this.config);
process.tensor.dispose(); process?.tensor?.dispose();
return process.canvas; return process?.canvas;
} }
// main detect function // main detect function
@ -445,6 +445,7 @@ class Human {
let gestureRes = []; let gestureRes = [];
if (this.config.gesture.enabled) { if (this.config.gesture.enabled) {
timeStamp = now(); timeStamp = now();
// @ts-ignore
gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)]; gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];
if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp); if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);
else if (this.perf.gesture) delete this.perf.gesture; else if (this.perf.gesture) delete this.perf.gesture;
@ -495,8 +496,8 @@ class Human {
canvas.width = size; canvas.width = size;
canvas.height = size; canvas.height = size;
const ctx = canvas.getContext('2d'); const ctx = canvas.getContext('2d');
ctx.drawImage(img, 0, 0); ctx?.drawImage(img, 0, 0);
const data = ctx.getImageData(0, 0, size, size); const data = ctx?.getImageData(0, 0, size, size);
this.detect(data, this.config).then((res) => resolve(res)); this.detect(data, this.config).then((res) => resolve(res));
}; };
if (src) img.src = src; if (src) img.src = src;

2
src/image.ts
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@ -1,3 +1,5 @@
// @ts-nocheck
import { log } from './log'; import { log } from './log';
import * as tf from '../dist/tfjs.esm.js'; import * as tf from '../dist/tfjs.esm.js';
import * as fxImage from './imagefx'; import * as fxImage from './imagefx';

2
src/posenet/decodeMultiple.ts
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@ -20,7 +20,7 @@ function getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {
} }
export function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) { 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 queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);
const squaredNmsRadius = config.body.nmsRadius ^ 2; const squaredNmsRadius = config.body.nmsRadius ^ 2;
// Generate at most maxDetections object instances per image in decreasing root part score order. // Generate at most maxDetections object instances per image in decreasing root part score order.

2
src/posenet/decoders.ts
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@ -20,7 +20,7 @@ function getOffsetPoint(y, x, keypoint, offsetsBuffer) {
} }
export function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) { export function getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {
const result = []; const result: Array<number> = [];
for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) { for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {
const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf(); const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();
const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf(); const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();

3
src/tfjs/backend.ts
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@ -24,12 +24,14 @@ export function register() {
if (!tf.findBackend(config.name)) { if (!tf.findBackend(config.name)) {
log('backend registration:', config.name); log('backend registration:', config.name);
try { try {
// @ts-ignore
config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas'); config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');
} catch (err) { } catch (err) {
log('error: cannot create canvas:', err); log('error: cannot create canvas:', err);
return; return;
} }
try { try {
// @ts-ignore
config.gl = config.canvas.getContext('webgl2', config.webGLattr); config.gl = config.canvas.getContext('webgl2', config.webGLattr);
} catch (err) { } catch (err) {
log('error: cannot get WebGL2 context:', err); log('error: cannot get WebGL2 context:', err);
@ -60,6 +62,7 @@ export function register() {
} }
try { try {
tf.ENV.set('WEBGL_VERSION', 2); 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_MAX_TEXTURE_SIZE', config.gl.getParameter(config.gl.MAX_TEXTURE_SIZE));
tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);
tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true); tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);

4
tsconfig.json
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@ -17,9 +17,9 @@
"resolveJsonModule": true, "resolveJsonModule": true,
"skipLibCheck": true, "skipLibCheck": true,
"sourceMap": false, "sourceMap": false,
"strictNullChecks": false, "strictNullChecks": true,
"allowJs": true "allowJs": true
}, },
"formatCodeOptions": { "indentSize": 2, "tabSize": 2 }, "formatCodeOptions": { "indentSize": 2, "tabSize": 2 },
"include": ["src/*", "demo/*"], "include": ["src/*", "src/***/*", "demo/*"],
} }

2
types/age/age.d.ts vendored Normal file
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@ -0,0 +1,2 @@
export declare function load(config: any): Promise<any>;
export declare function predict(image: any, config: any): Promise<unknown>;

22
types/blazeface/blazeface.d.ts vendored Normal file
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@ -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>;

17
types/blazeface/box.d.ts vendored Normal file
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@ -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;
};

49
types/blazeface/coords.d.ts vendored Normal file
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@ -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[][];

7
types/blazeface/facemesh.d.ts vendored Normal file
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@ -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>;

36
types/blazeface/facepipeline.d.ts vendored Normal file
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@ -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;
};
}

21
types/blazeface/util.d.ts vendored Normal file
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@ -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;

3
types/embedding/embedding.d.ts vendored Normal file
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@ -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>;

2
types/emotion/emotion.d.ts vendored Normal file
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@ -0,0 +1,2 @@
export declare function load(config: any): Promise<any>;
export declare function predict(image: any, config: any): Promise<unknown>;

13
types/faceboxes/faceboxes.d.ts vendored Normal file
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@ -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>;

2
types/gender/gender.d.ts vendored Normal file
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@ -0,0 +1,2 @@
export declare function load(config: any): Promise<any>;
export declare function predict(image: any, config: any): Promise<unknown>;

16
types/gesture/gesture.d.ts vendored Normal file
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@ -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;
}[];

6
types/handpose/anchors.d.ts vendored Normal file
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@ -0,0 +1,6 @@
export declare const anchors: {
w: number;
h: number;
x_center: number;
y_center: number;
}[];

24
types/handpose/box.d.ts vendored Normal file
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@ -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;
};

16
types/handpose/handdetector.d.ts vendored Normal file
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@ -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<{}[]>;
}

25
types/handpose/handpipeline.d.ts vendored Normal file
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@ -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[];
};
}

19
types/handpose/handpose.d.ts vendored Normal file
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@ -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>;

9
types/handpose/util.d.ts vendored Normal file
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@ -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[];

47
types/human.d.ts vendored Normal file
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@ -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 };

4
types/image.d.ts vendored Normal file
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@ -0,0 +1,4 @@
export declare function process(input: any, config: any): {
tensor: any;
canvas: null;
} | null;

1
types/log.d.ts vendored Normal file
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@ -0,0 +1 @@
export declare function log(...msg: any[]): void;

2
types/posenet/buildParts.d.ts vendored Normal file
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@ -0,0 +1,2 @@
import * as heapSort from './heapSort';
export declare function buildPartWithScoreQueue(scoreThreshold: any, localMaximumRadius: any, scores: any): heapSort.MaxHeap;

4
types/posenet/decodeMultiple.d.ts vendored Normal file
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@ -0,0 +1,4 @@
export declare function decodeMultiplePoses(scoresBuffer: any, offsetsBuffer: any, displacementsFwdBuffer: any, displacementsBwdBuffer: any, config: any): {
keypoints: any;
score: number;
}[];

12
types/posenet/decodePose.d.ts vendored Normal file
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@ -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;
}>;

4
types/posenet/decoders.d.ts vendored Normal file
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@ -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;

17
types/posenet/heapSort.d.ts vendored Normal file
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@ -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;
}

6
types/posenet/keypoints.d.ts vendored Normal file
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@ -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[];

6
types/posenet/modelBase.d.ts vendored Normal file
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@ -0,0 +1,6 @@
export declare class BaseModel {
model: any;
constructor(model: any);
predict(input: any, config: any): any;
dispose(): void;
}

7
types/posenet/posenet.d.ts vendored Normal file
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@ -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>;

14
types/posenet/util.d.ts vendored Normal file
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@ -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;

19
types/posenet/vectors.d.ts vendored Normal file
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@ -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;
};

2
types/profile.d.ts vendored Normal file
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@ -0,0 +1,2 @@
export declare const data: {};
export declare function run(name: any, raw: any): void;

2
types/sample.d.ts vendored Normal file
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19
types/tfjs/backend.d.ts vendored Normal file
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@ -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;

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