mirror of https://github.com/vladmandic/human
100 lines
3.0 KiB
JavaScript
100 lines
3.0 KiB
JavaScript
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const log = require('@vladmandic/pilogger');
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const tf = require('@tensorflow/tfjs');
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const Human = require('../dist/human.node-wasm.js').default;
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const config = {
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backend: 'wasm',
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wasmPath: 'assets/',
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debug: false,
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videoOptimized: false,
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async: false,
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modelBasePath: 'http://localhost:10030/models',
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filter: {
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enabled: true,
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},
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face: {
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enabled: true,
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detector: { enabled: true, rotation: false },
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mesh: { enabled: true },
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iris: { enabled: true },
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description: { enabled: true },
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emotion: { enabled: true },
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},
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hand: {
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enabled: true,
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},
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// body: { modelPath: 'efficientpose.json', enabled: true },
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// body: { modelPath: 'blazepose.json', enabled: true },
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body: { modelPath: 'posenet.json', enabled: true },
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object: { enabled: false }, // Error: Kernel 'SparseToDense' not registered for backend 'wasm'
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};
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async function testInstance(human) {
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if (human) log.state('passed: create human');
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else log.error('failed: create human');
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// if (!human.tf) human.tf = tf;
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log.info('human version:', human.version);
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log.info('tfjs version:', human.tf.version_core);
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log.info('platform:', human.sysinfo.platform);
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log.info('agent:', human.sysinfo.agent);
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await human.load();
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if (human.models) {
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log.state('passed: load models');
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const keys = Object.keys(human.models);
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const loaded = keys.filter((model) => human.models[model]);
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log.data(' result: defined models:', keys.length, 'loaded models:', loaded.length);
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} else {
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log.error('failed: load models');
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}
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let warmup;
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try {
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warmup = await human.warmup();
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} catch (err) {
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log.error('error warmup');
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}
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if (warmup) {
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log.state('passed: warmup:', config.warmup);
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log.data(' result: face:', warmup.face?.length, 'body:', warmup.body?.length, 'hand:', warmup.hand?.length, 'gesture:', warmup.gesture?.length, 'object:', warmup.object?.length);
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log.data(' result: performance:', 'load:', warmup.performance?.load, 'total:', warmup.performance?.total);
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} else {
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log.error('failed: warmup');
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}
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const random = tf.randomNormal([1, 1024, 1024, 3]);
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let detect;
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try {
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detect = await human.detect(random);
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} catch (err) {
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log.error('error: detect', err);
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}
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tf.dispose(random);
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if (detect) {
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log.state('passed: detect:', 'random');
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log.data(' result: face:', detect.face?.length, 'body:', detect.body?.length, 'hand:', detect.hand?.length, 'gesture:', detect.gesture?.length, 'object:', detect.object?.length);
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log.data(' result: performance:', 'load:', detect?.performance.load, 'total:', detect.performance?.total);
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} else {
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log.error('failed: detect');
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}
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}
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async function test() {
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log.info('testing instance#1 - none');
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config.warmup = 'none';
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const human1 = new Human(config);
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await testInstance(human1);
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log.info('testing instance#2 - face');
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config.warmup = 'face';
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const human2 = new Human(config);
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await testInstance(human2);
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log.info('testing instance#3 - body');
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config.warmup = 'body';
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const human3 = new Human(config);
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await testInstance(human3);
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}
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test();
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