mirror of https://github.com/vladmandic/human
366 lines
17 KiB
JavaScript
366 lines
17 KiB
JavaScript
const fs = require('fs');
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const process = require('process');
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const canvasJS = require('canvas');
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let fetch; // fetch is dynamically imported later
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let config;
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const log = (status, ...data) => {
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if (typeof process.send !== 'undefined') process.send([status, data]); // send to parent process over ipc
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// eslint-disable-next-line no-console
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else console.log(status, ...data); // write to console if no parent process
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};
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async function testHTTP() {
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if (config.modelBasePath.startsWith('file:')) return true;
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return new Promise((resolve) => {
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fetch(config.modelBasePath)
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.then((res) => {
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if (res && res.ok) log('state', 'passed: model server:', config.modelBasePath);
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else log('error', 'failed: model server:', config.modelBasePath);
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resolve(res && res.ok);
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})
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.catch((err) => {
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log('error', 'failed: model server:', err.message);
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resolve(false);
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});
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});
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}
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async function getImage(human, input) {
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let img;
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try {
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img = await canvasJS.loadImage(input);
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} catch (err) {
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log('error', 'failed: load image', input, err.message);
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return img;
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}
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const canvas = canvasJS.createCanvas(img.width, img.height);
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const ctx = canvas.getContext('2d');
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ctx.drawImage(img, 0, 0, img.width, img.height);
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const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
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const res = human.tf.tidy(() => {
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const tensor = human.tf.tensor(Array.from(imageData.data), [canvas.height, canvas.width, 4], 'float32'); // create rgba image tensor from flat array
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const channels = human.tf.split(tensor, 4, 2); // split rgba to channels
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const rgb = human.tf.stack([channels[0], channels[1], channels[2]], 2); // stack channels back to rgb
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const reshape = human.tf.reshape(rgb, [1, canvas.height, canvas.width, 3]); // move extra dim from the end of tensor and use it as batch number instead
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return reshape;
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});
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const sum = human.tf.sum(res);
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if (res && res.shape[0] === 1 && res.shape[3] === 3) log('state', 'passed: load image:', input, res.shape, { checksum: sum.dataSync()[0] });
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else log('error', 'failed: load image:', input, res);
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human.tf.dispose(sum);
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return res;
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}
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function printResults(detect) {
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const person = (detect.face && detect.face[0]) ? { score: detect.face[0].score, age: detect.face[0].age, gender: detect.face[0].gender } : {};
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const object = (detect.object && detect.object[0]) ? { score: detect.object[0].score, class: detect.object[0].label } : {};
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const body = (detect.body && detect.body[0]) ? { score: detect.body[0].score, keypoints: detect.body[0].keypoints.length } : {};
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const persons = detect.persons;
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if (detect.face) log('data', ' result: face:', detect.face?.length, 'body:', detect.body?.length, 'hand:', detect.hand?.length, 'gesture:', detect.gesture?.length, 'object:', detect.object?.length, 'person:', persons.length, person, object, body);
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if (detect.performance) log('data', ' result: performance:', 'load:', detect?.performance.load, 'total:', detect.performance?.total);
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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', 'platform:', human.env.platform, 'agent:', human.env.agent);
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log('info', 'tfjs version:', human.tf.version.tfjs);
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await human.load();
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if (config.backend === human.tf.getBackend()) log('state', 'passed: set backend:', config.backend);
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else log('error', 'failed: set backend:', config.backend);
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log('state', 'tensors', human.tf.memory().numTensors);
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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('state', ' result: defined models:', keys.length, 'loaded models:', loaded.length);
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return true;
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}
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log('error', 'failed: load models');
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return false;
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}
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async function testWarmup(human, title) {
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let warmup;
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try {
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warmup = await human.warmup(config);
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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, title);
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// const count = human.tf.engine().state.numTensors;
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// if (count - tensors > 0) log('warn', 'failed: memory', config.warmup, title, 'tensors:', count - tensors);
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printResults(warmup);
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} else {
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log('error', 'failed: warmup:', config.warmup, title);
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}
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return warmup;
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}
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async function testDetect(human, input, title, checkLeak = true) {
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await human.load(config);
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const tensors = human.tf.engine().state.numTensors;
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const image = input ? await getImage(human, input) : human.tf.randomNormal([1, 1024, 1024, 3]);
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if (!image) {
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log('error', 'failed: detect: input is null');
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return false;
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}
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let detect;
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try {
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detect = await human.detect(image, config);
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} catch (err) {
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log('error', 'error: detect', err);
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}
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if (image instanceof human.tf.Tensor) human.tf.dispose(image);
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if (detect) {
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log('state', 'passed: detect:', input || 'random', title);
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// const count = human.tf.engine().state.numTensors;
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// if (count - tensors > 0) log('warn', 'failed: memory', config.warmup, title, 'tensors:', count - tensors);
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printResults(detect);
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} else {
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log('error', 'failed: detect', input || 'random', title);
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}
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// check tensor leaks
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if (checkLeak) {
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const leak = human.tf.engine().state.numTensors - tensors;
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if (leak !== 0) log('error', 'failed: memory leak', leak);
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}
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return detect;
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}
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const evt = { image: 0, detect: 0, warmup: 0 };
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async function events(event) {
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log('state', 'event:', event);
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evt[event]++;
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}
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async function test(Human, inputConfig) {
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config = inputConfig;
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fetch = (await import('node-fetch')).default;
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const ok = await testHTTP();
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if (!ok) {
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log('error', 'aborting test');
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return;
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}
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const t0 = process.hrtime.bigint();
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let res;
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// test event emitter
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const human = new Human(config);
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human.events.addEventListener('warmup', () => events('warmup'));
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human.events.addEventListener('image', () => events('image'));
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human.events.addEventListener('detect', () => events('detect'));
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// test configuration validation
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let invalid = human.validate();
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if (invalid.length === 0) log('state', 'passed: configuration default validation', invalid);
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else log('error', 'failed: configuration default validation', invalid);
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config.invalid = true;
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invalid = human.validate(config);
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if (invalid.length === 1) log('state', 'passed: configuration invalid validation', invalid);
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else log('error', 'failed: configuration default validation', invalid);
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delete config.invalid;
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// test model loading
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await human.load();
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const models = Object.keys(human.models).map((model) => ({ name: model, loaded: (human.models[model] !== null) }));
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const loaded = models.filter((model) => model.loaded);
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if (models.length === 19 && loaded.length === 10) log('state', 'passed: models loaded', models);
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else log('error', 'failed: models loaded', models);
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// test warmup sequences
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await testInstance(human);
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config.warmup = 'none';
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res = await testWarmup(human, 'default');
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if (res.error !== 'null') log('error', 'failed: warmup none result mismatch');
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else log('state', 'passed: warmup none result match');
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config.warmup = 'face';
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res = await testWarmup(human, 'default');
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if (!res || res?.face?.length !== 1 || res?.body?.length !== 1 || res?.hand?.length !== 0 || res?.gesture?.length !== 3) log('error', 'failed: warmup face result mismatch', res?.face?.length, res?.body?.length, res?.hand?.length, res?.gesture?.length);
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else log('state', 'passed: warmup face result match');
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config.warmup = 'body';
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res = await testWarmup(human, 'default');
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if (!res || res?.face?.length !== 1 || res?.body?.length !== 1 || res?.hand?.length !== 0 || res?.gesture?.length !== 3) log('error', 'failed: warmup body result mismatch', res?.face?.length, res?.body?.length, res?.hand?.length, res?.gesture?.length);
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else log('state', 'passed: warmup body result match');
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// test default config async
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log('info', 'test default');
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human.reset();
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config.async = true;
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config.cacheSensitivity = 0;
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res = await testDetect(human, 'samples/ai-body.jpg', 'default');
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if (!res || res?.face?.length !== 1 || res?.face[0].gender !== 'female') log('error', 'failed: default result face mismatch', res?.face?.length, res?.body?.length, res?.hand?.length, res?.gesture?.length);
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else log('state', 'passed: default result face match');
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// test default config sync
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log('info', 'test sync');
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human.reset();
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config.async = false;
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config.cacheSensitivity = 0;
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res = await testDetect(human, 'samples/ai-body.jpg', 'default');
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if (!res || res?.face?.length !== 1 || res?.face[0].gender !== 'female') log('error', 'failed: default sync', res?.face?.length, res?.body?.length, res?.hand?.length, res?.gesture?.length);
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else log('state', 'passed: default sync');
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// test image processing
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const img1 = await human.image(null);
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const img2 = await human.image(await getImage(human, 'samples/ai-face.jpg'));
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if (!img1 || !img2 || img1.tensor !== null || img2.tensor?.shape?.length !== 4) log('error', 'failed: image input', img1?.tensor?.shape, img2?.tensor?.shape);
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else log('state', 'passed: image input', img1?.tensor?.shape, img2?.tensor?.shape);
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// test null input
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res = await human.detect(null);
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if (!res || !res.error) log('error', 'failed: invalid input', res);
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else log('state', 'passed: invalid input', res);
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// test face similarity
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log('info', 'test face similarity');
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human.reset();
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config.async = false;
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config.cacheSensitivity = 0;
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let res1 = await testDetect(human, 'samples/ai-face.jpg', 'default');
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let res2 = await testDetect(human, 'samples/ai-body.jpg', 'default');
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let res3 = await testDetect(human, 'samples/ai-upper.jpg', 'default');
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const desc1 = res1 && res1.face && res1.face[0] && res1.face[0].embedding ? [...res1.face[0].embedding] : null;
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const desc2 = res2 && res2.face && res2.face[0] && res2.face[0].embedding ? [...res2.face[0].embedding] : null;
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const desc3 = res3 && res3.face && res3.face[0] && res3.face[0].embedding ? [...res3.face[0].embedding] : null;
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if (!desc1 || !desc2 || !desc3 || desc1.length !== 1024 || desc2.length !== 1024 || desc3.length !== 1024) log('error', 'failed: face descriptor', desc1?.length, desc2?.length, desc3?.length);
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else log('state', 'passed: face descriptor');
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res1 = Math.round(100 * human.similarity(desc1, desc2));
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res2 = Math.round(100 * human.similarity(desc1, desc3));
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res3 = Math.round(100 * human.similarity(desc2, desc3));
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if (res1 !== 51 || res2 !== 49 || res3 !== 53) log('error', 'failed: face similarity ', res1, res2, res3);
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else log('state', 'passed: face similarity');
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// test face matching
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log('info', 'test face matching');
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let db = [];
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try {
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db = JSON.parse(fs.readFileSync('demo/facematch/faces.json').toString());
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} catch { /***/ }
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if (db.length < 100) log('error', 'failed: face database ', db.length);
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else log('state', 'passed: face database', db.length);
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res1 = human.match(desc1, db);
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res2 = human.match(desc2, db);
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res3 = human.match(desc3, db);
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if (!res1 || !res1['name'] || !res2 || !res2['name'] || !res3 || !res3['name']) log('error', 'failed: face match ', res1, res2, res3);
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else log('state', 'passed: face match', { first: { name: res1.name, similarity: res1.similarity } }, { second: { name: res2.name, similarity: res2.similarity } }, { third: { name: res3.name, similarity: res3.similarity } });
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// test object detection
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log('info', 'test object');
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human.reset();
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config.object = { enabled: true };
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res = await testDetect(human, 'samples/ai-body.jpg', 'default');
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if (!res || res?.object?.length !== 1 || res?.object[0]?.label !== 'person') log('error', 'failed: object result mismatch', res?.object?.length);
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else log('state', 'passed: object result match');
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// test sensitive config
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log('info', 'test sensitive');
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human.reset();
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config.cacheSensitivity = 0;
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config.face = { detector: { minConfidence: 0.0001, maxDetected: 1 } };
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config.body = { minConfidence: 0.0001, maxDetected: 1 };
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config.hand = { minConfidence: 0.0001, maxDetected: 3 };
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res = await testDetect(human, 'samples/ai-body.jpg', 'default');
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if (!res || res?.face?.length !== 1 || res?.body?.length !== 1 || res?.hand?.length !== 3 || res?.gesture?.length !== 9) log('error', 'failed: sensitive result mismatch', res?.face?.length, res?.body?.length, res?.hand?.length, res?.gesture?.length);
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else log('state', 'passed: sensitive result match');
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// test sensitive details face
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const face = res && res.face ? res.face[0] : null;
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if (!face || face?.box?.length !== 4 || face?.mesh?.length !== 478 || face?.emotion?.length !== 4 || face?.embedding?.length !== 1024 || face?.rotation?.matrix?.length !== 9) {
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log('error', 'failed: sensitive face result mismatch', res?.face?.length, face?.box?.length, face?.mesh?.length, face?.emotion?.length, face?.embedding?.length, face?.rotation?.matrix?.length);
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} else log('state', 'passed: sensitive face result match');
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// test sensitive details body
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const body = res && res.body ? res.body[0] : null;
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if (!body || body?.box?.length !== 4 || body?.keypoints?.length !== 17) log('error', 'failed: sensitive body result mismatch', body);
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else log('state', 'passed: sensitive body result match');
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// test sensitive details hand
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const hand = res && res.hand ? res.hand[0] : null;
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if (!hand || hand?.box?.length !== 4 || hand?.keypoints?.length !== 21) log('error', 'failed: sensitive hand result mismatch', hand?.keypoints?.length);
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else log('state', 'passed: sensitive hand result match');
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// test detectors only
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log('info', 'test detectors');
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human.reset();
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config.face = { mesh: { enabled: false }, iris: { enabled: false }, description: { enabled: false }, emotion: { enabled: false } };
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config.hand = { landmarks: false };
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res = await testDetect(human, 'samples/ai-body.jpg', 'default');
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if (!res || res?.face?.length !== 1 || res?.face[0]?.gender || res?.face[0]?.age || res?.face[0]?.embedding) log('error', 'failed: detectors result face mismatch', res?.face);
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else log('state', 'passed: detector result face match');
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if (!res || res?.hand?.length !== 1 || res?.hand[0]?.landmarks) log('error', 'failed: detectors result hand mismatch', res?.hand?.length);
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else log('state', 'passed: detector result hand match');
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// test posenet and movenet
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log('info', 'test body variants');
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config.body = { modelPath: 'posenet.json' };
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await testDetect(human, 'samples/ai-body.jpg', 'posenet');
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config.body = { modelPath: 'movenet-lightning.json' };
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await testDetect(human, 'samples/ai-body.jpg', 'movenet');
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// test multiple instances
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const first = new Human(config);
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const second = new Human(config);
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await testDetect(human, null, 'default');
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log('info', 'test: first instance');
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await testDetect(first, 'samples/ai-upper.jpg', 'default');
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log('info', 'test: second instance');
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await testDetect(second, 'samples/ai-upper.jpg', 'default');
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// test async multiple instances
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log('info', 'test: concurrent');
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await Promise.all([
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testDetect(human, 'samples/ai-face.jpg', 'default', false),
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testDetect(first, 'samples/ai-face.jpg', 'default', false),
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testDetect(second, 'samples/ai-face.jpg', 'default', false),
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testDetect(human, 'samples/ai-body.jpg', 'default', false),
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testDetect(first, 'samples/ai-body.jpg', 'default', false),
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testDetect(second, 'samples/ai-body.jpg', 'default', false),
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testDetect(human, 'samples/ai-upper.jpg', 'default', false),
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testDetect(first, 'samples/ai-upper.jpg', 'default', false),
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testDetect(second, 'samples/ai-upper.jpg', 'default', false),
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]);
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// test monkey-patch
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globalThis.Canvas = canvasJS.Canvas; // monkey-patch to use external canvas library
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globalThis.ImageData = canvasJS.ImageData; // monkey-patch to use external canvas library
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const inputImage = await canvasJS.loadImage('samples/ai-face.jpg'); // load image using canvas library
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const inputCanvas = new canvasJS.Canvas(inputImage.width, inputImage.height); // create canvas
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const ctx = inputCanvas.getContext('2d');
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ctx.drawImage(inputImage, 0, 0); // draw input image onto canvas
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res = await human.detect(inputCanvas);
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if (!res || res?.face?.length !== 1) log('error', 'failed: monkey patch');
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else log('state', 'passed: monkey patch');
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// test segmentation
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res = await human.segmentation(inputCanvas, inputCanvas);
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if (!res || !res.data || !res.canvas) log('error', 'failed: segmentation');
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else log('state', 'passed: segmentation', [res.data.length]);
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human.env.Canvas = undefined;
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// tests end
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const t1 = process.hrtime.bigint();
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// check if all instances reported same
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const tensors1 = human.tf.engine().state.numTensors;
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const tensors2 = first.tf.engine().state.numTensors;
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const tensors3 = second.tf.engine().state.numTensors;
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if (tensors1 === tensors2 && tensors1 === tensors3 && tensors2 === tensors3) log('state', 'passeed: equal usage');
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else log('error', 'failed: equal usage', tensors1, tensors2, tensors3);
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// report end
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log('info', 'events:', evt);
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log('info', 'tensors', human.tf.memory().numTensors);
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log('info', 'test complete:', Math.trunc(Number(t1 - t0) / 1000 / 1000), 'ms');
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}
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exports.test = test;
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