mirror of https://github.com/vladmandic/human
implement event emitters
Vladimir Mandic
parent
1702b6a833
commit
9ae3f10ded
29
README.md
29
README.md
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@ -195,11 +195,11 @@ draw output on screen using internal draw helper functions
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// create instance of human with simple configuration using default values
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const config = { backend: 'webgl' };
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const human = new Human(config);
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function detectVideo() {
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// select input HTMLVideoElement and output HTMLCanvasElement from page
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const inputVideo = document.getElementById('video-id');
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const outputCanvas = document.getElementById('canvas-id');
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function detectVideo() {
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// perform processing using default configuration
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human.detect(inputVideo).then((result) => {
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// result object will contain detected details
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@ -225,10 +225,10 @@ or using `async/await`:
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// create instance of human with simple configuration using default values
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const config = { backend: 'webgl' };
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const human = new Human(config); // create instance of Human
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async function detectVideo() {
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const inputVideo = document.getElementById('video-id');
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const outputCanvas = document.getElementById('canvas-id');
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async function detectVideo() {
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const result = await human.detect(inputVideo); // run detection
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human.draw.all(outputCanvas, result); // draw all results
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requestAnimationFrame(detectVideo); // run loop
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@ -237,6 +237,27 @@ async function detectVideo() {
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detectVideo(); // start loop
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```
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or using `Events`:
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```js
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// create instance of human with simple configuration using default values
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const config = { backend: 'webgl' };
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const human = new Human(config); // create instance of Human
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const inputVideo = document.getElementById('video-id');
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const outputCanvas = document.getElementById('canvas-id');
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human.events.addEventListener('detect', () => { // event gets triggered when detect is complete
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human.draw.all(outputCanvas, human.result); // draw all results
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});
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function detectVideo() {
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human.detect(inputVideo) // run detection
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.then(() => requestAnimationFrame(detectVideo)); // upon detect complete start processing of the next frame
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}
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detectVideo(); // start loop
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```
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or using interpolated results for smooth video processing by separating detection and drawing loops:
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```js
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@ -7,8 +7,6 @@ const fs = require('fs');
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const process = require('process');
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const canvas = require('canvas');
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let fetch; // fetch is dynamically imported later
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// for NodeJS, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
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// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
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const tf = require('@tensorflow/tfjs-node'); // or const tf = require('@tensorflow/tfjs-node-gpu');
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@ -51,25 +49,26 @@ async function init() {
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}
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async function detect(input, output) {
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// read input image file and create tensor to be used for processing
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// read input image from file or url into buffer
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let buffer;
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log.info('Loading image:', input);
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if (input.startsWith('http:') || input.startsWith('https:')) {
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const fetch = (await import('node-fetch')).default;
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const res = await fetch(input);
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if (res && res.ok) buffer = await res.buffer();
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else log.error('Invalid image URL:', input, res.status, res.statusText, res.headers.get('content-type'));
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} else {
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buffer = fs.readFileSync(input);
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}
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if (!buffer) return {};
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// decode image using tfjs-node so we don't need external depenencies
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// can also be done using canvas.js or some other 3rd party image library
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if (!buffer) return {};
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/*
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const tensor = human.tf.tidy(() => {
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const decode = human.tf.node.decodeImage(buffer, 3);
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let expand;
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if (decode.shape[2] === 4) { // input is in rgba format, need to convert to rgb
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const channels = human.tf.split(decode, 4, 2); // tf.split(tensor, 4, 2); // split rgba to channels
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const channels = human.tf.split(decode, 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 and ignore alpha
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expand = human.tf.reshape(rgb, [1, decode.shape[0], decode.shape[1], 3]); // move extra dim from the end of tensor and use it as batch number instead
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} else {
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@ -78,6 +77,22 @@ async function detect(input, output) {
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const cast = human.tf.cast(expand, 'float32');
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return cast;
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});
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*/
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// decode image using canvas library
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const inputImage = await canvas.loadImage(input);
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const inputCanvas = new canvas.Canvas(inputImage.width, inputImage.height, 'image');
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const inputCtx = inputCanvas.getContext('2d');
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inputCtx.drawImage(inputImage, 0, 0);
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const inputData = inputCtx.getImageData(0, 0, inputImage.width, inputImage.height);
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const tensor = human.tf.tidy(() => {
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const data = tf.tensor(Array.from(inputData.data), [inputImage.width, inputImage.height, 4]);
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const channels = human.tf.split(data, 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 and ignore alpha
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const expand = human.tf.reshape(rgb, [1, data.shape[0], data.shape[1], 3]); // move extra dim from the end of tensor and use it as batch number instead
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const cast = human.tf.cast(expand, 'float32');
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return cast;
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});
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// image shape contains image dimensions and depth
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log.state('Processing:', tensor['shape']);
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@ -130,7 +145,6 @@ async function detect(input, output) {
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async function main() {
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log.header();
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log.info('Current folder:', process.env.PWD);
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fetch = (await import('node-fetch')).default;
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await init();
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const input = process.argv[2];
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const output = process.argv[3];
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@ -0,0 +1,110 @@
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/**
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* Human demo for NodeJS
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*/
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const log = require('@vladmandic/pilogger');
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const fs = require('fs');
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const process = require('process');
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let fetch; // fetch is dynamically imported later
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// for NodeJS, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
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// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
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const tf = require('@tensorflow/tfjs-node'); // or const tf = require('@tensorflow/tfjs-node-gpu');
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// load specific version of Human library that matches TensorFlow mode
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const Human = require('../../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default;
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let human = null;
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const myConfig = {
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backend: 'tensorflow',
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modelBasePath: 'file://models/',
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debug: false,
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async: true,
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filter: { enabled: false },
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face: {
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enabled: true,
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detector: { enabled: true },
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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: { enabled: true },
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body: { enabled: true },
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object: { enabled: true },
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};
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async function detect(input) {
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// read input image from file or url into buffer
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let buffer;
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log.info('Loading image:', input);
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if (input.startsWith('http:') || input.startsWith('https:')) {
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fetch = (await import('node-fetch')).default;
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const res = await fetch(input);
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if (res && res.ok) buffer = await res.buffer();
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else log.error('Invalid image URL:', input, res.status, res.statusText, res.headers.get('content-type'));
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} else {
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buffer = fs.readFileSync(input);
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}
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// decode image using tfjs-node so we don't need external depenencies
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if (!buffer) return;
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const tensor = human.tf.tidy(() => {
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const decode = human.tf.node.decodeImage(buffer, 3);
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let expand;
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if (decode.shape[2] === 4) { // input is in rgba format, need to convert to rgb
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const channels = human.tf.split(decode, 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 and ignore alpha
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expand = human.tf.reshape(rgb, [1, decode.shape[0], decode.shape[1], 3]); // move extra dim from the end of tensor and use it as batch number instead
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} else {
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expand = human.tf.expandDims(decode, 0);
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}
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const cast = human.tf.cast(expand, 'float32');
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return cast;
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});
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// run detection
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await human.detect(tensor, myConfig);
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human.tf.dispose(tensor); // dispose image tensor as we no longer need it
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}
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async function main() {
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log.header();
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human = new Human(myConfig);
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human.events.addEventListener('warmup', () => {
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log.info('Event Warmup');
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});
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human.events.addEventListener('load', () => {
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const loaded = Object.keys(human.models).filter((a) => human.models[a]);
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log.info('Event Loaded:', loaded, human.tf.engine().memory());
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});
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human.events.addEventListener('image', () => {
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log.info('Event Image:', human.process.tensor.shape);
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});
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human.events.addEventListener('detect', () => {
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log.data('Event Detected:');
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const persons = human.result.persons;
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for (let i = 0; i < persons.length; i++) {
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const face = persons[i].face;
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const faceTxt = face ? `score:${face.score} age:${face.age} gender:${face.gender} iris:${face.iris}` : null;
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const body = persons[i].body;
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const bodyTxt = body ? `score:${body.score} keypoints:${body.keypoints?.length}` : null;
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log.data(` #${i}: Face:${faceTxt} Body:${bodyTxt} LeftHand:${persons[i].hands.left ? 'yes' : 'no'} RightHand:${persons[i].hands.right ? 'yes' : 'no'} Gestures:${persons[i].gestures.length}`);
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}
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});
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await human.tf.ready(); // wait until tf is ready
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const input = process.argv[2]; // process input
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if (input) await detect(input);
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else log.error('Missing <input>');
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}
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main();
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@ -11726,7 +11726,7 @@ lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
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var version2 = "2.1.5";
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// src/human.ts
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var _numTensors, _analyzeMemoryLeaks, _checkSanity, _firstRun, _lastInputSum, _lastCacheDiff, _sanity, _checkBackend, _skipFrame, _warmupBitmap, _warmupCanvas, _warmupNode;
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var _numTensors, _analyzeMemoryLeaks, _checkSanity, _firstRun, _lastInputSum, _lastCacheDiff, _sanity, _emit, _checkBackend, _skipFrame, _warmupBitmap, _warmupCanvas, _warmupNode;
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var Human = class {
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constructor(userConfig) {
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__privateAdd(this, _numTensors, void 0);
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@ -11759,6 +11759,10 @@ var Human = class {
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}
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return null;
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});
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__privateAdd(this, _emit, (event) => {
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var _a;
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return (_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event));
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});
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__privateAdd(this, _checkBackend, async (force = false) => {
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var _a;
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if (this.config.backend && this.config.backend.length > 0 && force || this.tf.getBackend() !== this.config.backend) {
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@ -11942,6 +11946,7 @@ var Human = class {
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__privateSet(this, _firstRun, true);
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__privateSet(this, _lastCacheDiff, 0);
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this.performance = { backend: 0, load: 0, image: 0, frames: 0, cached: 0, changed: 0, total: 0, draw: 0 };
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this.events = new EventTarget();
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this.models = {
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face: null,
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posenet: null,
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@ -11960,10 +11965,12 @@ var Human = class {
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};
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this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [] };
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this.image = (input) => process4(input, this.config);
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this.process = { tensor: null, canvas: null };
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this.faceTriangulation = triangulation;
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this.faceUVMap = uvmap;
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this.sysinfo = info();
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__privateSet(this, _lastInputSum, 1);
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__privateGet(this, _emit).call(this, "create");
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}
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similarity(embedding1, embedding2) {
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return similarity(embedding1, embedding2);
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@ -11980,6 +11987,7 @@ var Human = class {
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async load(userConfig) {
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this.state = "load";
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const timeStamp = now();
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const count2 = Object.values(this.models).filter((model10) => model10).length;
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if (userConfig)
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this.config = mergeDeep(this.config, userConfig);
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if (__privateGet(this, _firstRun)) {
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@ -12005,12 +12013,16 @@ var Human = class {
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log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
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__privateSet(this, _firstRun, false);
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}
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const loaded = Object.values(this.models).filter((model10) => model10).length;
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if (loaded !== count2)
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__privateGet(this, _emit).call(this, "load");
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const current = Math.trunc(now() - timeStamp);
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if (current > (this.performance.load || 0))
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this.performance.load = current;
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}
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async detect(input, userConfig) {
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return new Promise(async (resolve) => {
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var _a, _b;
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this.state = "config";
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let timeStamp;
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let elapsedTime;
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@ -12025,30 +12037,31 @@ var Human = class {
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await __privateGet(this, _checkBackend).call(this);
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await this.load();
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timeStamp = now();
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let process6 = process4(input, this.config);
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this.process = process4(input, this.config);
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this.performance.image = Math.trunc(now() - timeStamp);
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this.analyze("Get Image:");
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if (this.config.segmentation.enabled && process6 && process6.tensor) {
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if (this.config.segmentation.enabled && this.process && this.process.tensor) {
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this.analyze("Start Segmentation:");
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this.state = "run:segmentation";
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timeStamp = now();
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await predict11(process6);
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await predict11(this.process);
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elapsedTime = Math.trunc(now() - timeStamp);
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if (elapsedTime > 0)
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this.performance.segmentation = elapsedTime;
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if (process6.canvas) {
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tfjs_esm_exports.dispose(process6.tensor);
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process6 = process4(process6.canvas, this.config);
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if (this.process.canvas) {
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tfjs_esm_exports.dispose(this.process.tensor);
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this.process = process4(this.process.canvas, this.config);
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}
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this.analyze("End Segmentation:");
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}
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if (!process6 || !process6.tensor) {
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if (!this.process || !this.process.tensor) {
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log("could not convert input to tensor");
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resolve({ error: "could not convert input to tensor" });
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return;
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}
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__privateGet(this, _emit).call(this, "image");
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timeStamp = now();
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this.config.skipFrame = await __privateGet(this, _skipFrame).call(this, process6.tensor);
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this.config.skipFrame = await __privateGet(this, _skipFrame).call(this, this.process.tensor);
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if (!this.performance.frames)
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this.performance.frames = 0;
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if (!this.performance.cached)
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@ -12063,13 +12076,13 @@ var Human = class {
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let handRes = [];
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let objectRes = [];
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if (this.config.async) {
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faceRes = this.config.face.enabled ? detectFace(this, process6.tensor) : [];
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faceRes = this.config.face.enabled ? detectFace(this, this.process.tensor) : [];
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if (this.performance.face)
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delete this.performance.face;
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} else {
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this.state = "run:face";
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timeStamp = now();
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faceRes = this.config.face.enabled ? await detectFace(this, process6.tensor) : [];
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faceRes = this.config.face.enabled ? await detectFace(this, this.process.tensor) : [];
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elapsedTime = Math.trunc(now() - timeStamp);
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if (elapsedTime > 0)
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this.performance.face = elapsedTime;
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@ -12077,26 +12090,26 @@ var Human = class {
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this.analyze("Start Body:");
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if (this.config.async) {
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if (this.config.body.modelPath.includes("posenet"))
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bodyRes = this.config.body.enabled ? predict4(process6.tensor, this.config) : [];
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bodyRes = this.config.body.enabled ? predict4(this.process.tensor, this.config) : [];
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else if (this.config.body.modelPath.includes("blazepose"))
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bodyRes = this.config.body.enabled ? predict6(process6.tensor, this.config) : [];
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bodyRes = this.config.body.enabled ? predict6(this.process.tensor, this.config) : [];
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else if (this.config.body.modelPath.includes("efficientpose"))
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bodyRes = this.config.body.enabled ? predict7(process6.tensor, this.config) : [];
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bodyRes = this.config.body.enabled ? predict7(this.process.tensor, this.config) : [];
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else if (this.config.body.modelPath.includes("movenet"))
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bodyRes = this.config.body.enabled ? predict8(process6.tensor, this.config) : [];
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bodyRes = this.config.body.enabled ? predict8(this.process.tensor, this.config) : [];
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if (this.performance.body)
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delete this.performance.body;
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} else {
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this.state = "run:body";
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timeStamp = now();
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if (this.config.body.modelPath.includes("posenet"))
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bodyRes = this.config.body.enabled ? await predict4(process6.tensor, this.config) : [];
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bodyRes = this.config.body.enabled ? await predict4(this.process.tensor, this.config) : [];
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else if (this.config.body.modelPath.includes("blazepose"))
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bodyRes = this.config.body.enabled ? await predict6(process6.tensor, this.config) : [];
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bodyRes = this.config.body.enabled ? await predict6(this.process.tensor, this.config) : [];
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else if (this.config.body.modelPath.includes("efficientpose"))
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bodyRes = this.config.body.enabled ? await predict7(process6.tensor, this.config) : [];
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bodyRes = this.config.body.enabled ? await predict7(this.process.tensor, this.config) : [];
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else if (this.config.body.modelPath.includes("movenet"))
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bodyRes = this.config.body.enabled ? await predict8(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict8(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.body = elapsedTime;
|
||||
|
|
@ -12104,13 +12117,13 @@ var Human = class {
|
|||
this.analyze("End Body:");
|
||||
this.analyze("Start Hand:");
|
||||
if (this.config.async) {
|
||||
handRes = this.config.hand.enabled ? predict5(process6.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? predict5(this.process.tensor, this.config) : [];
|
||||
if (this.performance.hand)
|
||||
delete this.performance.hand;
|
||||
} else {
|
||||
this.state = "run:hand";
|
||||
timeStamp = now();
|
||||
handRes = this.config.hand.enabled ? await predict5(process6.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? await predict5(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.hand = elapsedTime;
|
||||
|
|
@ -12119,18 +12132,18 @@ var Human = class {
|
|||
this.analyze("Start Object:");
|
||||
if (this.config.async) {
|
||||
if (this.config.object.modelPath.includes("nanodet"))
|
||||
objectRes = this.config.object.enabled ? predict9(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? predict9(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes("centernet"))
|
||||
objectRes = this.config.object.enabled ? predict10(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? predict10(this.process.tensor, this.config) : [];
|
||||
if (this.performance.object)
|
||||
delete this.performance.object;
|
||||
} else {
|
||||
this.state = "run:object";
|
||||
timeStamp = now();
|
||||
if (this.config.object.modelPath.includes("nanodet"))
|
||||
objectRes = this.config.object.enabled ? await predict9(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? await predict9(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes("centernet"))
|
||||
objectRes = this.config.object.enabled ? await predict10(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? await predict10(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.object = elapsedTime;
|
||||
|
|
@ -12149,6 +12162,7 @@ var Human = class {
|
|||
}
|
||||
this.performance.total = Math.trunc(now() - timeStart);
|
||||
this.state = "idle";
|
||||
const shape = ((_b = (_a = this.process) == null ? void 0 : _a.tensor) == null ? void 0 : _b.shape) || [];
|
||||
this.result = {
|
||||
face: faceRes,
|
||||
body: bodyRes,
|
||||
|
|
@ -12156,14 +12170,14 @@ var Human = class {
|
|||
gesture: gestureRes,
|
||||
object: objectRes,
|
||||
performance: this.performance,
|
||||
canvas: process6.canvas,
|
||||
canvas: this.process.canvas,
|
||||
timestamp: Date.now(),
|
||||
get persons() {
|
||||
var _a;
|
||||
return join2(faceRes, bodyRes, handRes, gestureRes, (_a = process6 == null ? void 0 : process6.tensor) == null ? void 0 : _a.shape);
|
||||
return join2(faceRes, bodyRes, handRes, gestureRes, shape);
|
||||
}
|
||||
};
|
||||
tfjs_esm_exports.dispose(process6.tensor);
|
||||
tfjs_esm_exports.dispose(this.process.tensor);
|
||||
__privateGet(this, _emit).call(this, "detect");
|
||||
resolve(this.result);
|
||||
});
|
||||
}
|
||||
|
|
@ -12183,6 +12197,7 @@ var Human = class {
|
|||
const t1 = now();
|
||||
if (this.config.debug)
|
||||
log("Warmup", this.config.warmup, Math.round(t1 - t0), "ms", res);
|
||||
__privateGet(this, _emit).call(this, "warmup");
|
||||
return res;
|
||||
}
|
||||
};
|
||||
|
|
@ -12193,6 +12208,7 @@ _firstRun = new WeakMap();
|
|||
_lastInputSum = new WeakMap();
|
||||
_lastCacheDiff = new WeakMap();
|
||||
_sanity = new WeakMap();
|
||||
_emit = new WeakMap();
|
||||
_checkBackend = new WeakMap();
|
||||
_skipFrame = new WeakMap();
|
||||
_warmupBitmap = new WeakMap();
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -71234,7 +71234,7 @@ lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
|
|||
var version17 = "2.1.5";
|
||||
|
||||
// src/human.ts
|
||||
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _firstRun, _lastInputSum, _lastCacheDiff, _sanity, _checkBackend, _skipFrame, _warmupBitmap, _warmupCanvas, _warmupNode;
|
||||
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _firstRun, _lastInputSum, _lastCacheDiff, _sanity, _emit, _checkBackend, _skipFrame, _warmupBitmap, _warmupCanvas, _warmupNode;
|
||||
var Human = class {
|
||||
constructor(userConfig) {
|
||||
__privateAdd(this, _numTensors, void 0);
|
||||
|
|
@ -71267,6 +71267,10 @@ var Human = class {
|
|||
}
|
||||
return null;
|
||||
});
|
||||
__privateAdd(this, _emit, (event) => {
|
||||
var _a;
|
||||
return (_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event));
|
||||
});
|
||||
__privateAdd(this, _checkBackend, async (force = false) => {
|
||||
var _a;
|
||||
if (this.config.backend && this.config.backend.length > 0 && force || this.tf.getBackend() !== this.config.backend) {
|
||||
|
|
@ -71450,6 +71454,7 @@ var Human = class {
|
|||
__privateSet(this, _firstRun, true);
|
||||
__privateSet(this, _lastCacheDiff, 0);
|
||||
this.performance = { backend: 0, load: 0, image: 0, frames: 0, cached: 0, changed: 0, total: 0, draw: 0 };
|
||||
this.events = new EventTarget();
|
||||
this.models = {
|
||||
face: null,
|
||||
posenet: null,
|
||||
|
|
@ -71468,10 +71473,12 @@ var Human = class {
|
|||
};
|
||||
this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [] };
|
||||
this.image = (input2) => process4(input2, this.config);
|
||||
this.process = { tensor: null, canvas: null };
|
||||
this.faceTriangulation = triangulation;
|
||||
this.faceUVMap = uvmap;
|
||||
this.sysinfo = info();
|
||||
__privateSet(this, _lastInputSum, 1);
|
||||
__privateGet(this, _emit).call(this, "create");
|
||||
}
|
||||
similarity(embedding1, embedding2) {
|
||||
return similarity(embedding1, embedding2);
|
||||
|
|
@ -71488,6 +71495,7 @@ var Human = class {
|
|||
async load(userConfig) {
|
||||
this.state = "load";
|
||||
const timeStamp = now();
|
||||
const count3 = Object.values(this.models).filter((model11) => model11).length;
|
||||
if (userConfig)
|
||||
this.config = mergeDeep(this.config, userConfig);
|
||||
if (__privateGet(this, _firstRun)) {
|
||||
|
|
@ -71513,12 +71521,16 @@ var Human = class {
|
|||
log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
|
||||
__privateSet(this, _firstRun, false);
|
||||
}
|
||||
const loaded = Object.values(this.models).filter((model11) => model11).length;
|
||||
if (loaded !== count3)
|
||||
__privateGet(this, _emit).call(this, "load");
|
||||
const current = Math.trunc(now() - timeStamp);
|
||||
if (current > (this.performance.load || 0))
|
||||
this.performance.load = current;
|
||||
}
|
||||
async detect(input2, userConfig) {
|
||||
return new Promise(async (resolve) => {
|
||||
var _a, _b;
|
||||
this.state = "config";
|
||||
let timeStamp;
|
||||
let elapsedTime;
|
||||
|
|
@ -71533,30 +71545,31 @@ var Human = class {
|
|||
await __privateGet(this, _checkBackend).call(this);
|
||||
await this.load();
|
||||
timeStamp = now();
|
||||
let process6 = process4(input2, this.config);
|
||||
this.process = process4(input2, this.config);
|
||||
this.performance.image = Math.trunc(now() - timeStamp);
|
||||
this.analyze("Get Image:");
|
||||
if (this.config.segmentation.enabled && process6 && process6.tensor) {
|
||||
if (this.config.segmentation.enabled && this.process && this.process.tensor) {
|
||||
this.analyze("Start Segmentation:");
|
||||
this.state = "run:segmentation";
|
||||
timeStamp = now();
|
||||
await predict11(process6);
|
||||
await predict11(this.process);
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.segmentation = elapsedTime;
|
||||
if (process6.canvas) {
|
||||
dispose(process6.tensor);
|
||||
process6 = process4(process6.canvas, this.config);
|
||||
if (this.process.canvas) {
|
||||
dispose(this.process.tensor);
|
||||
this.process = process4(this.process.canvas, this.config);
|
||||
}
|
||||
this.analyze("End Segmentation:");
|
||||
}
|
||||
if (!process6 || !process6.tensor) {
|
||||
if (!this.process || !this.process.tensor) {
|
||||
log("could not convert input to tensor");
|
||||
resolve({ error: "could not convert input to tensor" });
|
||||
return;
|
||||
}
|
||||
__privateGet(this, _emit).call(this, "image");
|
||||
timeStamp = now();
|
||||
this.config.skipFrame = await __privateGet(this, _skipFrame).call(this, process6.tensor);
|
||||
this.config.skipFrame = await __privateGet(this, _skipFrame).call(this, this.process.tensor);
|
||||
if (!this.performance.frames)
|
||||
this.performance.frames = 0;
|
||||
if (!this.performance.cached)
|
||||
|
|
@ -71571,13 +71584,13 @@ var Human = class {
|
|||
let handRes = [];
|
||||
let objectRes = [];
|
||||
if (this.config.async) {
|
||||
faceRes = this.config.face.enabled ? detectFace(this, process6.tensor) : [];
|
||||
faceRes = this.config.face.enabled ? detectFace(this, this.process.tensor) : [];
|
||||
if (this.performance.face)
|
||||
delete this.performance.face;
|
||||
} else {
|
||||
this.state = "run:face";
|
||||
timeStamp = now();
|
||||
faceRes = this.config.face.enabled ? await detectFace(this, process6.tensor) : [];
|
||||
faceRes = this.config.face.enabled ? await detectFace(this, this.process.tensor) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.face = elapsedTime;
|
||||
|
|
@ -71585,26 +71598,26 @@ var Human = class {
|
|||
this.analyze("Start Body:");
|
||||
if (this.config.async) {
|
||||
if (this.config.body.modelPath.includes("posenet"))
|
||||
bodyRes = this.config.body.enabled ? predict4(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict4(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("blazepose"))
|
||||
bodyRes = this.config.body.enabled ? predict6(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict6(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("efficientpose"))
|
||||
bodyRes = this.config.body.enabled ? predict7(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict7(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("movenet"))
|
||||
bodyRes = this.config.body.enabled ? predict8(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict8(this.process.tensor, this.config) : [];
|
||||
if (this.performance.body)
|
||||
delete this.performance.body;
|
||||
} else {
|
||||
this.state = "run:body";
|
||||
timeStamp = now();
|
||||
if (this.config.body.modelPath.includes("posenet"))
|
||||
bodyRes = this.config.body.enabled ? await predict4(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict4(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("blazepose"))
|
||||
bodyRes = this.config.body.enabled ? await predict6(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict6(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("efficientpose"))
|
||||
bodyRes = this.config.body.enabled ? await predict7(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict7(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("movenet"))
|
||||
bodyRes = this.config.body.enabled ? await predict8(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict8(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.body = elapsedTime;
|
||||
|
|
@ -71612,13 +71625,13 @@ var Human = class {
|
|||
this.analyze("End Body:");
|
||||
this.analyze("Start Hand:");
|
||||
if (this.config.async) {
|
||||
handRes = this.config.hand.enabled ? predict5(process6.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? predict5(this.process.tensor, this.config) : [];
|
||||
if (this.performance.hand)
|
||||
delete this.performance.hand;
|
||||
} else {
|
||||
this.state = "run:hand";
|
||||
timeStamp = now();
|
||||
handRes = this.config.hand.enabled ? await predict5(process6.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? await predict5(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.hand = elapsedTime;
|
||||
|
|
@ -71627,18 +71640,18 @@ var Human = class {
|
|||
this.analyze("Start Object:");
|
||||
if (this.config.async) {
|
||||
if (this.config.object.modelPath.includes("nanodet"))
|
||||
objectRes = this.config.object.enabled ? predict9(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? predict9(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes("centernet"))
|
||||
objectRes = this.config.object.enabled ? predict10(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? predict10(this.process.tensor, this.config) : [];
|
||||
if (this.performance.object)
|
||||
delete this.performance.object;
|
||||
} else {
|
||||
this.state = "run:object";
|
||||
timeStamp = now();
|
||||
if (this.config.object.modelPath.includes("nanodet"))
|
||||
objectRes = this.config.object.enabled ? await predict9(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? await predict9(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes("centernet"))
|
||||
objectRes = this.config.object.enabled ? await predict10(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? await predict10(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.object = elapsedTime;
|
||||
|
|
@ -71657,6 +71670,7 @@ var Human = class {
|
|||
}
|
||||
this.performance.total = Math.trunc(now() - timeStart);
|
||||
this.state = "idle";
|
||||
const shape = ((_b = (_a = this.process) == null ? void 0 : _a.tensor) == null ? void 0 : _b.shape) || [];
|
||||
this.result = {
|
||||
face: faceRes,
|
||||
body: bodyRes,
|
||||
|
|
@ -71664,14 +71678,14 @@ var Human = class {
|
|||
gesture: gestureRes,
|
||||
object: objectRes,
|
||||
performance: this.performance,
|
||||
canvas: process6.canvas,
|
||||
canvas: this.process.canvas,
|
||||
timestamp: Date.now(),
|
||||
get persons() {
|
||||
var _a;
|
||||
return join2(faceRes, bodyRes, handRes, gestureRes, (_a = process6 == null ? void 0 : process6.tensor) == null ? void 0 : _a.shape);
|
||||
return join2(faceRes, bodyRes, handRes, gestureRes, shape);
|
||||
}
|
||||
};
|
||||
dispose(process6.tensor);
|
||||
dispose(this.process.tensor);
|
||||
__privateGet(this, _emit).call(this, "detect");
|
||||
resolve(this.result);
|
||||
});
|
||||
}
|
||||
|
|
@ -71691,6 +71705,7 @@ var Human = class {
|
|||
const t1 = now();
|
||||
if (this.config.debug)
|
||||
log("Warmup", this.config.warmup, Math.round(t1 - t0), "ms", res);
|
||||
__privateGet(this, _emit).call(this, "warmup");
|
||||
return res;
|
||||
}
|
||||
};
|
||||
|
|
@ -71701,6 +71716,7 @@ _firstRun = new WeakMap();
|
|||
_lastInputSum = new WeakMap();
|
||||
_lastCacheDiff = new WeakMap();
|
||||
_sanity = new WeakMap();
|
||||
_emit = new WeakMap();
|
||||
_checkBackend = new WeakMap();
|
||||
_skipFrame = new WeakMap();
|
||||
_warmupBitmap = new WeakMap();
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -11776,7 +11776,7 @@ lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
|
|||
var version = "2.1.5";
|
||||
|
||||
// src/human.ts
|
||||
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _firstRun, _lastInputSum, _lastCacheDiff, _sanity, _checkBackend, _skipFrame, _warmupBitmap, _warmupCanvas, _warmupNode;
|
||||
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _firstRun, _lastInputSum, _lastCacheDiff, _sanity, _emit, _checkBackend, _skipFrame, _warmupBitmap, _warmupCanvas, _warmupNode;
|
||||
var Human = class {
|
||||
constructor(userConfig) {
|
||||
__privateAdd(this, _numTensors, void 0);
|
||||
|
|
@ -11809,6 +11809,10 @@ var Human = class {
|
|||
}
|
||||
return null;
|
||||
});
|
||||
__privateAdd(this, _emit, (event) => {
|
||||
var _a;
|
||||
return (_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event));
|
||||
});
|
||||
__privateAdd(this, _checkBackend, async (force = false) => {
|
||||
var _a;
|
||||
if (this.config.backend && this.config.backend.length > 0 && force || this.tf.getBackend() !== this.config.backend) {
|
||||
|
|
@ -11992,6 +11996,7 @@ var Human = class {
|
|||
__privateSet(this, _firstRun, true);
|
||||
__privateSet(this, _lastCacheDiff, 0);
|
||||
this.performance = { backend: 0, load: 0, image: 0, frames: 0, cached: 0, changed: 0, total: 0, draw: 0 };
|
||||
this.events = new EventTarget();
|
||||
this.models = {
|
||||
face: null,
|
||||
posenet: null,
|
||||
|
|
@ -12010,10 +12015,12 @@ var Human = class {
|
|||
};
|
||||
this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [] };
|
||||
this.image = (input) => process4(input, this.config);
|
||||
this.process = { tensor: null, canvas: null };
|
||||
this.faceTriangulation = triangulation;
|
||||
this.faceUVMap = uvmap;
|
||||
this.sysinfo = info();
|
||||
__privateSet(this, _lastInputSum, 1);
|
||||
__privateGet(this, _emit).call(this, "create");
|
||||
}
|
||||
similarity(embedding1, embedding2) {
|
||||
return similarity(embedding1, embedding2);
|
||||
|
|
@ -12030,6 +12037,7 @@ var Human = class {
|
|||
async load(userConfig) {
|
||||
this.state = "load";
|
||||
const timeStamp = now();
|
||||
const count2 = Object.values(this.models).filter((model10) => model10).length;
|
||||
if (userConfig)
|
||||
this.config = mergeDeep(this.config, userConfig);
|
||||
if (__privateGet(this, _firstRun)) {
|
||||
|
|
@ -12055,12 +12063,16 @@ var Human = class {
|
|||
log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
|
||||
__privateSet(this, _firstRun, false);
|
||||
}
|
||||
const loaded = Object.values(this.models).filter((model10) => model10).length;
|
||||
if (loaded !== count2)
|
||||
__privateGet(this, _emit).call(this, "load");
|
||||
const current = Math.trunc(now() - timeStamp);
|
||||
if (current > (this.performance.load || 0))
|
||||
this.performance.load = current;
|
||||
}
|
||||
async detect(input, userConfig) {
|
||||
return new Promise(async (resolve) => {
|
||||
var _a, _b;
|
||||
this.state = "config";
|
||||
let timeStamp;
|
||||
let elapsedTime;
|
||||
|
|
@ -12075,30 +12087,31 @@ var Human = class {
|
|||
await __privateGet(this, _checkBackend).call(this);
|
||||
await this.load();
|
||||
timeStamp = now();
|
||||
let process6 = process4(input, this.config);
|
||||
this.process = process4(input, this.config);
|
||||
this.performance.image = Math.trunc(now() - timeStamp);
|
||||
this.analyze("Get Image:");
|
||||
if (this.config.segmentation.enabled && process6 && process6.tensor) {
|
||||
if (this.config.segmentation.enabled && this.process && this.process.tensor) {
|
||||
this.analyze("Start Segmentation:");
|
||||
this.state = "run:segmentation";
|
||||
timeStamp = now();
|
||||
await predict11(process6);
|
||||
await predict11(this.process);
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.segmentation = elapsedTime;
|
||||
if (process6.canvas) {
|
||||
tf21.dispose(process6.tensor);
|
||||
process6 = process4(process6.canvas, this.config);
|
||||
if (this.process.canvas) {
|
||||
tf21.dispose(this.process.tensor);
|
||||
this.process = process4(this.process.canvas, this.config);
|
||||
}
|
||||
this.analyze("End Segmentation:");
|
||||
}
|
||||
if (!process6 || !process6.tensor) {
|
||||
if (!this.process || !this.process.tensor) {
|
||||
log("could not convert input to tensor");
|
||||
resolve({ error: "could not convert input to tensor" });
|
||||
return;
|
||||
}
|
||||
__privateGet(this, _emit).call(this, "image");
|
||||
timeStamp = now();
|
||||
this.config.skipFrame = await __privateGet(this, _skipFrame).call(this, process6.tensor);
|
||||
this.config.skipFrame = await __privateGet(this, _skipFrame).call(this, this.process.tensor);
|
||||
if (!this.performance.frames)
|
||||
this.performance.frames = 0;
|
||||
if (!this.performance.cached)
|
||||
|
|
@ -12113,13 +12126,13 @@ var Human = class {
|
|||
let handRes = [];
|
||||
let objectRes = [];
|
||||
if (this.config.async) {
|
||||
faceRes = this.config.face.enabled ? detectFace(this, process6.tensor) : [];
|
||||
faceRes = this.config.face.enabled ? detectFace(this, this.process.tensor) : [];
|
||||
if (this.performance.face)
|
||||
delete this.performance.face;
|
||||
} else {
|
||||
this.state = "run:face";
|
||||
timeStamp = now();
|
||||
faceRes = this.config.face.enabled ? await detectFace(this, process6.tensor) : [];
|
||||
faceRes = this.config.face.enabled ? await detectFace(this, this.process.tensor) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.face = elapsedTime;
|
||||
|
|
@ -12127,26 +12140,26 @@ var Human = class {
|
|||
this.analyze("Start Body:");
|
||||
if (this.config.async) {
|
||||
if (this.config.body.modelPath.includes("posenet"))
|
||||
bodyRes = this.config.body.enabled ? predict4(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict4(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("blazepose"))
|
||||
bodyRes = this.config.body.enabled ? predict6(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict6(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("efficientpose"))
|
||||
bodyRes = this.config.body.enabled ? predict7(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict7(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("movenet"))
|
||||
bodyRes = this.config.body.enabled ? predict8(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict8(this.process.tensor, this.config) : [];
|
||||
if (this.performance.body)
|
||||
delete this.performance.body;
|
||||
} else {
|
||||
this.state = "run:body";
|
||||
timeStamp = now();
|
||||
if (this.config.body.modelPath.includes("posenet"))
|
||||
bodyRes = this.config.body.enabled ? await predict4(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict4(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("blazepose"))
|
||||
bodyRes = this.config.body.enabled ? await predict6(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict6(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("efficientpose"))
|
||||
bodyRes = this.config.body.enabled ? await predict7(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict7(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("movenet"))
|
||||
bodyRes = this.config.body.enabled ? await predict8(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict8(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.body = elapsedTime;
|
||||
|
|
@ -12154,13 +12167,13 @@ var Human = class {
|
|||
this.analyze("End Body:");
|
||||
this.analyze("Start Hand:");
|
||||
if (this.config.async) {
|
||||
handRes = this.config.hand.enabled ? predict5(process6.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? predict5(this.process.tensor, this.config) : [];
|
||||
if (this.performance.hand)
|
||||
delete this.performance.hand;
|
||||
} else {
|
||||
this.state = "run:hand";
|
||||
timeStamp = now();
|
||||
handRes = this.config.hand.enabled ? await predict5(process6.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? await predict5(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.hand = elapsedTime;
|
||||
|
|
@ -12169,18 +12182,18 @@ var Human = class {
|
|||
this.analyze("Start Object:");
|
||||
if (this.config.async) {
|
||||
if (this.config.object.modelPath.includes("nanodet"))
|
||||
objectRes = this.config.object.enabled ? predict9(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? predict9(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes("centernet"))
|
||||
objectRes = this.config.object.enabled ? predict10(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? predict10(this.process.tensor, this.config) : [];
|
||||
if (this.performance.object)
|
||||
delete this.performance.object;
|
||||
} else {
|
||||
this.state = "run:object";
|
||||
timeStamp = now();
|
||||
if (this.config.object.modelPath.includes("nanodet"))
|
||||
objectRes = this.config.object.enabled ? await predict9(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? await predict9(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes("centernet"))
|
||||
objectRes = this.config.object.enabled ? await predict10(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? await predict10(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.object = elapsedTime;
|
||||
|
|
@ -12199,6 +12212,7 @@ var Human = class {
|
|||
}
|
||||
this.performance.total = Math.trunc(now() - timeStart);
|
||||
this.state = "idle";
|
||||
const shape = ((_b = (_a = this.process) == null ? void 0 : _a.tensor) == null ? void 0 : _b.shape) || [];
|
||||
this.result = {
|
||||
face: faceRes,
|
||||
body: bodyRes,
|
||||
|
|
@ -12206,14 +12220,14 @@ var Human = class {
|
|||
gesture: gestureRes,
|
||||
object: objectRes,
|
||||
performance: this.performance,
|
||||
canvas: process6.canvas,
|
||||
canvas: this.process.canvas,
|
||||
timestamp: Date.now(),
|
||||
get persons() {
|
||||
var _a;
|
||||
return join2(faceRes, bodyRes, handRes, gestureRes, (_a = process6 == null ? void 0 : process6.tensor) == null ? void 0 : _a.shape);
|
||||
return join2(faceRes, bodyRes, handRes, gestureRes, shape);
|
||||
}
|
||||
};
|
||||
tf21.dispose(process6.tensor);
|
||||
tf21.dispose(this.process.tensor);
|
||||
__privateGet(this, _emit).call(this, "detect");
|
||||
resolve(this.result);
|
||||
});
|
||||
}
|
||||
|
|
@ -12233,6 +12247,7 @@ var Human = class {
|
|||
const t1 = now();
|
||||
if (this.config.debug)
|
||||
log("Warmup", this.config.warmup, Math.round(t1 - t0), "ms", res);
|
||||
__privateGet(this, _emit).call(this, "warmup");
|
||||
return res;
|
||||
}
|
||||
};
|
||||
|
|
@ -12243,6 +12258,7 @@ _firstRun = new WeakMap();
|
|||
_lastInputSum = new WeakMap();
|
||||
_lastCacheDiff = new WeakMap();
|
||||
_sanity = new WeakMap();
|
||||
_emit = new WeakMap();
|
||||
_checkBackend = new WeakMap();
|
||||
_skipFrame = new WeakMap();
|
||||
_warmupBitmap = new WeakMap();
|
||||
|
|
|
|||
|
|
@ -11777,7 +11777,7 @@ lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
|
|||
var version = "2.1.5";
|
||||
|
||||
// src/human.ts
|
||||
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _firstRun, _lastInputSum, _lastCacheDiff, _sanity, _checkBackend, _skipFrame, _warmupBitmap, _warmupCanvas, _warmupNode;
|
||||
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _firstRun, _lastInputSum, _lastCacheDiff, _sanity, _emit, _checkBackend, _skipFrame, _warmupBitmap, _warmupCanvas, _warmupNode;
|
||||
var Human = class {
|
||||
constructor(userConfig) {
|
||||
__privateAdd(this, _numTensors, void 0);
|
||||
|
|
@ -11810,6 +11810,10 @@ var Human = class {
|
|||
}
|
||||
return null;
|
||||
});
|
||||
__privateAdd(this, _emit, (event) => {
|
||||
var _a;
|
||||
return (_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event));
|
||||
});
|
||||
__privateAdd(this, _checkBackend, async (force = false) => {
|
||||
var _a;
|
||||
if (this.config.backend && this.config.backend.length > 0 && force || this.tf.getBackend() !== this.config.backend) {
|
||||
|
|
@ -11993,6 +11997,7 @@ var Human = class {
|
|||
__privateSet(this, _firstRun, true);
|
||||
__privateSet(this, _lastCacheDiff, 0);
|
||||
this.performance = { backend: 0, load: 0, image: 0, frames: 0, cached: 0, changed: 0, total: 0, draw: 0 };
|
||||
this.events = new EventTarget();
|
||||
this.models = {
|
||||
face: null,
|
||||
posenet: null,
|
||||
|
|
@ -12011,10 +12016,12 @@ var Human = class {
|
|||
};
|
||||
this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [] };
|
||||
this.image = (input) => process4(input, this.config);
|
||||
this.process = { tensor: null, canvas: null };
|
||||
this.faceTriangulation = triangulation;
|
||||
this.faceUVMap = uvmap;
|
||||
this.sysinfo = info();
|
||||
__privateSet(this, _lastInputSum, 1);
|
||||
__privateGet(this, _emit).call(this, "create");
|
||||
}
|
||||
similarity(embedding1, embedding2) {
|
||||
return similarity(embedding1, embedding2);
|
||||
|
|
@ -12031,6 +12038,7 @@ var Human = class {
|
|||
async load(userConfig) {
|
||||
this.state = "load";
|
||||
const timeStamp = now();
|
||||
const count2 = Object.values(this.models).filter((model10) => model10).length;
|
||||
if (userConfig)
|
||||
this.config = mergeDeep(this.config, userConfig);
|
||||
if (__privateGet(this, _firstRun)) {
|
||||
|
|
@ -12056,12 +12064,16 @@ var Human = class {
|
|||
log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
|
||||
__privateSet(this, _firstRun, false);
|
||||
}
|
||||
const loaded = Object.values(this.models).filter((model10) => model10).length;
|
||||
if (loaded !== count2)
|
||||
__privateGet(this, _emit).call(this, "load");
|
||||
const current = Math.trunc(now() - timeStamp);
|
||||
if (current > (this.performance.load || 0))
|
||||
this.performance.load = current;
|
||||
}
|
||||
async detect(input, userConfig) {
|
||||
return new Promise(async (resolve) => {
|
||||
var _a, _b;
|
||||
this.state = "config";
|
||||
let timeStamp;
|
||||
let elapsedTime;
|
||||
|
|
@ -12076,30 +12088,31 @@ var Human = class {
|
|||
await __privateGet(this, _checkBackend).call(this);
|
||||
await this.load();
|
||||
timeStamp = now();
|
||||
let process6 = process4(input, this.config);
|
||||
this.process = process4(input, this.config);
|
||||
this.performance.image = Math.trunc(now() - timeStamp);
|
||||
this.analyze("Get Image:");
|
||||
if (this.config.segmentation.enabled && process6 && process6.tensor) {
|
||||
if (this.config.segmentation.enabled && this.process && this.process.tensor) {
|
||||
this.analyze("Start Segmentation:");
|
||||
this.state = "run:segmentation";
|
||||
timeStamp = now();
|
||||
await predict11(process6);
|
||||
await predict11(this.process);
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.segmentation = elapsedTime;
|
||||
if (process6.canvas) {
|
||||
tf21.dispose(process6.tensor);
|
||||
process6 = process4(process6.canvas, this.config);
|
||||
if (this.process.canvas) {
|
||||
tf21.dispose(this.process.tensor);
|
||||
this.process = process4(this.process.canvas, this.config);
|
||||
}
|
||||
this.analyze("End Segmentation:");
|
||||
}
|
||||
if (!process6 || !process6.tensor) {
|
||||
if (!this.process || !this.process.tensor) {
|
||||
log("could not convert input to tensor");
|
||||
resolve({ error: "could not convert input to tensor" });
|
||||
return;
|
||||
}
|
||||
__privateGet(this, _emit).call(this, "image");
|
||||
timeStamp = now();
|
||||
this.config.skipFrame = await __privateGet(this, _skipFrame).call(this, process6.tensor);
|
||||
this.config.skipFrame = await __privateGet(this, _skipFrame).call(this, this.process.tensor);
|
||||
if (!this.performance.frames)
|
||||
this.performance.frames = 0;
|
||||
if (!this.performance.cached)
|
||||
|
|
@ -12114,13 +12127,13 @@ var Human = class {
|
|||
let handRes = [];
|
||||
let objectRes = [];
|
||||
if (this.config.async) {
|
||||
faceRes = this.config.face.enabled ? detectFace(this, process6.tensor) : [];
|
||||
faceRes = this.config.face.enabled ? detectFace(this, this.process.tensor) : [];
|
||||
if (this.performance.face)
|
||||
delete this.performance.face;
|
||||
} else {
|
||||
this.state = "run:face";
|
||||
timeStamp = now();
|
||||
faceRes = this.config.face.enabled ? await detectFace(this, process6.tensor) : [];
|
||||
faceRes = this.config.face.enabled ? await detectFace(this, this.process.tensor) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.face = elapsedTime;
|
||||
|
|
@ -12128,26 +12141,26 @@ var Human = class {
|
|||
this.analyze("Start Body:");
|
||||
if (this.config.async) {
|
||||
if (this.config.body.modelPath.includes("posenet"))
|
||||
bodyRes = this.config.body.enabled ? predict4(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict4(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("blazepose"))
|
||||
bodyRes = this.config.body.enabled ? predict6(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict6(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("efficientpose"))
|
||||
bodyRes = this.config.body.enabled ? predict7(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict7(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("movenet"))
|
||||
bodyRes = this.config.body.enabled ? predict8(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict8(this.process.tensor, this.config) : [];
|
||||
if (this.performance.body)
|
||||
delete this.performance.body;
|
||||
} else {
|
||||
this.state = "run:body";
|
||||
timeStamp = now();
|
||||
if (this.config.body.modelPath.includes("posenet"))
|
||||
bodyRes = this.config.body.enabled ? await predict4(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict4(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("blazepose"))
|
||||
bodyRes = this.config.body.enabled ? await predict6(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict6(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("efficientpose"))
|
||||
bodyRes = this.config.body.enabled ? await predict7(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict7(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("movenet"))
|
||||
bodyRes = this.config.body.enabled ? await predict8(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict8(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.body = elapsedTime;
|
||||
|
|
@ -12155,13 +12168,13 @@ var Human = class {
|
|||
this.analyze("End Body:");
|
||||
this.analyze("Start Hand:");
|
||||
if (this.config.async) {
|
||||
handRes = this.config.hand.enabled ? predict5(process6.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? predict5(this.process.tensor, this.config) : [];
|
||||
if (this.performance.hand)
|
||||
delete this.performance.hand;
|
||||
} else {
|
||||
this.state = "run:hand";
|
||||
timeStamp = now();
|
||||
handRes = this.config.hand.enabled ? await predict5(process6.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? await predict5(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.hand = elapsedTime;
|
||||
|
|
@ -12170,18 +12183,18 @@ var Human = class {
|
|||
this.analyze("Start Object:");
|
||||
if (this.config.async) {
|
||||
if (this.config.object.modelPath.includes("nanodet"))
|
||||
objectRes = this.config.object.enabled ? predict9(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? predict9(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes("centernet"))
|
||||
objectRes = this.config.object.enabled ? predict10(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? predict10(this.process.tensor, this.config) : [];
|
||||
if (this.performance.object)
|
||||
delete this.performance.object;
|
||||
} else {
|
||||
this.state = "run:object";
|
||||
timeStamp = now();
|
||||
if (this.config.object.modelPath.includes("nanodet"))
|
||||
objectRes = this.config.object.enabled ? await predict9(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? await predict9(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes("centernet"))
|
||||
objectRes = this.config.object.enabled ? await predict10(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? await predict10(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.object = elapsedTime;
|
||||
|
|
@ -12200,6 +12213,7 @@ var Human = class {
|
|||
}
|
||||
this.performance.total = Math.trunc(now() - timeStart);
|
||||
this.state = "idle";
|
||||
const shape = ((_b = (_a = this.process) == null ? void 0 : _a.tensor) == null ? void 0 : _b.shape) || [];
|
||||
this.result = {
|
||||
face: faceRes,
|
||||
body: bodyRes,
|
||||
|
|
@ -12207,14 +12221,14 @@ var Human = class {
|
|||
gesture: gestureRes,
|
||||
object: objectRes,
|
||||
performance: this.performance,
|
||||
canvas: process6.canvas,
|
||||
canvas: this.process.canvas,
|
||||
timestamp: Date.now(),
|
||||
get persons() {
|
||||
var _a;
|
||||
return join2(faceRes, bodyRes, handRes, gestureRes, (_a = process6 == null ? void 0 : process6.tensor) == null ? void 0 : _a.shape);
|
||||
return join2(faceRes, bodyRes, handRes, gestureRes, shape);
|
||||
}
|
||||
};
|
||||
tf21.dispose(process6.tensor);
|
||||
tf21.dispose(this.process.tensor);
|
||||
__privateGet(this, _emit).call(this, "detect");
|
||||
resolve(this.result);
|
||||
});
|
||||
}
|
||||
|
|
@ -12234,6 +12248,7 @@ var Human = class {
|
|||
const t1 = now();
|
||||
if (this.config.debug)
|
||||
log("Warmup", this.config.warmup, Math.round(t1 - t0), "ms", res);
|
||||
__privateGet(this, _emit).call(this, "warmup");
|
||||
return res;
|
||||
}
|
||||
};
|
||||
|
|
@ -12244,6 +12259,7 @@ _firstRun = new WeakMap();
|
|||
_lastInputSum = new WeakMap();
|
||||
_lastCacheDiff = new WeakMap();
|
||||
_sanity = new WeakMap();
|
||||
_emit = new WeakMap();
|
||||
_checkBackend = new WeakMap();
|
||||
_skipFrame = new WeakMap();
|
||||
_warmupBitmap = new WeakMap();
|
||||
|
|
|
|||
|
|
@ -11776,7 +11776,7 @@ lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
|
|||
var version = "2.1.5";
|
||||
|
||||
// src/human.ts
|
||||
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _firstRun, _lastInputSum, _lastCacheDiff, _sanity, _checkBackend, _skipFrame, _warmupBitmap, _warmupCanvas, _warmupNode;
|
||||
var _numTensors, _analyzeMemoryLeaks, _checkSanity, _firstRun, _lastInputSum, _lastCacheDiff, _sanity, _emit, _checkBackend, _skipFrame, _warmupBitmap, _warmupCanvas, _warmupNode;
|
||||
var Human = class {
|
||||
constructor(userConfig) {
|
||||
__privateAdd(this, _numTensors, void 0);
|
||||
|
|
@ -11809,6 +11809,10 @@ var Human = class {
|
|||
}
|
||||
return null;
|
||||
});
|
||||
__privateAdd(this, _emit, (event) => {
|
||||
var _a;
|
||||
return (_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event));
|
||||
});
|
||||
__privateAdd(this, _checkBackend, async (force = false) => {
|
||||
var _a;
|
||||
if (this.config.backend && this.config.backend.length > 0 && force || this.tf.getBackend() !== this.config.backend) {
|
||||
|
|
@ -11992,6 +11996,7 @@ var Human = class {
|
|||
__privateSet(this, _firstRun, true);
|
||||
__privateSet(this, _lastCacheDiff, 0);
|
||||
this.performance = { backend: 0, load: 0, image: 0, frames: 0, cached: 0, changed: 0, total: 0, draw: 0 };
|
||||
this.events = new EventTarget();
|
||||
this.models = {
|
||||
face: null,
|
||||
posenet: null,
|
||||
|
|
@ -12010,10 +12015,12 @@ var Human = class {
|
|||
};
|
||||
this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [] };
|
||||
this.image = (input) => process4(input, this.config);
|
||||
this.process = { tensor: null, canvas: null };
|
||||
this.faceTriangulation = triangulation;
|
||||
this.faceUVMap = uvmap;
|
||||
this.sysinfo = info();
|
||||
__privateSet(this, _lastInputSum, 1);
|
||||
__privateGet(this, _emit).call(this, "create");
|
||||
}
|
||||
similarity(embedding1, embedding2) {
|
||||
return similarity(embedding1, embedding2);
|
||||
|
|
@ -12030,6 +12037,7 @@ var Human = class {
|
|||
async load(userConfig) {
|
||||
this.state = "load";
|
||||
const timeStamp = now();
|
||||
const count2 = Object.values(this.models).filter((model10) => model10).length;
|
||||
if (userConfig)
|
||||
this.config = mergeDeep(this.config, userConfig);
|
||||
if (__privateGet(this, _firstRun)) {
|
||||
|
|
@ -12055,12 +12063,16 @@ var Human = class {
|
|||
log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
|
||||
__privateSet(this, _firstRun, false);
|
||||
}
|
||||
const loaded = Object.values(this.models).filter((model10) => model10).length;
|
||||
if (loaded !== count2)
|
||||
__privateGet(this, _emit).call(this, "load");
|
||||
const current = Math.trunc(now() - timeStamp);
|
||||
if (current > (this.performance.load || 0))
|
||||
this.performance.load = current;
|
||||
}
|
||||
async detect(input, userConfig) {
|
||||
return new Promise(async (resolve) => {
|
||||
var _a, _b;
|
||||
this.state = "config";
|
||||
let timeStamp;
|
||||
let elapsedTime;
|
||||
|
|
@ -12075,30 +12087,31 @@ var Human = class {
|
|||
await __privateGet(this, _checkBackend).call(this);
|
||||
await this.load();
|
||||
timeStamp = now();
|
||||
let process6 = process4(input, this.config);
|
||||
this.process = process4(input, this.config);
|
||||
this.performance.image = Math.trunc(now() - timeStamp);
|
||||
this.analyze("Get Image:");
|
||||
if (this.config.segmentation.enabled && process6 && process6.tensor) {
|
||||
if (this.config.segmentation.enabled && this.process && this.process.tensor) {
|
||||
this.analyze("Start Segmentation:");
|
||||
this.state = "run:segmentation";
|
||||
timeStamp = now();
|
||||
await predict11(process6);
|
||||
await predict11(this.process);
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.segmentation = elapsedTime;
|
||||
if (process6.canvas) {
|
||||
tf21.dispose(process6.tensor);
|
||||
process6 = process4(process6.canvas, this.config);
|
||||
if (this.process.canvas) {
|
||||
tf21.dispose(this.process.tensor);
|
||||
this.process = process4(this.process.canvas, this.config);
|
||||
}
|
||||
this.analyze("End Segmentation:");
|
||||
}
|
||||
if (!process6 || !process6.tensor) {
|
||||
if (!this.process || !this.process.tensor) {
|
||||
log("could not convert input to tensor");
|
||||
resolve({ error: "could not convert input to tensor" });
|
||||
return;
|
||||
}
|
||||
__privateGet(this, _emit).call(this, "image");
|
||||
timeStamp = now();
|
||||
this.config.skipFrame = await __privateGet(this, _skipFrame).call(this, process6.tensor);
|
||||
this.config.skipFrame = await __privateGet(this, _skipFrame).call(this, this.process.tensor);
|
||||
if (!this.performance.frames)
|
||||
this.performance.frames = 0;
|
||||
if (!this.performance.cached)
|
||||
|
|
@ -12113,13 +12126,13 @@ var Human = class {
|
|||
let handRes = [];
|
||||
let objectRes = [];
|
||||
if (this.config.async) {
|
||||
faceRes = this.config.face.enabled ? detectFace(this, process6.tensor) : [];
|
||||
faceRes = this.config.face.enabled ? detectFace(this, this.process.tensor) : [];
|
||||
if (this.performance.face)
|
||||
delete this.performance.face;
|
||||
} else {
|
||||
this.state = "run:face";
|
||||
timeStamp = now();
|
||||
faceRes = this.config.face.enabled ? await detectFace(this, process6.tensor) : [];
|
||||
faceRes = this.config.face.enabled ? await detectFace(this, this.process.tensor) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.face = elapsedTime;
|
||||
|
|
@ -12127,26 +12140,26 @@ var Human = class {
|
|||
this.analyze("Start Body:");
|
||||
if (this.config.async) {
|
||||
if (this.config.body.modelPath.includes("posenet"))
|
||||
bodyRes = this.config.body.enabled ? predict4(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict4(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("blazepose"))
|
||||
bodyRes = this.config.body.enabled ? predict6(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict6(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("efficientpose"))
|
||||
bodyRes = this.config.body.enabled ? predict7(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict7(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("movenet"))
|
||||
bodyRes = this.config.body.enabled ? predict8(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? predict8(this.process.tensor, this.config) : [];
|
||||
if (this.performance.body)
|
||||
delete this.performance.body;
|
||||
} else {
|
||||
this.state = "run:body";
|
||||
timeStamp = now();
|
||||
if (this.config.body.modelPath.includes("posenet"))
|
||||
bodyRes = this.config.body.enabled ? await predict4(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict4(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("blazepose"))
|
||||
bodyRes = this.config.body.enabled ? await predict6(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict6(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("efficientpose"))
|
||||
bodyRes = this.config.body.enabled ? await predict7(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict7(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes("movenet"))
|
||||
bodyRes = this.config.body.enabled ? await predict8(process6.tensor, this.config) : [];
|
||||
bodyRes = this.config.body.enabled ? await predict8(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.body = elapsedTime;
|
||||
|
|
@ -12154,13 +12167,13 @@ var Human = class {
|
|||
this.analyze("End Body:");
|
||||
this.analyze("Start Hand:");
|
||||
if (this.config.async) {
|
||||
handRes = this.config.hand.enabled ? predict5(process6.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? predict5(this.process.tensor, this.config) : [];
|
||||
if (this.performance.hand)
|
||||
delete this.performance.hand;
|
||||
} else {
|
||||
this.state = "run:hand";
|
||||
timeStamp = now();
|
||||
handRes = this.config.hand.enabled ? await predict5(process6.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? await predict5(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.hand = elapsedTime;
|
||||
|
|
@ -12169,18 +12182,18 @@ var Human = class {
|
|||
this.analyze("Start Object:");
|
||||
if (this.config.async) {
|
||||
if (this.config.object.modelPath.includes("nanodet"))
|
||||
objectRes = this.config.object.enabled ? predict9(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? predict9(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes("centernet"))
|
||||
objectRes = this.config.object.enabled ? predict10(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? predict10(this.process.tensor, this.config) : [];
|
||||
if (this.performance.object)
|
||||
delete this.performance.object;
|
||||
} else {
|
||||
this.state = "run:object";
|
||||
timeStamp = now();
|
||||
if (this.config.object.modelPath.includes("nanodet"))
|
||||
objectRes = this.config.object.enabled ? await predict9(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? await predict9(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes("centernet"))
|
||||
objectRes = this.config.object.enabled ? await predict10(process6.tensor, this.config) : [];
|
||||
objectRes = this.config.object.enabled ? await predict10(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0)
|
||||
this.performance.object = elapsedTime;
|
||||
|
|
@ -12199,6 +12212,7 @@ var Human = class {
|
|||
}
|
||||
this.performance.total = Math.trunc(now() - timeStart);
|
||||
this.state = "idle";
|
||||
const shape = ((_b = (_a = this.process) == null ? void 0 : _a.tensor) == null ? void 0 : _b.shape) || [];
|
||||
this.result = {
|
||||
face: faceRes,
|
||||
body: bodyRes,
|
||||
|
|
@ -12206,14 +12220,14 @@ var Human = class {
|
|||
gesture: gestureRes,
|
||||
object: objectRes,
|
||||
performance: this.performance,
|
||||
canvas: process6.canvas,
|
||||
canvas: this.process.canvas,
|
||||
timestamp: Date.now(),
|
||||
get persons() {
|
||||
var _a;
|
||||
return join2(faceRes, bodyRes, handRes, gestureRes, (_a = process6 == null ? void 0 : process6.tensor) == null ? void 0 : _a.shape);
|
||||
return join2(faceRes, bodyRes, handRes, gestureRes, shape);
|
||||
}
|
||||
};
|
||||
tf21.dispose(process6.tensor);
|
||||
tf21.dispose(this.process.tensor);
|
||||
__privateGet(this, _emit).call(this, "detect");
|
||||
resolve(this.result);
|
||||
});
|
||||
}
|
||||
|
|
@ -12233,6 +12247,7 @@ var Human = class {
|
|||
const t1 = now();
|
||||
if (this.config.debug)
|
||||
log("Warmup", this.config.warmup, Math.round(t1 - t0), "ms", res);
|
||||
__privateGet(this, _emit).call(this, "warmup");
|
||||
return res;
|
||||
}
|
||||
};
|
||||
|
|
@ -12243,6 +12258,7 @@ _firstRun = new WeakMap();
|
|||
_lastInputSum = new WeakMap();
|
||||
_lastCacheDiff = new WeakMap();
|
||||
_sanity = new WeakMap();
|
||||
_emit = new WeakMap();
|
||||
_checkBackend = new WeakMap();
|
||||
_skipFrame = new WeakMap();
|
||||
_warmupBitmap = new WeakMap();
|
||||
|
|
|
|||
101
src/human.ts
101
src/human.ts
|
|
@ -76,8 +76,10 @@ export class Human {
|
|||
* - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
|
||||
*/
|
||||
state: string;
|
||||
/** @internal: Instance of current image being processed */
|
||||
image: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };
|
||||
/** process input and return tensor and canvas */
|
||||
image: typeof image.process;
|
||||
/** currenty processed image tensor and canvas */
|
||||
process: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null };
|
||||
/** @internal: Instance of TensorFlow/JS used by Human
|
||||
* - Can be embedded or externally provided
|
||||
*/
|
||||
|
|
@ -87,7 +89,7 @@ export class Human {
|
|||
* - face: draw detected faces
|
||||
* - body: draw detected people and body parts
|
||||
* - hand: draw detected hands and hand parts
|
||||
* - canvas: draw processed canvas which is a processed copy of the input
|
||||
* - canvas: draw this.processed canvas which is a this.processed copy of the input
|
||||
* - all: meta-function that performs: canvas, face, body, hand
|
||||
*/
|
||||
draw: {
|
||||
|
|
@ -126,6 +128,17 @@ export class Human {
|
|||
faceres: GraphModel | null,
|
||||
segmentation: GraphModel | null,
|
||||
};
|
||||
/** Container for events dispatched by Human
|
||||
*
|
||||
* Possible events:
|
||||
* - `create`: triggered when Human object is instantiated
|
||||
* - `load`: triggered when models are loaded (explicitly or on-demand)
|
||||
* - `image`: triggered when input image is this.processed
|
||||
* - `result`: triggered when detection is complete
|
||||
* - `warmup`: triggered when warmup is complete
|
||||
*/
|
||||
|
||||
events: EventTarget;
|
||||
/** Reference face triangualtion array of 468 points, used for triangle references between points */
|
||||
faceTriangulation: typeof facemesh.triangulation;
|
||||
/** Refernce UV map of 468 values, used for 3D mapping of the face mesh */
|
||||
|
|
@ -161,6 +174,7 @@ export class Human {
|
|||
this.#firstRun = true;
|
||||
this.#lastCacheDiff = 0;
|
||||
this.performance = { backend: 0, load: 0, image: 0, frames: 0, cached: 0, changed: 0, total: 0, draw: 0 };
|
||||
this.events = new EventTarget();
|
||||
// object that contains all initialized models
|
||||
this.models = {
|
||||
face: null,
|
||||
|
|
@ -179,15 +193,17 @@ export class Human {
|
|||
segmentation: null,
|
||||
};
|
||||
this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [] };
|
||||
// export access to image processing
|
||||
// export access to image this.processing
|
||||
// @ts-ignore eslint-typescript cannot correctly infer type in anonymous function
|
||||
this.image = (input: Input) => image.process(input, this.config);
|
||||
this.process = { tensor: null, canvas: null };
|
||||
// export raw access to underlying models
|
||||
this.faceTriangulation = facemesh.triangulation;
|
||||
this.faceUVMap = facemesh.uvmap;
|
||||
// include platform info
|
||||
this.sysinfo = sysinfo.info();
|
||||
this.#lastInputSum = 1;
|
||||
this.#emit('create');
|
||||
}
|
||||
|
||||
// helper function: measure tensor leak
|
||||
|
|
@ -228,9 +244,9 @@ export class Human {
|
|||
}
|
||||
|
||||
/**
|
||||
* Segmentation method takes any input and returns processed canvas with body segmentation
|
||||
* Segmentation method takes any input and returns this.processed canvas with body segmentation
|
||||
* Optional parameter background is used to fill the background with specific input
|
||||
* Segmentation is not triggered as part of detect process
|
||||
* Segmentation is not triggered as part of detect this.process
|
||||
*
|
||||
* @param input: {@link Input}
|
||||
* @param background?: {@link Input}
|
||||
|
|
@ -240,7 +256,7 @@ export class Human {
|
|||
return segmentation.process(input, background, this.config);
|
||||
}
|
||||
|
||||
/** Enhance method performs additional enhacements to face image previously detected for futher processing
|
||||
/** Enhance method performs additional enhacements to face image previously detected for futher this.processing
|
||||
* @param input: Tensor as provided in human.result.face[n].tensor
|
||||
* @returns Tensor
|
||||
*/
|
||||
|
|
@ -267,6 +283,7 @@ export class Human {
|
|||
async load(userConfig?: Config | Record<string, unknown>) {
|
||||
this.state = 'load';
|
||||
const timeStamp = now();
|
||||
const count = Object.values(this.models).filter((model) => model).length;
|
||||
if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;
|
||||
|
||||
if (this.#firstRun) { // print version info on first run and check for correct backend setup
|
||||
|
|
@ -289,10 +306,16 @@ export class Human {
|
|||
this.#firstRun = false;
|
||||
}
|
||||
|
||||
const loaded = Object.values(this.models).filter((model) => model).length;
|
||||
if (loaded !== count) this.#emit('load');
|
||||
const current = Math.trunc(now() - timeStamp);
|
||||
if (current > (this.performance.load as number || 0)) this.performance.load = current;
|
||||
}
|
||||
|
||||
// emit event
|
||||
/** @hidden */
|
||||
#emit = (event: string) => this.events?.dispatchEvent(new Event(event));
|
||||
|
||||
// check if backend needs initialization if it changed
|
||||
/** @hidden */
|
||||
#checkBackend = async (force = false) => {
|
||||
|
|
@ -433,9 +456,9 @@ export class Human {
|
|||
|
||||
/** Main detection method
|
||||
* - Analyze configuration: {@link Config}
|
||||
* - Pre-process input: {@link Input}
|
||||
* - Pre-this.process input: {@link Input}
|
||||
* - Run inference for all configured models
|
||||
* - Process and return result: {@link Result}
|
||||
* - this.process and return result: {@link Result}
|
||||
*
|
||||
* @param input: Input
|
||||
* @param userConfig?: {@link Config}
|
||||
|
|
@ -468,34 +491,35 @@ export class Human {
|
|||
await this.load();
|
||||
|
||||
timeStamp = now();
|
||||
let process = image.process(input, this.config);
|
||||
this.process = image.process(input, this.config);
|
||||
this.performance.image = Math.trunc(now() - timeStamp);
|
||||
this.analyze('Get Image:');
|
||||
|
||||
// run segmentation preprocessing
|
||||
if (this.config.segmentation.enabled && process && process.tensor) {
|
||||
// run segmentation prethis.processing
|
||||
if (this.config.segmentation.enabled && this.process && this.process.tensor) {
|
||||
this.analyze('Start Segmentation:');
|
||||
this.state = 'run:segmentation';
|
||||
timeStamp = now();
|
||||
await segmentation.predict(process);
|
||||
await segmentation.predict(this.process);
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0) this.performance.segmentation = elapsedTime;
|
||||
if (process.canvas) {
|
||||
if (this.process.canvas) {
|
||||
// replace input
|
||||
tf.dispose(process.tensor);
|
||||
process = image.process(process.canvas, this.config);
|
||||
tf.dispose(this.process.tensor);
|
||||
this.process = image.process(this.process.canvas, this.config);
|
||||
}
|
||||
this.analyze('End Segmentation:');
|
||||
}
|
||||
|
||||
if (!process || !process.tensor) {
|
||||
if (!this.process || !this.process.tensor) {
|
||||
log('could not convert input to tensor');
|
||||
resolve({ error: 'could not convert input to tensor' });
|
||||
return;
|
||||
}
|
||||
this.#emit('image');
|
||||
|
||||
timeStamp = now();
|
||||
this.config.skipFrame = await this.#skipFrame(process.tensor);
|
||||
this.config.skipFrame = await this.#skipFrame(this.process.tensor);
|
||||
if (!this.performance.frames) this.performance.frames = 0;
|
||||
if (!this.performance.cached) this.performance.cached = 0;
|
||||
(this.performance.frames as number)++;
|
||||
|
|
@ -512,12 +536,12 @@ export class Human {
|
|||
|
||||
// run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion
|
||||
if (this.config.async) {
|
||||
faceRes = this.config.face.enabled ? face.detectFace(this, process.tensor) : [];
|
||||
faceRes = this.config.face.enabled ? face.detectFace(this, this.process.tensor) : [];
|
||||
if (this.performance.face) delete this.performance.face;
|
||||
} else {
|
||||
this.state = 'run:face';
|
||||
timeStamp = now();
|
||||
faceRes = this.config.face.enabled ? await face.detectFace(this, process.tensor) : [];
|
||||
faceRes = this.config.face.enabled ? await face.detectFace(this, this.process.tensor) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0) this.performance.face = elapsedTime;
|
||||
}
|
||||
|
|
@ -525,18 +549,18 @@ export class Human {
|
|||
// run body: can be posenet, blazepose, efficientpose, movenet
|
||||
this.analyze('Start Body:');
|
||||
if (this.config.async) {
|
||||
if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(process.tensor, this.config) : [];
|
||||
if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(this.process.tensor, this.config) : [];
|
||||
if (this.performance.body) delete this.performance.body;
|
||||
} else {
|
||||
this.state = 'run:body';
|
||||
timeStamp = now();
|
||||
if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(process.tensor, this.config) : [];
|
||||
if (this.config.body.modelPath.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(this.process.tensor, this.config) : [];
|
||||
else if (this.config.body.modelPath.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0) this.performance.body = elapsedTime;
|
||||
}
|
||||
|
|
@ -545,12 +569,12 @@ export class Human {
|
|||
// run handpose
|
||||
this.analyze('Start Hand:');
|
||||
if (this.config.async) {
|
||||
handRes = this.config.hand.enabled ? handpose.predict(process.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? handpose.predict(this.process.tensor, this.config) : [];
|
||||
if (this.performance.hand) delete this.performance.hand;
|
||||
} else {
|
||||
this.state = 'run:hand';
|
||||
timeStamp = now();
|
||||
handRes = this.config.hand.enabled ? await handpose.predict(process.tensor, this.config) : [];
|
||||
handRes = this.config.hand.enabled ? await handpose.predict(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0) this.performance.hand = elapsedTime;
|
||||
}
|
||||
|
|
@ -559,14 +583,14 @@ export class Human {
|
|||
// run nanodet
|
||||
this.analyze('Start Object:');
|
||||
if (this.config.async) {
|
||||
if (this.config.object.modelPath.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(process.tensor, this.config) : [];
|
||||
if (this.config.object.modelPath.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(this.process.tensor, this.config) : [];
|
||||
if (this.performance.object) delete this.performance.object;
|
||||
} else {
|
||||
this.state = 'run:object';
|
||||
timeStamp = now();
|
||||
if (this.config.object.modelPath.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(process.tensor, this.config) : [];
|
||||
if (this.config.object.modelPath.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(this.process.tensor, this.config) : [];
|
||||
else if (this.config.object.modelPath.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(this.process.tensor, this.config) : [];
|
||||
elapsedTime = Math.trunc(now() - timeStamp);
|
||||
if (elapsedTime > 0) this.performance.object = elapsedTime;
|
||||
}
|
||||
|
|
@ -586,6 +610,7 @@ export class Human {
|
|||
|
||||
this.performance.total = Math.trunc(now() - timeStart);
|
||||
this.state = 'idle';
|
||||
const shape = this.process?.tensor?.shape || [];
|
||||
this.result = {
|
||||
face: faceRes as Face[],
|
||||
body: bodyRes as Body[],
|
||||
|
|
@ -593,15 +618,16 @@ export class Human {
|
|||
gesture: gestureRes,
|
||||
object: objectRes as Item[],
|
||||
performance: this.performance,
|
||||
canvas: process.canvas,
|
||||
canvas: this.process.canvas,
|
||||
timestamp: Date.now(),
|
||||
get persons() { return persons.join(faceRes as Face[], bodyRes as Body[], handRes as Hand[], gestureRes, process?.tensor?.shape); },
|
||||
get persons() { return persons.join(faceRes as Face[], bodyRes as Body[], handRes as Hand[], gestureRes, shape); },
|
||||
};
|
||||
|
||||
// finally dispose input tensor
|
||||
tf.dispose(process.tensor);
|
||||
tf.dispose(this.process.tensor);
|
||||
|
||||
// log('Result:', result);
|
||||
this.#emit('detect');
|
||||
resolve(this.result);
|
||||
});
|
||||
}
|
||||
|
|
@ -700,6 +726,7 @@ export class Human {
|
|||
else res = await this.#warmupNode();
|
||||
const t1 = now();
|
||||
if (this.config.debug) log('Warmup', this.config.warmup, Math.round(t1 - t0), 'ms', res);
|
||||
this.#emit('warmup');
|
||||
return res;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -186,7 +186,7 @@ export interface Result {
|
|||
/** global performance object with timing values for each operation */
|
||||
performance: Record<string, unknown>,
|
||||
/** optional processed canvas that can be used to draw input on screen */
|
||||
canvas?: OffscreenCanvas | HTMLCanvasElement,
|
||||
canvas?: OffscreenCanvas | HTMLCanvasElement | null,
|
||||
/** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */
|
||||
readonly timestamp: number,
|
||||
/** getter property that returns unified persons object */
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ export async function load(config: Config): Promise<GraphModel> {
|
|||
return model;
|
||||
}
|
||||
|
||||
export async function predict(input: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement }): Promise<Uint8ClampedArray | null> {
|
||||
export async function predict(input: { tensor: Tensor | null, canvas: OffscreenCanvas | HTMLCanvasElement | null }): Promise<Uint8ClampedArray | null> {
|
||||
const width = input.tensor?.shape[1] || 0;
|
||||
const height = input.tensor?.shape[2] || 0;
|
||||
if (!input.tensor) return null;
|
||||
|
|
|
|||
427
test/build.log
427
test/build.log
|
|
@ -496,3 +496,430 @@
|
|||
2021-09-11 11:42:16 [35mSTATE:[39m Lint: {"locations":["src/**/*.ts","test/*.js","demo/**/*.js"],"files":75,"errors":0,"warnings":0}
|
||||
2021-09-11 11:42:16 [35mSTATE:[39m ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"}
|
||||
2021-09-11 11:42:16 [36mINFO: [39m Done...
|
||||
2021-09-11 13:36:32 [36mINFO: [39m @vladmandic/human version 2.1.5
|
||||
2021-09-11 13:36:32 [36mINFO: [39m User: vlado Platform: linux Arch: x64 Node: v16.5.0
|
||||
2021-09-11 13:36:32 [36mINFO: [39m Application: {"name":"@vladmandic/human","version":"2.1.5"}
|
||||
2021-09-11 13:36:32 [36mINFO: [39m Environment: {"profile":"development","config":"build.json","tsconfig":true,"eslintrc":true,"git":true}
|
||||
2021-09-11 13:36:32 [36mINFO: [39m Toolchain: {"build":"0.4.1","esbuild":"0.12.26","typescript":"4.4.3","typedoc":"0.21.9","eslint":"7.32.0"}
|
||||
2021-09-11 13:36:32 [36mINFO: [39m Build: {"profile":"development","steps":["serve","watch","compile"]}
|
||||
2021-09-11 13:36:32 [35mSTATE:[39m WebServer: {"ssl":false,"port":10030,"root":"."}
|
||||
2021-09-11 13:36:32 [35mSTATE:[39m WebServer: {"ssl":true,"port":10031,"root":".","sslKey":"node_modules/@vladmandic/build/cert/https.key","sslCrt":"node_modules/@vladmandic/build/cert/https.crt"}
|
||||
2021-09-11 13:36:32 [35mSTATE:[39m Watch: {"locations":["src/**","src/**","tfjs/*"]}
|
||||
2021-09-11 13:36:32 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1416}
|
||||
2021-09-11 13:36:32 [35mSTATE:[39m Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":47,"inputBytes":456782,"outputBytes":396579}
|
||||
2021-09-11 13:36:32 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1424}
|
||||
2021-09-11 13:36:32 [35mSTATE:[39m Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":47,"inputBytes":456790,"outputBytes":396583}
|
||||
2021-09-11 13:36:32 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1491}
|
||||
2021-09-11 13:36:33 [35mSTATE:[39m Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":47,"inputBytes":456857,"outputBytes":396655}
|
||||
2021-09-11 13:36:33 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":2168,"outputBytes":1590}
|
||||
2021-09-11 13:36:33 [35mSTATE:[39m Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":47,"inputBytes":456956,"outputBytes":398559}
|
||||
2021-09-11 13:36:33 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":7,"inputBytes":2168,"outputBytes":2343983}
|
||||
2021-09-11 13:36:33 [35mSTATE:[39m Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":47,"inputBytes":2799349,"outputBytes":1391563}
|
||||
2021-09-11 13:36:34 [35mSTATE:[39m Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":47,"inputBytes":2799349,"outputBytes":2583497}
|
||||
2021-09-11 13:36:34 [36mINFO: [39m Listening...
|
||||
2021-09-11 13:38:06 [36mINFO: [39m Watch: {"event":"modify","input":"src/interpolate.ts"}
|
||||
2021-09-11 13:38:06 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1416}
|
||||
2021-09-11 13:38:06 [35mSTATE:[39m Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":47,"inputBytes":456804,"outputBytes":396600}
|
||||
2021-09-11 13:38:06 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1424}
|
||||
2021-09-11 13:38:07 [35mSTATE:[39m Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":47,"inputBytes":456812,"outputBytes":396604}
|
||||
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|
||||
2021-09-11 15:59:06 [35mSTATE:[39m Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":47,"inputBytes":2800567,"outputBytes":1392342}
|
||||
2021-09-11 15:59:07 [35mSTATE:[39m Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":47,"inputBytes":2800567,"outputBytes":2584375}
|
||||
2021-09-11 16:00:05 [36mINFO: [39m Watch: {"event":"modify","input":"src/human.ts"}
|
||||
2021-09-11 16:00:05 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1416}
|
||||
2021-09-11 16:00:05 [35mSTATE:[39m Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":47,"inputBytes":458000,"outputBytes":397457}
|
||||
2021-09-11 16:00:05 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1424}
|
||||
2021-09-11 16:00:05 [35mSTATE:[39m Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":47,"inputBytes":458008,"outputBytes":397461}
|
||||
2021-09-11 16:00:05 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1491}
|
||||
2021-09-11 16:00:05 [35mSTATE:[39m Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":47,"inputBytes":458075,"outputBytes":397533}
|
||||
2021-09-11 16:00:05 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":2168,"outputBytes":1590}
|
||||
2021-09-11 16:00:05 [35mSTATE:[39m Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":47,"inputBytes":458174,"outputBytes":399437}
|
||||
2021-09-11 16:00:06 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":7,"inputBytes":2168,"outputBytes":2343983}
|
||||
2021-09-11 16:00:06 [35mSTATE:[39m Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":47,"inputBytes":2800567,"outputBytes":1392342}
|
||||
2021-09-11 16:00:06 [35mSTATE:[39m Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":47,"inputBytes":2800567,"outputBytes":2584375}
|
||||
|
|
|
|||
Loading…
Reference in New Issue