human/demo/nodejs/node-fetch.js

32 lines
1.6 KiB
JavaScript

/**
* Human demo for NodeJS using http fetch to get image file
*
* Requires [node-fetch](https://www.npmjs.com/package/node-fetch) to provide `fetch` functionality in NodeJS environment
*/
const fs = require('fs');
const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
// in nodejs environments tfjs-node is required to be loaded before human
const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
// const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
const humanConfig = {
modelBasePath: 'https://vladmandic.github.io/human/models/',
};
async function main(inputFile) {
// @ts-ignore
global.fetch = (await import('node-fetch')).default; // eslint-disable-line node/no-unpublished-import
const human = new Human.Human(humanConfig); // create instance of human using default configuration
log.info('Human:', human.version, 'TF:', tf.version_core);
await human.load(); // optional as models would be loaded on-demand first time they are required
await human.warmup(); // optional as model warmup is performed on-demand first time its executed
const buffer = fs.readFileSync(inputFile); // read file data into buffer
const tensor = human.tf.node.decodeImage(buffer); // decode jpg data
const result = await human.detect(tensor); // run detection; will initialize backend and on-demand load models
log.data(result.gesture);
}
main('samples/in/ai-body.jpg');