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const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require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const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require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global.fetch = (await import('node-fetch')).default; // eslint-disable-line node/no-unpublished-import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