mirror of https://github.com/vladmandic/human
66 lines
2.2 KiB
JavaScript
66 lines
2.2 KiB
JavaScript
// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
|
|
const tf = require('@tensorflow/tfjs-node-gpu');
|
|
const log = require('@vladmandic/pilogger');
|
|
const canvasJS = require('canvas');
|
|
const Human = require('../../dist/human.node-gpu.js').default;
|
|
|
|
const input = './samples/in/group-1.jpg';
|
|
const loop = 20;
|
|
|
|
const myConfig = {
|
|
backend: 'tensorflow',
|
|
modelBasePath: 'https://vladmandic.github.io/human/models',
|
|
wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.9.0/dist/',
|
|
debug: true,
|
|
async: true,
|
|
cacheSensitivity: 0,
|
|
filter: { enabled: false },
|
|
face: {
|
|
enabled: true,
|
|
detector: { enabled: true, rotation: false },
|
|
mesh: { enabled: true },
|
|
iris: { enabled: true },
|
|
description: { enabled: true },
|
|
emotion: { enabled: true },
|
|
antispoof: { enabled: true },
|
|
liveness: { enabled: true },
|
|
},
|
|
hand: { enabled: true },
|
|
body: { enabled: true },
|
|
object: { enabled: true },
|
|
};
|
|
|
|
async function getImage(human) {
|
|
const img = await canvasJS.loadImage(input);
|
|
const canvas = canvasJS.createCanvas(img.width, img.height);
|
|
const ctx = canvas.getContext('2d');
|
|
ctx.drawImage(img, 0, 0, img.width, img.height);
|
|
const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
|
|
const tensor = human.tf.tensor(Array.from(imageData.data), [canvas.height, canvas.width, 4], 'int32'); // create rgba image tensor from flat array
|
|
log.info('Image:', input, tensor.shape);
|
|
return tensor;
|
|
}
|
|
|
|
async function main() {
|
|
log.header();
|
|
const human = new Human(myConfig);
|
|
await human.tf.ready();
|
|
log.info('Human:', human.version);
|
|
await human.load();
|
|
const loaded = Object.keys(human.models).filter((a) => human.models[a]);
|
|
log.info('Loaded:', loaded);
|
|
log.info('Memory state:', human.tf.engine().memory());
|
|
const tensor = await getImage(human);
|
|
log.state('Processing:', tensor['shape']);
|
|
const t0 = human.now();
|
|
await human.detect(tensor, myConfig);
|
|
const t1 = human.now();
|
|
log.state('Backend:', human.tf.getBackend());
|
|
log.data('Warmup:', Math.round(t1 - t0));
|
|
for (let i = 0; i < loop; i++) await human.detect(tensor, myConfig);
|
|
const t2 = human.now();
|
|
log.data('Average:', Math.round((t2 - t1) / loop));
|
|
}
|
|
|
|
main();
|