const tf = require('@tensorflow/tfjs-node');
const fs = require('fs');
const process = require('process');
const console = require('console');
const Human = require('..').default; // this resolves to project root which is '@vladmandic/human'
const logger = new console.Console({
stdout: process.stdout,
stderr: process.stderr,
ignoreErrors: true,
groupIndentation: 2,
inspectOptions: {
showHidden: true,
depth: 5,
colors: true,
showProxy: true,
maxArrayLength: 1024,
maxStringLength: 10240,
breakLength: 200,
compact: 64,
sorted: false,
getters: true,
},
});
const config = {
backend: 'tensorflow',
console: true,
videoOptimized: false,
face: {
detector: { modelPath: 'file://models/blazeface-back.json' },
mesh: { modelPath: 'file://models/facemesh.json' },
iris: { modelPath: 'file://models/iris.json' },
age: { modelPath: 'file://models/ssrnet-age-imdb.json' },
gender: { modelPath: 'file://models/ssrnet-gender-imdb.json' },
emotion: { modelPath: 'file://models/emotion.json' },
},
body: { modelPath: 'file://models/posenet.json' },
hand: {
detector: { modelPath: 'file://models/handdetect.json' },
skeleton: { modelPath: 'file://models/handskeleton.json' },
},
};
async function detect(input, output) {
await tf.setBackend('tensorflow');
await tf.ready();
logger.info('TFJS Flags:', tf.env().features);
logger.log('Loading:', input);
const buffer = fs.readFileSync(input);
const decoded = tf.node.decodeImage(buffer);
const casted = decoded.toFloat();
const image = casted.expandDims(0);
decoded.dispose();
casted.dispose();
logger.log('Processing:', image.shape);
const human = new Human();
const result = await human.detect(image, config);
image.dispose();
logger.log(result);
// Draw detected data and save processed image
logger.log('TODO Saving:', output);
}
async function main() {
if (process.argv.length !== 4) logger.error('Parameters: