const log = require('@vladmandic/pilogger');
const fs = require('fs');
const process = require('process');
// for Node, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
const tf = require('@tensorflow/tfjs-node'); // or const tf = require('@tensorflow/tfjs-node-gpu');
// load specific version of Human library that matches TensorFlow mode
const Human = require('../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default;

const myConfig = {
  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/age-ssrnet-imdb.json' },
    gender: { modelPath: 'file://models/gender-ssrnet-imdb.json' },
    emotion: { modelPath: 'file://models/emotion-large.json' },
  },
  body: { modelPath: 'file://models/posenet.json' },
  hand: {
    detector: { modelPath: 'file://models/handdetect.json' },
    skeleton: { modelPath: 'file://models/handskeleton.json' },
  },
};

async function detect(input) {
  // wait until tf is ready
  await tf.ready();
  // create instance of human
  const human = new Human(myConfig);
  // pre-load models
  await human.load();
  // read input image file and create tensor to be used for processing
  const buffer = fs.readFileSync(input);
  const decoded = human.tf.node.decodeImage(buffer);
  const casted = decoded.toFloat();
  const image = casted.expandDims(0);
  // console.log(image.arraySync());
  fs.writeFileSync('q.json', JSON.stringify(image.arraySync()));
  decoded.dispose();
  casted.dispose();
  // image shape contains image dimensions and depth
  log.state('Processing:', image.shape);
  // must disable face model when runing in tfjs-node as it's missing required ops
  // see <https://github.com/tensorflow/tfjs/issues/4066>
  myConfig.face.enabled = false;
  // run actual detection
  const result = await human.detect(image, myConfig);
  // dispose image tensor as we no longer need it
  image.dispose();
  // print data to console
  log.data(result);
}

async function main() {
  if (process.argv.length !== 3) log.error('Parameters: <input image>');
  else if (!fs.existsSync(process.argv[2])) log.error(`File not found: ${process.argv[2]}`);
  else detect(process.argv[2]);
}

main();