const fs = require('fs');
const path = require('path');
const log = require('@vladmandic/pilogger');
const tf = require('@tensorflow/tfjs-node');
const faceapi = require('../dist/face-api.node.js'); // this is equivalent to '@vladmandic/faceapi'

const modelPathRoot = '../model';
const minConfidence = 0.15;
const maxResults = 5;
let optionsSSDMobileNet;

async function image(img) {
  const buffer = fs.readFileSync(img);
  const decoded = tf.node.decodeImage(buffer);
  const casted = decoded.toFloat();
  const result = casted.expandDims(0);
  decoded.dispose();
  casted.dispose();
  return result;
}

async function detect(tensor) {
  try {
    const result = await faceapi
      .detectAllFaces(tensor, optionsSSDMobileNet)
      .withFaceLandmarks()
      .withFaceExpressions()
      .withFaceDescriptors()
      .withAgeAndGender();
    return result;
  } catch (err) {
    log.error('Caught error', err.message);
    return [];
  }
}

async function main() {
  await faceapi.tf.setBackend('tensorflow');
  await faceapi.tf.enableProdMode();
  await faceapi.tf.ENV.set('DEBUG', false);
  await faceapi.tf.ready();
  log.state('passed: tf initialized');

  const modelPath = path.join(__dirname, modelPathRoot);
  await faceapi.nets.ssdMobilenetv1.loadFromDisk(modelPath);
  await faceapi.nets.ageGenderNet.loadFromDisk(modelPath);
  await faceapi.nets.faceLandmark68Net.loadFromDisk(modelPath);
  await faceapi.nets.faceRecognitionNet.loadFromDisk(modelPath);
  await faceapi.nets.faceExpressionNet.loadFromDisk(modelPath);
  optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options({ minConfidence, maxResults });
  log.state('passed: models loaded');

  const tensor = await image('demo/sample1.jpg');
  if (tensor) log.state('passed: image loaded');
  else log.error('failed: image load');

  const result = await detect(tensor);
  if (result && result.length > 0) log.state('passed: detected faces', result.length);
  else log.error('failed: detect faces');
  tensor.dispose();
}

main();