"use strict";

process.env.TZ = "Etc/GMT";

require('@tensorflow/tfjs-node');

const config = require("config"),
  Promise = require("bluebird"),
  { exists, mkdir, unlink } = require("./lib/util"),
  faceapi = require("face-api.js"),
  fs = require("fs"),
  canvas = require("canvas");

const { Canvas, Image, ImageData } = canvas;

faceapi.env.monkeyPatch({ Canvas, Image, ImageData });

const maxConcurrency = require("os").cpus().length;

require("./console-line.js"); /* Monkey-patch console.log with line numbers */

faceapi.nets.ssdMobilenetv1.loadFromDisk('./models')
.then(() => {
  console.log("ssdMobileNetv1 loaded.");
  return faceapi.nets.faceLandmark68Net.loadFromDisk('./models');
}).then(() => {
  console.log("landmark68 loaded.");
  return faceapi.nets.faceRecognitionNet.loadFromDisk('./models');
}).then(async () => {
  console.log("faceRecognitionNet loaded.");
  let faces = [];
  for (let a = 2; a < process.argv.length; a++) {
    const file = process.argv[a];
    process.stdout.write(`Loading ${file}...`);
    const image = await canvas.loadImage(file),
      detectors = await faceapi.detectAllFaces(image, 
        new faceapi.SsdMobilenetv1Options({
          minConfidence: 0.8
        })
      ).withFaceLandmarks().withFaceDescriptors();
    process.stdout.write(`${detectors.length} faces.\n`);
    
    detectors.forEach((face, index) => {
      faces.push({
        file: file,
        index: index,
        descriptor: face.descriptor
      })
    });
  }
  
  for (let i = 0; i < faces.length; i++) {
    for (let j = 0; j < faces.length; j++) {
      const a = faces[i], b = faces[j];
      const distance = faceapi.euclideanDistance(a.descriptor, b.descriptor);
      console.log(`${a.file}.${a.index} to ${b.file}.${b.index} = ${distance}`);
    }
  }
}).then(() => {
  console.log("Face detection scanning completed.");
}).catch((error) => {
  console.error(error);
  process.exit(-1);
});