agent-app/rag-server/node_modules/@vladmandic/face-api/demo/node-image.js

/**
 * FaceAPI Demo for NodeJS
 * - Uses external library [@canvas/image](https://www.npmjs.com/package/@canvas/image) to decode image
 * - Loads image from provided param
 * - Outputs results to console
 */

// @canvas/image can decode jpeg, png, webp
// must be installed manually as it just a demo dependency and not actual face-api dependency
const image = require('@canvas/image'); // eslint-disable-line node/no-missing-require
const fs = require('fs');
const log = require('@vladmandic/pilogger');
const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before face-api
const faceapi = require('../dist/face-api.node.js'); // use this when using face-api in dev mode
// const faceapi = require('@vladmandic/face-api'); // use this when face-api is installed as module (majority of use cases)

const modelPath = 'model/';
const imageFile = 'demo/sample1.jpg';
const ssdOptions = { minConfidence: 0.1, maxResults: 10 };

async function main() {
  log.header();
  const buffer = fs.readFileSync(imageFile); // read image from disk
  const canvas = await image.imageFromBuffer(buffer); // decode to canvas
  const imageData = image.getImageData(canvas); // read decoded image data from canvas
  log.info('image:', imageFile, canvas.width, canvas.height);

  const tensor = tf.tidy(() => { // create tensor from image data
    const data = tf.tensor(Array.from(imageData?.data || []), [canvas.height, canvas.width, 4], 'int32'); // create rgba image tensor from flat array and flip to height x width
    const channels = tf.split(data, 4, 2); // split rgba to channels
    const rgb = tf.stack([channels[0], channels[1], channels[2]], 2); // stack channels back to rgb
    const reshape = tf.reshape(rgb, [1, canvas.height, canvas.width, 3]); // move extra dim from the end of tensor and use it as batch number instead
    return reshape;
  });
  log.info('tensor:', tensor.shape, tensor.size);

  // load models
  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);

  const optionsSSDMobileNet = new faceapi.SsdMobilenetv1Options(ssdOptions); // create options object
  const result = await faceapi // run detection
    .detectAllFaces(tensor, optionsSSDMobileNet)
    .withFaceLandmarks()
    .withFaceExpressions()
    .withFaceDescriptors()
    .withAgeAndGender();
  log.data('results:', result.length);
}

main();