From 35c974c673ff124d2a400292524d80e236940b0b Mon Sep 17 00:00:00 2001
From: Vladimir Mandic <mandic00@live.com>
Date: Mon, 30 May 2022 08:58:54 -0400
Subject: [PATCH] add faceboxes prototype

---
 src/face/faceboxes.ts | 65 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 65 insertions(+)
 create mode 100644 src/face/faceboxes.ts

diff --git a/src/face/faceboxes.ts b/src/face/faceboxes.ts
new file mode 100644
index 00000000..3690ffc0
--- /dev/null
+++ b/src/face/faceboxes.ts
@@ -0,0 +1,65 @@
+// https://github.com/TropComplique/FaceBoxes-tensorflow
+
+import { log } from '../util/util';
+import * as tf from '../../dist/tfjs.esm.js';
+import type { GraphModel, Tensor } from '../tfjs/types';
+import type { Config } from '../config';
+
+type Box = [number, number, number, number];
+
+export class FaceBoxes {
+  enlarge: number;
+  model: GraphModel;
+  config: Config;
+  inputSize: 0;
+
+  constructor(model, config: Config) {
+    this.enlarge = 1.1;
+    this.model = model;
+    this.config = config;
+    this.inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;
+  }
+
+  async estimateFaces(input, config) {
+    if (config) this.config = config;
+    const results: Array<{ confidence: number, box: Box, boxRaw: Box, image: Tensor }> = [];
+    const resizeT = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);
+    const castT = resizeT.toInt();
+    const [scoresT, boxesT, numT] = await this.model.executeAsync(castT) as Tensor[];
+    const scores = scoresT.dataSync();
+    const squeezeT = tf.squeeze(boxesT);
+    const boxes = squeezeT.arraySync();
+    scoresT.dispose();
+    boxesT.dispose();
+    squeezeT.dispose();
+    numT.dispose();
+    castT.dispose();
+    resizeT.dispose();
+    for (const i in boxes) {
+      if (scores[i] && scores[i] > (this.config.face?.detector?.minConfidence || 0.1)) {
+        const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];
+        const boxRaw: Box = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];
+        const box: Box = [
+          parseInt((boxRaw[0] * input.shape[2]).toString()),
+          parseInt((boxRaw[1] * input.shape[1]).toString()),
+          parseInt((boxRaw[2] * input.shape[2]).toString()),
+          parseInt((boxRaw[3] * input.shape[1]).toString())];
+        const resized = tf.image.cropAndResize(input, [crop], [0], [this.inputSize, this.inputSize]);
+        const image = tf.div(resized, [255]);
+        resized.dispose();
+        results.push({ confidence: scores[i], box, boxRaw, image });
+        // add mesh, meshRaw, annotations,
+      }
+    }
+    return results;
+  }
+}
+
+export async function load(config) {
+  const model = await tf.loadGraphModel(config.face.detector.modelPath);
+  if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\/(.*)\./)[1]}`);
+  const faceboxes = new FaceBoxes(model, config);
+  if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\/(.*)\./)[1]}`);
+  if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\/(.*)\./)[1]}`);
+  return faceboxes;
+}