mirror of https://github.com/vladmandic/human
70 lines
2.8 KiB
TypeScript
70 lines
2.8 KiB
TypeScript
import { log } from '../log';
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import * as tf from '../../dist/tfjs.esm.js';
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import * as profile from '../profile';
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export class FaceBoxes {
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enlarge: number;
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model: any;
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config: any;
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constructor(model, config) {
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this.enlarge = 1.1;
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this.model = model;
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this.config = config;
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}
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async estimateFaces(input, config) {
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if (config) this.config = config;
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const results: Array<{ confidence: number, box: any, boxRaw: any, image: any }> = [];
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const resizeT = tf.image.resizeBilinear(input, [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);
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const castT = resizeT.toInt();
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let scores;
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let boxes;
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if (!config.profile) {
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const [scoresT, boxesT, numT] = await this.model.executeAsync(castT);
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scores = scoresT.dataSync();
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const squeezeT = boxesT.squeeze();
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boxes = squeezeT.arraySync();
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scoresT.dispose();
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boxesT.dispose();
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squeezeT.dispose();
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numT.dispose();
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} else {
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const profileData = await tf.profile(() => this.model.executeAsync(castT));
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scores = profileData.result[0].dataSync();
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const squeezeT = profileData.result[1].squeeze();
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boxes = squeezeT.arraySync();
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profileData.result.forEach((t) => t.dispose());
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profile.run('faceboxes', profileData);
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}
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castT.dispose();
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resizeT.dispose();
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for (const i in boxes) {
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if (scores[i] && scores[i] > this.config.face.detector.minConfidence) {
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const crop = [boxes[i][0] / this.enlarge, boxes[i][1] / this.enlarge, boxes[i][2] * this.enlarge, boxes[i][3] * this.enlarge];
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const boxRaw = [crop[1], crop[0], (crop[3]) - (crop[1]), (crop[2]) - (crop[0])];
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const box = [
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parseInt((boxRaw[0] * input.shape[2]).toString()),
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parseInt((boxRaw[1] * input.shape[1]).toString()),
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parseInt((boxRaw[2] * input.shape[2]).toString()),
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parseInt((boxRaw[3] * input.shape[1]).toString())];
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const resized = tf.image.cropAndResize(input, [crop], [0], [this.config.face.detector.inputSize, this.config.face.detector.inputSize]);
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const image = resized.div([255]);
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resized.dispose();
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results.push({ confidence: scores[i], box, boxRaw: this.config.face.mesh.returnRawData ? boxRaw : null, image });
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// add mesh, meshRaw, annotations,
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}
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}
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return results;
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}
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}
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export async function load(config) {
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const model = await tf.loadGraphModel(config.face.detector.modelPath);
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if (config.debug) log(`load model: ${config.face.detector.modelPath.match(/\/(.*)\./)[1]}`);
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const faceboxes = new FaceBoxes(model, config);
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if (config.face.mesh.enabled && config.debug) log(`load model: ${config.face.mesh.modelPath.match(/\/(.*)\./)[1]}`);
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if (config.face.iris.enabled && config.debug) log(`load model: ${config.face.iris.modelPath.match(/\/(.*)\./)[1]}`);
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return faceboxes;
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}
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