mirror of https://github.com/vladmandic/human
62 lines
1.6 KiB
TypeScript
62 lines
1.6 KiB
TypeScript
import { log } from '../helpers';
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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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/*
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Prototype implementation for model processing
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Must implement
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- load()
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- predict()
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Must account for:
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- image processing, tfjs profiling
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*/
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let model;
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let last = { };
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let skipped = Number.MAX_SAFE_INTEGER;
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export async function load(config) {
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if (!model) {
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model = await tf.loadGraphModel(config.prototype.modelPath);
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if (config.debug) log(`load model: ${config.prototype.modelPath.match(/\/(.*)\./)[1]}`);
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}
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return model;
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}
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export async function predict(image, config) {
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if (!model) return null;
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if ((skipped < config.prototype.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) {
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skipped++;
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return last;
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}
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if (config.videoOptimized) skipped = 0;
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else skipped = Number.MAX_SAFE_INTEGER;
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return new Promise(async (resolve) => {
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const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
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const enhance = tf.mul(resize, [255.0]);
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tf.dispose(resize);
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let resT;
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if (!config.profile) {
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if (config.prototype.enabled) resT = await model.predict(enhance);
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} else {
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const profileT = config.prototype.enabled ? await tf.profile(() => model.predict(enhance)) : {};
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resT = profileT.result.clone();
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profileT.result.dispose();
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profile.run('prototype', profileT);
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}
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enhance.dispose();
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let obj = {};
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if (resT) {
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const data = resT.dataSync();
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obj = { data };
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tf.dispose(resT);
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}
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last = obj;
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resolve(obj);
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});
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}
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