mirror of https://github.com/vladmandic/human
69 lines
2.7 KiB
TypeScript
69 lines
2.7 KiB
TypeScript
/**
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* Emotion Module
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*/
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import { log, join } from '../helpers';
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import { Config } from '../config';
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import { Tensor, GraphModel } from '../tfjs/types';
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import * as tf from '../../dist/tfjs.esm.js';
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const annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];
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let model;
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// let last: Array<{ score: number, emotion: string }> = [];
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const last: Array<Array<{ score: number, emotion: string }>> = [];
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let lastCount = 0;
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let skipped = Number.MAX_SAFE_INTEGER;
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// tuning values
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const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale
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export async function load(config: Config): Promise<GraphModel> {
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if (!model) {
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model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion?.modelPath || ''));
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if (!model || !model.modelUrl) log('load model failed:', config.face.emotion?.modelPath || '');
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else if (config.debug) log('load model:', model.modelUrl);
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} else if (config.debug) log('cached model:', model.modelUrl);
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return model;
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}
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export async function predict(image: Tensor, config: Config, idx, count) {
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if (!model) return null;
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if ((skipped < (config.face.emotion?.skipFrames || 0)) && config.skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {
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skipped++;
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return last[idx];
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}
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skipped = 0;
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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 [red, green, blue] = tf.split(resize, 3, 3);
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tf.dispose(resize);
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// weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html
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const redNorm = tf.mul(red, rgb[0]);
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const greenNorm = tf.mul(green, rgb[1]);
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const blueNorm = tf.mul(blue, rgb[2]);
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tf.dispose(red);
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tf.dispose(green);
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tf.dispose(blue);
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const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);
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tf.dispose(redNorm);
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tf.dispose(greenNorm);
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tf.dispose(blueNorm);
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const normalize = tf.tidy(() => tf.mul(tf.sub(grayscale, 0.5), 2));
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tf.dispose(grayscale);
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const obj: Array<{ score: number, emotion: string }> = [];
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if (config.face.emotion?.enabled) {
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const emotionT = await model.predict(normalize); // result is already in range 0..1, no need for additional activation
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const data = await emotionT.data();
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tf.dispose(emotionT);
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for (let i = 0; i < data.length; i++) {
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if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
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}
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obj.sort((a, b) => b.score - a.score);
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}
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tf.dispose(normalize);
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last[idx] = obj;
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lastCount = count;
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resolve(obj);
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});
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}
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