mirror of https://github.com/vladmandic/human
76 lines
3.5 KiB
TypeScript
76 lines
3.5 KiB
TypeScript
import { log } from '../helpers';
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import * as tf from '../../dist/tfjs.esm.js';
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import * as blazeface from './blazeface';
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import * as facepipeline from './facepipeline';
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import * as coords from './coords';
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export class MediaPipeFaceMesh {
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facePipeline: any;
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config: any;
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constructor(blazeFace, blazeMeshModel, irisModel, config) {
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this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel);
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this.config = config;
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}
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async estimateFaces(input, config): Promise<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }[]> {
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const predictions = await this.facePipeline.predict(input, config);
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const results: Array<{ confidence, boxConfidence, faceConfidence, box, mesh, boxRaw, meshRaw, annotations, image }> = [];
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for (const prediction of (predictions || [])) {
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if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing
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const mesh = prediction.coords ? prediction.coords.arraySync() : [];
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const meshRaw = mesh.map((pt) => [
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pt[0] / input.shape[2],
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pt[1] / input.shape[1],
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pt[2] / this.facePipeline.meshSize,
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]);
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const annotations = {};
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if (mesh && mesh.length > 0) {
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for (const key of Object.keys(coords.MESH_ANNOTATIONS)) annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);
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}
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const box = prediction.box ? [
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Math.max(0, prediction.box.startPoint[0]),
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Math.max(0, prediction.box.startPoint[1]),
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Math.min(input.shape[1], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0]),
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Math.min(input.shape[2], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]),
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] : 0;
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const boxRaw = prediction.box ? [
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prediction.box.startPoint[0] / input.shape[2],
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prediction.box.startPoint[1] / input.shape[1],
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(prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],
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(prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],
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] : [];
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results.push({
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confidence: prediction.faceConfidence || prediction.boxConfidence || 0,
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boxConfidence: prediction.boxConfidence,
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faceConfidence: prediction.faceConfidence,
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box,
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boxRaw,
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mesh,
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meshRaw,
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annotations,
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image: prediction.image ? prediction.image.clone() : null,
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});
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if (prediction.coords) prediction.coords.dispose();
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if (prediction.image) prediction.image.dispose();
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}
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return results;
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}
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}
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let faceModels = [null, null, null];
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export async function load(config) {
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// @ts-ignore
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faceModels = await Promise.all([
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(!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,
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(!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,
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(!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,
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]);
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const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], 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 faceMesh;
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}
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export const triangulation = coords.TRI468;
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