mirror of https://github.com/vladmandic/human
79 lines
3.4 KiB
TypeScript
79 lines
3.4 KiB
TypeScript
import { log, join } from '../helpers';
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import * as tf from '../../dist/tfjs.esm.js';
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import * as handdetector from './handdetector';
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import * as handpipeline from './handpipeline';
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import * as anchors from './anchors';
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const MESH_ANNOTATIONS = {
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thumb: [1, 2, 3, 4],
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indexFinger: [5, 6, 7, 8],
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middleFinger: [9, 10, 11, 12],
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ringFinger: [13, 14, 15, 16],
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pinky: [17, 18, 19, 20],
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palmBase: [0],
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};
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export class HandPose {
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handPipeline: any;
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constructor(handPipeline) {
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this.handPipeline = handPipeline;
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}
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static getAnnotations() {
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return MESH_ANNOTATIONS;
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}
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async estimateHands(input, config) {
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const predictions = await this.handPipeline.estimateHands(input, config);
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if (!predictions) return [];
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const hands: Array<{ confidence: number, box: any, boxRaw: any, landmarks: any, annotations: any }> = [];
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for (const prediction of predictions) {
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const annotations = {};
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if (prediction.landmarks) {
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for (const key of Object.keys(MESH_ANNOTATIONS)) {
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annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);
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}
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}
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const box = prediction.box ? [
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Math.max(0, prediction.box.topLeft[0]),
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Math.max(0, prediction.box.topLeft[1]),
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Math.min(input.shape[2], prediction.box.bottomRight[0]) - Math.max(0, prediction.box.topLeft[0]),
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Math.min(input.shape[1], prediction.box.bottomRight[1]) - Math.max(0, prediction.box.topLeft[1]),
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] : [];
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const boxRaw = [
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(prediction.box.topLeft[0]) / input.shape[2],
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(prediction.box.topLeft[1]) / input.shape[1],
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(prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],
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(prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],
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];
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hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });
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}
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return hands;
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}
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}
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let handDetectorModel;
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let handPoseModel;
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export async function load(config): Promise<HandPose> {
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if (!handDetectorModel || !handPoseModel) {
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[handDetectorModel, handPoseModel] = await Promise.all([
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config.hand.enabled ? tf.loadGraphModel(join(config.modelBasePath, config.hand.detector.modelPath), { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,
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config.hand.landmarks ? tf.loadGraphModel(join(config.modelBasePath, config.hand.skeleton.modelPath), { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,
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]);
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if (config.hand.enabled) {
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if (!handDetectorModel || !handDetectorModel.modelUrl) log('load model failed:', config.hand.detector.modelPath);
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else if (config.debug) log('load model:', handDetectorModel.modelUrl);
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if (!handPoseModel || !handPoseModel.modelUrl) log('load model failed:', config.hand.skeleton.modelPath);
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else if (config.debug) log('load model:', handPoseModel.modelUrl);
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}
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} else {
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if (config.debug) log('cached model:', handDetectorModel.modelUrl);
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if (config.debug) log('cached model:', handPoseModel.modelUrl);
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}
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const handDetector = new handdetector.HandDetector(handDetectorModel, handDetectorModel?.inputs[0].shape[2], anchors.anchors);
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const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, handPoseModel?.inputs[0].shape[2]);
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const handPose = new HandPose(handPipeline);
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return handPose;
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}
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