mirror of https://github.com/vladmandic/human
93 lines
4.3 KiB
TypeScript
93 lines
4.3 KiB
TypeScript
/**
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* HandPose module entry point
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*/
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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 { Hand } from '../result';
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import { Tensor, GraphModel } from '../tfjs/types';
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import { Config } from '../config';
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const meshAnnotations = {
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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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let handDetectorModel: GraphModel | null;
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let handPoseModel: GraphModel | null;
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let handPipeline: handpipeline.HandPipeline;
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export async function predict(input: Tensor, config: Config): Promise<Hand[]> {
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const predictions = await handPipeline.estimateHands(input, config);
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if (!predictions) return [];
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const hands: Array<Hand> = [];
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for (let i = 0; i < predictions.length; i++) {
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const annotations = {};
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if (predictions[i].landmarks) {
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for (const key of Object.keys(meshAnnotations)) {
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// @ts-ignore landmarks are not undefined
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annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);
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}
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}
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const keypoints = predictions[i].landmarks as unknown as Array<[number, number, number]>;
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let box: [number, number, number, number] = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work
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let boxRaw: [number, number, number, number] = [0, 0, 0, 0];
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if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks
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for (const pt of keypoints) {
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if (pt[0] < box[0]) box[0] = pt[0];
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if (pt[1] < box[1]) box[1] = pt[1];
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if (pt[0] > box[2]) box[2] = pt[0];
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if (pt[1] > box[3]) box[3] = pt[1];
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}
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box[2] -= box[0];
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box[3] -= box[1];
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boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];
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} else { // otherwise use box from prediction
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box = predictions[i].box ? [
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Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
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Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
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Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
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Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),
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] : [0, 0, 0, 0];
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boxRaw = [
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(predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
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(predictions[i].box.topLeft[1]) / (input.shape[1] || 0),
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(predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
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(predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),
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];
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}
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hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box, boxRaw, keypoints, annotations });
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}
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return hands;
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}
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export async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {
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if (!handDetectorModel || !handPoseModel) {
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// @ts-ignore type mismatch on GraphModel
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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);
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handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);
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return [handDetectorModel, handPoseModel];
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}
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