mirror of https://github.com/vladmandic/human
169 lines
6.6 KiB
TypeScript
169 lines
6.6 KiB
TypeScript
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/**
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* MoveNet model implementation
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*
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* Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)
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*/
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import { log, join } from '../util/util';
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import { scale } from '../util/box';
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import * as tf from '../../dist/tfjs.esm.js';
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import type { BodyResult, Box, Point } from '../result';
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import type { GraphModel, Tensor } from '../tfjs/types';
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import type { Config } from '../config';
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import { fakeOps } from '../tfjs/backend';
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import { env } from '../util/env';
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let model: GraphModel | null;
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let inputSize = 0;
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const cachedBoxes: Array<Box> = [];
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type Keypoints = { score: number, part: string, position: Point, positionRaw: Point };
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type Body = { id: number, score: number, box: Box, boxRaw: Box, keypoints: Array<Keypoints> }
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let skipped = Number.MAX_SAFE_INTEGER;
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const keypoints: Array<Keypoints> = [];
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const bodyParts = ['nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder', 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist', 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle'];
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export async function load(config: Config): Promise<GraphModel> {
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if (env.initial) model = null;
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if (!model) {
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fakeOps(['size'], config);
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model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath || '')) as unknown as GraphModel;
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if (!model || !model['modelUrl']) log('load model failed:', config.body.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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inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;
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if (inputSize === -1) inputSize = 256;
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return model;
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}
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async function parseSinglePose(res, config, image, inputBox) {
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const kpt = res[0][0];
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keypoints.length = 0;
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let score = 0;
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for (let id = 0; id < kpt.length; id++) {
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score = kpt[id][2];
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if (score > config.body.minConfidence) {
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const positionRaw: [number, number] = [
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(inputBox[3] - inputBox[1]) * kpt[id][1] + inputBox[1],
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(inputBox[2] - inputBox[0]) * kpt[id][0] + inputBox[0],
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];
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keypoints.push({
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score: Math.round(100 * score) / 100,
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part: bodyParts[id],
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positionRaw,
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position: [ // normalized to input image size
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Math.round((image.shape[2] || 0) * positionRaw[0]),
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Math.round((image.shape[1] || 0) * positionRaw[1]),
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],
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});
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}
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}
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score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);
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const x = keypoints.map((a) => a.position[0]);
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const y = keypoints.map((a) => a.position[1]);
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const box: Box = [
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Math.min(...x),
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Math.min(...y),
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Math.max(...x) - Math.min(...x),
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Math.max(...y) - Math.min(...y),
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];
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const xRaw = keypoints.map((a) => a.positionRaw[0]);
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const yRaw = keypoints.map((a) => a.positionRaw[1]);
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const boxRaw: Box = [
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Math.min(...xRaw),
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Math.min(...yRaw),
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Math.max(...xRaw) - Math.min(...xRaw),
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Math.max(...yRaw) - Math.min(...yRaw),
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];
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const bodies: Array<Body> = [];
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bodies.push({ id: 0, score, box, boxRaw, keypoints });
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return bodies;
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}
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async function parseMultiPose(res, config, image, inputBox) {
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const bodies: Array<Body> = [];
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for (let id = 0; id < res[0].length; id++) {
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const kpt = res[0][id];
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const score = Math.round(100 * kpt[51 + 4]) / 100;
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// eslint-disable-next-line no-continue
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if (score < config.body.minConfidence) continue;
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keypoints.length = 0;
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for (let i = 0; i < 17; i++) {
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const partScore = Math.round(100 * kpt[3 * i + 2]) / 100;
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if (partScore > config.body.minConfidence) {
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const positionRaw: [number, number] = [
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(inputBox[3] - inputBox[1]) * kpt[3 * i + 1] + inputBox[1],
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(inputBox[2] - inputBox[0]) * kpt[3 * i + 0] + inputBox[0],
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];
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keypoints.push({
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part: bodyParts[i],
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score: partScore,
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positionRaw,
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position: [Math.trunc(positionRaw[0] * (image.shape[2] || 0)), Math.trunc(positionRaw[0] * (image.shape[1] || 0))],
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});
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}
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}
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const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];
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bodies.push({
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id,
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score,
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boxRaw,
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box: [
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Math.trunc(boxRaw[0] * (image.shape[2] || 0)),
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Math.trunc(boxRaw[1] * (image.shape[1] || 0)),
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Math.trunc(boxRaw[2] * (image.shape[2] || 0)),
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Math.trunc(boxRaw[3] * (image.shape[1] || 0)),
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],
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keypoints: [...keypoints],
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});
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}
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return bodies;
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}
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export async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {
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if (!model || !model?.inputs[0].shape) return [];
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return new Promise(async (resolve) => {
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const t: Record<string, Tensor> = {};
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let bodies: Array<Body> = [];
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if (!config.skipFrame) cachedBoxes.length = 0; // allowed to use cache or not
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skipped++;
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for (let i = 0; i < cachedBoxes.length; i++) { // run detection based on cached boxes
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t.crop = tf.image.cropAndResize(input, [cachedBoxes[i]], [0], [inputSize, inputSize], 'bilinear');
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t.cast = tf.cast(t.crop, 'int32');
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t.res = await model?.predict(t.cast) as Tensor;
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const res = await t.res.array();
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const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cachedBoxes[i]) : await parseMultiPose(res, config, input, cachedBoxes[i]);
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bodies = bodies.concat(newBodies);
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Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));
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}
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if ((bodies.length !== config.body.maxDetected) && (skipped > (config.body.skipFrames || 0))) { // run detection on full frame
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t.resized = tf.image.resizeBilinear(input, [inputSize, inputSize], false);
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t.cast = tf.cast(t.resized, 'int32');
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t.res = await model?.predict(t.cast) as Tensor;
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const res = await t.res.array();
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bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);
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Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));
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cachedBoxes.length = 0; // reset cache
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skipped = 0;
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}
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if (config.skipFrame) { // create box cache based on last detections
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cachedBoxes.length = 0;
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for (let i = 0; i < bodies.length; i++) {
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if (bodies[i].keypoints.length > 10) { // only update cache if we detected sufficient number of keypoints
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const kpts = bodies[i].keypoints.map((kpt) => kpt.position);
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const newBox = scale(kpts, 1.5, [input.shape[2], input.shape[1]]);
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cachedBoxes.push([...newBox.yxBox]);
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}
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}
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}
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resolve(bodies);
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});
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}
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