mirror of https://github.com/vladmandic/human
type definitions
Vladimir Mandic
parent
f796d39e60
commit
b0ed511405
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@ -1,6 +1,6 @@
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# @vladmandic/human
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Version: **1.1.8**
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Version: **1.1.9**
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Description: **Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition**
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Author: **Vladimir Mandic <mandic00@live.com>**
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@ -11,6 +11,10 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
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### **HEAD -> main** 2021/03/17 mandic00@live.com
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### **1.1.9** 2021/03/17 mandic00@live.com
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- fix box clamping and raw output
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- hierarchical readme notes
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### **1.1.8** 2021/03/17 mandic00@live.com
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@ -6,7 +6,7 @@
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"main": "dist/human.node.js",
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"module": "dist/human.esm.js",
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"browser": "dist/human.esm.js",
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"types": "types/src/human.d.ts",
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"types": "types/human.d.ts",
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"author": "Vladimir Mandic <mandic00@live.com>",
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"bugs": {
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"url": "https://github.com/vladmandic/human/issues"
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@ -1,7 +1,121 @@
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/* eslint-disable indent */
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/* eslint-disable no-multi-spaces */
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export default {
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/**
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* Configuration interface definition for **Human** library
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*
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* Contains all configurable parameters
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*/
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export interface Config {
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backend: String,
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wasmPath: String,
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debug: Boolean,
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async: Boolean,
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profile: Boolean,
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deallocate: Boolean,
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scoped: Boolean,
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videoOptimized: Boolean,
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warmup: String,
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filter: {
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enabled: Boolean,
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width: Number,
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height: Number,
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return: Boolean,
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brightness: Number,
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contrast: Number,
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sharpness: Number,
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blur: Number
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saturation: Number,
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hue: Number,
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negative: Boolean,
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sepia: Boolean,
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vintage: Boolean,
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kodachrome: Boolean,
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technicolor: Boolean,
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polaroid: Boolean,
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pixelate: Number,
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},
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gesture: {
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enabled: Boolean,
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},
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face: {
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enabled: Boolean,
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detector: {
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modelPath: String,
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rotation: Boolean,
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maxFaces: Number,
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skipFrames: Number,
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skipInitial: Boolean,
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minConfidence: Number,
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iouThreshold: Number,
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scoreThreshold: Number,
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return: Boolean,
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},
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mesh: {
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enabled: Boolean,
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modelPath: String,
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},
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iris: {
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enabled: Boolean,
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modelPath: String,
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},
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age: {
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enabled: Boolean,
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modelPath: String,
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skipFrames: Number,
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},
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gender: {
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enabled: Boolean,
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minConfidence: Number,
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modelPath: String,
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skipFrames: Number,
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},
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emotion: {
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enabled: Boolean,
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minConfidence: Number,
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skipFrames: Number,
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modelPath: String,
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},
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embedding: {
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enabled: Boolean,
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modelPath: String,
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},
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},
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body: {
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enabled: Boolean,
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modelPath: String,
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maxDetections: Number,
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scoreThreshold: Number,
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nmsRadius: Number,
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},
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hand: {
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enabled: Boolean,
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rotation: Boolean,
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skipFrames: Number,
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skipInitial: Boolean,
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minConfidence: Number,
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iouThreshold: Number,
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scoreThreshold: Number,
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maxHands: Number,
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landmarks: Boolean,
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detector: {
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modelPath: String,
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},
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skeleton: {
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modelPath: String,
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},
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},
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object: {
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enabled: Boolean,
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modelPath: String,
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minConfidence: Number,
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iouThreshold: Number,
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maxResults: Number,
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skipFrames: Number,
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},
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}
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const config: Config = {
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backend: 'webgl', // select tfjs backend to use
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// can be 'webgl', 'wasm', 'cpu', or 'humangl' which is a custom version of webgl
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// leave as empty string to continue using default backend
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@ -177,3 +291,4 @@ export default {
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skipFrames: 13, // how many frames to go without re-running the detector
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},
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};
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export { config as defaults };
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@ -1,5 +1,5 @@
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import config from '../config';
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import { TRI468 as triangulation } from './blazeface/coords';
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import { defaults } from '../config';
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import { TRI468 as triangulation } from '../blazeface/coords';
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export const drawOptions = {
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color: <string>'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel
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@ -86,7 +86,7 @@ function curves(ctx, points: number[] = []) {
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}
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}
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export async function gesture(inCanvas, result) {
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export async function gesture(inCanvas: HTMLCanvasElement, result: Array<any>) {
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if (!result || !inCanvas) return;
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if (!(inCanvas instanceof HTMLCanvasElement)) return;
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const ctx = inCanvas.getContext('2d');
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@ -112,7 +112,7 @@ export async function gesture(inCanvas, result) {
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}
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}
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export async function face(inCanvas, result) {
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export async function face(inCanvas: HTMLCanvasElement, result: Array<any>) {
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if (!result || !inCanvas) return;
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if (!(inCanvas instanceof HTMLCanvasElement)) return;
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const ctx = inCanvas.getContext('2d');
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@ -196,7 +196,7 @@ export async function face(inCanvas, result) {
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}
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const lastDrawnPose:any[] = [];
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export async function body(inCanvas, result) {
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export async function body(inCanvas: HTMLCanvasElement, result: Array<any>) {
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if (!result || !inCanvas) return;
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if (!(inCanvas instanceof HTMLCanvasElement)) return;
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const ctx = inCanvas.getContext('2d');
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@ -232,70 +232,70 @@ export async function body(inCanvas, result) {
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// torso
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points.length = 0;
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part = result[i].keypoints.find((a) => a.part === 'leftShoulder');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'rightShoulder');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'rightHip');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'leftHip');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'leftShoulder');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (points.length === 5) lines(ctx, points); // only draw if we have complete torso
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// leg left
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points.length = 0;
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part = result[i].keypoints.find((a) => a.part === 'leftHip');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'leftKnee');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'leftAnkle');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'leftHeel');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'leftFoot');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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curves(ctx, points);
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// leg right
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points.length = 0;
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part = result[i].keypoints.find((a) => a.part === 'rightHip');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'rightKnee');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'rightAnkle');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'rightHeel');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'rightFoot');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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curves(ctx, points);
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// arm left
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points.length = 0;
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part = result[i].keypoints.find((a) => a.part === 'leftShoulder');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'leftElbow');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'leftWrist');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'leftPalm');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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curves(ctx, points);
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// arm right
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points.length = 0;
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part = result[i].keypoints.find((a) => a.part === 'rightShoulder');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'rightElbow');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'rightWrist');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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part = result[i].keypoints.find((a) => a.part === 'rightPalm');
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if (part && part.score > config.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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if (part && part.score > defaults.body.scoreThreshold) points.push([part.position.x, part.position.y]);
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curves(ctx, points);
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// draw all
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}
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}
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}
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export async function hand(inCanvas, result) {
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export async function hand(inCanvas: HTMLCanvasElement, result: Array<any>) {
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if (!result || !inCanvas) return;
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if (!(inCanvas instanceof HTMLCanvasElement)) return;
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const ctx = inCanvas.getContext('2d');
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@ -348,7 +348,7 @@ export async function hand(inCanvas, result) {
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}
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}
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export async function object(inCanvas, result) {
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export async function object(inCanvas: HTMLCanvasElement, result: Array<any>) {
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if (!result || !inCanvas) return;
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if (!(inCanvas instanceof HTMLCanvasElement)) return;
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const ctx = inCanvas.getContext('2d');
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@ -375,14 +375,14 @@ export async function object(inCanvas, result) {
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}
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}
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export async function canvas(inCanvas, outCanvas) {
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export async function canvas(inCanvas: HTMLCanvasElement, outCanvas: HTMLCanvasElement) {
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if (!inCanvas || !outCanvas) return;
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if (!(inCanvas instanceof HTMLCanvasElement) || !(outCanvas instanceof HTMLCanvasElement)) return;
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const outCtx = inCanvas.getContext('2d');
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outCtx?.drawImage(inCanvas, 0, 0);
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}
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export async function all(inCanvas, result) {
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export async function all(inCanvas: HTMLCanvasElement, result:any) {
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if (!result || !inCanvas) return;
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if (!(inCanvas instanceof HTMLCanvasElement)) return;
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face(inCanvas, result.face);
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102
src/human.ts
102
src/human.ts
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@ -12,12 +12,25 @@ import * as handpose from './handpose/handpose';
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import * as blazepose from './blazepose/blazepose';
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import * as nanodet from './nanodet/nanodet';
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import * as gesture from './gesture/gesture';
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import * as image from './image';
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import * as image from './image/image';
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import * as draw from './draw/draw';
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import * as profile from './profile';
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import * as config from '../config';
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import { Config, defaults } from './config';
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import { Result } from './result';
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import * as sample from './sample';
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import * as app from '../package.json';
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import * as draw from './draw';
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type Tensor = {};
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type Model = {};
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export type { Config } from './config';
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export type { Result } from './result';
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/** Defines all possible input types for **Human** detection */
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export type Input = Tensor | ImageData | ImageBitmap | HTMLVideoElement | HTMLCanvasElement | OffscreenCanvas;
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/** Error message */
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export type Error = { error: String };
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export type TensorFlow = typeof tf;
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// helper function: gets elapsed time on both browser and nodejs
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const now = () => {
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@ -25,50 +38,6 @@ const now = () => {
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return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());
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};
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type Tensor = {};
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type Model = {};
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export type Result = {
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face: Array<{
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confidence: Number,
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boxConfidence: Number,
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faceConfidence: Number,
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box: [Number, Number, Number, Number],
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mesh: Array<[Number, Number, Number]>
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meshRaw: Array<[Number, Number, Number]>
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boxRaw: [Number, Number, Number, Number],
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annotations: Array<{ part: String, points: Array<[Number, Number, Number]>[] }>,
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age: Number,
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gender: String,
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genderConfidence: Number,
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emotion: Array<{ score: Number, emotion: String }>,
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embedding: Array<Number>,
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iris: Number,
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angle: { roll: Number, yaw: Number, pitch: Number },
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}>,
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body: Array<{
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id: Number,
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part: String,
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position: { x: Number, y: Number, z: Number },
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score: Number,
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presence: Number }>,
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hand: Array<{
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confidence: Number,
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box: [Number, Number, Number, Number],
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boxRaw: [Number, Number, Number, Number],
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landmarks: Array<[Number, Number, Number]>,
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annotations: Array<{ part: String, points: Array<[Number, Number, Number]>[] }>,
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}>,
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gesture: Array<{
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part: String,
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gesture: String,
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}>,
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object: Array<{ score: Number, strideSize: Number, class: Number, label: String, center: Number[], centerRaw: Number[], box: Number[], boxRaw: Number[] }>,
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performance: { any },
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canvas: OffscreenCanvas | HTMLCanvasElement,
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}
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export type { default as Config } from '../config';
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// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides
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function mergeDeep(...objects) {
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const isObject = (obj) => obj && typeof obj === 'object';
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@ -83,15 +52,31 @@ function mergeDeep(...objects) {
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return prev;
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}, {});
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}
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/**
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* Main Class for `Human` library
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*
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* All methods and properties are available only as members of Human class
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*
|
||||
* Configuration object definition: @link Config
|
||||
* Results object definition: @link Result
|
||||
* Possible inputs: @link Input
|
||||
*/
|
||||
export class Human {
|
||||
version: String;
|
||||
config: typeof config.default;
|
||||
config: Config;
|
||||
state: String;
|
||||
image: { tensor: Tensor, canvas: OffscreenCanvas | HTMLCanvasElement };
|
||||
// classes
|
||||
tf: typeof tf;
|
||||
draw: { drawOptions?: typeof draw.drawOptions, gesture: Function, face: Function, body: Function, hand: Function, canvas: Function, all: Function };
|
||||
tf: TensorFlow;
|
||||
draw: {
|
||||
drawOptions?: typeof draw.drawOptions,
|
||||
gesture: typeof draw.gesture,
|
||||
face: typeof draw.face,
|
||||
body: typeof draw.body,
|
||||
hand: typeof draw.hand,
|
||||
canvas: typeof draw.canvas,
|
||||
all: typeof draw.all,
|
||||
};
|
||||
// models
|
||||
models: {
|
||||
face: facemesh.MediaPipeFaceMesh | null,
|
||||
|
|
@ -123,12 +108,12 @@ export class Human {
|
|||
#firstRun: Boolean;
|
||||
// definition end
|
||||
|
||||
constructor(userConfig = {}) {
|
||||
constructor(userConfig: Config | Object = {}) {
|
||||
this.tf = tf;
|
||||
this.draw = draw;
|
||||
this.#package = app;
|
||||
this.version = app.version;
|
||||
this.config = mergeDeep(config.default, userConfig);
|
||||
this.config = mergeDeep(defaults, userConfig);
|
||||
this.state = 'idle';
|
||||
this.#numTensors = 0;
|
||||
this.#analyzeMemoryLeaks = false;
|
||||
|
|
@ -150,7 +135,7 @@ export class Human {
|
|||
};
|
||||
// export access to image processing
|
||||
// @ts-ignore
|
||||
this.image = (input: Tensor | ImageData | HTMLCanvasElement | HTMLVideoElement | OffscreenCanvas) => image.process(input, this.config);
|
||||
this.image = (input: Input) => image.process(input, this.config);
|
||||
// export raw access to underlying models
|
||||
this.classes = {
|
||||
facemesh,
|
||||
|
|
@ -211,7 +196,7 @@ export class Human {
|
|||
}
|
||||
|
||||
// preload models, not explicitly required as it's done automatically on first use
|
||||
async load(userConfig: Object = {}) {
|
||||
async load(userConfig: Config | Object = {}) {
|
||||
this.state = 'load';
|
||||
const timeStamp = now();
|
||||
if (userConfig) this.config = mergeDeep(this.config, userConfig);
|
||||
|
|
@ -480,7 +465,7 @@ export class Human {
|
|||
}
|
||||
|
||||
// main detect function
|
||||
async detect(input: Tensor | ImageData | HTMLCanvasElement | HTMLVideoElement | OffscreenCanvas, userConfig: Object = {}): Promise<Result | { error: String }> {
|
||||
async detect(input: Input, userConfig: Config | Object = {}): Promise<Result | Error> {
|
||||
// detection happens inside a promise
|
||||
return new Promise(async (resolve) => {
|
||||
this.state = 'config';
|
||||
|
|
@ -675,7 +660,7 @@ export class Human {
|
|||
return res;
|
||||
}
|
||||
|
||||
async warmup(userConfig: Object = {}): Promise<Result | { error }> {
|
||||
async warmup(userConfig: Config | Object = {}): Promise<Result | { error }> {
|
||||
const t0 = now();
|
||||
if (userConfig) this.config = mergeDeep(this.config, userConfig);
|
||||
const save = this.config.videoOptimized;
|
||||
|
|
@ -691,4 +676,7 @@ export class Human {
|
|||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Class Human is also available as default export
|
||||
*/
|
||||
export { Human as default };
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
// @ts-nocheck
|
||||
|
||||
import { log } from './log';
|
||||
import * as tf from '../dist/tfjs.esm.js';
|
||||
import { log } from '../log';
|
||||
import * as tf from '../../dist/tfjs.esm.js';
|
||||
import * as fxImage from './imagefx';
|
||||
|
||||
const maxSize = 2048;
|
||||
|
|
@ -0,0 +1,112 @@
|
|||
/**
|
||||
* Result interface definition for **Human** library
|
||||
*
|
||||
* Contains all possible detection results
|
||||
*/
|
||||
export interface Result {
|
||||
/** Face results
|
||||
* Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
|
||||
* Some values may be null if specific model is not enabled
|
||||
*
|
||||
* Array of individual results with one object per detected face
|
||||
* Each result has:
|
||||
* - overal detection confidence value
|
||||
* - box detection confidence value
|
||||
* - mesh detection confidence value
|
||||
* - box as array of [x, y, width, height], normalized to image resolution
|
||||
* - boxRaw as array of [x, y, width, height], normalized to range 0..1
|
||||
* - mesh as array of [x, y, z] points of face mesh, normalized to image resolution
|
||||
* - meshRaw as array of [x, y, z] points of face mesh, normalized to range 0..1
|
||||
* - annotations as array of annotated face mesh points
|
||||
* - age as value
|
||||
* - gender as value
|
||||
* - genderConfidence as value
|
||||
* - emotion as array of possible emotions with their individual scores
|
||||
* - iris as distance value
|
||||
* - angle as object with values for roll, yaw and pitch angles
|
||||
*/
|
||||
face: Array<{
|
||||
confidence: Number,
|
||||
boxConfidence: Number,
|
||||
faceConfidence: Number,
|
||||
box: [Number, Number, Number, Number],
|
||||
boxRaw: [Number, Number, Number, Number],
|
||||
mesh: Array<[Number, Number, Number]>
|
||||
meshRaw: Array<[Number, Number, Number]>
|
||||
annotations: Array<{ part: String, points: Array<[Number, Number, Number]>[] }>,
|
||||
age: Number,
|
||||
gender: String,
|
||||
genderConfidence: Number,
|
||||
emotion: Array<{ score: Number, emotion: String }>,
|
||||
embedding: Array<Number>,
|
||||
iris: Number,
|
||||
angle: { roll: Number, yaw: Number, pitch: Number },
|
||||
}>,
|
||||
/** Body results
|
||||
*
|
||||
* Array of individual results with one object per detected body
|
||||
* Each results has:
|
||||
* - body id number
|
||||
* - body part name
|
||||
* - part position with x,y,z coordinates
|
||||
* - body part score value
|
||||
* - body part presence value
|
||||
*/
|
||||
body: Array<{
|
||||
id: Number,
|
||||
part: String,
|
||||
position: { x: Number, y: Number, z: Number },
|
||||
score: Number,
|
||||
presence: Number }>,
|
||||
/** Hand results
|
||||
*
|
||||
* Array of individual results with one object per detected hand
|
||||
* Each result has:
|
||||
* - confidence as value
|
||||
* - box as array of [x, y, width, height], normalized to image resolution
|
||||
* - boxRaw as array of [x, y, width, height], normalized to range 0..1
|
||||
* - landmarks as array of [x, y, z] points of hand, normalized to image resolution
|
||||
* - annotations as array of annotated face landmark points
|
||||
*/
|
||||
hand: Array<{
|
||||
confidence: Number,
|
||||
box: [Number, Number, Number, Number],
|
||||
boxRaw: [Number, Number, Number, Number],
|
||||
landmarks: Array<[Number, Number, Number]>,
|
||||
annotations: Array<{ part: String, points: Array<[Number, Number, Number]>[] }>,
|
||||
}>,
|
||||
/** Gesture results
|
||||
*
|
||||
* Array of individual results with one object per detected gesture
|
||||
* Each result has:
|
||||
* - part where gesture was detected
|
||||
* - gesture detected
|
||||
*/
|
||||
gesture: Array<{
|
||||
part: String,
|
||||
gesture: String,
|
||||
}>,
|
||||
/** Object results
|
||||
*
|
||||
* Array of individual results with one object per detected gesture
|
||||
* Each result has:
|
||||
* - score as value
|
||||
* - label as detected class name
|
||||
* - center as array of [x, y], normalized to image resolution
|
||||
* - centerRaw as array of [x, y], normalized to range 0..1
|
||||
* - box as array of [x, y, width, height], normalized to image resolution
|
||||
* - boxRaw as array of [x, y, width, height], normalized to range 0..1
|
||||
*/
|
||||
object: Array<{
|
||||
score: Number,
|
||||
strideSize: Number,
|
||||
class: Number,
|
||||
label: String,
|
||||
center: Number[],
|
||||
centerRaw: Number[],
|
||||
box: Number[],
|
||||
boxRaw: Number[],
|
||||
}>,
|
||||
performance: { any },
|
||||
canvas: OffscreenCanvas | HTMLCanvasElement,
|
||||
}
|
||||
2
wiki
2
wiki
|
|
@ -1 +1 @@
|
|||
Subproject commit 91af84e9543762c4f31be41dda15fb2a5549d8a6
|
||||
Subproject commit 936e36ce0fd331030b48db735836381f2f22134a
|
||||
Loading…
Reference in New Issue