From 768c8c062cbf7acfa314d0abb58ae81f3dad414c Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Thu, 21 Apr 2022 09:38:36 -0400 Subject: [PATCH] update tfjs --- CHANGELOG.md | 5 +- api-extractor.json | 2 +- build.js | 3 +- demo/faceid/index.js | 325 +- demo/faceid/index.js.map | 2 +- demo/typescript/index.js | 97 +- demo/typescript/index.js.map | 4 +- demo/typescript/index.ts | 3 +- dist/human.esm-nobundle.js | 13127 ++++- dist/human.esm-nobundle.js.map | 4 +- dist/human.esm.js | 90657 ++++++++++++++++++++---------- dist/human.esm.js.map | 4 +- dist/human.js | 3159 +- dist/human.node-gpu.js | 13205 ++++- dist/human.node-wasm.js | 13206 ++++- dist/human.node.js | 13205 ++++- dist/tfjs.esm.js | 68661 +++++++++++++++++++++- dist/tfjs.version.js | 35 +- package.json | 32 +- test/build.log | 379 +- typedoc/classes/GraphModel.html | 20 +- typedoc/classes/Tensor.html | 44 +- typedoc/enums/Rank.html | 2 +- typedoc/index.html | 2 +- 24 files changed, 183795 insertions(+), 32388 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9cbb1878..fbe66ece 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,9 +11,8 @@ ### **HEAD -> main** 2022/04/18 mandic00@live.com - -### **origin/main** 2022/04/15 mandic00@live.com - +- fix coloring function +- enable precompile as part of warmup - prepare release beta - change default face crop - face attention model is available in human-models diff --git a/api-extractor.json b/api-extractor.json index 7d96a995..007617e4 100644 --- a/api-extractor.json +++ b/api-extractor.json @@ -1,7 +1,7 @@ { "$schema": "https://developer.microsoft.com/json-schemas/api-extractor/v7/api-extractor.schema.json", "mainEntryPointFilePath": "types/lib/src/human.d.ts", - "bundledPackages": ["@types/offscreencanvas", "@tensorflow/tfjs-core", "@tensorflow/tfjs-converter"], + "bundledPackages": ["@types/offscreencanvas", "@tensorflow/tfjs-core", "@tensorflow/tfjs-converter", "@tensorflow/tfjs-data"], "compiler": { "skipLibCheck": false }, diff --git a/build.js b/build.js index 16bff6d0..c25ac27e 100644 --- a/build.js +++ b/build.js @@ -9,6 +9,7 @@ function copy(src, dst) { fs.writeFileSync(dst, buffer); } +// eslint-disable-next-line @typescript-eslint/no-unused-vars const apiExtractorIgnoreList = [ 'ae-missing-release-tag', 'tsdoc-param-tag-missing-hyphen', @@ -18,7 +19,7 @@ const apiExtractorIgnoreList = [ 'ae-unresolved-link', 'ae-forgotten-export', 'tsdoc-malformed-inline-tag', - 'tsdoc-unnecessary-backslash' + 'tsdoc-unnecessary-backslash', ]; async function main() { diff --git a/demo/faceid/index.js b/demo/faceid/index.js index c29a04e1..9184b9bb 100644 --- a/demo/faceid/index.js +++ b/demo/faceid/index.js @@ -4,8 +4,329 @@ author: ' */ -import{Human as H}from"../../dist/human.esm.js";var d,R="human",m="person",g=(...t)=>console.log("indexdb",...t);async function b(){return d?!0:new Promise(t=>{let i=indexedDB.open(R,1);i.onerror=s=>g("error:",s),i.onupgradeneeded=s=>{g("create:",s.target),d=s.target.result,d.createObjectStore(m,{keyPath:"id",autoIncrement:!0})},i.onsuccess=s=>{d=s.target.result,g("open:",d),t(!0)}})}async function C(){let t=[];return d||await b(),new Promise(i=>{let s=d.transaction([m],"readwrite").objectStore(m).openCursor(null,"next");s.onerror=o=>g("load error:",o),s.onsuccess=o=>{o.target.result?(t.push(o.target.result.value),o.target.result.continue()):i(t)}})}async function k(){return d||await b(),new Promise(t=>{let i=d.transaction([m],"readwrite").objectStore(m).count();i.onerror=s=>g("count error:",s),i.onsuccess=()=>t(i.result)})}async function x(t){d||await b();let i={name:t.name,descriptor:t.descriptor,image:t.image};d.transaction([m],"readwrite").objectStore(m).put(i),g("save:",i)}async function D(t){d||await b(),d.transaction([m],"readwrite").objectStore(m).delete(t.id),g("delete:",t)}var v={modelBasePath:"../../models",filter:{equalization:!0},face:{enabled:!0,detector:{rotation:!0,return:!0,cropFactor:1.6,mask:!1},description:{enabled:!0},mobilefacenet:{enabled:!1,modelPath:"https://vladmandic.github.io/human-models/models/mobilefacenet.json"},iris:{enabled:!0},emotion:{enabled:!1},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},gesture:{enabled:!0}},I={order:2,multiplier:25,min:.2,max:.8},c={minConfidence:.6,minSize:224,maxTime:1e4,blinkMin:10,blinkMax:800,threshold:.5,mask:v.face.detector.mask,rotation:v.face.detector.rotation,cropFactor:v.face.detector.cropFactor,...I},n={faceCount:!1,faceConfidence:!1,facingCenter:!1,lookingCenter:!1,blinkDetected:!1,faceSize:!1,antispoofCheck:!1,livenessCheck:!1,elapsedMs:0},M=()=>n.faceCount&&n.faceSize&&n.blinkDetected&&n.facingCenter&&n.lookingCenter&&n.faceConfidence&&n.antispoofCheck&&n.livenessCheck,r={face:null,record:null},l={start:0,end:0,time:0},a=new H(v);a.env.perfadd=!1;a.draw.options.font='small-caps 18px "Lato"';a.draw.options.lineHeight=20;var e={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("fps"),match:document.getElementById("match"),name:document.getElementById("name"),save:document.getElementById("save"),delete:document.getElementById("delete"),retry:document.getElementById("retry"),source:document.getElementById("source"),ok:document.getElementById("ok")},h={detect:0,draw:0},y={detect:0,draw:0},E=0,p=(...t)=>{e.log.innerText+=t.join(" ")+` -`,console.log(...t)},w=t=>e.fps.innerText=t;async function S(){w("starting webcam...");let t={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth}}},i=await navigator.mediaDevices.getUserMedia(t),s=new Promise(o=>{e.video.onloadeddata=()=>o(!0)});e.video.srcObject=i,e.video.play(),await s,e.canvas.width=e.video.videoWidth,e.canvas.height=e.video.videoHeight,a.env.initial&&p("video:",e.video.videoWidth,e.video.videoHeight,"|",i.getVideoTracks()[0].label),e.canvas.onclick=()=>{e.video.paused?e.video.play():e.video.pause()}}async function T(){if(!e.video.paused){r.face&&r.face.tensor&&a.tf.dispose(r.face.tensor),await a.detect(e.video);let t=a.now();y.detect=1e3/(t-h.detect),h.detect=t,requestAnimationFrame(T)}}async function L(){let t=await a.next(a.result);await a.draw.canvas(e.video,e.canvas),await a.draw.all(e.canvas,t);let i=a.now();if(y.draw=1e3/(i-h.draw),h.draw=i,w(`fps: ${y.detect.toFixed(1).padStart(5," ")} detect | ${y.draw.toFixed(1).padStart(5," ")} draw`),n.faceCount=a.result.face.length===1,n.faceCount){let o=Object.values(a.result.gesture).map(f=>f.gesture);(o.includes("blink left eye")||o.includes("blink right eye"))&&(l.start=a.now()),l.start>0&&!o.includes("blink left eye")&&!o.includes("blink right eye")&&(l.end=a.now()),n.blinkDetected=n.blinkDetected||Math.abs(l.end-l.start)>c.blinkMin&&Math.abs(l.end-l.start)c.minConfidence&&(a.result.face[0].faceScore||0)>c.minConfidence&&(a.result.face[0].genderScore||0)>c.minConfidence,n.antispoofCheck=(a.result.face[0].real||0)>c.minConfidence,n.livenessCheck=(a.result.face[0].live||0)>c.minConfidence,n.faceSize=a.result.face[0].box[2]>=c.minSize&&a.result.face[0].box[3]>=c.minSize}let s=32;for(let[o,f]of Object.entries(n)){let u=document.getElementById(`ok-${o}`);u||(u=document.createElement("div"),u.innerText=o,u.className="ok",u.style.top=`${s}px`,e.ok.appendChild(u)),typeof f=="boolean"?u.style.backgroundColor=f?"lightgreen":"lightcoral":u.innerText=`${o}:${f}`,s+=28}return M()||n.elapsedMs>c.maxTime?(e.video.pause(),a.result.face[0]):(n.elapsedMs=Math.trunc(a.now()-E),new Promise(o=>{setTimeout(async()=>{await L()&&o(a.result.face[0])},30)}))}async function P(){var t,i;if(e.name.value.length>0){let s=(t=e.canvas.getContext("2d"))==null?void 0:t.getImageData(0,0,e.canvas.width,e.canvas.height),o={id:0,name:e.name.value,descriptor:(i=r.face)==null?void 0:i.embedding,image:s};await x(o),p("saved face record:",o.name)}else p("invalid name")}async function z(){r.record&&r.record.id>0&&await D(r.record)}async function j(){var o,f;if((o=e.canvas.getContext("2d"))==null||o.clearRect(0,0,c.minSize,c.minSize),!r.face||!r.face.tensor||!r.face.embedding)return!1;if(console.log("face record:",r.face),a.tf.browser.toPixels(r.face.tensor,e.canvas),await k()===0)return p("face database is empty"),document.body.style.background="black",e.delete.style.display="none",!1;let t=await C(),i=t.map(u=>u.descriptor),s=await a.match(r.face.embedding,i,I);return r.record=t[s.index]||null,r.record&&(p(`best match: ${r.record.name} | id: ${r.record.id} | similarity: ${Math.round(1e3*s.similarity)/10}%`),e.name.value=r.record.name,e.source.style.display="",(f=e.source.getContext("2d"))==null||f.putImageData(r.record.image,0,0)),document.body.style.background=s.similarity>c.threshold?"darkgreen":"maroon",s.similarity>c.threshold}async function B(){var t,i,s,o;return n.faceCount=!1,n.faceConfidence=!1,n.facingCenter=!1,n.blinkDetected=!1,n.faceSize=!1,n.antispoofCheck=!1,n.livenessCheck=!1,n.elapsedMs=0,e.match.style.display="none",e.retry.style.display="none",e.source.style.display="none",document.body.style.background="black",await S(),await T(),E=a.now(),r.face=await L(),e.canvas.width=((i=(t=r.face)==null?void 0:t.tensor)==null?void 0:i.shape[1])||c.minSize,e.canvas.height=((o=(s=r.face)==null?void 0:s.tensor)==null?void 0:o.shape[0])||c.minSize,e.source.width=e.canvas.width,e.source.height=e.canvas.height,e.canvas.style.width="",e.match.style.display="flex",e.save.style.display="flex",e.delete.style.display="flex",e.retry.style.display="block",M()?j():(p("did not find valid face"),!1)}async function q(){p("human version:",a.version,"| tfjs version:",a.tf.version["tfjs-core"]),p("options:",JSON.stringify(c).replace(/{|}|"|\[|\]/g,"").replace(/,/g," ")),w("loading..."),p("known face records:",await k()),await S(),await a.load(),w("initializing..."),e.retry.addEventListener("click",B),e.save.addEventListener("click",P),e.delete.addEventListener("click",z),await a.warmup(),await B()}window.onload=q; +// demo/faceid/index.ts +import { Human } from "../../dist/human.esm.js"; + +// demo/faceid/indexdb.ts +var db; +var database = "human"; +var table = "person"; +var log = (...msg) => console.log("indexdb", ...msg); +async function open() { + if (db) + return true; + return new Promise((resolve) => { + const request = indexedDB.open(database, 1); + request.onerror = (evt) => log("error:", evt); + request.onupgradeneeded = (evt) => { + log("create:", evt.target); + db = evt.target.result; + db.createObjectStore(table, { keyPath: "id", autoIncrement: true }); + }; + request.onsuccess = (evt) => { + db = evt.target.result; + log("open:", db); + resolve(true); + }; + }); +} +async function load() { + const faceDB = []; + if (!db) + await open(); + return new Promise((resolve) => { + const cursor = db.transaction([table], "readwrite").objectStore(table).openCursor(null, "next"); + cursor.onerror = (evt) => log("load error:", evt); + cursor.onsuccess = (evt) => { + if (evt.target.result) { + faceDB.push(evt.target.result.value); + evt.target.result.continue(); + } else { + resolve(faceDB); + } + }; + }); +} +async function count() { + if (!db) + await open(); + return new Promise((resolve) => { + const store = db.transaction([table], "readwrite").objectStore(table).count(); + store.onerror = (evt) => log("count error:", evt); + store.onsuccess = () => resolve(store.result); + }); +} +async function save(faceRecord) { + if (!db) + await open(); + const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; + db.transaction([table], "readwrite").objectStore(table).put(newRecord); + log("save:", newRecord); +} +async function remove(faceRecord) { + if (!db) + await open(); + db.transaction([table], "readwrite").objectStore(table).delete(faceRecord.id); + log("delete:", faceRecord); +} + +// demo/faceid/index.ts +var humanConfig = { + modelBasePath: "../../models", + filter: { equalization: true }, + face: { + enabled: true, + detector: { rotation: true, return: true, cropFactor: 1.6, mask: false }, + description: { enabled: true }, + mobilefacenet: { enabled: false, modelPath: "https://vladmandic.github.io/human-models/models/mobilefacenet.json" }, + iris: { enabled: true }, + emotion: { enabled: false }, + antispoof: { enabled: true }, + liveness: { enabled: true } + }, + body: { enabled: false }, + hand: { enabled: false }, + object: { enabled: false }, + gesture: { enabled: true } +}; +var matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; +var options = { + minConfidence: 0.6, + minSize: 224, + maxTime: 1e4, + blinkMin: 10, + blinkMax: 800, + threshold: 0.5, + mask: humanConfig.face.detector.mask, + rotation: humanConfig.face.detector.rotation, + cropFactor: humanConfig.face.detector.cropFactor, + ...matchOptions +}; +var ok = { + faceCount: false, + faceConfidence: false, + facingCenter: false, + lookingCenter: false, + blinkDetected: false, + faceSize: false, + antispoofCheck: false, + livenessCheck: false, + elapsedMs: 0 +}; +var allOk = () => ok.faceCount && ok.faceSize && ok.blinkDetected && ok.facingCenter && ok.lookingCenter && ok.faceConfidence && ok.antispoofCheck && ok.livenessCheck; +var current = { face: null, record: null }; +var blink = { + start: 0, + end: 0, + time: 0 +}; +var human = new Human(humanConfig); +human.env["perfadd"] = false; +human.draw.options.font = 'small-caps 18px "Lato"'; +human.draw.options.lineHeight = 20; +var dom = { + video: document.getElementById("video"), + canvas: document.getElementById("canvas"), + log: document.getElementById("log"), + fps: document.getElementById("fps"), + match: document.getElementById("match"), + name: document.getElementById("name"), + save: document.getElementById("save"), + delete: document.getElementById("delete"), + retry: document.getElementById("retry"), + source: document.getElementById("source"), + ok: document.getElementById("ok") +}; +var timestamp = { detect: 0, draw: 0 }; +var fps = { detect: 0, draw: 0 }; +var startTime = 0; +var log2 = (...msg) => { + dom.log.innerText += msg.join(" ") + "\n"; + console.log(...msg); +}; +var printFPS = (msg) => dom.fps.innerText = msg; +async function webCam() { + printFPS("starting webcam..."); + const cameraOptions = { audio: false, video: { facingMode: "user", resizeMode: "none", width: { ideal: document.body.clientWidth } } }; + const stream = await navigator.mediaDevices.getUserMedia(cameraOptions); + const ready = new Promise((resolve) => { + dom.video.onloadeddata = () => resolve(true); + }); + dom.video.srcObject = stream; + dom.video.play(); + await ready; + dom.canvas.width = dom.video.videoWidth; + dom.canvas.height = dom.video.videoHeight; + if (human.env.initial) + log2("video:", dom.video.videoWidth, dom.video.videoHeight, "|", stream.getVideoTracks()[0].label); + dom.canvas.onclick = () => { + if (dom.video.paused) + dom.video.play(); + else + dom.video.pause(); + }; +} +async function detectionLoop() { + if (!dom.video.paused) { + if (current.face && current.face.tensor) + human.tf.dispose(current.face.tensor); + await human.detect(dom.video); + const now = human.now(); + fps.detect = 1e3 / (now - timestamp.detect); + timestamp.detect = now; + requestAnimationFrame(detectionLoop); + } +} +async function validationLoop() { + const interpolated = await human.next(human.result); + await human.draw.canvas(dom.video, dom.canvas); + await human.draw.all(dom.canvas, interpolated); + const now = human.now(); + fps.draw = 1e3 / (now - timestamp.draw); + timestamp.draw = now; + printFPS(`fps: ${fps.detect.toFixed(1).padStart(5, " ")} detect | ${fps.draw.toFixed(1).padStart(5, " ")} draw`); + ok.faceCount = human.result.face.length === 1; + if (ok.faceCount) { + const gestures = Object.values(human.result.gesture).map((gesture) => gesture.gesture); + if (gestures.includes("blink left eye") || gestures.includes("blink right eye")) + blink.start = human.now(); + if (blink.start > 0 && !gestures.includes("blink left eye") && !gestures.includes("blink right eye")) + blink.end = human.now(); + ok.blinkDetected = ok.blinkDetected || Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax; + if (ok.blinkDetected && blink.time === 0) + blink.time = Math.trunc(blink.end - blink.start); + ok.facingCenter = gestures.includes("facing center"); + ok.lookingCenter = gestures.includes("looking center"); + ok.faceConfidence = (human.result.face[0].boxScore || 0) > options.minConfidence && (human.result.face[0].faceScore || 0) > options.minConfidence && (human.result.face[0].genderScore || 0) > options.minConfidence; + ok.antispoofCheck = (human.result.face[0].real || 0) > options.minConfidence; + ok.livenessCheck = (human.result.face[0].live || 0) > options.minConfidence; + ok.faceSize = human.result.face[0].box[2] >= options.minSize && human.result.face[0].box[3] >= options.minSize; + } + let y = 32; + for (const [key, val] of Object.entries(ok)) { + let el = document.getElementById(`ok-${key}`); + if (!el) { + el = document.createElement("div"); + el.innerText = key; + el.className = "ok"; + el.style.top = `${y}px`; + dom.ok.appendChild(el); + } + if (typeof val === "boolean") + el.style.backgroundColor = val ? "lightgreen" : "lightcoral"; + else + el.innerText = `${key}:${val}`; + y += 28; + } + if (allOk()) { + dom.video.pause(); + return human.result.face[0]; + } + if (ok.elapsedMs > options.maxTime) { + dom.video.pause(); + return human.result.face[0]; + } else { + ok.elapsedMs = Math.trunc(human.now() - startTime); + return new Promise((resolve) => { + setTimeout(async () => { + const res = await validationLoop(); + if (res) + resolve(human.result.face[0]); + }, 30); + }); + } +} +async function saveRecords() { + var _a, _b; + if (dom.name.value.length > 0) { + const image = (_a = dom.canvas.getContext("2d")) == null ? void 0 : _a.getImageData(0, 0, dom.canvas.width, dom.canvas.height); + const rec = { id: 0, name: dom.name.value, descriptor: (_b = current.face) == null ? void 0 : _b.embedding, image }; + await save(rec); + log2("saved face record:", rec.name); + } else { + log2("invalid name"); + } +} +async function deleteRecord() { + if (current.record && current.record.id > 0) { + await remove(current.record); + } +} +async function detectFace() { + var _a, _b; + (_a = dom.canvas.getContext("2d")) == null ? void 0 : _a.clearRect(0, 0, options.minSize, options.minSize); + if (!current.face || !current.face.tensor || !current.face.embedding) + return false; + console.log("face record:", current.face); + human.tf.browser.toPixels(current.face.tensor, dom.canvas); + if (await count() === 0) { + log2("face database is empty"); + document.body.style.background = "black"; + dom.delete.style.display = "none"; + return false; + } + const db2 = await load(); + const descriptors = db2.map((rec) => rec.descriptor); + const res = await human.match(current.face.embedding, descriptors, matchOptions); + current.record = db2[res.index] || null; + if (current.record) { + log2(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1e3 * res.similarity) / 10}%`); + dom.name.value = current.record.name; + dom.source.style.display = ""; + (_b = dom.source.getContext("2d")) == null ? void 0 : _b.putImageData(current.record.image, 0, 0); + } + document.body.style.background = res.similarity > options.threshold ? "darkgreen" : "maroon"; + return res.similarity > options.threshold; +} +async function main() { + var _a, _b, _c, _d; + ok.faceCount = false; + ok.faceConfidence = false; + ok.facingCenter = false; + ok.blinkDetected = false; + ok.faceSize = false; + ok.antispoofCheck = false; + ok.livenessCheck = false; + ok.elapsedMs = 0; + dom.match.style.display = "none"; + dom.retry.style.display = "none"; + dom.source.style.display = "none"; + document.body.style.background = "black"; + await webCam(); + await detectionLoop(); + startTime = human.now(); + current.face = await validationLoop(); + dom.canvas.width = ((_b = (_a = current.face) == null ? void 0 : _a.tensor) == null ? void 0 : _b.shape[1]) || options.minSize; + dom.canvas.height = ((_d = (_c = current.face) == null ? void 0 : _c.tensor) == null ? void 0 : _d.shape[0]) || options.minSize; + dom.source.width = dom.canvas.width; + dom.source.height = dom.canvas.height; + dom.canvas.style.width = ""; + dom.match.style.display = "flex"; + dom.save.style.display = "flex"; + dom.delete.style.display = "flex"; + dom.retry.style.display = "block"; + if (!allOk()) { + log2("did not find valid face"); + return false; + } else { + return detectFace(); + } +} +async function init() { + log2("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]); + log2("options:", JSON.stringify(options).replace(/{|}|"|\[|\]/g, "").replace(/,/g, " ")); + printFPS("loading..."); + log2("known face records:", await count()); + await webCam(); + await human.load(); + printFPS("initializing..."); + dom.retry.addEventListener("click", main); + dom.save.addEventListener("click", saveRecords); + dom.delete.addEventListener("click", deleteRecord); + await human.warmup(); + await main(); +} +window.onload = init; /** * Human demo for browsers * @default Human Library diff --git a/demo/faceid/index.js.map b/demo/faceid/index.js.map index de684c96..76145ea1 100644 --- a/demo/faceid/index.js.map +++ b/demo/faceid/index.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["index.ts", "indexdb.ts"], "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport { Human, TensorLike, FaceResult } from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\nimport * as indexDb from './indexdb'; // methods to deal with indexdb\n\nconst humanConfig = { // user configuration for human, used to fine-tune behavior\n modelBasePath: '../../models',\n filter: { equalization: true }, // lets run with histogram equilizer\n face: {\n enabled: true,\n detector: { rotation: true, return: true, cropFactor: 1.6, mask: false }, // return tensor is used to get detected face image\n description: { enabled: true }, // default model for face descriptor extraction is faceres\n mobilefacenet: { enabled: false, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model\n iris: { enabled: true }, // needed to determine gaze direction\n emotion: { enabled: false }, // not needed\n antispoof: { enabled: true }, // enable optional antispoof module\n liveness: { enabled: true }, // enable optional liveness module\n },\n body: { enabled: false },\n hand: { enabled: false },\n object: { enabled: false },\n gesture: { enabled: true }, // parses face and iris gestures\n};\n\n// const matchOptions = { order: 2, multiplier: 1000, min: 0.0, max: 1.0 }; // for embedding model\nconst matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; // for faceres model\n\nconst options = {\n minConfidence: 0.6, // overal face confidence for box, face, gender, real, live\n minSize: 224, // min input to face descriptor model before degradation\n maxTime: 10000, // max time before giving up\n blinkMin: 10, // minimum duration of a valid blink\n blinkMax: 800, // maximum duration of a valid blink\n threshold: 0.5, // minimum similarity\n mask: humanConfig.face.detector.mask,\n rotation: humanConfig.face.detector.rotation,\n cropFactor: humanConfig.face.detector.cropFactor,\n ...matchOptions,\n};\n\nconst ok = { // must meet all rules\n faceCount: false,\n faceConfidence: false,\n facingCenter: false,\n lookingCenter: false,\n blinkDetected: false,\n faceSize: false,\n antispoofCheck: false,\n livenessCheck: false,\n elapsedMs: 0, // total time while waiting for valid face\n};\nconst allOk = () => ok.faceCount && ok.faceSize && ok.blinkDetected && ok.facingCenter && ok.lookingCenter && ok.faceConfidence && ok.antispoofCheck && ok.livenessCheck;\nconst current: { face: FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record\n\nconst blink = { // internal timers for blink start/end/duration\n start: 0,\n end: 0,\n time: 0,\n};\n\n// let db: Array<{ name: string, source: string, embedding: number[] }> = []; // holds loaded face descriptor database\nconst human = new Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env['perfadd'] = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('fps') as HTMLPreElement,\n match: document.getElementById('match') as HTMLDivElement,\n name: document.getElementById('name') as HTMLInputElement,\n save: document.getElementById('save') as HTMLSpanElement,\n delete: document.getElementById('delete') as HTMLSpanElement,\n retry: document.getElementById('retry') as HTMLDivElement,\n source: document.getElementById('source') as HTMLCanvasElement,\n ok: document.getElementById('ok') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detect: 0, draw: 0 }; // holds calculated fps information for both detect and screen refresh\nlet startTime = 0;\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n // eslint-disable-next-line no-console\n console.log(...msg);\n};\nconst printFPS = (msg) => dom.fps.innerText = msg; // print status element\n\nasync function webCam() { // initialize webcam\n printFPS('starting webcam...');\n // @ts-ignore resizeMode is not yet defined in tslib\n const cameraOptions: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };\n const stream: MediaStream = await navigator.mediaDevices.getUserMedia(cameraOptions);\n const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n dom.video.srcObject = stream;\n dom.video.play();\n await ready;\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n if (human.env.initial) log('video:', dom.video.videoWidth, dom.video.videoHeight, '|', stream.getVideoTracks()[0].label);\n dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n if (dom.video.paused) dom.video.play();\n else dom.video.pause();\n };\n}\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (current.face && current.face.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const now = human.now();\n fps.detect = 1000 / (now - timestamp.detect);\n timestamp.detect = now;\n requestAnimationFrame(detectionLoop); // start new frame immediately\n }\n}\n\nasync function validationLoop(): Promise { // main screen refresh loop\n const interpolated = await human.next(human.result); // smoothen result using last-known results\n await human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n const now = human.now();\n fps.draw = 1000 / (now - timestamp.draw);\n timestamp.draw = now;\n printFPS(`fps: ${fps.detect.toFixed(1).padStart(5, ' ')} detect | ${fps.draw.toFixed(1).padStart(5, ' ')} draw`); // write status\n ok.faceCount = human.result.face.length === 1; // must be exactly detected face\n if (ok.faceCount) { // skip the rest if no face\n const gestures: string[] = Object.values(human.result.gesture).map((gesture) => gesture.gesture); // flatten all gestures\n if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed\n if (blink.start > 0 && !gestures.includes('blink left eye') && !gestures.includes('blink right eye')) blink.end = human.now(); // if blink started how long until eyes are back open\n ok.blinkDetected = ok.blinkDetected || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);\n if (ok.blinkDetected && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);\n ok.facingCenter = gestures.includes('facing center');\n ok.lookingCenter = gestures.includes('looking center'); // must face camera and look at camera\n ok.faceConfidence = (human.result.face[0].boxScore || 0) > options.minConfidence && (human.result.face[0].faceScore || 0) > options.minConfidence && (human.result.face[0].genderScore || 0) > options.minConfidence;\n ok.antispoofCheck = (human.result.face[0].real || 0) > options.minConfidence;\n ok.livenessCheck = (human.result.face[0].live || 0) > options.minConfidence;\n ok.faceSize = human.result.face[0].box[2] >= options.minSize && human.result.face[0].box[3] >= options.minSize;\n }\n let y = 32;\n for (const [key, val] of Object.entries(ok)) {\n let el = document.getElementById(`ok-${key}`);\n if (!el) {\n el = document.createElement('div');\n el.innerText = key;\n el.className = 'ok';\n el.style.top = `${y}px`;\n dom.ok.appendChild(el);\n }\n if (typeof val === 'boolean') el.style.backgroundColor = val ? 'lightgreen' : 'lightcoral';\n else el.innerText = `${key}:${val}`;\n y += 28;\n }\n if (allOk()) { // all criteria met\n dom.video.pause();\n return human.result.face[0];\n }\n if (ok.elapsedMs > options.maxTime) { // give up\n dom.video.pause();\n return human.result.face[0];\n } else { // run again\n ok.elapsedMs = Math.trunc(human.now() - startTime);\n return new Promise((resolve) => {\n setTimeout(async () => {\n const res = await validationLoop(); // run validation loop until conditions are met\n if (res) resolve(human.result.face[0]); // recursive promise resolve\n }, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n });\n }\n}\n\nasync function saveRecords() {\n if (dom.name.value.length > 0) {\n const image = dom.canvas.getContext('2d')?.getImageData(0, 0, dom.canvas.width, dom.canvas.height) as ImageData;\n const rec = { id: 0, name: dom.name.value, descriptor: current.face?.embedding as number[], image };\n await indexDb.save(rec);\n log('saved face record:', rec.name);\n } else {\n log('invalid name');\n }\n}\n\nasync function deleteRecord() {\n if (current.record && current.record.id > 0) {\n await indexDb.remove(current.record);\n }\n}\n\nasync function detectFace() {\n dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);\n if (!current.face || !current.face.tensor || !current.face.embedding) return false;\n // eslint-disable-next-line no-console\n console.log('face record:', current.face);\n human.tf.browser.toPixels(current.face.tensor as unknown as TensorLike, dom.canvas);\n if (await indexDb.count() === 0) {\n log('face database is empty');\n document.body.style.background = 'black';\n dom.delete.style.display = 'none';\n return false;\n }\n const db = await indexDb.load();\n const descriptors = db.map((rec) => rec.descriptor);\n const res = await human.match(current.face.embedding, descriptors, matchOptions);\n current.record = db[res.index] || null;\n if (current.record) {\n log(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1000 * res.similarity) / 10}%`);\n dom.name.value = current.record.name;\n dom.source.style.display = '';\n dom.source.getContext('2d')?.putImageData(current.record.image, 0, 0);\n }\n document.body.style.background = res.similarity > options.threshold ? 'darkgreen' : 'maroon';\n return res.similarity > options.threshold;\n}\n\nasync function main() { // main entry point\n ok.faceCount = false;\n ok.faceConfidence = false;\n ok.facingCenter = false;\n ok.blinkDetected = false;\n ok.faceSize = false;\n ok.antispoofCheck = false;\n ok.livenessCheck = false;\n ok.elapsedMs = 0;\n dom.match.style.display = 'none';\n dom.retry.style.display = 'none';\n dom.source.style.display = 'none';\n document.body.style.background = 'black';\n await webCam();\n await detectionLoop(); // start detection loop\n startTime = human.now();\n current.face = await validationLoop(); // start validation loop\n dom.canvas.width = current.face?.tensor?.shape[1] || options.minSize;\n dom.canvas.height = current.face?.tensor?.shape[0] || options.minSize;\n dom.source.width = dom.canvas.width;\n dom.source.height = dom.canvas.height;\n dom.canvas.style.width = '';\n dom.match.style.display = 'flex';\n dom.save.style.display = 'flex';\n dom.delete.style.display = 'flex';\n dom.retry.style.display = 'block';\n if (!allOk()) { // is all criteria met?\n log('did not find valid face');\n return false;\n } else {\n return detectFace();\n }\n}\n\nasync function init() {\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('options:', JSON.stringify(options).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ' '));\n printFPS('loading...');\n log('known face records:', await indexDb.count());\n await webCam(); // start webcam\n await human.load(); // preload all models\n printFPS('initializing...');\n dom.retry.addEventListener('click', main);\n dom.save.addEventListener('click', saveRecords);\n dom.delete.addEventListener('click', deleteRecord);\n await human.warmup(); // warmup function to initialize backend for future faster detection\n await main();\n}\n\nwindow.onload = init;\n", "let db: IDBDatabase; // instance of indexdb\n\nconst database = 'human';\nconst table = 'person';\n\nexport type FaceRecord = { id: number, name: string, descriptor: number[], image: ImageData };\n\n// eslint-disable-next-line no-console\nconst log = (...msg) => console.log('indexdb', ...msg);\n\nexport async function open() {\n if (db) return true;\n return new Promise((resolve) => {\n const request: IDBOpenDBRequest = indexedDB.open(database, 1);\n request.onerror = (evt) => log('error:', evt);\n request.onupgradeneeded = (evt: IDBVersionChangeEvent) => { // create if doesnt exist\n log('create:', evt.target);\n db = (evt.target as IDBOpenDBRequest).result;\n db.createObjectStore(table, { keyPath: 'id', autoIncrement: true });\n };\n request.onsuccess = (evt) => { // open\n db = (evt.target as IDBOpenDBRequest).result as IDBDatabase;\n log('open:', db);\n resolve(true);\n };\n });\n}\n\nexport async function load(): Promise {\n const faceDB: Array = [];\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const cursor: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).openCursor(null, 'next');\n cursor.onerror = (evt) => log('load error:', evt);\n cursor.onsuccess = (evt) => {\n if ((evt.target as IDBRequest).result) {\n faceDB.push((evt.target as IDBRequest).result.value);\n (evt.target as IDBRequest).result.continue();\n } else {\n resolve(faceDB);\n }\n };\n });\n}\n\nexport async function count(): Promise {\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const store: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).count();\n store.onerror = (evt) => log('count error:', evt);\n store.onsuccess = () => resolve(store.result);\n });\n}\n\nexport async function save(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; // omit id as its autoincrement\n db.transaction([table], 'readwrite').objectStore(table).put(newRecord);\n log('save:', newRecord);\n}\n\nexport async function remove(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n db.transaction([table], 'readwrite').objectStore(table).delete(faceRecord.id); // delete based on id\n log('delete:', faceRecord);\n}\n"], - "mappings": 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"names": [] } diff --git a/demo/typescript/index.js b/demo/typescript/index.js index 271c9736..fa7b379b 100644 --- a/demo/typescript/index.js +++ b/demo/typescript/index.js @@ -4,8 +4,101 @@ author: ' */ -import{Human as p}from"../../dist/human.esm.js";var w={modelBasePath:"../../models",filter:{enabled:!0,equalization:!1},face:{enabled:!0,detector:{rotation:!1},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0}},body:{enabled:!0},hand:{enabled:!0},object:{enabled:!1},gesture:{enabled:!0}},t=new p(w);t.env.perfadd=!1;t.draw.options.font='small-caps 18px "Lato"';t.draw.options.lineHeight=20;var e={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},i={detect:0,draw:0,tensors:0},d={detect:0,draw:0},s=(...a)=>{e.log.innerText+=a.join(" ")+` -`,console.log(...a)},r=a=>e.fps.innerText=a,b=a=>e.perf.innerText="tensors:"+t.tf.memory().numTensors+" | performance: "+JSON.stringify(a).replace(/"|{|}/g,"").replace(/,/g," | ");async function h(){r("starting webcam...");let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth}}},n=await navigator.mediaDevices.getUserMedia(a),m=new Promise(f=>{e.video.onloadeddata=()=>f(!0)});e.video.srcObject=n,e.video.play(),await m,e.canvas.width=e.video.videoWidth,e.canvas.height=e.video.videoHeight;let o=n.getVideoTracks()[0],v=o.getCapabilities?o.getCapabilities():"",g=o.getSettings?o.getSettings():"",u=o.getConstraints?o.getConstraints():"";s("video:",e.video.videoWidth,e.video.videoHeight,o.label,{stream:n,track:o,settings:g,constraints:u,capabilities:v}),e.canvas.onclick=()=>{e.video.paused?e.video.play():e.video.pause()}}async function c(){if(!e.video.paused){await t.detect(e.video);let n=t.tf.memory().numTensors;n-i.tensors!==0&&s("allocated tensors:",n-i.tensors),i.tensors=n}let a=t.now();d.detect=1e3/(a-i.detect),i.detect=a,requestAnimationFrame(c)}async function l(){if(!e.video.paused){let n=await t.next(t.result);await t.draw.canvas(e.video,e.canvas),await t.draw.all(e.canvas,n),b(n.performance)}let a=t.now();d.draw=1e3/(a-i.draw),i.draw=a,r(e.video.paused?"paused":`fps: ${d.detect.toFixed(1).padStart(5," ")} detect | ${d.draw.toFixed(1).padStart(5," ")} draw`),setTimeout(l,30)}async function y(){s("human version:",t.version,"| tfjs version:",t.tf.version["tfjs-core"]),s("platform:",t.env.platform,"| agent:",t.env.agent),r("loading..."),await t.load(),s("backend:",t.tf.getBackend(),"| available:",t.env.backends),s("loaded models:",Object.values(t.models).filter(a=>a!==null).length),r("initializing..."),await t.warmup(),await h(),await c(),await l()}window.onload=y; +// demo/typescript/index.ts +import { Human } from "../../dist/human.esm.js"; +var humanConfig = { + modelBasePath: "../../models", + filter: { enabled: true, equalization: false }, + face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } }, + body: { enabled: true }, + hand: { enabled: true }, + object: { enabled: false }, + gesture: { enabled: true } +}; +var human = new Human(humanConfig); +human.env["perfadd"] = false; +human.draw.options.font = 'small-caps 18px "Lato"'; +human.draw.options.lineHeight = 20; +var dom = { + video: document.getElementById("video"), + canvas: document.getElementById("canvas"), + log: document.getElementById("log"), + fps: document.getElementById("status"), + perf: document.getElementById("performance") +}; +var timestamp = { detect: 0, draw: 0, tensors: 0 }; +var fps = { detect: 0, draw: 0 }; +var log = (...msg) => { + dom.log.innerText += msg.join(" ") + "\n"; + console.log(...msg); +}; +var status = (msg) => dom.fps.innerText = msg; +var perf = (msg) => dom.perf.innerText = "tensors:" + human.tf.memory().numTensors + " | performance: " + JSON.stringify(msg).replace(/"|{|}/g, "").replace(/,/g, " | "); +async function webCam() { + status("starting webcam..."); + const options = { audio: false, video: { facingMode: "user", resizeMode: "none", width: { ideal: document.body.clientWidth }, height: { ideal: document.body.clientHeight } } }; + const stream = await navigator.mediaDevices.getUserMedia(options); + const ready = new Promise((resolve) => { + dom.video.onloadeddata = () => resolve(true); + }); + dom.video.srcObject = stream; + dom.video.play(); + await ready; + dom.canvas.width = dom.video.videoWidth; + dom.canvas.height = dom.video.videoHeight; + const track = stream.getVideoTracks()[0]; + const capabilities = track.getCapabilities ? track.getCapabilities() : ""; + const settings = track.getSettings ? track.getSettings() : ""; + const constraints = track.getConstraints ? track.getConstraints() : ""; + log("video:", dom.video.videoWidth, dom.video.videoHeight, track.label, { stream, track, settings, constraints, capabilities }); + dom.canvas.onclick = () => { + if (dom.video.paused) + dom.video.play(); + else + dom.video.pause(); + }; +} +async function detectionLoop() { + if (!dom.video.paused) { + await human.detect(dom.video); + const tensors = human.tf.memory().numTensors; + if (tensors - timestamp.tensors !== 0) + log("allocated tensors:", tensors - timestamp.tensors); + timestamp.tensors = tensors; + } + const now = human.now(); + fps.detect = 1e3 / (now - timestamp.detect); + timestamp.detect = now; + requestAnimationFrame(detectionLoop); +} +async function drawLoop() { + if (!dom.video.paused) { + const interpolated = await human.next(human.result); + await human.draw.canvas(dom.video, dom.canvas); + await human.draw.all(dom.canvas, interpolated); + console.log(dom.canvas.width, dom.canvas.height); + perf(interpolated.performance); + } + const now = human.now(); + fps.draw = 1e3 / (now - timestamp.draw); + timestamp.draw = now; + status(dom.video.paused ? "paused" : `fps: ${fps.detect.toFixed(1).padStart(5, " ")} detect | ${fps.draw.toFixed(1).padStart(5, " ")} draw`); + setTimeout(drawLoop, 30); +} +async function main() { + log("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]); + log("platform:", human.env.platform, "| agent:", human.env.agent); + status("loading..."); + await human.load(); + log("backend:", human.tf.getBackend(), "| available:", human.env.backends); + log("loaded models:", Object.values(human.models).filter((model) => model !== null).length); + status("initializing..."); + await human.warmup(); + await webCam(); + await detectionLoop(); + await drawLoop(); +} +window.onload = main; /** * Human demo for browsers * @default Human Library diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map index 11e7c7e8..c3f01432 100644 --- a/demo/typescript/index.js.map +++ b/demo/typescript/index.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["index.ts"], - "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport { Human, Config } from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n // backend: 'webgpu' as const,\n // async: true,\n modelBasePath: '../../models',\n filter: { enabled: true, equalization: false },\n // cacheSensitivity: 0,\n face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },\n body: { enabled: true },\n hand: { enabled: true },\n object: { enabled: false },\n gesture: { enabled: true },\n};\n\nconst human = new Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env['perfadd'] = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('status') as HTMLPreElement,\n perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detect: 0, draw: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n // eslint-disable-next-line no-console\n console.log(...msg);\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function webCam() { // initialize webcam\n status('starting webcam...');\n // @ts-ignore resizeMode is not yet defined in tslib\n const options: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };\n const stream: MediaStream = await navigator.mediaDevices.getUserMedia(options);\n const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n dom.video.srcObject = stream;\n dom.video.play();\n await ready;\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n const track: MediaStreamTrack = stream.getVideoTracks()[0];\n const capabilities: MediaTrackCapabilities | string = track.getCapabilities ? 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// equivalent of @vladmandic/Human\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n // backend: 'webgpu' as const,\n // async: true,\n modelBasePath: '../../models',\n filter: { enabled: true, equalization: false },\n // cacheSensitivity: 0,\n face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },\n body: { enabled: true },\n hand: { enabled: true },\n object: { enabled: false },\n gesture: { enabled: true },\n};\n\nconst human = new Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env['perfadd'] = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('status') as HTMLPreElement,\n perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detect: 0, draw: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n // eslint-disable-next-line no-console\n console.log(...msg);\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function webCam() { // initialize webcam\n status('starting webcam...');\n // @ts-ignore resizeMode is not yet defined in tslib\n const options: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth }, height: { ideal: document.body.clientHeight } } };\n const stream: MediaStream = await navigator.mediaDevices.getUserMedia(options);\n const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n dom.video.srcObject = stream;\n dom.video.play();\n await ready;\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n const track: MediaStreamTrack = stream.getVideoTracks()[0];\n const capabilities: MediaTrackCapabilities | string = track.getCapabilities ? track.getCapabilities() : '';\n const settings: MediaTrackSettings | string = track.getSettings ? track.getSettings() : '';\n const constraints: MediaTrackConstraints | string = track.getConstraints ? track.getConstraints() : '';\n log('video:', dom.video.videoWidth, dom.video.videoHeight, track.label, { stream, track, settings, constraints, capabilities });\n dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n if (dom.video.paused) dom.video.play();\n else dom.video.pause();\n };\n}\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n // console.log('profiling data:', await human.profile(dom.video));\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n timestamp.tensors = tensors;\n }\n const now = human.now();\n fps.detect = 1000 / (now - timestamp.detect);\n timestamp.detect = now;\n requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n if (!dom.video.paused) {\n const interpolated = await human.next(human.result); // smoothen result using last-known results\n await human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n console.log(dom.canvas.width, dom.canvas.height);\n perf(interpolated.performance); 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// @ts-ignore resizeMode is not yet defined in tslib - const options: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } }; + const options: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth }, height: { ideal: document.body.clientHeight } } }; const stream: MediaStream = await navigator.mediaDevices.getUserMedia(options); const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); }); dom.video.srcObject = stream; @@ -87,6 +87,7 @@ async function drawLoop() { // main screen refresh loop const interpolated = await human.next(human.result); // smoothen result using last-known results await human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc. + console.log(dom.canvas.width, dom.canvas.height); perf(interpolated.performance); // write performance data } const now = human.now(); diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js index a89ca6f4..0cbcc4a0 100644 --- a/dist/human.esm-nobundle.js +++ b/dist/human.esm-nobundle.js @@ -4,7 +4,262 @@ author: ' */ -var Z5=Object.defineProperty;var mn=Object.getOwnPropertyDescriptor;var pn=Object.getOwnPropertyNames;var un=Object.prototype.hasOwnProperty;var hn=(e,t,o)=>t in e?Z5(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var ne=(e,t)=>{for(var o in t)Z5(e,o,{get:t[o],enumerable:!0})},w2=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of pn(t))!un.call(e,r)&&r!==o&&Z5(e,r,{get:()=>t[r],enumerable:!(n=mn(t,r))||n.enumerable});return e},X=(e,t,o)=>(w2(e,t,"default"),o&&w2(o,t,"default"));var k=(e,t,o)=>(hn(e,typeof t!="symbol"?t+"":t,o),o),k2=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var Ge=(e,t,o)=>(k2(e,t,"read from private field"),o?o.call(e):t.get(e)),Be=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},He=(e,t,o,n)=>(k2(e,t,"write to private field"),n?n.call(e,o):t.set(e,o),o);function h(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function E2(e,t){let o=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var g=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function D5(e,t,o="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")D5(e[r],t[r],r,n);else{let s=e&&typeof e[r]!="undefined";s||n.push({reason:"unknown property",where:`${o}.${r} = ${t[r]}`});let a=e&&typeof e[r]==typeof t[r];s&&!a&&n.push({reason:"property type mismatch",where:`${o}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&o==="config"&&n.length>0&&h("invalid configuration",n),n}function K(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,n)=>(Object.keys(n||{}).forEach(r=>{let s=o[r],a=n[r];Array.isArray(s)&&Array.isArray(a)?o[r]=s.concat(...a):t(s)&&t(a)?o[r]=K(s,a):o[r]=a}),o),{})}var re={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!0,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json"},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var A={};ne(A,{GraphModel:()=>X5,Tensor:()=>fe,version:()=>Ve});X(A,MA);X(A,RA);import*as MA from"@tensorflow/tfjs/dist/index.js";import*as RA from"@tensorflow/tfjs-backend-webgl/dist/index.js";import{Tensor as fe}from"@tensorflow/tfjs/dist/index.js";import{GraphModel as X5}from"@tensorflow/tfjs-converter/dist/index";var bn="3.15.0",gn="3.15.0",Pn="3.15.0",vn="3.15.0",Mn="3.15.0",Rn="3.15.0",Tn="3.15.0",Ve={tfjs:bn,"tfjs-core":gn,"tfjs-data":Pn,"tfjs-layers":vn,"tfjs-converter":Mn,"tfjs-backend-webgl":Rn,"tfjs-backend-wasm":Tn};var z2=` +var __defProp = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; +var __export = (target, all2) => { + for (var name in all2) + __defProp(target, name, { get: all2[name], enumerable: true }); +}; +var __copyProps = (to, from, except, desc) => { + if (from && typeof from === "object" || typeof from === "function") { + for (let key of __getOwnPropNames(from)) + if (!__hasOwnProp.call(to, key) && key !== except) + __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); + } + return to; +}; +var __reExport = (target, mod3, secondTarget) => (__copyProps(target, mod3, "default"), secondTarget && __copyProps(secondTarget, mod3, "default")); +var __publicField = (obj, key, value) => { + __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); + return value; +}; +var __accessCheck = (obj, member, msg) => { + if (!member.has(obj)) + throw TypeError("Cannot " + msg); +}; +var __privateGet = (obj, member, getter) => { + __accessCheck(obj, member, "read from private field"); + return getter ? getter.call(obj) : member.get(obj); +}; +var __privateAdd = (obj, member, value) => { + if (member.has(obj)) + throw TypeError("Cannot add the same private member more than once"); + member instanceof WeakSet ? member.add(obj) : member.set(obj, value); +}; +var __privateSet = (obj, member, value, setter) => { + __accessCheck(obj, member, "write to private field"); + setter ? setter.call(obj, value) : member.set(obj, value); + return value; +}; + +// src/util/util.ts +function log(...msg) { + const dt = new Date(); + const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; + if (msg) + console.log(ts, "Human:", ...msg); +} +function join(folder, file) { + const separator = folder.endsWith("/") ? "" : "/"; + const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); + const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; + if (!path.toLocaleLowerCase().includes(".json")) + throw new Error(`modelpath error: expecting json file: ${path}`); + return path; +} +var now = () => { + if (typeof performance !== "undefined") + return performance.now(); + return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); +}; +function validate(defaults, config3, parent = "config", msgs = []) { + for (const key of Object.keys(config3)) { + if (typeof config3[key] === "object") { + validate(defaults[key], config3[key], key, msgs); + } else { + const defined = defaults && typeof defaults[key] !== "undefined"; + if (!defined) + msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); + const same = defaults && typeof defaults[key] === typeof config3[key]; + if (defined && !same) + msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); + } + } + if (config3.debug && parent === "config" && msgs.length > 0) + log("invalid configuration", msgs); + return msgs; +} +function mergeDeep(...objects) { + const isObject = (obj) => obj && typeof obj === "object"; + return objects.reduce((prev, obj) => { + Object.keys(obj || {}).forEach((key) => { + const pVal = prev[key]; + const oVal = obj[key]; + if (Array.isArray(pVal) && Array.isArray(oVal)) + prev[key] = pVal.concat(...oVal); + else if (isObject(pVal) && isObject(oVal)) + prev[key] = mergeDeep(pVal, oVal); + else + prev[key] = oVal; + }); + return prev; + }, {}); +} + +// src/config.ts +var config = { + backend: "", + modelBasePath: "", + cacheModels: true, + wasmPath: "", + wasmPlatformFetch: false, + debug: true, + async: true, + warmup: "full", + cacheSensitivity: 0.7, + skipAllowed: false, + deallocate: false, + filter: { + enabled: true, + equalization: false, + width: 0, + height: 0, + flip: false, + return: true, + brightness: 0, + contrast: 0, + sharpness: 0, + blur: 0, + saturation: 0, + hue: 0, + negative: false, + sepia: false, + vintage: false, + kodachrome: false, + technicolor: false, + polaroid: false, + pixelate: 0 + }, + gesture: { + enabled: true + }, + face: { + enabled: true, + detector: { + modelPath: "blazeface.json", + rotation: true, + maxDetected: 1, + skipFrames: 99, + skipTime: 2500, + minConfidence: 0.2, + iouThreshold: 0.1, + mask: false, + return: false + }, + mesh: { + enabled: true, + modelPath: "facemesh.json" + }, + attention: { + enabled: false, + modelPath: "facemesh-attention.json" + }, + iris: { + enabled: true, + modelPath: "iris.json" + }, + emotion: { + enabled: true, + minConfidence: 0.1, + skipFrames: 99, + skipTime: 1500, + modelPath: "emotion.json" + }, + description: { + enabled: true, + modelPath: "faceres.json", + skipFrames: 99, + skipTime: 3e3, + minConfidence: 0.1 + }, + antispoof: { + enabled: false, + skipFrames: 99, + skipTime: 4e3, + modelPath: "antispoof.json" + }, + liveness: { + enabled: false, + skipFrames: 99, + skipTime: 4e3, + modelPath: "liveness.json" + } + }, + body: { + enabled: true, + modelPath: "movenet-lightning.json", + maxDetected: -1, + minConfidence: 0.3, + skipFrames: 1, + skipTime: 200 + }, + hand: { + enabled: true, + rotation: true, + skipFrames: 99, + skipTime: 1e3, + minConfidence: 0.5, + iouThreshold: 0.2, + maxDetected: -1, + landmarks: true, + detector: { + modelPath: "handtrack.json" + }, + skeleton: { + modelPath: "handlandmark-full.json" + } + }, + object: { + enabled: false, + modelPath: "mb3-centernet.json", + minConfidence: 0.2, + iouThreshold: 0.4, + maxDetected: 10, + skipFrames: 99, + skipTime: 2e3 + }, + segmentation: { + enabled: false, + modelPath: "selfie.json", + blur: 8 + } +}; + +// dist/tfjs.esm.js +var tfjs_esm_exports = {}; +__export(tfjs_esm_exports, { + GraphModel: () => GraphModel, + Tensor: () => Tensor, + version: () => version8 +}); +__reExport(tfjs_esm_exports, dist_star); +__reExport(tfjs_esm_exports, dist_star2); +import * as dist_star from "@tensorflow/tfjs/dist/index.js"; +import * as dist_star2 from "@tensorflow/tfjs-backend-webgl/dist/index.js"; +import { Tensor } from "@tensorflow/tfjs/dist/index.js"; +import { GraphModel } from "@tensorflow/tfjs-converter/dist/index"; +var version = "3.16.0"; +var version2 = "3.16.0"; +var version3 = "3.16.0"; +var version4 = "3.16.0"; +var version5 = "3.16.0"; +var version6 = "3.16.0"; +var version7 = "3.16.0"; +var version8 = { + tfjs: version, + "tfjs-core": version2, + "tfjs-data": version3, + "tfjs-layers": version4, + "tfjs-converter": version5, + "tfjs-backend-webgl": version6, + "tfjs-backend-wasm": version7 +}; + +// src/image/imagefxshaders.ts +var vertexIdentity = ` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -14,7 +269,8 @@ var Z5=Object.defineProperty;var mn=Object.getOwnPropertyDescriptor;var pn=Objec vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`;var j2=` +`; +var colorMatrixWithAlpha = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -26,7 +282,8 @@ var Z5=Object.defineProperty;var mn=Object.getOwnPropertyDescriptor;var pn=Objec gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`,S2=` +`; +var colorMatrixWithoutAlpha = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -38,7 +295,8 @@ var Z5=Object.defineProperty;var mn=Object.getOwnPropertyDescriptor;var pn=Objec gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`,C2=` +`; +var pixelate = ` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -51,7 +309,8 @@ var Z5=Object.defineProperty;var mn=Object.getOwnPropertyDescriptor;var pn=Objec vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`,W2=` +`; +var blur = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -74,7 +333,8 @@ var Z5=Object.defineProperty;var mn=Object.getOwnPropertyDescriptor;var pn=Objec gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`,O2=` +`; +var convolution = ` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -96,19 +356,12342 @@ var Z5=Object.defineProperty;var mn=Object.getOwnPropertyDescriptor;var pn=Objec c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`;var q5=(e,t,o)=>{let n=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(n,(r,s)=>(o[s]=0,r))},U5=class{constructor(t,o,n){k(this,"uniform",{});k(this,"attribute",{});k(this,"gl");k(this,"id");k(this,"compile",(t,o)=>{let n=this.gl.createShader(o);return n?(this.gl.shaderSource(n,t),this.gl.compileShader(n),this.gl.getShaderParameter(n,this.gl.COMPILE_STATUS)?n:(h(`filter: gl compile failed: ${this.gl.getShaderInfoLog(n)}`),null)):(h("filter: could not create shader"),null)});this.gl=t;let r=this.compile(o,this.gl.VERTEX_SHADER),s=this.compile(n,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!s)){if(!this.id){h("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,s),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){h(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);return}this.gl.useProgram(this.id),q5(o,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=this.gl.getAttribLocation(this.id,a);q5(o,"uniform",this.uniform),q5(n,"uniform",this.uniform);for(let a in this.uniform)this.uniform[a]=this.gl.getUniformLocation(this.id,a)}}};function L2(){let e=0,t=null,o=!1,n=-1,r=[null,null],s=[],a=null,i=null,l=i0(100,100),x={},d={INTERMEDIATE:1},y=l.getContext("webgl");if(!y){h("filter: cannot get webgl context");return}this.gl=y;function c(u,p){if(!(u===l.width&&p===l.height)){if(l.width=u,l.height=p,!a){let v=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);a=y.createBuffer(),y.bindBuffer(y.ARRAY_BUFFER,a),y.bufferData(y.ARRAY_BUFFER,v,y.STATIC_DRAW),y.pixelStorei(y.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}y.viewport(0,0,l.width,l.height),r=[null,null]}}function f(u,p){let v=y.createFramebuffer();y.bindFramebuffer(y.FRAMEBUFFER,v);let w=y.createRenderbuffer();y.bindRenderbuffer(y.RENDERBUFFER,w);let S=y.createTexture();return y.bindTexture(y.TEXTURE_2D,S),y.texImage2D(y.TEXTURE_2D,0,y.RGBA,u,p,0,y.RGBA,y.UNSIGNED_BYTE,null),y.texParameteri(y.TEXTURE_2D,y.TEXTURE_MAG_FILTER,y.LINEAR),y.texParameteri(y.TEXTURE_2D,y.TEXTURE_MIN_FILTER,y.LINEAR),y.texParameteri(y.TEXTURE_2D,y.TEXTURE_WRAP_S,y.CLAMP_TO_EDGE),y.texParameteri(y.TEXTURE_2D,y.TEXTURE_WRAP_T,y.CLAMP_TO_EDGE),y.framebufferTexture2D(y.FRAMEBUFFER,y.COLOR_ATTACHMENT0,y.TEXTURE_2D,S,0),y.bindTexture(y.TEXTURE_2D,null),y.bindFramebuffer(y.FRAMEBUFFER,null),{fbo:v,texture:S}}function b(u){return r[u]=r[u]||f(l.width,l.height),r[u]}function m(u=0){if(!i)return;let p=null,v=null,w=!1;e===0?p=t:p=b(n).texture||null,e++,o&&!(u&d.INTERMEDIATE)?(v=null,w=e%2===0):(n=(n+1)%2,v=b(n).fbo||null),y.bindTexture(y.TEXTURE_2D,p),y.bindFramebuffer(y.FRAMEBUFFER,v),y.uniform1f(i.uniform.flipY,w?-1:1),y.drawArrays(y.TRIANGLES,0,6)}function M(u){if(x[u])return i=x[u],y.useProgram((i?i.id:null)||null),i;if(i=new U5(y,z2,u),!i)return h("filter: could not get webgl program"),null;let p=Float32Array.BYTES_PER_ELEMENT,v=4*p;return y.enableVertexAttribArray(i.attribute.pos),y.vertexAttribPointer(i.attribute.pos,2,y.FLOAT,!1,v,0*p),y.enableVertexAttribArray(i.attribute.uv),y.vertexAttribPointer(i.attribute.uv,2,y.FLOAT,!1,v,2*p),x[u]=i,i}let P={colorMatrix:u=>{let p=new Float32Array(u);p[4]/=255,p[9]/=255,p[14]/=255,p[19]/=255;let v=p[18]===1&&p[3]===0&&p[8]===0&&p[13]===0&&p[15]===0&&p[16]===0&&p[17]===0&&p[19]===0?S2:j2,w=M(v);!w||(y.uniform1fv(w.uniform.m,p),m())},brightness:u=>{let p=(u||0)+1;P.colorMatrix([p,0,0,0,0,0,p,0,0,0,0,0,p,0,0,0,0,0,1,0])},saturation:u=>{let p=(u||0)*2/3+1,v=(p-1)*-.5;P.colorMatrix([p,v,v,0,0,v,p,v,0,0,v,v,p,0,0,0,0,0,1,0])},desaturate:()=>{P.saturation(-1)},contrast:u=>{let p=(u||0)+1,v=-128*(p-1);P.colorMatrix([p,0,0,0,v,0,p,0,0,v,0,0,p,0,v,0,0,0,1,0])},negative:()=>{P.contrast(-2)},hue:u=>{u=(u||0)/180*Math.PI;let p=Math.cos(u),v=Math.sin(u),w=.213,S=.715,O=.072;P.colorMatrix([w+p*(1-w)+v*-w,S+p*-S+v*-S,O+p*-O+v*(1-O),0,0,w+p*-w+v*.143,S+p*(1-S)+v*.14,O+p*-O+v*-.283,0,0,w+p*-w+v*-(1-w),S+p*-S+v*S,O+p*(1-O)+v*O,0,0,0,0,0,1,0])},desaturateLuminance:()=>{P.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{P.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{P.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{P.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{P.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{P.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{P.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{P.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:u=>{let p=new Float32Array(u),v=1/l.width,w=1/l.height,S=M(O2);!S||(y.uniform1fv(S.uniform.m,p),y.uniform2f(S.uniform.px,v,w),m())},detectEdges:()=>{P.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{P.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{P.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:u=>{let p=u||1;P.convolution.call(this,[0,-1*p,0,-1*p,1+4*p,-1*p,0,-1*p,0])},emboss:u=>{let p=u||1;P.convolution.call(this,[-2*p,-1*p,0,-1*p,1,1*p,0,1*p,2*p])},blur:u=>{let p=u/7/l.width,v=u/7/l.height,w=M(W2);!w||(y.uniform2f(w.uniform.px,0,v),m(d.INTERMEDIATE),y.uniform2f(w.uniform.px,p,0),m())},pixelate:u=>{let p=u/l.width,v=u/l.height,w=M(C2);!w||(y.uniform2f(w.uniform.size,p,v),m())}};this.add=function(u){let p=Array.prototype.slice.call(arguments,1),v=P[u];s.push({func:v,args:p})},this.reset=function(){s=[]},this.get=function(){return s},this.apply=function(u){c(u.width,u.height),e=0,t||(t=y.createTexture()),y.bindTexture(y.TEXTURE_2D,t),y.texParameteri(y.TEXTURE_2D,y.TEXTURE_WRAP_S,y.CLAMP_TO_EDGE),y.texParameteri(y.TEXTURE_2D,y.TEXTURE_WRAP_T,y.CLAMP_TO_EDGE),y.texParameteri(y.TEXTURE_2D,y.TEXTURE_MIN_FILTER,y.NEAREST),y.texParameteri(y.TEXTURE_2D,y.TEXTURE_MAG_FILTER,y.NEAREST),y.texImage2D(y.TEXTURE_2D,0,y.RGBA,y.RGBA,y.UNSIGNED_BYTE,u);for(let p=0;pf.data())),a=.99*Math.max(s[0][0],s[1][0],s[2][0]),i=[A.sub(o[0],n[0]),A.sub(o[1],n[1]),A.sub(o[2],n[2])],l=[A.sub(r[0],n[0]),A.sub(r[1],n[1]),A.sub(r[2],n[2])],x=[A.div(a,l[0]),A.div(a,l[1]),A.div(a,l[2])],d=[A.mul(i[0],x[0]),A.mul(i[1],x[1]),A.mul(i[2],x[2])],y=A.stack([d[0],d[1],d[2]],2),c=A.reshape(y,[1,t.shape[0],t.shape[1],3]);return A.dispose([...o,...n,...r,...i,...l,...x,...d,y,t]),c}var n5=2048,B=null,$=null,me=null,q,I0={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function i0(e,t){let o;if(T.browser)if(T.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");o=new OffscreenCanvas(e,t)}else{if(typeof document=="undefined")throw new Error("canvas error: attempted to run in browser but DOM is not defined");o=document.createElement("canvas"),o.width=e,o.height=t}else typeof T.Canvas!="undefined"?o=new T.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(o=new globalThis.Canvas(e,t));return o}function r5(e,t){let o=t||i0(e.width,e.height);return o.getContext("2d").drawImage(e,0,0),o}async function pe(e,t,o=!0){if(!e)return t.debug&&h("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof fe)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof T.Canvas!="undefined"&&e instanceof T.Canvas)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type is not recognized");if(e instanceof fe){let n=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new Error("input error: attempted to use tensor without a shape");if(e.shape.length===3){if(e.shape[2]===3)n=A.expandDims(e,0);else if(e.shape[2]===4){let r=A.slice3d(e,[0,0,0],[-1,-1,3]);n=A.expandDims(r,0),A.dispose(r)}}else e.shape.length===4&&(e.shape[3]===3?n=A.clone(e):e.shape[3]===4&&(n=A.slice4d(e,[0,0,0,0],[-1,-1,-1,3])));if(n==null||n.shape.length!==4||n.shape[0]!==1||n.shape[3]!==3)throw new Error(`input error: attempted to use tensor with unrecognized shape: ${e.shape}`);if(n.dtype==="int32"){let r=A.cast(n,"float32");A.dispose(n),n=r}return{tensor:n,canvas:t.filter.return?$:null}}else{if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&h("input stream is not ready"),{tensor:null,canvas:B};let n=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!n||!r)return t.debug&&h("cannot determine input dimensions"),{tensor:null,canvas:B};let s=n,a=r;if(s>n5&&(s=n5,a=Math.trunc(s*r/n)),a>n5&&(a=n5,s=Math.trunc(a*n/r)),(t.filter.width||0)>0?s=t.filter.width:(t.filter.height||0)>0&&(s=n*((t.filter.height||0)/r)),(t.filter.height||0)>0?a=t.filter.height:(t.filter.width||0)>0&&(a=r*((t.filter.width||0)/n)),!s||!a)throw new Error("input error: cannot determine dimension");(!B||(B==null?void 0:B.width)!==s||(B==null?void 0:B.height)!==a)&&(B=i0(s,a));let i=B.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?i.putImageData(e,0,0):t.filter.flip&&typeof i.translate!="undefined"?(i.translate(n,0),i.scale(-1,1),i.drawImage(e,0,0,n,r,0,0,B==null?void 0:B.width,B==null?void 0:B.height),i.setTransform(1,0,0,1,0,0)):i.drawImage(e,0,0,n,r,0,0,B==null?void 0:B.width,B==null?void 0:B.height),(!$||B.width!==$.width||(B==null?void 0:B.height)!==($==null?void 0:$.height))&&($=i0(B.width,B.height)),t.filter.enabled&&T.webgl.supported?(q||(q=T.browser?new L2:null),T.filter=!!q,!q||!q.add?(t.debug&&h("input process error: cannot initialize filters"),T.webgl.supported=!1,t.filter.enabled=!1,r5(B,$)):(q.reset(),t.filter.brightness!==0&&q.add("brightness",t.filter.brightness),t.filter.contrast!==0&&q.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&q.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&q.add("blur",t.filter.blur),t.filter.saturation!==0&&q.add("saturation",t.filter.saturation),t.filter.hue!==0&&q.add("hue",t.filter.hue),t.filter.negative&&q.add("negative"),t.filter.sepia&&q.add("sepia"),t.filter.vintage&&q.add("brownie"),t.filter.sepia&&q.add("sepia"),t.filter.kodachrome&&q.add("kodachrome"),t.filter.technicolor&&q.add("technicolor"),t.filter.polaroid&&q.add("polaroid"),t.filter.pixelate!==0&&q.add("pixelate",t.filter.pixelate),q.get()>0?$=q.apply(B):$=q.draw(B))):(r5(B,$),q&&(q=null),T.filter=!!q),!o)return{tensor:null,canvas:$};if(!$)throw new Error("canvas error: cannot create output");let l,x=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(T.browser&&A.browser)l=A.browser?A.browser.fromPixels(e):null;else{x=e.data.length/e.height/e.width;let c=new Uint8Array(e.data.buffer);l=A.tensor(c,[e.height,e.width,x],"int32")}else if((!me||$.width!==me.width||$.height!==me.height)&&(me=i0($.width,$.height)),A.browser&&T.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=A.browser.fromPixels($):(me=r5($),l=A.browser.fromPixels(me));else{let b=r5($).getContext("2d").getImageData(0,0,s,a);x=b.data.length/s/a;let m=new Uint8Array(b.data.buffer);l=A.tensor(m,[s,a,x])}if(x===4){let c=A.slice3d(l,[0,0,0],[-1,-1,3]);A.dispose(l),l=c}if(!l)throw new Error("input error: cannot create tensor");let d=A.cast(l,"float32"),y=t.filter.equalization?await o5(d):A.expandDims(d,0);return A.dispose([l,d]),{tensor:y,canvas:t.filter.return?$:null}}}async function I2(e,t){let o=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>2048||t.shape[2]>2048)return o;if(!I0.inputTensor)I0.inputTensor=A.clone(t);else if(I0.inputTensor.shape[1]!==t.shape[1]||I0.inputTensor.shape[2]!==t.shape[2])A.dispose(I0.inputTensor),I0.inputTensor=A.clone(t);else{let n={};n.diff=A.sub(t,I0.inputTensor),n.squared=A.mul(n.diff,n.diff),n.sum=A.sum(n.squared);let s=(await n.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;A.dispose([I0.inputTensor,n.diff,n.squared,n.sum]),I0.inputTensor=A.clone(t),o=s<=(e.cacheSensitivity||0)}return o}async function N2(e,t,o){let n={};if(!t||!o||t.shape.length!==4||t.shape.length!==o.shape.length)return e.debug||h("invalid input tensor or tensor shapes do not match:",t.shape,o.shape),0;if(t.shape[0]!==1||o.shape[0]!==1||t.shape[3]!==3||o.shape[3]!==3)return e.debug||h("input tensors must be of shape [1, height, width, 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P1={initial:!0},c0={detector:null,landmarks:null},he={detector:[224,224],landmarks:[256,256]},pt=Number.MAX_SAFE_INTEGER,nr={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},p5=null,Ue,J0=[[0,0],[0,0],[0,0],[0,0]],b1=0,g1=e=>1-1/(1+Math.exp(e));async function v1(e){if(P1.initial&&(c0.detector=null),!c0.detector&&e.body.detector&&e.body.detector.modelPath){c0.detector=await I(e.body.detector.modelPath);let t=Object.values(c0.detector.modelSignature.inputs);he.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,he.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&c0.detector&&h("cached model:",c0.detector.modelUrl);return await u1(),c0.detector}async function M1(e){if(P1.initial&&(c0.landmarks=null),c0.landmarks)e.debug&&h("cached model:",c0.landmarks.modelUrl);else{c0.landmarks=await I(e.body.modelPath);let 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drier"},{class:80,label:"toothbrush"}];var G0,ae=0,ht=[],T1=0,bt=Number.MAX_SAFE_INTEGER;async function w1(e){if(T.initial&&(G0=null),G0)e.debug&&h("cached model:",G0.modelUrl);else{G0=await I(e.object.modelPath);let t=Object.values(G0.modelSignature.inputs);ae=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return G0}async function ir(e,t,o){if(!e)return[];let n={},r=[],s=await e.array();n.squeeze=A.squeeze(e);let a=A.split(n.squeeze,6,1);n.stack=A.stack([a[1],a[0],a[3],a[2]],1),n.boxes=A.squeeze(n.stack),n.scores=A.squeeze(a[4]),n.classes=A.squeeze(a[5]),A.dispose([e,...a]),n.nms=await A.image.nonMaxSuppressionAsync(n.boxes,n.scores,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence||0);let i=await n.nms.data(),l=0;for(let x of Array.from(i)){let 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Pt=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],vt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var A0,E1=0,l0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Mt=Number.MAX_SAFE_INTEGER;async function z1(e){return T.initial&&(A0=null),A0?e.debug&&h("cached model:",A0.modelUrl):A0=await I(e.body.modelPath),A0}async function lr(e,t){let[o,n]=e.shape,r=A.reshape(e,[n*o]),s=A.max(r,0),a=(await s.data())[0];if(A.dispose([r,s]),a>t){let i=A.argMax(r,0),l=A.mod(i,o),x=(await l.data())[0],d=A.div(i,A.scalar(o,"int32")),y=(await d.data())[0];return A.dispose([l,d]),[x,y,a]}return[0,0,a]}async function Rt(e,t){let o=(t.body.skipTime||0)>g()-E1,n=Mt<(t.body.skipFrames||0);return t.skipAllowed&&o&&n&&Object.keys(l0.keypoints).length>0?(Mt++,[l0]):(Mt=0,new Promise(async r=>{var y;let s=A.tidy(()=>{if(!(A0!=null&&A0.inputs[0].shape))return null;let c=A.image.resizeBilinear(e,[A0.inputs[0].shape[2],A0.inputs[0].shape[1]],!1),f=A.mul(c,N.tf2);return A.sub(f,N.tf1)}),a;if(t.body.enabled&&(a=A0==null?void 0:A0.execute(s)),E1=g(),A.dispose(s),a){l0.keypoints.length=0;let c=a.squeeze();A.dispose(a);let f=c.unstack(2);A.dispose(c);for(let b=0;b(((y=t.body)==null?void 0:y.minConfidence)||0)&&l0.keypoints.push({score:Math.round(100*P)/100,part:Pt[b],positionRaw:[m/A0.inputs[0].shape[2],M/A0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/A0.inputs[0].shape[2]),Math.round(e.shape[1]*M/A0.inputs[0].shape[1])]})}f.forEach(b=>A.dispose(b))}l0.score=l0.keypoints.reduce((c,f)=>f.score>c?f.score:c,0);let i=l0.keypoints.map(c=>c.position[0]),l=l0.keypoints.map(c=>c.position[1]);l0.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let x=l0.keypoints.map(c=>c.positionRaw[0]),d=l0.keypoints.map(c=>c.positionRaw[1]);l0.boxRaw=[Math.min(...x),Math.min(...d),Math.max(...x)-Math.min(...x),Math.max(...d)-Math.min(...d)];for(let[c,f]of Object.entries(vt)){let b=[];for(let m=0;mu.part===f[m]),P=l0.keypoints.find(u=>u.part===f[m+1]);M&&P&&M.score>(t.body.minConfidence||0)&&P.score>(t.body.minConfidence||0)&&b.push([M.position,P.position])}l0.annotations[c]=b}r([l0])}))}var yr=["angry","disgust","fear","happy","sad","surprise","neutral"],b0,h5=[],S1=0,C1=0,Tt=Number.MAX_SAFE_INTEGER;async function W1(e){var t;return T.initial&&(b0=null),b0?e.debug&&h("cached model:",b0.modelUrl):b0=await I((t=e.face.emotion)==null?void 0:t.modelPath),b0}async function wt(e,t,o,n){var a,i;if(!b0)return[];let r=Tt<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),s=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>g()-C1;return t.skipAllowed&&s&&r&&S1===n&&h5[o]&&h5[o].length>0?(Tt++,h5[o]):(Tt=0,new Promise(async l=>{var d,y;let x=[];if((d=t.face.emotion)!=null&&d.enabled){let c={},f=b0!=null&&b0.inputs[0].shape?b0.inputs[0].shape[2]:0;c.resize=A.image.resizeBilinear(e,[f,f],!1),c.channels=A.mul(c.resize,N.rgb),c.grayscale=A.sum(c.channels,3,!0),c.grayscaleSub=A.sub(c.grayscale,N.tf05),c.grayscaleMul=A.mul(c.grayscaleSub,N.tf2),c.emotion=b0==null?void 0:b0.execute(c.grayscaleMul),C1=g();let b=await c.emotion.data();for(let m=0;m(((y=t.face.emotion)==null?void 0:y.minConfidence)||0)&&x.push({score:Math.min(.99,Math.trunc(100*b[m])/100),emotion:yr[m]});x.sort((m,M)=>M.score-m.score),Object.keys(c).forEach(m=>A.dispose(c[m]))}h5[o]=x,S1=n,l(x)}))}var d0,kt=[],L1=0,I1=0,N1=Number.MAX_SAFE_INTEGER;async function F1(e){return T.initial&&(d0=null),d0?e.debug&&h("cached model:",d0.modelUrl):d0=await I(e.face.mobilefacenet.modelPath),d0}async function Et(e,t,o,n){var a,i;if(!d0)return[];let r=N1<(((a=t.face.embedding)==null?void 0:a.skipFrames)||0),s=(((i=t.face.embedding)==null?void 0:i.skipTime)||0)>g()-I1;return t.skipAllowed&&s&&r&&L1===n&&kt[o]?(N1++,kt[o]):new Promise(async l=>{var d;let x=[];if(((d=t.face.embedding)==null?void 0:d.enabled)&&(d0==null?void 0:d0.inputs[0].shape)){let y={};y.crop=A.image.resizeBilinear(e,[d0.inputs[0].shape[2],d0.inputs[0].shape[1]],!1),y.data=d0==null?void 0:d0.execute(y.crop);let c=await y.data.data();x=Array.from(c)}kt[o]=x,L1=n,I1=g(),l(x)})}var B0,Y0=0,xr=2.3,zt=M0.leftEyeLower0,jt=M0.rightEyeLower0,ge={leftBounds:[zt[0],zt[zt.length-1]],rightBounds:[jt[0],jt[jt.length-1]]},Pe={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function Z1(e){var t;return T.initial&&(B0=null),B0?e.debug&&h("cached model:",B0.modelUrl):B0=await I((t=e.face.iris)==null?void 0:t.modelPath),Y0=B0.inputs[0].shape?B0.inputs[0].shape[2]:0,Y0===-1&&(Y0=64),B0}function b5(e,t,o,n){for(let r=0;r{let t=e[ge.leftBounds[0]][2],o=e[ge.rightBounds[0]][2];return t-o},B1=(e,t,o,n,r,s=!1)=>{let a=y5(l5(A1([e[o],e[n]]),xr)),i=ue(a),l=A.image.cropAndResize(t,[[a.startPoint[1]/r,a.startPoint[0]/r,a.endPoint[1]/r,a.endPoint[0]/r]],[0],[Y0,Y0]);if(s&&T.kernels.includes("flipleftright")){let x=A.image.flipLeftRight(l);A.dispose(l),l=x}return{box:a,boxSize:i,crop:l}},H1=(e,t,o,n=!1)=>{let r=[];for(let s=0;s{let n=e[M0[`${o}EyeUpper0`][Pe.upperCenter]][2],r=e[M0[`${o}EyeLower0`][Pe.lowerCenter]][2],s=(n+r)/2;return t.map((a,i)=>{let l=s;return i===2?l=n:i===4&&(l=r),[a[0],a[1],l]})};async function D1(e,t,o,n){if(!B0)return o.debug&&h("face mesh iris detection requested, but model is not 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f0={eyeLLower:[33,7,163,144,145,153,154,155,133],eyeRLower:[263,249,390,373,374,380,381,382,362],lips:[185,96,90,181,84,17,314,405,320,307,409,40,39,73,37,0,267,269,270,409,40,88,178,178,87,14,268,402,318,324,409,80,41,38,87,12,268,303,318,324,185,95,80,81,85,16,315,404,319,325,409,40,39,73,72,0,302,303,270,408,185,88,88,81,82,15,316,403,319,324,409,80,41,38,87,12,268,303,318,324],eyeL:[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],eyeR:[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417]};async function q1(e,t){let o={irisL:t[3].dataSync(),irisR:t[1].dataSync(),eyeL:t[0].dataSync(),eyeR:t[6].dataSync(),lips:t[5].dataSync()},n=f0.eyeRLower.reduce((s,a)=>s+=e[a][2],0)/f0.eyeRLower.length;for(let s=0;ss+=e[a][2],0)/f0.eyeLLower.length;for(let s=0;sg()-j0.timestamp,n=j0.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!o||!n||j0.boxes.length===0?(j0.boxes=await f1(e,t),j0.timestamp=g(),j0.skipped=0):j0.skipped++;let r=[],s=[],a=0;for(let P=0;PG.shape[G.shape.length-1]===1),C=S.find(G=>G.shape[G.shape.length-1]===1404),L=await O.data();w.faceScore=Math.round(100*L[0])/100;let Z=A.reshape(C,[-1,3]),F=await Z.array();if(w.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1))u.confidence=w.faceScore;else{(b=t.face.attention)!=null&&b.enabled?F=await q1(F,S):(m=t.face.iris)!=null&&m.enabled&&(F=await D1(F,w.tensor,t,ve)),w.mesh=i1(F,u,p,v,ve),w.meshRaw=w.mesh.map(Y=>[Y[0]/(e.shape[2]||0),Y[1]/(e.shape[1]||0),(Y[2]||0)/ve]);for(let Y of Object.keys(M0))w.annotations[Y]=M0[Y].map(z=>w.mesh[z]);w.score=w.faceScore;let G={...y1(w.mesh,u),confidence:u.confidence,landmarks:u.landmarks};w.box=xt(G,e),w.boxRaw=ct(G,e),s.push(G)}A.dispose([...S,Z])}else{w.box=xt(u,e),w.boxRaw=ct(u,e),w.score=w.boxScore,w.mesh=u.landmarks.map(S=>[(u.startPoint[0]+u.endPoint[0])/2+(u.endPoint[0]+u.startPoint[0])*S[0]/x5(),(u.startPoint[1]+u.endPoint[1])/2+(u.endPoint[1]+u.startPoint[1])*S[1]/x5()]),w.meshRaw=w.mesh.map(S=>[S[0]/(e.shape[2]||0),S[1]/(e.shape[1]||0),(S[2]||0)/ve]);for(let S of Object.keys(De))w.annotations[S]=[w.mesh[De[S]]]}w.score>(((M=t.face.detector)==null?void 0:M.minConfidence)||1)?r.push(w):A.dispose(w.tensor)}return j0.boxes=s,r}async function J1(e){var t,o,n;return T.initial&&(S0=null),S0?e.debug&&h("cached model:",S0.modelUrl):(t=e.face.attention)!=null&&t.enabled?S0=await I((o=e.face.attention)==null?void 0:o.modelPath):S0=await I((n=e.face.mesh)==null?void 0:n.modelPath),ve=S0.inputs[0].shape?S0.inputs[0].shape[2]:0,S0}var Y1=Ae,K1=Xe;var m0,g5=[],Q1=0,_1=0,Ct=Number.MAX_SAFE_INTEGER;async function $1(e){var t;return T.initial&&(m0=null),m0?e.debug&&h("cached model:",m0.modelUrl):m0=await I((t=e.face.description)==null?void 0:t.modelPath),m0}function Wt(e){let t=e.image||e.tensor||e;if(!(m0!=null&&m0.inputs[0].shape))return t;let o=A.image.resizeBilinear(t,[m0.inputs[0].shape[2],m0.inputs[0].shape[1]],!1),n=A.mul(o,N.tf255);return A.dispose(o),n}async function Ot(e,t,o,n){var a,i,l,x;if(!m0)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let r=Ct<(((a=t.face.description)==null?void 0:a.skipFrames)||0),s=(((i=t.face.description)==null?void 0:i.skipTime)||0)>g()-Q1;return t.skipAllowed&&r&&s&&_1===n&&((l=g5[o])==null?void 0:l.age)&&((x=g5[o])==null?void 0:x.age)>0?(Ct++,g5[o]):(Ct=0,new Promise(async d=>{var c,f;let y={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((c=t.face.description)!=null&&c.enabled){let b=Wt(e),m=m0==null?void 0:m0.execute(b);Q1=g(),A.dispose(b);let P=await(await m.find(L=>L.shape[1]===1)).data(),u=Math.trunc(200*Math.abs(P[0]-.5))/100;u>(((f=t.face.description)==null?void 0:f.minConfidence)||0)&&(y.gender=P[0]<=.5?"female":"male",y.genderScore=Math.min(.99,u));let p=A.argMax(m.find(L=>L.shape[1]===100),1),v=(await p.data())[0];A.dispose(p);let S=await m.find(L=>L.shape[1]===100).data();y.age=Math.round(S[v-1]>S[v+1]?10*v-100*S[v-1]:10*v+100*S[v+1])/10;let O=m.find(L=>L.shape[1]===1024),C=O?await O.data():[];y.descriptor=Array.from(C),m.forEach(L=>A.dispose(L))}g5[o]=y,_1=n,d(y)}))}function P5(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Je(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function oo(e,t,o){let n=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/n,e.startPoint[0]/r,e.endPoint[1]/n,e.endPoint[0]/r]];return A.image.cropAndResize(t,s,[0],o)}function no(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:o,endPoint:n,palmLandmarks:r,confidence:e.confidence}}function v5(e,t=1.5){let o=Je(e),n=P5(e),r=[t*n[0]/2,t*n[1]/2],s=[o[0]-r[0],o[1]-r[1]],a=[o[0]+r[0],o[1]+r[1]];return{startPoint:s,endPoint:a,palmLandmarks:e.palmLandmarks}}function M5(e){let t=Je(e),o=P5(e),r=Math.max(...o)/2,s=[t[0]-r,t[1]-r],a=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:a,palmLandmarks:e.palmLandmarks}}function fr(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function ro(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return fr(o)}var eo=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function K0(e,t){let o=0;for(let n=0;n[o.x,o.y]),this.anchorsTensor=A.tensor2d(this.anchors),this.inputSize=this.model&&this.model.inputs&&this.model.inputs[0].shape?this.model.inputs[0].shape[2]:0,this.inputSizeTensor=A.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=A.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let o={};o.boxOffsets=A.slice(t,[0,0],[-1,2]),o.boxSizes=A.slice(t,[0,2],[-1,2]),o.div=A.div(o.boxOffsets,this.inputSizeTensor),o.boxCenterPoints=A.add(o.div,this.anchorsTensor),o.halfBoxSizes=A.div(o.boxSizes,this.doubleInputSizeTensor),o.sub=A.sub(o.boxCenterPoints,o.halfBoxSizes),o.startPoints=A.mul(o.sub,this.inputSizeTensor),o.add=A.add(o.boxCenterPoints,o.halfBoxSizes),o.endPoints=A.mul(o.add,this.inputSizeTensor);let n=A.concat2d([o.startPoints,o.endPoints],1);return Object.keys(o).forEach(r=>A.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=A.reshape(t,[-1,7,2]),n.div=A.div(n.reshape,this.inputSizeTensor),n.landmarks=A.add(n.div,this.anchors[o]);let r=A.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>A.dispose(n[s])),r}async predict(t,o){let n={};n.resize=A.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=A.div(n.resize,N.tf127),n.image=A.sub(n.div,N.tf1),n.batched=this.model.execute(n.image),n.predictions=A.squeeze(n.batched),n.slice=A.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=A.sigmoid(n.slice),n.scores=A.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=A.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await A.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let s=await n.nms.array(),a=[];for(let i of s){let l={};l.box=A.slice(n.norm,[i,0],[1,-1]),l.slice=A.slice(n.predictions,[i,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,i),l.palmLandmarks=A.reshape(l.norm,[-1,2]);let x=await l.box.data(),d=x.slice(0,2),y=x.slice(2,4),c=await l.palmLandmarks.array(),f={startPoint:d,endPoint:y,palmLandmarks:c,confidence:r[i]},b=no(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);a.push(b),Object.keys(l).forEach(m=>A.dispose(l[m]))}return Object.keys(n).forEach(i=>A.dispose(n[i])),a}};var hr=5,io=1.65,lo=[0,5,9,13,17,1,2],br=0,gr=2,yo=0,T5=class{constructor(t,o){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(a=>a[0]),n=t.map(a=>a[1]),r=[Math.min(...o),Math.min(...n)],s=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,o){let n=t.map(s=>Nt([...s,1],o)),r=this.calculateLandmarksBoundingBox(n);return 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O=this.transformRawCoords(S,m,d,b),C=this.getBoxForHandLandmarks(O);this.storedBoxes[l]={...C,confidence:v};let L={landmarks:O,confidence:v,boxConfidence:x.confidence,fingerConfidence:v,box:{topLeft:C.startPoint,bottomRight:C.endPoint}};i.push(L)}else this.storedBoxes[l]=null;A.dispose(p)}else{let d=v5(M5(x),io),y={confidence:x.confidence,boxConfidence:x.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};i.push(y)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>o.hand.maxDetected&&(i.length=o.hand.maxDetected),i}};var 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i=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],l=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let x=w5(a);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:a,annotations:s,landmarks:x})}return n}async function Ht(e){var o,n;T.initial&&(we=null,ke=null),!we||!ke?[we,ke]=await Promise.all([e.hand.enabled?I((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?I((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&h("cached 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i=A.unstack(n.scores,1);A.dispose(i[Mo]),i.splice(Mo,1),n.filtered=A.stack(i,1),A.dispose(i),n.max=A.max(n.filtered,1),n.argmax=A.argMax(n.filtered,1);let l=0;n.nms=await A.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let x=await n.nms.data(),d=await n.max.data(),y=await n.argmax.data();for(let c of Array.from(x)){let f=A.slice(n.boxes,c,1),b=await f.data();A.dispose(f);let m=[b[1],b[0],b[3]-b[1],b[2]-b[0]],M=f5(m,Cr),P=[Math.trunc(m[0]*Z0[0]),Math.trunc(m[1]*Z0[1]),Math.trunc(m[2]*Z0[0]),Math.trunc(m[3]*Z0[1])],u=d[c],p=jr[y[c]],v={id:l++,score:u,box:P,boxRaw:M,label:p};o.push(v)}return Object.keys(n).forEach(c=>A.dispose(n[c])),o.sort((c,f)=>f.score-c.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function Zt(e,t,o){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&o0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=A.image.cropAndResize(e,[s],[0],[oe[1][0],oe[1][1]],"bilinear"),r.div=A.div(r.crop,N.tf255),[r.score,r.keypoints]=o0[1].execute(r.div,["Identity_1","Identity"]);let a=(await r.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(o.hand.minConfidence||0)){n.fingerScore=i,r.reshaped=A.reshape(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(y=>[y[0]/oe[1][1],y[1]/oe[1][0],y[2]||0]).map(y=>[y[0]*t.boxRaw[2],y[1]*t.boxRaw[3],y[2]||0]);n.keypoints=d.map(y=>[Z0[0]*(y[0]+t.boxRaw[0]),Z0[1]*(y[1]+t.boxRaw[1]),y[2]||0]),n.landmarks=w5(n.keypoints);for(let y of 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0:M.enabled)&&!e.models.emotion&&(e.models.emotion=W1(e.config)),e.config.face.enabled&&((P=e.config.face.iris)==null?void 0:P.enabled)&&!((u=e.config.face.attention)!=null&&u.enabled)&&!e.models.faceiris&&(e.models.faceiris=Z1(e.config)),e.config.face.enabled&&((p=e.config.face.mesh)==null?void 0:p.enabled)&&!e.models.facemesh&&(e.models.facemesh=J1(e.config)),e.config.face.enabled&&((v=e.config.face.gear)==null?void 0:v.enabled)&&!e.models.gear&&(e.models.gear=H2(e.config)),e.config.face.enabled&&((w=e.config.face.ssrnet)==null?void 0:w.enabled)&&!e.models.ssrnetage&&(e.models.ssrnetage=q2(e.config)),e.config.face.enabled&&((S=e.config.face.ssrnet)==null?void 0:S.enabled)&&!e.models.ssrnetgender&&(e.models.ssrnetgender=K2(e.config)),e.config.face.enabled&&((O=e.config.face.mobilefacenet)==null?void 0:O.enabled)&&!e.models.mobilefacenet&&(e.models.mobilefacenet=F1(e.config)),e.config.hand.enabled&&!e.models.handtrack&&((L=(C=e.config.hand.detector)==null?void 0:C.modelPath)==null?void 0:L.includes("handtrack"))&&(e.models.handtrack=wo(e.config)),e.config.hand.enabled&&e.config.hand.landmarks&&!e.models.handskeleton&&((F=(Z=e.config.hand.detector)==null?void 0:Z.modelPath)==null?void 0:F.includes("handtrack"))&&(e.models.handskeleton=ko(e.config)),e.config.object.enabled&&!e.models.centernet&&((Y=(G=e.config.object)==null?void 0:G.modelPath)==null?void 0:Y.includes("centernet"))&&(e.models.centernet=w1(e.config)),e.config.object.enabled&&!e.models.nanodet&&((R0=(z=e.config.object)==null?void 0:z.modelPath)==null?void 0:R0.includes("nanodet"))&&(e.models.nanodet=Bo(e.config)),e.config.segmentation.enabled&&!e.models.segmentation&&(e.models.segmentation=a2(e.config));for await(let h0 of Object.keys(e.models))e.models[h0]&&typeof e.models[h0]!="undefined"&&(e.models[h0]=await e.models[h0])}async function l2(e){let t=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"];for(let o of Object.keys(e.models)){let n=e.models[o];if(!n)continue;let 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A.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");e.config.debug&&h(`wasm execution: ${r?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),e.config.debug&&!r&&h("warning: wasm simd support is not enabled")}try{await A.setBackend(e.config.backend),await A.ready(),Z2()}catch(r){return h("error: cannot set backend:",e.config.backend,r),!1}}if(A.getBackend()==="humangl"&&(A.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),A.ENV.set("WEBGL_CPU_FORWARD",!0),A.ENV.set("WEBGL_USE_SHAPES_UNIFORMS",!0),A.ENV.set("CPU_HANDOFF_SIZE_THRESHOLD",256),typeof e.config.deallocate!="undefined"&&e.config.deallocate&&(h("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),A.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0)),A.backend().getGPGPUContext)){let n=await A.backend().getGPGPUContext().gl;e.config.debug&&h(`gl version:${n.getParameter(n.VERSION)} renderer:${n.getParameter(n.RENDERER)}`)}A.getBackend(),A.enableProdMode(),await A.ready(),e.performance.initBackend=Math.trunc(g()-o),e.config.backend=A.getBackend(),await T.updateBackend(),Yr()}return!0}function E5(e,t){for(let o of e){let n={kernelName:o,backendName:t.backend,kernelFunc:()=>{t.debug&&h("kernelFunc",o,t.backend)}};A.registerKernel(n)}T.kernels=A.getKernelsForBackend(A.getBackend()).map(o=>o.kernelName.toLowerCase())}var tn={};ne(tn,{all:()=>p2,body:()=>Se,canvas:()=>m2,face:()=>je,gesture:()=>Oe,hand:()=>Ce,object:()=>We,options:()=>a0,person:()=>f2});var g0=e=>{if(!e)h("draw error: invalid canvas");else if(!e.getContext)h("draw error: canvas context not defined");else{let t=e.getContext("2d");if(!t)h("draw error: cannot get canvas context");else return t}return null},ce=e=>Math.round(e*180/Math.PI),D0=(e,t)=>{if(!t.useDepth||typeof e=="undefined")return t.color;let o=Uint8ClampedArray.from([127+2*e,127-2*e,255]);return`rgba(${o[0]}, ${o[1]}, ${o[2]}, ${t.alpha})`};function X0(e,t,o,n,r){e.fillStyle=D0(n,r),e.beginPath(),e.arc(t,o,r.pointSize,0,2*Math.PI),e.fill()}function W0(e,t,o,n,r,s){if(e.beginPath(),e.lineWidth=s.lineWidth,s.useCurves){let a=(t+t+n)/2,i=(o+o+r)/2;e.ellipse(a,i,n/2,r/2,0,0,2*Math.PI)}else e.moveTo(t+s.roundRect,o),e.lineTo(t+n-s.roundRect,o),e.quadraticCurveTo(t+n,o,t+n,o+s.roundRect),e.lineTo(t+n,o+r-s.roundRect),e.quadraticCurveTo(t+n,o+r,t+n-s.roundRect,o+r),e.lineTo(t+s.roundRect,o+r),e.quadraticCurveTo(t,o+r,t,o+r-s.roundRect),e.lineTo(t,o+s.roundRect),e.quadraticCurveTo(t,o,t+s.roundRect,o),e.closePath();e.stroke()}function x2(e,t,o){if(!(t.length<2)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let n of t)e.strokeStyle=D0(n[2],o),e.lineTo(Math.trunc(n[0]),Math.trunc(n[1]));e.stroke(),o.fillPolygons&&(e.closePath(),e.fill())}}function en(e,t,o){if(!(t.length<2)){if(e.lineWidth=o.lineWidth,!o.useCurves||t.length<=2){x2(e,t,o);return}e.moveTo(t[0][0],t[0][1]);for(let n=0;n0){let n=e.emotion.map(r=>`${Math.trunc(100*r.score)}% ${r.emotion}`);n.length>3&&(n.length=3),o.push(n.join(" "))}e.rotation&&e.rotation.angle&&e.rotation.gaze&&(e.rotation.angle.roll&&o.push(`roll: ${ce(e.rotation.angle.roll)}\xB0 yaw:${ce(e.rotation.angle.yaw)}\xB0 pitch:${ce(e.rotation.angle.pitch)}\xB0`),e.rotation.gaze.bearing&&o.push(`gaze: ${ce(e.rotation.gaze.bearing)}\xB0`)),o.length===0&&o.push("face"),t.fillStyle=H.color;for(let n=o.length-1;n>=0;n--){let r=Math.max(e.box[0],0),s=n*H.lineHeight+e.box[1];H.shadowColor&&H.shadowColor!==""&&(t.fillStyle=H.shadowColor,t.fillText(o[n],r+5,s+16)),t.fillStyle=H.labelColor,t.fillText(o[n],r+4,s+15)}}}function _r(e,t){if(e.annotations&&e.annotations.leftEyeIris&&e.annotations.leftEyeIris[0]){t.strokeStyle=H.useDepth?"rgba(255, 200, 255, 0.3)":H.color,t.beginPath();let o=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,n=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),H.fillPolygons&&(t.fillStyle=H.useDepth?"rgba(255, 255, 200, 0.3)":H.color,t.fill())}if(e.annotations&&e.annotations.rightEyeIris&&e.annotations.rightEyeIris[0]){t.strokeStyle=H.useDepth?"rgba(255, 200, 255, 0.3)":H.color,t.beginPath();let o=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),H.fillPolygons&&(t.fillStyle=H.useDepth?"rgba(255, 255, 200, 0.3)":H.color,t.fill())}}function $r(e,t){var o;if(H.drawGaze&&((o=e.rotation)==null?void 0:o.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*ce(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*ce(e.rotation.angle.pitch)/90,s=new Path2D(` - M ${e.box[0]+e.box[2]/2} ${e.box[1]} +`; + +// src/image/imagefx.ts +var collect = (source, prefix, collection) => { + const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); + source.replace(r, (match3, name) => { + collection[name] = 0; + return match3; + }); +}; +var GLProgram = class { + constructor(gl, vertexSource, fragmentSource) { + __publicField(this, "uniform", {}); + __publicField(this, "attribute", {}); + __publicField(this, "gl"); + __publicField(this, "id"); + __publicField(this, "compile", (source, type) => { + const shader = this.gl.createShader(type); + if (!shader) { + log("filter: could not create shader"); + return null; + } + this.gl.shaderSource(shader, source); + this.gl.compileShader(shader); + if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { + log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`); + return null; + } + return shader; + }); + this.gl = gl; + const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); + const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); + this.id = this.gl.createProgram(); + if (!vertexShader || !fragmentShader) + return; + if (!this.id) { + log("filter: could not create webgl program"); + return; + } + this.gl.attachShader(this.id, vertexShader); + this.gl.attachShader(this.id, fragmentShader); + this.gl.linkProgram(this.id); + if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { + log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`); + return; + } + this.gl.useProgram(this.id); + collect(vertexSource, "attribute", this.attribute); + for (const a in this.attribute) + this.attribute[a] = this.gl.getAttribLocation(this.id, a); + collect(vertexSource, "uniform", this.uniform); + collect(fragmentSource, "uniform", this.uniform); + for (const u in this.uniform) + this.uniform[u] = this.gl.getUniformLocation(this.id, u); + } +}; +function GLImageFilter() { + let drawCount = 0; + let sourceTexture = null; + let lastInChain = false; + let currentFramebufferIndex = -1; + let tempFramebuffers = [null, null]; + let filterChain = []; + let vertexBuffer = null; + let currentProgram = null; + const fxcanvas = canvas(100, 100); + const shaderProgramCache = {}; + const DRAW = { INTERMEDIATE: 1 }; + const gl = fxcanvas.getContext("webgl"); + if (!gl) { + log("filter: cannot get webgl context"); + return; + } + this.gl = gl; + function resize(width, height) { + if (width === fxcanvas.width && height === fxcanvas.height) + return; + fxcanvas.width = width; + fxcanvas.height = height; + if (!vertexBuffer) { + const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); + vertexBuffer = gl.createBuffer(); + gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); + gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); + gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); + } + gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); + tempFramebuffers = [null, null]; + } + function createFramebufferTexture(width, height) { + const fbo = gl.createFramebuffer(); + gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); + const renderbuffer = gl.createRenderbuffer(); + gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); + const texture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, texture); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); + gl.bindTexture(gl.TEXTURE_2D, null); + gl.bindFramebuffer(gl.FRAMEBUFFER, null); + return { fbo, texture }; + } + function getTempFramebuffer(index2) { + tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); + return tempFramebuffers[index2]; + } + function draw(flags = 0) { + if (!currentProgram) + return; + let source = null; + let target = null; + let flipY = false; + if (drawCount === 0) + source = sourceTexture; + else + source = getTempFramebuffer(currentFramebufferIndex).texture || null; + drawCount++; + if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { + target = null; + flipY = drawCount % 2 === 0; + } else { + currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; + target = getTempFramebuffer(currentFramebufferIndex).fbo || null; + } + gl.bindTexture(gl.TEXTURE_2D, source); + gl.bindFramebuffer(gl.FRAMEBUFFER, target); + gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); + gl.drawArrays(gl.TRIANGLES, 0, 6); + } + function compileShader(fragmentSource) { + if (shaderProgramCache[fragmentSource]) { + currentProgram = shaderProgramCache[fragmentSource]; + gl.useProgram((currentProgram ? currentProgram.id : null) || null); + return currentProgram; + } + currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); + if (!currentProgram) { + log("filter: could not get webgl program"); + return null; + } + const floatSize = Float32Array.BYTES_PER_ELEMENT; + const vertSize = 4 * floatSize; + gl.enableVertexAttribArray(currentProgram.attribute["pos"]); + gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); + gl.enableVertexAttribArray(currentProgram.attribute["uv"]); + gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); + shaderProgramCache[fragmentSource] = currentProgram; + return currentProgram; + } + const filter = { + colorMatrix: (matrix) => { + const m = new Float32Array(matrix); + m[4] /= 255; + m[9] /= 255; + m[14] /= 255; + m[19] /= 255; + const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; + const program = compileShader(shader); + if (!program) + return; + gl.uniform1fv(program.uniform["m"], m); + draw(); + }, + brightness: (brightness) => { + const b = (brightness || 0) + 1; + filter.colorMatrix([ + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + b, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + saturation: (amount) => { + const x = (amount || 0) * 2 / 3 + 1; + const y = (x - 1) * -0.5; + filter.colorMatrix([ + x, + y, + y, + 0, + 0, + y, + x, + y, + 0, + 0, + y, + y, + x, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + desaturate: () => { + filter.saturation(-1); + }, + contrast: (amount) => { + const v = (amount || 0) + 1; + const o = -128 * (v - 1); + filter.colorMatrix([ + v, + 0, + 0, + 0, + o, + 0, + v, + 0, + 0, + o, + 0, + 0, + v, + 0, + o, + 0, + 0, + 0, + 1, + 0 + ]); + }, + negative: () => { + filter.contrast(-2); + }, + hue: (rotation) => { + rotation = (rotation || 0) / 180 * Math.PI; + const cos = Math.cos(rotation); + const sin = Math.sin(rotation); + const lumR = 0.213; + const lumG = 0.715; + const lumB = 0.072; + filter.colorMatrix([ + lumR + cos * (1 - lumR) + sin * -lumR, + lumG + cos * -lumG + sin * -lumG, + lumB + cos * -lumB + sin * (1 - lumB), + 0, + 0, + lumR + cos * -lumR + sin * 0.143, + lumG + cos * (1 - lumG) + sin * 0.14, + lumB + cos * -lumB + sin * -0.283, + 0, + 0, + lumR + cos * -lumR + sin * -(1 - lumR), + lumG + cos * -lumG + sin * lumG, + lumB + cos * (1 - lumB) + sin * lumB, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + desaturateLuminance: () => { + filter.colorMatrix([ + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0.2764723, + 0.929708, + 0.0938197, + 0, + -37.1, + 0, + 0, + 0, + 1, + 0 + ]); + }, + sepia: () => { + filter.colorMatrix([ + 0.393, + 0.7689999, + 0.18899999, + 0, + 0, + 0.349, + 0.6859999, + 0.16799999, + 0, + 0, + 0.272, + 0.5339999, + 0.13099999, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + brownie: () => { + filter.colorMatrix([ + 0.5997023498159715, + 0.34553243048391263, + -0.2708298674538042, + 0, + 47.43192855600873, + -0.037703249837783157, + 0.8609577587992641, + 0.15059552388459913, + 0, + -36.96841498319127, + 0.24113635128153335, + -0.07441037908422492, + 0.44972182064877153, + 0, + -7.562075277591283, + 0, + 0, + 0, + 1, + 0 + ]); + }, + vintagePinhole: () => { + filter.colorMatrix([ + 0.6279345635605994, + 0.3202183420819367, + -0.03965408211312453, + 0, + 9.651285835294123, + 0.02578397704808868, + 0.6441188644374771, + 0.03259127616149294, + 0, + 7.462829176470591, + 0.0466055556782719, + -0.0851232987247891, + 0.5241648018700465, + 0, + 5.159190588235296, + 0, + 0, + 0, + 1, + 0 + ]); + }, + kodachrome: () => { + filter.colorMatrix([ + 1.1285582396593525, + -0.3967382283601348, + -0.03992559172921793, + 0, + 63.72958762196502, + -0.16404339962244616, + 1.0835251566291304, + -0.05498805115633132, + 0, + 24.732407896706203, + -0.16786010706155763, + -0.5603416277695248, + 1.6014850761964943, + 0, + 35.62982807460946, + 0, + 0, + 0, + 1, + 0 + ]); + }, + technicolor: () => { + filter.colorMatrix([ + 1.9125277891456083, + -0.8545344976951645, + -0.09155508482755585, + 0, + 11.793603434377337, + -0.3087833385928097, + 1.7658908555458428, + -0.10601743074722245, + 0, + -70.35205161461398, + -0.231103377548616, + -0.7501899197440212, + 1.847597816108189, + 0, + 30.950940869491138, + 0, + 0, + 0, + 1, + 0 + ]); + }, + polaroid: () => { + filter.colorMatrix([ + 1.438, + -0.062, + -0.062, + 0, + 0, + -0.122, + 1.378, + -0.122, + 0, + 0, + -0.016, + -0.016, + 1.483, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + shiftToBGR: () => { + filter.colorMatrix([ + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0 + ]); + }, + convolution: (matrix) => { + const m = new Float32Array(matrix); + const pixelSizeX = 1 / fxcanvas.width; + const pixelSizeY = 1 / fxcanvas.height; + const program = compileShader(convolution); + if (!program) + return; + gl.uniform1fv(program.uniform["m"], m); + gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); + draw(); + }, + detectEdges: () => { + filter.convolution.call(this, [ + 0, + 1, + 0, + 1, + -4, + 1, + 0, + 1, + 0 + ]); + }, + sobelX: () => { + filter.convolution.call(this, [ + -1, + 0, + 1, + -2, + 0, + 2, + -1, + 0, + 1 + ]); + }, + sobelY: () => { + filter.convolution.call(this, [ + -1, + -2, + -1, + 0, + 0, + 0, + 1, + 2, + 1 + ]); + }, + sharpen: (amount) => { + const a = amount || 1; + filter.convolution.call(this, [ + 0, + -1 * a, + 0, + -1 * a, + 1 + 4 * a, + -1 * a, + 0, + -1 * a, + 0 + ]); + }, + emboss: (size2) => { + const s = size2 || 1; + filter.convolution.call(this, [ + -2 * s, + -1 * s, + 0, + -1 * s, + 1, + 1 * s, + 0, + 1 * s, + 2 * s + ]); + }, + blur: (size2) => { + const blurSizeX = size2 / 7 / fxcanvas.width; + const blurSizeY = size2 / 7 / fxcanvas.height; + const program = compileShader(blur); + if (!program) + return; + gl.uniform2f(program.uniform["px"], 0, blurSizeY); + draw(DRAW.INTERMEDIATE); + gl.uniform2f(program.uniform["px"], blurSizeX, 0); + draw(); + }, + pixelate: (size2) => { + const blurSizeX = size2 / fxcanvas.width; + const blurSizeY = size2 / fxcanvas.height; + const program = compileShader(pixelate); + if (!program) + return; + gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); + draw(); + } + }; + this.add = function(name) { + const args = Array.prototype.slice.call(arguments, 1); + const func = filter[name]; + filterChain.push({ func, args }); + }; + this.reset = function() { + filterChain = []; + }; + this.get = function() { + return filterChain; + }; + this.apply = function(image24) { + resize(image24.width, image24.height); + drawCount = 0; + if (!sourceTexture) + sourceTexture = gl.createTexture(); + gl.bindTexture(gl.TEXTURE_2D, sourceTexture); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image24); + for (let i = 0; i < filterChain.length; i++) { + lastInChain = i === filterChain.length - 1; + const f = filterChain[i]; + f.func.apply(this, f.args || []); + } + return fxcanvas; + }; + this.draw = function(image24) { + this.add("brightness", 0); + return this.apply(image24); + }; +} + +// src/image/enhance.ts +async function histogramEqualization(inputImage) { + const squeeze11 = inputImage.shape.length === 4 ? tfjs_esm_exports.squeeze(inputImage) : inputImage; + const channels = tfjs_esm_exports.split(squeeze11, 3, 2); + const min2 = [tfjs_esm_exports.min(channels[0]), tfjs_esm_exports.min(channels[1]), tfjs_esm_exports.min(channels[2])]; + const max4 = [tfjs_esm_exports.max(channels[0]), tfjs_esm_exports.max(channels[1]), tfjs_esm_exports.max(channels[2])]; + const absMax = await Promise.all(max4.map((channel) => channel.data())); + const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); + const sub11 = [tfjs_esm_exports.sub(channels[0], min2[0]), tfjs_esm_exports.sub(channels[1], min2[1]), tfjs_esm_exports.sub(channels[2], min2[2])]; + const range = [tfjs_esm_exports.sub(max4[0], min2[0]), tfjs_esm_exports.sub(max4[1], min2[1]), tfjs_esm_exports.sub(max4[2], min2[2])]; + const fact = [tfjs_esm_exports.div(maxValue, range[0]), tfjs_esm_exports.div(maxValue, range[1]), tfjs_esm_exports.div(maxValue, range[2])]; + const enh = [tfjs_esm_exports.mul(sub11[0], fact[0]), tfjs_esm_exports.mul(sub11[1], fact[1]), tfjs_esm_exports.mul(sub11[2], fact[2])]; + const rgb2 = tfjs_esm_exports.stack([enh[0], enh[1], enh[2]], 2); + const reshape8 = tfjs_esm_exports.reshape(rgb2, [1, squeeze11.shape[0], squeeze11.shape[1], 3]); + tfjs_esm_exports.dispose([...channels, ...min2, ...max4, ...sub11, ...range, ...fact, ...enh, rgb2, squeeze11]); + return reshape8; +} + +// src/image/image.ts +var maxSize = 2048; +var inCanvas = null; +var outCanvas = null; +var tmpCanvas = null; +var fx; +var last = { + inputSum: 0, + cacheDiff: 1, + sumMethod: 0, + inputTensor: void 0 +}; +function canvas(width, height) { + let c; + if (env.browser) { + if (env.worker) { + if (typeof OffscreenCanvas === "undefined") + throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); + c = new OffscreenCanvas(width, height); + } else { + if (typeof document === "undefined") + throw new Error("canvas error: attempted to run in browser but DOM is not defined"); + c = document.createElement("canvas"); + c.width = width; + c.height = height; + } + } else { + if (typeof env.Canvas !== "undefined") + c = new env.Canvas(width, height); + else if (typeof globalThis.Canvas !== "undefined") + c = new globalThis.Canvas(width, height); + } + return c; +} +function copy(input, output) { + const outputCanvas = output || canvas(input.width, input.height); + const ctx = outputCanvas.getContext("2d"); + ctx.drawImage(input, 0, 0); + return outputCanvas; +} +async function process2(input, config3, getTensor = true) { + if (!input) { + if (config3.debug) + log("input error: input is missing"); + return { tensor: null, canvas: null }; + } + if (!(input instanceof Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof env.Canvas !== "undefined" && input instanceof env.Canvas) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { + throw new Error("input error: type is not recognized"); + } + if (input instanceof Tensor) { + let tensor3 = null; + if (input["isDisposedInternal"]) + throw new Error("input error: attempted to use tensor but it is disposed"); + if (!input["shape"]) + throw new Error("input error: attempted to use tensor without a shape"); + if (input.shape.length === 3) { + if (input.shape[2] === 3) { + tensor3 = tfjs_esm_exports.expandDims(input, 0); + } else if (input.shape[2] === 4) { + const rgb2 = tfjs_esm_exports.slice3d(input, [0, 0, 0], [-1, -1, 3]); + tensor3 = tfjs_esm_exports.expandDims(rgb2, 0); + tfjs_esm_exports.dispose(rgb2); + } + } else if (input.shape.length === 4) { + if (input.shape[3] === 3) { + tensor3 = tfjs_esm_exports.clone(input); + } else if (input.shape[3] === 4) { + tensor3 = tfjs_esm_exports.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); + } + } + if (tensor3 == null || tensor3.shape.length !== 4 || tensor3.shape[0] !== 1 || tensor3.shape[3] !== 3) + throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input["shape"]}`); + if (tensor3.dtype === "int32") { + const cast5 = tfjs_esm_exports.cast(tensor3, "float32"); + tfjs_esm_exports.dispose(tensor3); + tensor3 = cast5; + } + return { tensor: tensor3, canvas: config3.filter.return ? outCanvas : null }; + } else { + if (typeof input["readyState"] !== "undefined" && input["readyState"] <= 2) { + if (config3.debug) + log("input stream is not ready"); + return { tensor: null, canvas: inCanvas }; + } + const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; + const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; + if (!originalWidth || !originalHeight) { + if (config3.debug) + log("cannot determine input dimensions"); + return { tensor: null, canvas: inCanvas }; + } + let targetWidth = originalWidth; + let targetHeight = originalHeight; + if (targetWidth > maxSize) { + targetWidth = maxSize; + targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); + } + if (targetHeight > maxSize) { + targetHeight = maxSize; + targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); + } + if ((config3.filter.width || 0) > 0) + targetWidth = config3.filter.width; + else if ((config3.filter.height || 0) > 0) + targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); + if ((config3.filter.height || 0) > 0) + targetHeight = config3.filter.height; + else if ((config3.filter.width || 0) > 0) + targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); + if (!targetWidth || !targetHeight) + throw new Error("input error: cannot determine dimension"); + if (!inCanvas || (inCanvas == null ? void 0 : inCanvas.width) !== targetWidth || (inCanvas == null ? void 0 : inCanvas.height) !== targetHeight) + inCanvas = canvas(targetWidth, targetHeight); + const inCtx = inCanvas.getContext("2d"); + if (typeof ImageData !== "undefined" && input instanceof ImageData) { + inCtx.putImageData(input, 0, 0); + } else { + if (config3.filter.flip && typeof inCtx.translate !== "undefined") { + inCtx.translate(originalWidth, 0); + inCtx.scale(-1, 1); + inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height); + inCtx.setTransform(1, 0, 0, 1, 0, 0); + } else { + inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height); + } + } + if (!outCanvas || inCanvas.width !== outCanvas.width || (inCanvas == null ? void 0 : inCanvas.height) !== (outCanvas == null ? void 0 : outCanvas.height)) + outCanvas = canvas(inCanvas.width, inCanvas.height); + if (config3.filter.enabled && env.webgl.supported) { + if (!fx) + fx = env.browser ? new GLImageFilter() : null; + env.filter = !!fx; + if (!fx || !fx.add) { + if (config3.debug) + log("input process error: cannot initialize filters"); + env.webgl.supported = false; + config3.filter.enabled = false; + copy(inCanvas, outCanvas); + } else { + fx.reset(); + if (config3.filter.brightness !== 0) + fx.add("brightness", config3.filter.brightness); + if (config3.filter.contrast !== 0) + fx.add("contrast", config3.filter.contrast); + if (config3.filter.sharpness !== 0) + fx.add("sharpen", config3.filter.sharpness); + if (config3.filter.blur !== 0) + fx.add("blur", config3.filter.blur); + if (config3.filter.saturation !== 0) + fx.add("saturation", config3.filter.saturation); + if (config3.filter.hue !== 0) + fx.add("hue", config3.filter.hue); + if (config3.filter.negative) + fx.add("negative"); + if (config3.filter.sepia) + fx.add("sepia"); + if (config3.filter.vintage) + fx.add("brownie"); + if (config3.filter.sepia) + fx.add("sepia"); + if (config3.filter.kodachrome) + fx.add("kodachrome"); + if (config3.filter.technicolor) + fx.add("technicolor"); + if (config3.filter.polaroid) + fx.add("polaroid"); + if (config3.filter.pixelate !== 0) + fx.add("pixelate", config3.filter.pixelate); + if (fx.get() > 0) + outCanvas = fx.apply(inCanvas); + else + outCanvas = fx.draw(inCanvas); + } + } else { + copy(inCanvas, outCanvas); + if (fx) + fx = null; + env.filter = !!fx; + } + if (!getTensor) + return { tensor: null, canvas: outCanvas }; + if (!outCanvas) + throw new Error("canvas error: cannot create output"); + let pixels; + let depth = 3; + if (typeof ImageData !== "undefined" && input instanceof ImageData || input["data"] && input["width"] && input["height"]) { + if (env.browser && tfjs_esm_exports.browser) { + pixels = tfjs_esm_exports.browser ? tfjs_esm_exports.browser.fromPixels(input) : null; + } else { + depth = input["data"].length / input["height"] / input["width"]; + const arr = new Uint8Array(input["data"]["buffer"]); + pixels = tfjs_esm_exports.tensor(arr, [input["height"], input["width"], depth], "int32"); + } + } else { + if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) + tmpCanvas = canvas(outCanvas.width, outCanvas.height); + if (tfjs_esm_exports.browser && env.browser) { + if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { + pixels = tfjs_esm_exports.browser.fromPixels(outCanvas); + } else { + tmpCanvas = copy(outCanvas); + pixels = tfjs_esm_exports.browser.fromPixels(tmpCanvas); + } + } else { + const tempCanvas = copy(outCanvas); + const tempCtx = tempCanvas.getContext("2d"); + const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); + depth = tempData.data.length / targetWidth / targetHeight; + const arr = new Uint8Array(tempData.data.buffer); + pixels = tfjs_esm_exports.tensor(arr, [targetWidth, targetHeight, depth]); + } + } + if (depth === 4) { + const rgb2 = tfjs_esm_exports.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); + tfjs_esm_exports.dispose(pixels); + pixels = rgb2; + } + if (!pixels) + throw new Error("input error: cannot create tensor"); + const casted = tfjs_esm_exports.cast(pixels, "float32"); + const tensor3 = config3.filter.equalization ? await histogramEqualization(casted) : tfjs_esm_exports.expandDims(casted, 0); + tfjs_esm_exports.dispose([pixels, casted]); + return { tensor: tensor3, canvas: config3.filter.return ? outCanvas : null }; + } +} +async function skip(config3, input) { + let skipFrame = false; + if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) + return skipFrame; + if (!last.inputTensor) { + last.inputTensor = tfjs_esm_exports.clone(input); + } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { + tfjs_esm_exports.dispose(last.inputTensor); + last.inputTensor = tfjs_esm_exports.clone(input); + } else { + const t = {}; + t.diff = tfjs_esm_exports.sub(input, last.inputTensor); + t.squared = tfjs_esm_exports.mul(t.diff, t.diff); + t.sum = tfjs_esm_exports.sum(t.squared); + const diffSum = await t.sum.data(); + const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; + tfjs_esm_exports.dispose([last.inputTensor, t.diff, t.squared, t.sum]); + last.inputTensor = tfjs_esm_exports.clone(input); + skipFrame = diffRelative <= (config3.cacheSensitivity || 0); + } + return skipFrame; +} +async function compare(config3, input1, input2) { + const t = {}; + if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { + if (!config3.debug) + log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); + return 0; + } + if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { + if (!config3.debug) + log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); + return 0; + } + t.input1 = tfjs_esm_exports.clone(input1); + t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tfjs_esm_exports.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tfjs_esm_exports.clone(input2); + t.diff = tfjs_esm_exports.sub(t.input1, t.input2); + t.squared = tfjs_esm_exports.mul(t.diff, t.diff); + t.sum = tfjs_esm_exports.sum(t.squared); + const diffSum = await t.sum.data(); + const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; + tfjs_esm_exports.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]); + return diffRelative; +} + +// src/util/env.ts +var Env = class { + constructor() { + __publicField(this, "browser"); + __publicField(this, "node"); + __publicField(this, "worker"); + __publicField(this, "platform", ""); + __publicField(this, "agent", ""); + __publicField(this, "backends", []); + __publicField(this, "initial"); + __publicField(this, "filter"); + __publicField(this, "tfjs"); + __publicField(this, "offscreen"); + __publicField(this, "perfadd", false); + __publicField(this, "wasm", { + supported: void 0, + backend: void 0, + simd: void 0, + multithread: void 0 + }); + __publicField(this, "webgl", { + supported: void 0, + backend: void 0, + version: void 0, + renderer: void 0 + }); + __publicField(this, "webgpu", { + supported: void 0, + backend: void 0, + adapter: void 0 + }); + __publicField(this, "cpu", { + model: void 0, + flags: [] + }); + __publicField(this, "kernels", []); + __publicField(this, "Canvas"); + __publicField(this, "Image"); + __publicField(this, "ImageData"); + this.browser = typeof navigator !== "undefined"; + this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; + this.tfjs = { version: version8["tfjs-core"] }; + this.offscreen = typeof OffscreenCanvas !== "undefined"; + this.initial = true; + this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; + if (typeof navigator !== "undefined") { + const raw = navigator.userAgent.match(/\(([^()]+)\)/g); + if (raw && raw[0]) { + const platformMatch = raw[0].match(/\(([^()]+)\)/g); + this.platform = platformMatch && platformMatch[0] ? platformMatch[0].replace(/\(|\)/g, "") : ""; + this.agent = navigator.userAgent.replace(raw[0], ""); + if (this.platform[1]) + this.agent = this.agent.replace(raw[1], ""); + this.agent = this.agent.replace(/ /g, " "); + } + } else if (typeof process !== "undefined") { + this.platform = `${process.platform} ${process.arch}`; + this.agent = `NodeJS ${process.version}`; + } + } + async updateBackend() { + this.backends = Object.keys(tfjs_esm_exports.engine().registryFactory); + this.wasm.supported = typeof WebAssembly !== "undefined"; + this.wasm.backend = this.backends.includes("wasm"); + if (this.wasm.supported && this.wasm.backend && tfjs_esm_exports.getBackend() === "wasm") { + this.wasm.simd = await tfjs_esm_exports.env().getAsync("WASM_HAS_SIMD_SUPPORT"); + this.wasm.multithread = await tfjs_esm_exports.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + } + const c = canvas(100, 100); + const ctx = c ? c.getContext("webgl2") : void 0; + this.webgl.supported = typeof ctx !== "undefined"; + this.webgl.backend = this.backends.includes("webgl"); + if (this.webgl.supported && this.webgl.backend && (tfjs_esm_exports.getBackend() === "webgl" || tfjs_esm_exports.getBackend() === "humangl")) { + const gl = tfjs_esm_exports.backend().gpgpu !== "undefined" ? await tfjs_esm_exports.backend().getGPGPUContext().gl : null; + if (gl) { + this.webgl.version = gl.getParameter(gl.VERSION); + this.webgl.renderer = gl.getParameter(gl.RENDERER); + } + } + this.webgpu.supported = this.browser && typeof navigator["gpu"] !== "undefined"; + this.webgpu.backend = this.backends.includes("webgpu"); + try { + if (this.webgpu.supported) + this.webgpu.adapter = (await navigator["gpu"].requestAdapter()).name; + } catch (e) { + this.webgpu.supported = false; + } + try { + this.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); + } catch (e) { + } + } + async updateCPU() { + const cpu = { model: "", flags: [] }; + if (this.node && this.platform.startsWith("linux")) { + } + if (!this["cpu"]) + Object.defineProperty(this, "cpu", { value: cpu }); + else + this["cpu"] = cpu; + } +}; +var env = new Env(); + +// src/tfjs/load.ts +var options = { + cacheModels: false, + verbose: true, + debug: false, + modelBasePath: "" +}; +async function httpHandler(url, init2) { + if (options.debug) + log("load model fetch:", url, init2); + return fetch(url, init2); +} +function setModelLoadOptions(config3) { + options.cacheModels = config3.cacheModels; + options.verbose = config3.debug; + options.modelBasePath = config3.modelBasePath; +} +async function loadModel(modelPath) { + const modelUrl = join(options.modelBasePath, modelPath || ""); + const modelPathSegments = modelUrl.split("/"); + const cachedModelName = "indexeddb://" + modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); + const cachedModels = await tfjs_esm_exports.io.listModels(); + const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); + const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init2) => httpHandler(url, init2) }; + const model18 = new GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions); + let loaded = false; + try { + model18.findIOHandler(); + if (options.debug) + log("model load handler:", model18.handler); + const artifacts = await model18.handler.load(); + model18.loadSync(artifacts); + if (options.verbose) + log("load model:", model18["modelUrl"]); + loaded = true; + } catch (err) { + log("error loading model:", modelUrl, err); + } + if (loaded && options.cacheModels && !modelCached) { + try { + const saveResult = await model18.save(cachedModelName); + log("model saved:", cachedModelName, saveResult); + } catch (err) { + log("error saving model:", modelUrl, err); + } + } + return model18; +} + +// package.json +var version9 = "2.7.0"; + +// src/models.ts +var models_exports = {}; +__export(models_exports, { + Models: () => Models, + load: () => load19, + reset: () => reset, + validate: () => validate2 +}); + +// src/gear/gear.ts +var model; +var last2 = []; +var raceNames = ["white", "black", "asian", "indian", "other"]; +var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; +var lastCount = 0; +var lastTime = 0; +var skipped = Number.MAX_SAFE_INTEGER; +async function load(config3) { + if (env.initial) + model = null; + if (!model) + model = await loadModel(config3.face["gear"]); + else if (config3.debug) + log("cached model:", model["modelUrl"]); + return model; +} +async function predict(image24, config3, idx, count2) { + var _a, _b; + if (!model) + return { age: 0, gender: "unknown", genderScore: 0, race: [] }; + const skipFrame = skipped < (((_a = config3.face["gear"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["gear"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime; + if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last2[idx]) { + skipped++; + return last2[idx]; + } + skipped = 0; + return new Promise(async (resolve) => { + var _a2, _b2; + if (!(model == null ? void 0 : model.inputs[0].shape)) + return; + const t = {}; + const box = [[0, 0.1, 0.9, 0.9]]; + t.resize = tfjs_esm_exports.image.cropAndResize(image24, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]); + const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; + if ((_a2 = config3.face["gear"]) == null ? void 0 : _a2.enabled) + [t.age, t.gender, t.race] = model.execute(t.resize, ["age_output", "gender_output", "race_output"]); + const gender = await t.gender.data(); + obj.gender = gender[0] > gender[1] ? "male" : "female"; + obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100; + const race = await t.race.data(); + for (let i = 0; i < race.length; i++) { + if (race[i] > (((_b2 = config3.face["gear"]) == null ? void 0 : _b2.minConfidence) || 0.2)) + obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); + } + obj.race.sort((a, b) => b.score - a.score); + const ageDistribution = Array.from(await t.age.data()); + const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); + let age = ageSorted[0][0]; + for (let i = 1; i < ageSorted.length; i++) + age += ageSorted[i][1] * (ageSorted[i][0] - age); + obj.age = Math.round(10 * age) / 10; + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + last2[idx] = obj; + lastCount = count2; + lastTime = now(); + resolve(obj); + }); +} + +// src/tfjs/constants.ts +var constants = { + tf255: 255, + tf1: 1, + tf2: 2, + tf05: 0.5, + tf127: 127.5, + rgb: [0.2989, 0.587, 0.114] +}; +function init() { + constants.tf255 = tfjs_esm_exports.scalar(255, "float32"); + constants.tf1 = tfjs_esm_exports.scalar(1, "float32"); + constants.tf2 = tfjs_esm_exports.scalar(2, "float32"); + constants.tf05 = tfjs_esm_exports.scalar(0.5, "float32"); + constants.tf127 = tfjs_esm_exports.scalar(127.5, "float32"); + constants.rgb = tfjs_esm_exports.tensor1d([0.2989, 0.587, 0.114], "float32"); +} + +// src/gear/ssrnet-age.ts +var model2; +var last3 = []; +var lastCount2 = 0; +var lastTime2 = 0; +var skipped2 = Number.MAX_SAFE_INTEGER; +async function load2(config3) { + if (env.initial) + model2 = null; + if (!model2) + model2 = await loadModel(config3.face["ssrnet"].modelPathAge); + else if (config3.debug) + log("cached model:", model2["modelUrl"]); + return model2; +} +async function predict2(image24, config3, idx, count2) { + var _a, _b, _c, _d; + if (!model2) + return { age: 0 }; + const skipFrame = skipped2 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime2; + if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) { + skipped2++; + return last3[idx]; + } + skipped2 = 0; + return new Promise(async (resolve) => { + if (!(model2 == null ? void 0 : model2.inputs) || !model2.inputs[0] || !model2.inputs[0].shape) + return; + const t = {}; + t.resize = tfjs_esm_exports.image.resizeBilinear(image24, [model2.inputs[0].shape[2], model2.inputs[0].shape[1]], false); + t.enhance = tfjs_esm_exports.mul(t.resize, constants.tf255); + const obj = { age: 0 }; + if (config3.face["ssrnet"].enabled) + t.age = model2.execute(t.enhance); + if (t.age) { + const data = await t.age.data(); + obj.age = Math.trunc(10 * data[0]) / 10; + } + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + last3[idx] = obj; + lastCount2 = count2; + lastTime2 = now(); + resolve(obj); + }); +} + +// src/gear/ssrnet-gender.ts +var model3; +var last4 = []; +var lastCount3 = 0; +var lastTime3 = 0; +var skipped3 = Number.MAX_SAFE_INTEGER; +var rgb = [0.2989, 0.587, 0.114]; +async function load3(config3) { + if (env.initial) + model3 = null; + if (!model3) + model3 = await loadModel(config3.face["ssrnet"].modelPathGender); + else if (config3.debug) + log("cached model:", model3["modelUrl"]); + return model3; +} +async function predict3(image24, config3, idx, count2) { + var _a, _b, _c, _d; + if (!model3) + return { gender: "unknown", genderScore: 0 }; + const skipFrame = skipped3 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime3; + if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last4[idx]) == null ? void 0 : _c.gender) && ((_d = last4[idx]) == null ? void 0 : _d.genderScore) > 0) { + skipped3++; + return last4[idx]; + } + skipped3 = 0; + return new Promise(async (resolve) => { + if (!(model3 == null ? void 0 : model3.inputs[0].shape)) + return; + const t = {}; + t.resize = tfjs_esm_exports.image.resizeBilinear(image24, [model3.inputs[0].shape[2], model3.inputs[0].shape[1]], false); + t.enhance = tfjs_esm_exports.tidy(() => { + const [red, green, blue] = tfjs_esm_exports.split(t.resize, 3, 3); + const redNorm = tfjs_esm_exports.mul(red, rgb[0]); + const greenNorm = tfjs_esm_exports.mul(green, rgb[1]); + const blueNorm = tfjs_esm_exports.mul(blue, rgb[2]); + const grayscale = tfjs_esm_exports.addN([redNorm, greenNorm, blueNorm]); + const normalize = tfjs_esm_exports.mul(tfjs_esm_exports.sub(grayscale, constants.tf05), 2); + return normalize; + }); + const obj = { gender: "unknown", genderScore: 0 }; + if (config3.face["ssrnet"].enabled) + t.gender = model3.execute(t.enhance); + const data = await t.gender.data(); + obj.gender = data[0] > data[1] ? "female" : "male"; + obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + last4[idx] = obj; + lastCount3 = count2; + lastTime3 = now(); + resolve(obj); + }); +} + +// src/face/antispoof.ts +var model4; +var cached = []; +var skipped4 = Number.MAX_SAFE_INTEGER; +var lastCount4 = 0; +var lastTime4 = 0; +async function load4(config3) { + var _a; + if (env.initial) + model4 = null; + if (!model4) + model4 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model4["modelUrl"]); + return model4; +} +async function predict4(image24, config3, idx, count2) { + var _a, _b; + if (!model4) + return 0; + const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime4; + const skipFrame = skipped4 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); + if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached[idx]) { + skipped4++; + return cached[idx]; + } + skipped4 = 0; + return new Promise(async (resolve) => { + const resize = tfjs_esm_exports.image.resizeBilinear(image24, [(model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[2] : 0, (model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[1] : 0], false); + const res = model4 == null ? void 0 : model4.execute(resize); + const num = (await res.data())[0]; + cached[idx] = Math.round(100 * num) / 100; + lastCount4 = count2; + lastTime4 = now(); + tfjs_esm_exports.dispose([resize, res]); + resolve(cached[idx]); + }); +} + +// src/face/facemeshcoords.ts +var meshAnnotations = { + silhouette: [ + 10, + 338, + 297, + 332, + 284, + 251, + 389, + 356, + 454, + 323, + 361, + 288, + 397, + 365, + 379, + 378, + 400, + 377, + 152, + 148, + 176, + 149, + 150, + 136, + 172, + 58, + 132, + 93, + 234, + 127, + 162, + 21, + 54, + 103, + 67, + 109 + ], + lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], + lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], + lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], + lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], + rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], + rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], + rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], + rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], + rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], + rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], + rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], + rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], + rightEyebrowLower: [35, 124, 46, 53, 52, 65], + rightEyeIris: [473, 474, 475, 476, 477], + leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], + leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], + leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], + leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], + leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], + leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], + leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], + leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], + leftEyebrowLower: [265, 353, 276, 283, 282, 295], + leftEyeIris: [468, 469, 470, 471, 472], + midwayBetweenEyes: [168], + noseTip: [1], + noseBottom: [2], + noseRightCorner: [98], + noseLeftCorner: [327], + rightCheek: [205], + leftCheek: [425] +}; +var meshLandmarks = { + count: 468, + mouth: 13, + symmetryLine: [13, meshAnnotations["midwayBetweenEyes"][0]] +}; +var blazeFaceLandmarks = { + leftEye: 0, + rightEye: 1, + nose: 2, + mouth: 3, + leftEar: 4, + rightEar: 5, + symmetryLine: [3, 2] +}; +var irisIndices = [ + { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, + { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, + { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, + { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, + { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, + { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, + { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, + { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, + { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] } +]; +var UV468 = [ + [0.499976992607117, 0.652534008026123], + [0.500025987625122, 0.547487020492554], + [0.499974012374878, 0.602371990680695], + [0.482113003730774, 0.471979022026062], + [0.500150978565216, 0.527155995368958], + [0.499909996986389, 0.498252987861633], + [0.499523013830185, 0.40106201171875], + [0.289712011814117, 0.380764007568359], + [0.499954998493195, 0.312398016452789], + [0.499987006187439, 0.269918978214264], + [0.500023007392883, 0.107050001621246], + [0.500023007392883, 0.666234016418457], + [0.5000159740448, 0.679224014282227], + [0.500023007392883, 0.692348003387451], + [0.499976992607117, 0.695277988910675], + [0.499976992607117, 0.70593398809433], + [0.499976992607117, 0.719385027885437], + [0.499976992607117, 0.737019002437592], + [0.499967992305756, 0.781370997428894], + [0.499816000461578, 0.562981009483337], + [0.473773002624512, 0.573909997940063], + [0.104906998574734, 0.254140973091125], + [0.365929991006851, 0.409575998783112], + [0.338757991790771, 0.41302502155304], + [0.311120003461838, 0.409460008144379], + [0.274657994508743, 0.389131009578705], + [0.393361985683441, 0.403706014156342], + [0.345234006643295, 0.344011008739471], + [0.370094001293182, 0.346076011657715], + [0.319321990013123, 0.347265005111694], + [0.297903001308441, 0.353591024875641], + [0.24779200553894, 0.410809993743896], + [0.396889001131058, 0.842755019664764], + [0.280097991228104, 0.375599980354309], + [0.106310002505779, 0.399955987930298], + [0.2099249958992, 0.391353011131287], + [0.355807989835739, 0.534406006336212], 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+ 456, + 360, + 420, + 363, + 361, + 401, + 288, + 265, + 372, + 353, + 390, + 339, + 249, + 339, + 448, + 255 +]; +var VTX68 = [ + 127, + 234, + 132, + 58, + 172, + 150, + 149, + 148, + 152, + 377, + 378, + 379, + 397, + 288, + 361, + 454, + 356, + 70, + 63, + 105, + 66, + 107, + 336, + 296, + 334, + 293, + 300, + 168, + 6, + 195, + 4, + 98, + 97, + 2, + 326, + 327, + 33, + 160, + 158, + 133, + 153, + 144, + 362, + 385, + 387, + 263, + 373, + 380, + 57, + 40, + 37, + 0, + 267, + 270, + 287, + 321, + 314, + 17, + 84, + 91, + 78, + 81, + 13, + 311, + 308, + 402, + 14, + 178 +]; +var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152]; +var VTX7 = [33, 133, 362, 263, 1, 78, 308]; +var UV68 = VTX68.map((x) => UV468[x]); +var UV33 = VTX33.map((x) => UV468[x]); +var UV7 = VTX7.map((x) => UV468[x]); +function connectionsToIndices(connections) { + const indices = connections.map((connection) => connection[0]); + indices.push(connections[connections.length - 1][1]); + return indices; +} +var pairsLips = [ + [61, 146], + [146, 91], + [91, 181], + [181, 84], + [84, 17], + [17, 314], + [314, 405], + [405, 321], + [321, 375], + [375, 291], + [61, 185], + [185, 40], + [40, 39], + [39, 37], + [37, 0], + [0, 267], + [267, 269], + [269, 270], + [270, 409], + [409, 291], + [78, 95], + [95, 88], + [88, 178], + [178, 87], + [87, 14], + [14, 317], + [317, 402], + [402, 318], + [318, 324], + [324, 308], + [78, 191], + [191, 80], + [80, 81], + [81, 82], + [82, 13], + [13, 312], + [312, 311], + [311, 310], + [310, 415], + [415, 308] +]; +var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; +var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; +var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; +var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; +var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; +var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; +var pairsFaceContour = [ + [10, 338], + [338, 297], + [297, 332], + [332, 284], + [284, 251], + [251, 389], + [389, 356], + [356, 454], + [454, 323], + [323, 361], + [361, 288], + [288, 397], + [397, 365], + [365, 379], + [379, 378], + [378, 400], + [400, 377], + [377, 152], + [152, 148], + [148, 176], + [176, 149], + [149, 150], + [150, 136], + [136, 172], + [172, 58], + [58, 132], + [132, 93], + [93, 234], + [234, 127], + [127, 162], + [162, 21], + [21, 54], + [54, 103], + [103, 67], + [67, 109], + [109, 10] +]; +var contourKeypoints = { + lips: connectionsToIndices(pairsLips), + leftEye: connectionsToIndices(pairsLeftEye), + leftEyebrow: connectionsToIndices(pairsLeftEyebrow), + leftIris: connectionsToIndices(pairsLeftIris), + rightEye: connectionsToIndices(pairsRightEye), + rightEyebrow: connectionsToIndices(pairsRightEyebrow), + rightIris: connectionsToIndices(pairsRightIris), + faceOval: connectionsToIndices(pairsFaceContour) +}; + +// src/face/facemeshutil.ts +var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; +var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; +var clampBox = (box, input) => box ? [ + Math.trunc(Math.max(0, box.startPoint[0])), + Math.trunc(Math.max(0, box.startPoint[1])), + Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), + Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) +] : [0, 0, 0, 0]; +var getRawBox = (box, input) => box ? [ + box.startPoint[0] / (input.shape[2] || 0), + box.startPoint[1] / (input.shape[1] || 0), + (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), + (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) +] : [0, 0, 0, 0]; +var scaleBoxCoordinates = (box, factor) => { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence }; +}; +var cutAndResize = (box, image24, cropSize) => { + const h = image24.shape[1]; + const w = image24.shape[2]; + const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; + const crop = tfjs_esm_exports.image.cropAndResize(image24, [cutBox], [0], cropSize); + const norm = tfjs_esm_exports.div(crop, constants.tf255); + tfjs_esm_exports.dispose(crop); + return norm; +}; +var enlargeBox = (box, factor) => { + const center = getBoxCenter(box); + const size2 = getBoxSize(box); + const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; + return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence }; +}; +var squarifyBox = (box) => { + const centers = getBoxCenter(box); + const size2 = getBoxSize(box); + const halfSize = Math.max(...size2) / 2; + return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence }; +}; +var calculateLandmarksBoundingBox = (landmarks) => { + const x = landmarks.map((d) => d[0]); + const y = landmarks.map((d) => d[1]); + return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks }; +}; +var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; +var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); +var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; +var dot = (v1, v2) => { + let product = 0; + for (let i = 0; i < v1.length; i++) + product += v1[i] * v2[i]; + return product; +}; +var getColumnFrom2DArr = (arr, columnIndex) => { + const column = []; + for (let i = 0; i < arr.length; i++) + column.push(arr[i][columnIndex]); + return column; +}; +var multiplyTransformMatrices = (mat1, mat2) => { + const product = []; + const size2 = mat1.length; + for (let row = 0; row < size2; row++) { + product.push([]); + for (let col = 0; col < size2; col++) + product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); + } + return product; +}; +var buildRotationMatrix = (rotation, center) => { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); + return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); +}; +var invertTransformMatrix = (matrix) => { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; + return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; +}; +var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; +function generateAnchors(inputSize10) { + const spec = { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; + const anchors3 = []; + for (let i = 0; i < spec.strides.length; i++) { + const stride = spec.strides[i]; + const gridRows = Math.floor((inputSize10 + stride - 1) / stride); + const gridCols = Math.floor((inputSize10 + stride - 1) / stride); + const anchorsNum = spec.anchors[i]; + for (let gridY = 0; gridY < gridRows; gridY++) { + const anchorY = stride * (gridY + 0.5); + for (let gridX = 0; gridX < gridCols; gridX++) { + const anchorX = stride * (gridX + 0.5); + for (let n = 0; n < anchorsNum; n++) + anchors3.push([anchorX, anchorY]); + } + } + } + return anchors3; +} +function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { + const boxSize = getBoxSize(box); + const coordsScaled = coordsRaw.map((coord) => [ + boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), + boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), + coord[2] || 0 + ]); + const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; + const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; + const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; + const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; + const boxCenter = getBoxCenter(box); + const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; + return coordsRotated.map((coord) => [ + Math.trunc(coord[0] + offsets[0]), + Math.trunc(coord[1] + offsets[1]), + Math.trunc(coord[2] || 0) + ]); +} +function correctFaceRotation(rotate, box, input, inputSize10) { + const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; + let angle = 0; + let rotationMatrix = fixedRotationMatrix; + let face4; + if (rotate && env.kernels.includes("rotatewithoffset")) { + angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); + const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; + if (largeAngle) { + const center = getBoxCenter(box); + const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; + const rotated = tfjs_esm_exports.image.rotateWithOffset(input, angle, 0, centerRaw); + rotationMatrix = buildRotationMatrix(-angle, center); + face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); + tfjs_esm_exports.dispose(rotated); + } else { + face4 = cutAndResize(box, input, [inputSize10, inputSize10]); + } + } else { + face4 = cutAndResize(box, input, [inputSize10, inputSize10]); + } + return [angle, rotationMatrix, face4]; +} +var findFaceCenter = (mesh) => { + const x = mesh.map((m) => m[0]); + const y = mesh.map((m) => m[1]); + return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; +}; +var calculateFaceBox = (mesh, previousBox) => { + const center = findFaceCenter(mesh); + const boxSize = getBoxSize(previousBox); + const calculatedBox = { + startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], + endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] + }; + return calculatedBox; +}; + +// src/face/blazeface.ts +var keypointsCount = 6; +var faceBoxScaleFactor = 1.4; +var model5; +var anchors = null; +var inputSize = 0; +var inputSizeT = null; +var size = () => inputSize; +async function load5(config3) { + var _a; + if (env.initial) + model5 = null; + if (!model5) + model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model5["modelUrl"]); + inputSize = model5.inputs[0].shape ? model5.inputs[0].shape[2] : 0; + inputSizeT = tfjs_esm_exports.scalar(inputSize, "int32"); + anchors = tfjs_esm_exports.tensor2d(generateAnchors(inputSize)); + return model5; +} +function decodeBounds(boxOutputs) { + const t = {}; + t.boxStarts = tfjs_esm_exports.slice(boxOutputs, [0, 1], [-1, 2]); + t.centers = tfjs_esm_exports.add(t.boxStarts, anchors); + t.boxSizes = tfjs_esm_exports.slice(boxOutputs, [0, 3], [-1, 2]); + t.boxSizesNormalized = tfjs_esm_exports.div(t.boxSizes, inputSizeT); + t.centersNormalized = tfjs_esm_exports.div(t.centers, inputSizeT); + t.halfBoxSize = tfjs_esm_exports.div(t.boxSizesNormalized, constants.tf2); + t.starts = tfjs_esm_exports.sub(t.centersNormalized, t.halfBoxSize); + t.ends = tfjs_esm_exports.add(t.centersNormalized, t.halfBoxSize); + t.startNormalized = tfjs_esm_exports.mul(t.starts, inputSizeT); + t.endNormalized = tfjs_esm_exports.mul(t.ends, inputSizeT); + const boxes = tfjs_esm_exports.concat2d([t.startNormalized, t.endNormalized], 1); + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + return boxes; +} +async function getBoxes(inputImage, config3) { + var _a, _b, _c, _d; + if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) + return []; + const t = {}; + t.resized = tfjs_esm_exports.image.resizeBilinear(inputImage, [inputSize, inputSize]); + t.div = tfjs_esm_exports.div(t.resized, constants.tf127); + t.normalized = tfjs_esm_exports.sub(t.div, constants.tf05); + const res = model5 == null ? void 0 : model5.execute(t.normalized); + if (Array.isArray(res)) { + const sorted = res.sort((a, b) => a.size - b.size); + t.concat384 = tfjs_esm_exports.concat([sorted[0], sorted[2]], 2); + t.concat512 = tfjs_esm_exports.concat([sorted[1], sorted[3]], 2); + t.concat = tfjs_esm_exports.concat([t.concat512, t.concat384], 1); + t.batch = tfjs_esm_exports.squeeze(t.concat, 0); + } else { + t.batch = tfjs_esm_exports.squeeze(res); + } + tfjs_esm_exports.dispose(res); + t.boxes = decodeBounds(t.batch); + t.logits = tfjs_esm_exports.slice(t.batch, [0, 0], [-1, 1]); + t.sigmoid = tfjs_esm_exports.sigmoid(t.logits); + t.scores = tfjs_esm_exports.squeeze(t.sigmoid); + t.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t.boxes, t.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0); + const nms = await t.nms.array(); + const boxes = []; + const scores = await t.scores.data(); + for (let i = 0; i < nms.length; i++) { + const confidence = scores[nms[i]]; + if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) { + const b = {}; + b.bbox = tfjs_esm_exports.slice(t.boxes, [nms[i], 0], [1, -1]); + b.slice = tfjs_esm_exports.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]); + b.squeeze = tfjs_esm_exports.squeeze(b.slice); + b.landmarks = tfjs_esm_exports.reshape(b.squeeze, [keypointsCount, -1]); + const points = await b.bbox.data(); + const rawBox = { + startPoint: [points[0], points[1]], + endPoint: [points[2], points[3]], + landmarks: await b.landmarks.array(), + confidence + }; + const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]); + const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor); + const squaredBox = squarifyBox(enlargedBox); + boxes.push(squaredBox); + Object.keys(b).forEach((tensor3) => tfjs_esm_exports.dispose(b[tensor3])); + } + } + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + return boxes; +} + +// src/body/blazeposecoords.ts +var blazeposecoords_exports = {}; +__export(blazeposecoords_exports, { + connected: () => connected, + kpt: () => kpt +}); +var kpt = [ + "nose", + "leftEyeInside", + "leftEye", + "leftEyeOutside", + "rightEyeInside", + "rightEye", + "rightEyeOutside", + "leftEar", + "rightEar", + "leftMouth", + "rightMouth", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftPinky", + "rightPinky", + "leftIndex", + "rightIndex", + "leftThumb", + "rightThumb", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle", + "leftHeel", + "rightHeel", + "leftFoot", + "rightFoot", + "bodyCenter", + "bodyTop", + "leftPalm", + "leftHand", + "rightPalm", + "rightHand" +]; +var connected = { + shoulders: ["leftShoulder", "rightShoulder"], + hips: ["rightHip", "leftHip"], + mouth: ["leftMouth", "rightMouth"], + leftLegUpper: ["leftHip", "leftKnee"], + leftLegLower: ["leftKnee", "leftAnkle"], + leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], + leftTorso: ["leftShoulder", "leftHip"], + leftArmUpper: ["leftShoulder", "leftElbow"], + leftArmLower: ["leftElbow", "leftWrist"], + leftHand: ["leftWrist", "leftPalm"], + leftHandPinky: ["leftPalm", "leftPinky"], + leftHandIndex: ["leftPalm", "leftIndex"], + leftHandThumb: ["leftPalm", "leftThumb"], + leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], + rightLegUpper: ["rightHip", "rightKnee"], + rightLegLower: ["rightKnee", "rightAnkle"], + rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], + rightTorso: ["rightShoulder", "rightHip"], + rightArmUpper: ["rightShoulder", "rightElbow"], + rightArmLower: ["rightElbow", "rightWrist"], + rightHand: ["rightWrist", "rightPalm"], + rightHandPinky: ["rightPalm", "rightPinky"], + rightHandIndex: ["rightPalm", "rightIndex"], + rightHandThumb: ["rightPalm", "rightThumb"], + rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] +}; + +// src/body/blazeposedetector.ts +var inputSize2 = 224; +var anchorTensor; +var numLayers = 5; +var strides = [8, 16, 32, 32, 32]; +async function createAnchors() { + const anchors3 = []; + let layerId = 0; + while (layerId < numLayers) { + let anchorCount = 0; + let lastSameStrideLayer = layerId; + while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { + anchorCount += 2; + lastSameStrideLayer++; + } + const stride = strides[layerId]; + const featureMapHeight = Math.ceil(inputSize2 / stride); + const featureMapWidth = Math.ceil(inputSize2 / stride); + for (let y = 0; y < featureMapHeight; ++y) { + for (let x = 0; x < featureMapWidth; ++x) { + for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { + anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); + } + } + } + layerId = lastSameStrideLayer; + } + anchorTensor = { x: tfjs_esm_exports.tensor1d(anchors3.map((a) => a.x)), y: tfjs_esm_exports.tensor1d(anchors3.map((a) => a.y)) }; +} + +// src/util/box.ts +function calc(keypoints, outputSize2 = [1, 1]) { + const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; + const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; + const max4 = [Math.max(...coords[0]), Math.max(...coords[1])]; + const box = [min2[0], min2[1], max4[0] - min2[0], max4[1] - min2[1]]; + const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; + return { box, boxRaw }; +} +function square(keypoints, outputSize2 = [1, 1]) { + const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; + const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; + const max4 = [Math.max(...coords[0]), Math.max(...coords[1])]; + const center = [(min2[0] + max4[0]) / 2, (min2[1] + max4[1]) / 2]; + const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max4[0], -center[1] + max4[1]); + const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; + const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; + return { box, boxRaw }; +} +function scale(box, scaleFact) { + const dist = [box[2] * scaleFact, box[3] * scaleFact]; + const newBox = [ + box[0] - (dist[0] - box[2]) / 2, + box[1] - (dist[1] - box[3]) / 2, + dist[0], + dist[1] + ]; + return newBox; +} + +// src/body/blazepose.ts +var env3 = { initial: true }; +var models = { detector: null, landmarks: null }; +var inputSize3 = { detector: [224, 224], landmarks: [256, 256] }; +var skipped5 = Number.MAX_SAFE_INTEGER; +var outputNodes = { + landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], + detector: [] +}; +var cache = null; +var cropBox; +var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; +var lastTime5 = 0; +var sigmoid3 = (x) => 1 - 1 / (1 + Math.exp(x)); +async function loadDetect(config3) { + if (env3.initial) + models.detector = null; + if (!models.detector && config3.body["detector"] && config3.body["detector"]["modelPath"] || "") { + models.detector = await loadModel(config3.body["detector"]["modelPath"]); + const inputs = Object.values(models.detector.modelSignature["inputs"]); + inputSize3.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize3.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug && models.detector) + log("cached model:", models.detector["modelUrl"]); + await createAnchors(); + return models.detector; +} +async function loadPose(config3) { + if (env3.initial) + models.landmarks = null; + if (!models.landmarks) { + models.landmarks = await loadModel(config3.body.modelPath); + const inputs = Object.values(models.landmarks.modelSignature["inputs"]); + inputSize3.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize3.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", models.landmarks["modelUrl"]); + return models.landmarks; +} +async function prepareImage(input, size2) { + const t = {}; + if (!input.shape || !input.shape[1] || !input.shape[2]) + return input; + let final; + if (cropBox) { + t.cropped = tfjs_esm_exports.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); + } + if (input.shape[1] !== input.shape[2]) { + const height = [ + input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, + input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 + ]; + const width = [ + input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, + input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 + ]; + padding = [ + [0, 0], + height, + width, + [0, 0] + ]; + t.pad = tfjs_esm_exports.pad(t.cropped || input, padding); + t.resize = tfjs_esm_exports.image.resizeBilinear(t.pad, [size2, size2]); + final = tfjs_esm_exports.div(t.resize, constants.tf255); + } else if (input.shape[1] !== size2) { + t.resize = tfjs_esm_exports.image.resizeBilinear(t.cropped || input, [size2, size2]); + final = tfjs_esm_exports.div(t.resize, constants.tf255); + } else { + final = tfjs_esm_exports.div(t.cropped || input, constants.tf255); + } + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + return final; +} +function rescaleKeypoints(keypoints, outputSize2) { + for (const kpt4 of keypoints) { + kpt4.position = [ + Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), + Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), + kpt4.position[2] + ]; + kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; + } + if (cropBox) { + for (const kpt4 of keypoints) { + kpt4.positionRaw = [ + kpt4.positionRaw[0] + cropBox[1], + kpt4.positionRaw[1] + cropBox[0], + kpt4.positionRaw[2] + ]; + kpt4.position = [ + Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), + Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), + kpt4.positionRaw[2] + ]; + } + } + return keypoints; +} +async function fixKeypoints(keypoints) { + const leftPalm = keypoints.find((k) => k.part === "leftPalm"); + const leftWrist = keypoints.find((k) => k.part === "leftWrist"); + const leftIndex = keypoints.find((k) => k.part === "leftIndex"); + leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; + const rightPalm = keypoints.find((k) => k.part === "rightPalm"); + const rightWrist = keypoints.find((k) => k.part === "rightWrist"); + const rightIndex = keypoints.find((k) => k.part === "rightIndex"); + rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; +} +async function detectLandmarks(input, config3, outputSize2) { + var _a; + const t = {}; + [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = (_a = models.landmarks) == null ? void 0 : _a.execute(input, outputNodes.landmarks); + const poseScore = (await t.poseflag.data())[0]; + const points = await t.ld.data(); + const distances = await t.world.data(); + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + const keypointsRelative = []; + const depth = 5; + for (let i = 0; i < points.length / depth; i++) { + const score = sigmoid3(points[depth * i + 3]); + const presence = sigmoid3(points[depth * i + 4]); + const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; + const positionRaw = [points[depth * i + 0] / inputSize3.landmarks[0], points[depth * i + 1] / inputSize3.landmarks[1], points[depth * i + 2] + 0]; + const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; + const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; + keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); + } + if (poseScore < (config3.body.minConfidence || 0)) + return null; + fixKeypoints(keypointsRelative); + const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); + const kpts = keypoints.map((k) => k.position); + const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); + const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); + if (pt0 && pt1) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; + return body4; +} +async function predict5(input, config3) { + const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; + const skipTime = (config3.body.skipTime || 0) > now() - lastTime5; + const skipFrame = skipped5 < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { + skipped5++; + } else { + const t = {}; + t.landmarks = await prepareImage(input, 256); + cache = await detectLandmarks(t.landmarks, config3, outputSize2); + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + lastTime5 = now(); + skipped5 = 0; + } + return cache ? [cache] : []; +} + +// src/object/labels.ts +var labels = [ + { class: 1, label: "person" }, + { class: 2, label: "bicycle" }, + { class: 3, label: "car" }, + { class: 4, label: "motorcycle" }, + { class: 5, label: "airplane" }, + { class: 6, label: "bus" }, + { class: 7, label: "train" }, + { class: 8, label: "truck" }, + { class: 9, label: "boat" }, + { class: 10, label: "traffic light" }, + { class: 11, label: "fire hydrant" }, + { class: 12, label: "stop sign" }, + { class: 13, label: "parking meter" }, + { class: 14, label: "bench" }, + { class: 15, label: "bird" }, + { class: 16, label: "cat" }, + { class: 17, label: "dog" }, + { class: 18, label: "horse" }, + { class: 19, label: "sheep" }, + { class: 20, label: "cow" }, + { class: 21, label: "elephant" }, + { class: 22, label: "bear" }, + { class: 23, label: "zebra" }, + { class: 24, label: "giraffe" }, + { class: 25, label: "backpack" }, + { class: 26, label: "umbrella" }, + { class: 27, label: "handbag" }, + { class: 28, label: "tie" }, + { class: 29, label: "suitcase" }, + { class: 30, label: "frisbee" }, + { class: 31, label: "skis" }, + { class: 32, label: "snowboard" }, + { class: 33, label: "sports ball" }, + { class: 34, label: "kite" }, + { class: 35, label: "baseball bat" }, + { class: 36, label: "baseball glove" }, + { class: 37, label: "skateboard" }, + { class: 38, label: "surfboard" }, + { class: 39, label: "tennis racket" }, + { class: 40, label: "bottle" }, + { class: 41, label: "wine glass" }, + { class: 42, label: "cup" }, + { class: 43, label: "fork" }, + { class: 44, label: "knife" }, + { class: 45, label: "spoon" }, + { class: 46, label: "bowl" }, + { class: 47, label: "banana" }, + { class: 48, label: "apple" }, + { class: 49, label: "sandwich" }, + { class: 50, label: "orange" }, + { class: 51, label: "broccoli" }, + { class: 52, label: "carrot" }, + { class: 53, label: "hot dog" }, + { class: 54, label: "pizza" }, + { class: 55, label: "donut" }, + { class: 56, label: "cake" }, + { class: 57, label: "chair" }, + { class: 58, label: "couch" }, + { class: 59, label: "potted plant" }, + { class: 60, label: "bed" }, + { class: 61, label: "dining table" }, + { class: 62, label: "toilet" }, + { class: 63, label: "tv" }, + { class: 64, label: "laptop" }, + { class: 65, label: "mouse" }, + { class: 66, label: "remote" }, + { class: 67, label: "keyboard" }, + { class: 68, label: "cell phone" }, + { class: 69, label: "microwave" }, + { class: 70, label: "oven" }, + { class: 71, label: "toaster" }, + { class: 72, label: "sink" }, + { class: 73, label: "refrigerator" }, + { class: 74, label: "book" }, + { class: 75, label: "clock" }, + { class: 76, label: "vase" }, + { class: 77, label: "scissors" }, + { class: 78, label: "teddy bear" }, + { class: 79, label: "hair drier" }, + { class: 80, label: "toothbrush" } +]; + +// src/object/centernet.ts +var model6; +var inputSize4 = 0; +var last5 = []; +var lastTime6 = 0; +var skipped6 = Number.MAX_SAFE_INTEGER; +async function load6(config3) { + if (env.initial) + model6 = null; + if (!model6) { + model6 = await loadModel(config3.object.modelPath); + const inputs = Object.values(model6.modelSignature["inputs"]); + inputSize4 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", model6["modelUrl"]); + return model6; +} +async function process3(res, outputShape, config3) { + if (!res) + return []; + const t = {}; + const results = []; + const detections = await res.array(); + t.squeeze = tfjs_esm_exports.squeeze(res); + const arr = tfjs_esm_exports.split(t.squeeze, 6, 1); + t.stack = tfjs_esm_exports.stack([arr[1], arr[0], arr[3], arr[2]], 1); + t.boxes = tfjs_esm_exports.squeeze(t.stack); + t.scores = tfjs_esm_exports.squeeze(arr[4]); + t.classes = tfjs_esm_exports.squeeze(arr[5]); + tfjs_esm_exports.dispose([res, ...arr]); + t.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t.boxes, t.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0); + const nms = await t.nms.data(); + let i = 0; + for (const id of Array.from(nms)) { + const score = Math.trunc(100 * detections[0][id][4]) / 100; + const classVal = detections[0][id][5]; + const label = labels[classVal].label; + const [x, y] = [ + detections[0][id][0] / inputSize4, + detections[0][id][1] / inputSize4 + ]; + const boxRaw = [ + x, + y, + detections[0][id][2] / inputSize4 - x, + detections[0][id][3] / inputSize4 - y + ]; + const box = [ + Math.trunc(boxRaw[0] * outputShape[0]), + Math.trunc(boxRaw[1] * outputShape[1]), + Math.trunc(boxRaw[2] * outputShape[0]), + Math.trunc(boxRaw[3] * outputShape[1]) + ]; + results.push({ id: i++, score, class: classVal, label, box, boxRaw }); + } + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + return results; +} +async function predict6(input, config3) { + const skipTime = (config3.object.skipTime || 0) > now() - lastTime6; + const skipFrame = skipped6 < (config3.object.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) { + skipped6++; + return last5; + } + skipped6 = 0; + return new Promise(async (resolve) => { + const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; + const resize = tfjs_esm_exports.image.resizeBilinear(input, [inputSize4, inputSize4]); + const objectT = config3.object.enabled ? model6 == null ? void 0 : model6.execute(resize, ["tower_0/detections"]) : null; + lastTime6 = now(); + tfjs_esm_exports.dispose(resize); + const obj = await process3(objectT, outputSize2, config3); + last5 = obj; + resolve(obj); + }); +} + +// src/body/efficientposecoords.ts +var efficientposecoords_exports = {}; +__export(efficientposecoords_exports, { + connected: () => connected2, + kpt: () => kpt2 +}); +var kpt2 = [ + "head", + "neck", + "rightShoulder", + "rightElbow", + "rightWrist", + "chest", + "leftShoulder", + "leftElbow", + "leftWrist", + "bodyCenter", + "rightHip", + "rightKnee", + "rightAnkle", + "leftHip", + "leftKnee", + "leftAnkle" +]; +var connected2 = { + leftLeg: ["leftHip", "leftKnee", "leftAnkle"], + rightLeg: ["rightHip", "rightKnee", "rightAnkle"], + torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], + leftArm: ["leftShoulder", "leftElbow", "leftWrist"], + rightArm: ["rightShoulder", "rightElbow", "rightWrist"], + head: [] +}; + +// src/body/efficientpose.ts +var model7; +var lastTime7 = 0; +var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; +var skipped7 = Number.MAX_SAFE_INTEGER; +async function load7(config3) { + if (env.initial) + model7 = null; + if (!model7) + model7 = await loadModel(config3.body.modelPath); + else if (config3.debug) + log("cached model:", model7["modelUrl"]); + return model7; +} +async function max2d(inputs, minScore) { + const [width, height] = inputs.shape; + const reshaped = tfjs_esm_exports.reshape(inputs, [height * width]); + const max4 = tfjs_esm_exports.max(reshaped, 0); + const newScore = (await max4.data())[0]; + tfjs_esm_exports.dispose([reshaped, max4]); + if (newScore > minScore) { + const coordinates = tfjs_esm_exports.argMax(reshaped, 0); + const mod3 = tfjs_esm_exports.mod(coordinates, width); + const x = (await mod3.data())[0]; + const div14 = tfjs_esm_exports.div(coordinates, tfjs_esm_exports.scalar(width, "int32")); + const y = (await div14.data())[0]; + tfjs_esm_exports.dispose([mod3, div14]); + return [x, y, newScore]; + } + return [0, 0, newScore]; +} +async function predict7(image24, config3) { + const skipTime = (config3.body.skipTime || 0) > now() - lastTime7; + const skipFrame = skipped7 < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { + skipped7++; + return [cache2]; + } + skipped7 = 0; + return new Promise(async (resolve) => { + var _a; + const tensor3 = tfjs_esm_exports.tidy(() => { + if (!(model7 == null ? void 0 : model7.inputs[0].shape)) + return null; + const resize = tfjs_esm_exports.image.resizeBilinear(image24, [model7.inputs[0].shape[2], model7.inputs[0].shape[1]], false); + const enhance2 = tfjs_esm_exports.mul(resize, constants.tf2); + const norm = tfjs_esm_exports.sub(enhance2, constants.tf1); + return norm; + }); + let resT; + if (config3.body.enabled) + resT = model7 == null ? void 0 : model7.execute(tensor3); + lastTime7 = now(); + tfjs_esm_exports.dispose(tensor3); + if (resT) { + cache2.keypoints.length = 0; + const squeeze11 = resT.squeeze(); + tfjs_esm_exports.dispose(resT); + const stack5 = squeeze11.unstack(2); + tfjs_esm_exports.dispose(squeeze11); + for (let id = 0; id < stack5.length; id++) { + const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); + if (partScore > (((_a = config3.body) == null ? void 0 : _a.minConfidence) || 0)) { + cache2.keypoints.push({ + score: Math.round(100 * partScore) / 100, + part: kpt2[id], + positionRaw: [ + x2 / model7.inputs[0].shape[2], + y2 / model7.inputs[0].shape[1] + ], + position: [ + Math.round(image24.shape[2] * x2 / model7.inputs[0].shape[2]), + Math.round(image24.shape[1] * y2 / model7.inputs[0].shape[1]) + ] + }); + } + } + stack5.forEach((s) => tfjs_esm_exports.dispose(s)); + } + cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const x = cache2.keypoints.map((a) => a.position[0]); + const y = cache2.keypoints.map((a) => a.position[1]); + cache2.box = [ + Math.min(...x), + Math.min(...y), + Math.max(...x) - Math.min(...x), + Math.max(...y) - Math.min(...y) + ]; + const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); + const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); + cache2.boxRaw = [ + Math.min(...xRaw), + Math.min(...yRaw), + Math.max(...xRaw) - Math.min(...xRaw), + Math.max(...yRaw) - Math.min(...yRaw) + ]; + for (const [name, indexes] of Object.entries(connected2)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); + const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + cache2.annotations[name] = pt; + } + resolve([cache2]); + }); +} + +// src/gear/emotion.ts +var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; +var model8; +var last6 = []; +var lastCount5 = 0; +var lastTime8 = 0; +var skipped8 = Number.MAX_SAFE_INTEGER; +async function load8(config3) { + var _a; + if (env.initial) + model8 = null; + if (!model8) + model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model8["modelUrl"]); + return model8; +} +async function predict8(image24, config3, idx, count2) { + var _a, _b; + if (!model8) + return []; + const skipFrame = skipped8 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; + if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last6[idx] && last6[idx].length > 0) { + skipped8++; + return last6[idx]; + } + skipped8 = 0; + return new Promise(async (resolve) => { + var _a2, _b2; + const obj = []; + if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { + const t = {}; + const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; + t.resize = tfjs_esm_exports.image.resizeBilinear(image24, [inputSize10, inputSize10], false); + t.channels = tfjs_esm_exports.mul(t.resize, constants.rgb); + t.grayscale = tfjs_esm_exports.sum(t.channels, 3, true); + t.grayscaleSub = tfjs_esm_exports.sub(t.grayscale, constants.tf05); + t.grayscaleMul = tfjs_esm_exports.mul(t.grayscaleSub, constants.tf2); + t.emotion = model8 == null ? void 0 : model8.execute(t.grayscaleMul); + lastTime8 = now(); + const data = await t.emotion.data(); + for (let i = 0; i < data.length; i++) { + if (data[i] > (((_b2 = config3.face.emotion) == null ? void 0 : _b2.minConfidence) || 0)) + obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); + } + obj.sort((a, b) => b.score - a.score); + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + } + last6[idx] = obj; + lastCount5 = count2; + resolve(obj); + }); +} + +// src/face/mobilefacenet.ts +var model9; +var last7 = []; +var lastCount6 = 0; +var lastTime9 = 0; +var skipped9 = Number.MAX_SAFE_INTEGER; +async function load9(config3) { + if (env.initial) + model9 = null; + if (!model9) + model9 = await loadModel(config3.face["mobilefacenet"].modelPath); + else if (config3.debug) + log("cached model:", model9["modelUrl"]); + return model9; +} +async function predict9(input, config3, idx, count2) { + var _a, _b; + if (!model9) + return []; + const skipFrame = skipped9 < (((_a = config3.face["embedding"]) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face["embedding"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; + if (config3.skipAllowed && skipTime && skipFrame && lastCount6 === count2 && last7[idx]) { + skipped9++; + return last7[idx]; + } + return new Promise(async (resolve) => { + var _a2; + let data = []; + if (((_a2 = config3.face["embedding"]) == null ? void 0 : _a2.enabled) && (model9 == null ? void 0 : model9.inputs[0].shape)) { + const t = {}; + t.crop = tfjs_esm_exports.image.resizeBilinear(input, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); + t.data = model9 == null ? void 0 : model9.execute(t.crop); + const output = await t.data.data(); + data = Array.from(output); + } + last7[idx] = data; + lastCount6 = count2; + lastTime9 = now(); + resolve(data); + }); +} + +// src/face/iris.ts +var model10; +var inputSize5 = 0; +var irisEnlarge = 2.3; +var leftOutline = meshAnnotations["leftEyeLower0"]; +var rightOutline = meshAnnotations["rightEyeLower0"]; +var eyeLandmarks = { + leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], + rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] +}; +var irisLandmarks = { + upperCenter: 3, + lowerCenter: 4, + index: 71, + numCoordinates: 76 +}; +async function load10(config3) { + var _a; + if (env.initial) + model10 = null; + if (!model10) + model10 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model10["modelUrl"]); + inputSize5 = model10.inputs[0].shape ? model10.inputs[0].shape[2] : 0; + if (inputSize5 === -1) + inputSize5 = 64; + return model10; +} +function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { + for (let i = 0; i < irisIndices.length; i++) { + const { key, indices } = irisIndices[i]; + const originalIndices = meshAnnotations[`${prefix}${key}`]; + if (!keys || keys.includes(key)) { + for (let j = 0; j < indices.length; j++) { + const index2 = indices[j]; + rawCoords[originalIndices[j]] = [ + newCoords[index2][0], + newCoords[index2][1], + (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 + ]; + } + } + } +} +var getLeftToRightEyeDepthDifference = (rawCoords) => { + const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; + const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; + return leftEyeZ - rightEyeZ; +}; +var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => { + const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge)); + const boxSize = getBoxSize(box); + let crop = tfjs_esm_exports.image.cropAndResize(face4, [[ + box.startPoint[1] / meshSize, + box.startPoint[0] / meshSize, + box.endPoint[1] / meshSize, + box.endPoint[0] / meshSize + ]], [0], [inputSize5, inputSize5]); + if (flip && env.kernels.includes("flipleftright")) { + const flipped = tfjs_esm_exports.image.flipLeftRight(crop); + tfjs_esm_exports.dispose(crop); + crop = flipped; + } + return { box, boxSize, crop }; +}; +var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { + const eyeRawCoords = []; + for (let i = 0; i < irisLandmarks.numCoordinates; i++) { + const x = eyeData[i * 3]; + const y = eyeData[i * 3 + 1]; + const z = eyeData[i * 3 + 2]; + eyeRawCoords.push([ + (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], + y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], + z + ]); + } + return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; +}; +var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { + const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; + const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; + const averageZ = (upperCenterZ + lowerCenterZ) / 2; + return irisCoords.map((coord, i) => { + let z = averageZ; + if (i === 2) { + z = upperCenterZ; + } else if (i === 4) { + z = lowerCenterZ; + } + return [coord[0], coord[1], z]; + }); +}; +async function augmentIris(rawCoords, face4, config3, meshSize) { + if (!model10) { + if (config3.debug) + log("face mesh iris detection requested, but model is not loaded"); + return rawCoords; + } + const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true); + const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true); + const combined = tfjs_esm_exports.concat([leftEyeCrop, rightEyeCrop]); + tfjs_esm_exports.dispose(leftEyeCrop); + tfjs_esm_exports.dispose(rightEyeCrop); + const eyePredictions = model10.execute(combined); + tfjs_esm_exports.dispose(combined); + const eyePredictionsData = await eyePredictions.data(); + tfjs_esm_exports.dispose(eyePredictions); + const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); + const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); + const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); + const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); + const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); + if (Math.abs(leftToRightEyeDepthDifference) < 30) { + replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); + replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); + } else if (leftToRightEyeDepthDifference < 1) { + replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); + } else { + replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); + } + const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); + const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); + const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); + return newCoords; +} + +// src/face/attention.ts +var attentionDefinitions = { + eyeLLower: [33, 7, 163, 144, 145, 153, 154, 155, 133], + eyeRLower: [263, 249, 390, 373, 374, 380, 381, 382, 362], + lips: [185, 96, 90, 181, 84, 17, 314, 405, 320, 307, 409, 40, 39, 73, 37, 0, 267, 269, 270, 409, 40, 88, 178, 178, 87, 14, 268, 402, 318, 324, 409, 80, 41, 38, 87, 12, 268, 303, 318, 324, 185, 95, 80, 81, 85, 16, 315, 404, 319, 325, 409, 40, 39, 73, 72, 0, 302, 303, 270, 408, 185, 88, 88, 81, 82, 15, 316, 403, 319, 324, 409, 80, 41, 38, 87, 12, 268, 303, 318, 324], + eyeL: [33, 7, 163, 144, 145, 153, 154, 155, 133, 246, 161, 160, 159, 158, 157, 173, 130, 25, 110, 24, 23, 22, 26, 112, 243, 247, 30, 29, 27, 28, 56, 190, 226, 31, 228, 229, 230, 231, 232, 233, 244, 113, 225, 224, 223, 222, 221, 189, 35, 124, 46, 53, 52, 65, 143, 111, 117, 118, 119, 120, 121, 128, 245, 156, 70, 63, 105, 66, 107, 55, 193], + eyeR: [263, 249, 390, 373, 374, 380, 381, 382, 362, 466, 388, 387, 386, 385, 384, 398, 359, 255, 339, 254, 253, 252, 256, 341, 463, 467, 260, 259, 257, 258, 286, 414, 446, 261, 448, 449, 450, 451, 452, 453, 464, 342, 445, 444, 443, 442, 441, 413, 265, 353, 276, 283, 282, 295, 372, 340, 346, 347, 348, 349, 350, 357, 465, 383, 300, 293, 334, 296, 336, 285, 417] +}; +async function augment(rawCoords, results) { + const t = { + irisL: results[3].dataSync(), + irisR: results[1].dataSync(), + eyeL: results[0].dataSync(), + eyeR: results[6].dataSync(), + lips: results[5].dataSync() + }; + const irisRDepth = attentionDefinitions.eyeRLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeRLower.length; + for (let i = 0; i < t.irisR.length / 2; i++) + rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]); + const irisLDepth = attentionDefinitions.eyeLLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeLLower.length; + for (let i = 0; i < t.irisL.length / 2; i++) + rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]); + for (let i = 0; i < t.eyeL.length / 2; i++) + rawCoords[attentionDefinitions.eyeL[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[attentionDefinitions.eyeL[i]][2]]; + for (let i = 0; i < t.eyeR.length / 2; i++) + rawCoords[attentionDefinitions.eyeR[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[attentionDefinitions.eyeR[i]][2]]; + for (let i = 0; i < t.lips.length / 2; i++) + rawCoords[attentionDefinitions.lips[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[attentionDefinitions.lips[i]][2]]; + return rawCoords; +} + +// src/face/facemesh.ts +var cache3 = { + boxes: [], + skipped: Number.MAX_SAFE_INTEGER, + timestamp: 0 +}; +var model11 = null; +var inputSize6 = 0; +async function predict10(input, config3) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; + const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; + const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); + if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { + cache3.boxes = await getBoxes(input, config3); + cache3.timestamp = now(); + cache3.skipped = 0; + } else { + cache3.skipped++; + } + const faces = []; + const newCache = []; + let id = 0; + for (let i = 0; i < cache3.boxes.length; i++) { + const box = cache3.boxes[i]; + let angle = 0; + let rotationMatrix; + const face4 = { + id: id++, + mesh: [], + meshRaw: [], + box: [0, 0, 0, 0], + boxRaw: [0, 0, 0, 0], + score: 0, + boxScore: 0, + faceScore: 0, + annotations: {} + }; + [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); + if ((_e = config3 == null ? void 0 : config3.filter) == null ? void 0 : _e.equalization) { + const equilized = await histogramEqualization(face4.tensor); + tfjs_esm_exports.dispose(face4.tensor); + face4.tensor = equilized; + } + face4.boxScore = Math.round(100 * box.confidence) / 100; + if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) { + face4.box = clampBox(box, input); + face4.boxRaw = getRawBox(box, input); + face4.score = face4.boxScore; + face4.mesh = box.landmarks.map((pt) => [ + (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(), + (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size() + ]); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize6]); + for (const key of Object.keys(blazeFaceLandmarks)) { + face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; + } + } else if (!model11) { + if (config3.debug) + log("face mesh detection requested, but model is not loaded"); + } else { + const results = model11.execute(face4.tensor); + const confidence = results.find((t) => t.shape[t.shape.length - 1] === 1); + const contourCoords = results.find((t) => t.shape[t.shape.length - 1] === 1404); + const faceConfidence = await confidence.data(); + face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; + const coordsReshaped = tfjs_esm_exports.reshape(contourCoords, [-1, 3]); + let rawCoords = await coordsReshaped.array(); + if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { + box.confidence = face4.faceScore; + } else { + if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { + rawCoords = await augment(rawCoords, results); + } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { + rawCoords = await augmentIris(rawCoords, face4.tensor, config3, inputSize6); + } + face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); + face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize6]); + for (const key of Object.keys(meshAnnotations)) + face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); + face4.score = face4.faceScore; + const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks }; + face4.box = clampBox(calculatedBox, input); + face4.boxRaw = getRawBox(calculatedBox, input); + newCache.push(calculatedBox); + } + tfjs_esm_exports.dispose([...results, coordsReshaped]); + } + if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) + faces.push(face4); + else + tfjs_esm_exports.dispose(face4.tensor); + } + cache3.boxes = newCache; + return faces; +} +async function load11(config3) { + var _a, _b, _c; + if (env.initial) + model11 = null; + if (!model11) { + if ((_a = config3.face.attention) == null ? void 0 : _a.enabled) + model11 = await loadModel((_b = config3.face.attention) == null ? void 0 : _b.modelPath); + else + model11 = await loadModel((_c = config3.face.mesh) == null ? void 0 : _c.modelPath); + } else if (config3.debug) { + log("cached model:", model11["modelUrl"]); + } + inputSize6 = model11.inputs[0].shape ? model11.inputs[0].shape[2] : 0; + return model11; +} +var triangulation = TRI468; +var uvmap = UV468; + +// src/face/faceres.ts +var model12; +var last8 = []; +var lastTime10 = 0; +var lastCount7 = 0; +var skipped10 = Number.MAX_SAFE_INTEGER; +async function load12(config3) { + var _a; + if (env.initial) + model12 = null; + if (!model12) + model12 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model12["modelUrl"]); + return model12; +} +function enhance(input) { + const tensor3 = input.image || input.tensor || input; + if (!(model12 == null ? void 0 : model12.inputs[0].shape)) + return tensor3; + const crop = tfjs_esm_exports.image.resizeBilinear(tensor3, [model12.inputs[0].shape[2], model12.inputs[0].shape[1]], false); + const norm = tfjs_esm_exports.mul(crop, constants.tf255); + tfjs_esm_exports.dispose(crop); + return norm; +} +async function predict11(image24, config3, idx, count2) { + var _a, _b, _c, _d; + if (!model12) + return { age: 0, gender: "unknown", genderScore: 0, descriptor: [] }; + const skipFrame = skipped10 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); + const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; + if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last8[idx]) == null ? void 0 : _c.age) && ((_d = last8[idx]) == null ? void 0 : _d.age) > 0) { + skipped10++; + return last8[idx]; + } + skipped10 = 0; + return new Promise(async (resolve) => { + var _a2, _b2; + const obj = { + age: 0, + gender: "unknown", + genderScore: 0, + descriptor: [] + }; + if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { + const enhanced = enhance(image24); + const resT = model12 == null ? void 0 : model12.execute(enhanced); + lastTime10 = now(); + tfjs_esm_exports.dispose(enhanced); + const genderT = await resT.find((t) => t.shape[1] === 1); + const gender = await genderT.data(); + const confidence = Math.trunc(200 * Math.abs(gender[0] - 0.5)) / 100; + if (confidence > (((_b2 = config3.face.description) == null ? void 0 : _b2.minConfidence) || 0)) { + obj.gender = gender[0] <= 0.5 ? "female" : "male"; + obj.genderScore = Math.min(0.99, confidence); + } + const argmax = tfjs_esm_exports.argMax(resT.find((t) => t.shape[1] === 100), 1); + const age = (await argmax.data())[0]; + tfjs_esm_exports.dispose(argmax); + const ageT = resT.find((t) => t.shape[1] === 100); + const all2 = await ageT.data(); + obj.age = Math.round(all2[age - 1] > all2[age + 1] ? 10 * age - 100 * all2[age - 1] : 10 * age + 100 * all2[age + 1]) / 10; + const desc = resT.find((t) => t.shape[1] === 1024); + const descriptor = desc ? await desc.data() : []; + obj.descriptor = Array.from(descriptor); + resT.forEach((t) => tfjs_esm_exports.dispose(t)); + } + last8[idx] = obj; + lastCount7 = count2; + resolve(obj); + }); +} + +// src/hand/handposeutil.ts +function getBoxSize2(box) { + return [ + Math.abs(box.endPoint[0] - box.startPoint[0]), + Math.abs(box.endPoint[1] - box.startPoint[1]) + ]; +} +function getBoxCenter2(box) { + return [ + box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, + box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 + ]; +} +function cutBoxFromImageAndResize(box, image24, cropSize) { + const h = image24.shape[1]; + const w = image24.shape[2]; + const boxes = [[ + box.startPoint[1] / h, + box.startPoint[0] / w, + box.endPoint[1] / h, + box.endPoint[0] / w + ]]; + return tfjs_esm_exports.image.cropAndResize(image24, boxes, [0], cropSize); +} +function scaleBoxCoordinates2(box, factor) { + const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; + const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; + const palmLandmarks = box.palmLandmarks.map((coord) => { + const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; + return scaledCoord; + }); + return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; +} +function enlargeBox2(box, factor = 1.5) { + const center = getBoxCenter2(box); + const size2 = getBoxSize2(box); + const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; + const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; + const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; + return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; +} +function squarifyBox2(box) { + const centers = getBoxCenter2(box); + const size2 = getBoxSize2(box); + const maxEdge = Math.max(...size2); + const halfSize = maxEdge / 2; + const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; + const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; + return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; +} +function normalizeRadians2(angle) { + return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); +} +function computeRotation2(point1, point2) { + const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); + return normalizeRadians2(radians); +} +var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; +function dot2(v1, v2) { + let product = 0; + for (let i = 0; i < v1.length; i++) { + product += v1[i] * v2[i]; + } + return product; +} +function getColumnFrom2DArr2(arr, columnIndex) { + const column = []; + for (let i = 0; i < arr.length; i++) { + column.push(arr[i][columnIndex]); + } + return column; +} +function multiplyTransformMatrices2(mat1, mat2) { + const product = []; + const size2 = mat1.length; + for (let row = 0; row < size2; row++) { + product.push([]); + for (let col = 0; col < size2; col++) { + product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); + } + } + return product; +} +function buildRotationMatrix2(rotation, center) { + const cosA = Math.cos(rotation); + const sinA = Math.sin(rotation); + const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; + const translationMatrix = buildTranslationMatrix2(center[0], center[1]); + const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); + const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); + return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); +} +function invertTransformMatrix2(matrix) { + const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; + const translationComponent = [matrix[0][2], matrix[1][2]]; + const invertedTranslation = [ + -dot2(rotationComponent[0], translationComponent), + -dot2(rotationComponent[1], translationComponent) + ]; + return [ + rotationComponent[0].concat(invertedTranslation[0]), + rotationComponent[1].concat(invertedTranslation[1]), + [0, 0, 1] + ]; +} +function rotatePoint2(homogeneousCoordinate, rotationMatrix) { + return [ + dot2(homogeneousCoordinate, rotationMatrix[0]), + dot2(homogeneousCoordinate, rotationMatrix[1]) + ]; +} + +// src/hand/handposeanchors.ts +var anchors2 = [ + { x: 0.015625, y: 0.015625 }, + { x: 0.015625, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.046875, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.078125, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.109375, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.140625, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.171875, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.203125, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.234375, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.265625, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.296875, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.328125, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.359375, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.390625, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.421875, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.453125, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.484375, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.515625, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.546875, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.578125, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.609375, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.640625, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.671875, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.703125, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.734375, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.765625, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.796875, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.828125, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.859375, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.890625, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.921875, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.953125, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.984375, y: 0.015625 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.015625, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.046875, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.078125, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.109375, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.140625, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.171875, y: 0.046875 }, + { x: 0.203125, y: 0.046875 }, + { x: 0.203125, y: 0.046875 }, + { x: 0.234375, y: 0.046875 }, + { x: 0.234375, y: 0.046875 }, + { x: 0.265625, y: 0.046875 }, + { x: 0.265625, y: 0.046875 }, + { x: 0.296875, y: 0.046875 }, + { x: 0.296875, y: 0.046875 }, + { x: 0.328125, y: 0.046875 }, + { x: 0.328125, y: 0.046875 }, + { x: 0.359375, y: 0.046875 }, + { x: 0.359375, y: 0.046875 }, + { x: 0.390625, y: 0.046875 }, + { x: 0.390625, y: 0.046875 }, + { x: 0.421875, y: 0.046875 }, + { x: 0.421875, y: 0.046875 }, + { x: 0.453125, y: 0.046875 }, + { x: 0.453125, y: 0.046875 }, + { x: 0.484375, y: 0.046875 }, + { x: 0.484375, y: 0.046875 }, + { x: 0.515625, y: 0.046875 }, + { x: 0.515625, y: 0.046875 }, + { x: 0.546875, y: 0.046875 }, + { x: 0.546875, y: 0.046875 }, + { x: 0.578125, y: 0.046875 }, + { x: 0.578125, y: 0.046875 }, + { x: 0.609375, y: 0.046875 }, + { x: 0.609375, y: 0.046875 }, + { x: 0.640625, y: 0.046875 }, + { x: 0.640625, y: 0.046875 }, + { x: 0.671875, y: 0.046875 }, + { x: 0.671875, y: 0.046875 }, + { x: 0.703125, y: 0.046875 }, + { x: 0.703125, y: 0.046875 }, + { x: 0.734375, y: 0.046875 }, + { x: 0.734375, y: 0.046875 }, + { x: 0.765625, y: 0.046875 }, + { x: 0.765625, y: 0.046875 }, + { x: 0.796875, y: 0.046875 }, + { x: 0.796875, y: 0.046875 }, + { x: 0.828125, y: 0.046875 }, + { x: 0.828125, y: 0.046875 }, + { x: 0.859375, y: 0.046875 }, + { x: 0.859375, y: 0.046875 }, + { x: 0.890625, y: 0.046875 }, + { x: 0.890625, y: 0.046875 }, + { x: 0.921875, y: 0.046875 }, + { x: 0.921875, y: 0.046875 }, + { x: 0.953125, y: 0.046875 }, + { x: 0.953125, y: 0.046875 }, + { x: 0.984375, y: 0.046875 }, + { x: 0.984375, y: 0.046875 }, + { x: 0.015625, y: 0.078125 }, + { x: 0.015625, y: 0.078125 }, + { x: 0.046875, y: 0.078125 }, + { x: 0.046875, y: 0.078125 }, + { x: 0.078125, y: 0.078125 }, + { x: 0.078125, y: 0.078125 }, + { x: 0.109375, y: 0.078125 }, + { x: 0.109375, y: 0.078125 }, + { x: 0.140625, y: 0.078125 }, + { x: 0.140625, y: 0.078125 }, + { x: 0.171875, y: 0.078125 }, + { x: 0.171875, y: 0.078125 }, + { x: 0.203125, y: 0.078125 }, + { x: 0.203125, y: 0.078125 }, + { x: 0.234375, y: 0.078125 }, + { x: 0.234375, y: 0.078125 }, + { x: 0.265625, y: 0.078125 }, + { x: 0.265625, y: 0.078125 }, + { x: 0.296875, y: 0.078125 }, + { x: 0.296875, y: 0.078125 }, + { x: 0.328125, y: 0.078125 }, + { x: 0.328125, y: 0.078125 }, + { x: 0.359375, y: 0.078125 }, + { x: 0.359375, y: 0.078125 }, + { x: 0.390625, y: 0.078125 }, + { x: 0.390625, y: 0.078125 }, + { x: 0.421875, y: 0.078125 }, + { x: 0.421875, y: 0.078125 }, + { x: 0.453125, y: 0.078125 }, + { x: 0.453125, y: 0.078125 }, + { x: 0.484375, y: 0.078125 }, + { x: 0.484375, y: 0.078125 }, + { x: 0.515625, y: 0.078125 }, + { x: 0.515625, y: 0.078125 }, + { x: 0.546875, y: 0.078125 }, + { x: 0.546875, y: 0.078125 }, + { x: 0.578125, y: 0.078125 }, + { x: 0.578125, y: 0.078125 }, + { x: 0.609375, y: 0.078125 }, + { x: 0.609375, y: 0.078125 }, + { x: 0.640625, y: 0.078125 }, + { x: 0.640625, y: 0.078125 }, + { x: 0.671875, y: 0.078125 }, + { x: 0.671875, y: 0.078125 }, + { x: 0.703125, y: 0.078125 }, + { x: 0.703125, y: 0.078125 }, + { x: 0.734375, y: 0.078125 }, + { x: 0.734375, y: 0.078125 }, + { x: 0.765625, y: 0.078125 }, + { x: 0.765625, y: 0.078125 }, + { x: 0.796875, y: 0.078125 }, + { x: 0.796875, y: 0.078125 }, + { x: 0.828125, y: 0.078125 }, + { x: 0.828125, y: 0.078125 }, + { x: 0.859375, y: 0.078125 }, + { x: 0.859375, y: 0.078125 }, + { x: 0.890625, y: 0.078125 }, + { x: 0.890625, y: 0.078125 }, + { x: 0.921875, y: 0.078125 }, + { x: 0.921875, y: 0.078125 }, + { x: 0.953125, y: 0.078125 }, + { x: 0.953125, y: 0.078125 }, + { x: 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0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.8125, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.9375, y: 0.3125 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.0625, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.1875, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.3125, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.4375, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.5625, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.6875, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.8125, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.9375, y: 0.4375 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.0625, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.1875, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.3125, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.4375, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.5625, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.6875, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.8125, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.9375, y: 0.5625 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.0625, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.1875, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.3125, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.4375, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.5625, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.6875, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.8125, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.9375, y: 0.6875 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.0625, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.1875, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.3125, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.4375, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.5625, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.6875, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.8125, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.9375, y: 0.8125 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.0625, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.1875, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.3125, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.4375, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.5625, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.6875, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.8125, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 }, + { x: 0.9375, y: 0.9375 } +]; + +// src/hand/handposedetector.ts +var HandDetector = class { + constructor(model18) { + __publicField(this, "model"); + __publicField(this, "anchors"); + __publicField(this, "anchorsTensor"); + __publicField(this, "inputSize"); + __publicField(this, "inputSizeTensor"); + __publicField(this, "doubleInputSizeTensor"); + this.model = model18; + this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); + this.anchorsTensor = tfjs_esm_exports.tensor2d(this.anchors); + this.inputSize = this.model && this.model.inputs && this.model.inputs[0].shape ? this.model.inputs[0].shape[2] : 0; + this.inputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize, this.inputSize]); + this.doubleInputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize * 2, this.inputSize * 2]); + } + normalizeBoxes(boxes) { + const t = {}; + t.boxOffsets = tfjs_esm_exports.slice(boxes, [0, 0], [-1, 2]); + t.boxSizes = tfjs_esm_exports.slice(boxes, [0, 2], [-1, 2]); + t.div = tfjs_esm_exports.div(t.boxOffsets, this.inputSizeTensor); + t.boxCenterPoints = tfjs_esm_exports.add(t.div, this.anchorsTensor); + t.halfBoxSizes = tfjs_esm_exports.div(t.boxSizes, this.doubleInputSizeTensor); + t.sub = tfjs_esm_exports.sub(t.boxCenterPoints, t.halfBoxSizes); + t.startPoints = tfjs_esm_exports.mul(t.sub, this.inputSizeTensor); + t.add = tfjs_esm_exports.add(t.boxCenterPoints, t.halfBoxSizes); + t.endPoints = tfjs_esm_exports.mul(t.add, this.inputSizeTensor); + const res = tfjs_esm_exports.concat2d([t.startPoints, t.endPoints], 1); + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + return res; + } + normalizeLandmarks(rawPalmLandmarks, index2) { + const t = {}; + t.reshape = tfjs_esm_exports.reshape(rawPalmLandmarks, [-1, 7, 2]); + t.div = tfjs_esm_exports.div(t.reshape, this.inputSizeTensor); + t.landmarks = tfjs_esm_exports.add(t.div, this.anchors[index2]); + const res = tfjs_esm_exports.mul(t.landmarks, this.inputSizeTensor); + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + return res; + } + async predict(input, config3) { + const t = {}; + t.resize = tfjs_esm_exports.image.resizeBilinear(input, [this.inputSize, this.inputSize]); + t.div = tfjs_esm_exports.div(t.resize, constants.tf127); + t.image = tfjs_esm_exports.sub(t.div, constants.tf1); + t.batched = this.model.execute(t.image); + t.predictions = tfjs_esm_exports.squeeze(t.batched); + t.slice = tfjs_esm_exports.slice(t.predictions, [0, 0], [-1, 1]); + t.sigmoid = tfjs_esm_exports.sigmoid(t.slice); + t.scores = tfjs_esm_exports.squeeze(t.sigmoid); + const scores = await t.scores.data(); + t.boxes = tfjs_esm_exports.slice(t.predictions, [0, 1], [-1, 4]); + t.norm = this.normalizeBoxes(t.boxes); + t.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence); + const nms = await t.nms.array(); + const hands = []; + for (const index2 of nms) { + const p = {}; + p.box = tfjs_esm_exports.slice(t.norm, [index2, 0], [1, -1]); + p.slice = tfjs_esm_exports.slice(t.predictions, [index2, 5], [1, 14]); + p.norm = this.normalizeLandmarks(p.slice, index2); + p.palmLandmarks = tfjs_esm_exports.reshape(p.norm, [-1, 2]); + const box = await p.box.data(); + const startPoint = box.slice(0, 2); + const endPoint = box.slice(2, 4); + const palmLandmarks = await p.palmLandmarks.array(); + const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; + const scaled = scaleBoxCoordinates2(hand3, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]); + hands.push(scaled); + Object.keys(p).forEach((tensor3) => tfjs_esm_exports.dispose(p[tensor3])); + } + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + return hands; + } +}; + +// src/hand/handposepipeline.ts +var palmBoxEnlargeFactor = 5; +var handBoxEnlargeFactor = 1.65; +var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; +var palmLandmarksPalmBase = 0; +var palmLandmarksMiddleFingerBase = 2; +var lastTime11 = 0; +var HandPipeline = class { + constructor(handDetector, handPoseModel2) { + __publicField(this, "handDetector"); + __publicField(this, "handPoseModel"); + __publicField(this, "inputSize"); + __publicField(this, "storedBoxes"); + __publicField(this, "skipped"); + __publicField(this, "detectedHands"); + this.handDetector = handDetector; + this.handPoseModel = handPoseModel2; + this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0; + this.storedBoxes = []; + this.skipped = Number.MAX_SAFE_INTEGER; + this.detectedHands = 0; + } + calculateLandmarksBoundingBox(landmarks) { + const xs = landmarks.map((d) => d[0]); + const ys = landmarks.map((d) => d[1]); + const startPoint = [Math.min(...xs), Math.min(...ys)]; + const endPoint = [Math.max(...xs), Math.max(...ys)]; + return { startPoint, endPoint }; + } + getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { + const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); + const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); + return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); + } + getBoxForHandLandmarks(landmarks) { + const boundingBox = this.calculateLandmarksBoundingBox(landmarks); + const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); + boxAroundHand.palmLandmarks = []; + for (let i = 0; i < palmLandmarkIds.length; i++) { + boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); + } + return boxAroundHand; + } + transformRawCoords(rawCoords, box2, angle, rotationMatrix) { + const boxSize = getBoxSize2(box2); + const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; + const coordsScaled = rawCoords.map((coord) => [ + scaleFactor[0] * (coord[0] - this.inputSize / 2), + scaleFactor[1] * (coord[1] - this.inputSize / 2), + scaleFactor[2] * coord[2] + ]); + const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); + const coordsRotated = coordsScaled.map((coord) => { + const rotated = rotatePoint2(coord, coordsRotationMatrix); + return [...rotated, coord[2]]; + }); + const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); + const boxCenter = [...getBoxCenter2(box2), 1]; + const originalBoxCenter = [ + dot2(boxCenter, inverseRotationMatrix[0]), + dot2(boxCenter, inverseRotationMatrix[1]) + ]; + return coordsRotated.map((coord) => [ + Math.trunc(coord[0] + originalBoxCenter[0]), + Math.trunc(coord[1] + originalBoxCenter[1]), + Math.trunc(coord[2]) + ]); + } + async estimateHands(image24, config3) { + let useFreshBox = false; + let boxes; + const skipTime = (config3.hand.skipTime || 0) > now() - lastTime11; + const skipFrame = this.skipped < (config3.hand.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + boxes = await this.handDetector.predict(image24, config3); + this.skipped = 0; + } + if (config3.skipAllowed) + this.skipped++; + if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { + this.detectedHands = 0; + this.storedBoxes = [...boxes]; + if (this.storedBoxes.length > 0) + useFreshBox = true; + } + const hands = []; + for (let i = 0; i < this.storedBoxes.length; i++) { + const currentBox = this.storedBoxes[i]; + if (!currentBox) + continue; + if (config3.hand.landmarks) { + const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; + const palmCenter = getBoxCenter2(currentBox); + const palmCenterNormalized = [palmCenter[0] / image24.shape[2], palmCenter[1] / image24.shape[1]]; + const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tfjs_esm_exports.image.rotateWithOffset(image24, angle, 0, palmCenterNormalized) : image24.clone(); + const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); + const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; + const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); + const handImage = tfjs_esm_exports.div(croppedInput, constants.tf255); + tfjs_esm_exports.dispose(croppedInput); + tfjs_esm_exports.dispose(rotatedImage); + const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); + lastTime11 = now(); + tfjs_esm_exports.dispose(handImage); + const confidence = (await confidenceT.data())[0]; + tfjs_esm_exports.dispose(confidenceT); + if (confidence >= config3.hand.minConfidence / 4) { + const keypointsReshaped = tfjs_esm_exports.reshape(keypoints, [-1, 3]); + const rawCoords = await keypointsReshaped.array(); + tfjs_esm_exports.dispose(keypoints); + tfjs_esm_exports.dispose(keypointsReshaped); + const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); + const nextBoundingBox = this.getBoxForHandLandmarks(coords); + this.storedBoxes[i] = { ...nextBoundingBox, confidence }; + const result = { + landmarks: coords, + confidence, + boxConfidence: currentBox.confidence, + fingerConfidence: confidence, + box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } + }; + hands.push(result); + } else { + this.storedBoxes[i] = null; + } + tfjs_esm_exports.dispose(keypoints); + } else { + const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); + const result = { + confidence: currentBox.confidence, + boxConfidence: currentBox.confidence, + fingerConfidence: 0, + box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, + landmarks: [] + }; + hands.push(result); + } + } + this.storedBoxes = this.storedBoxes.filter((a) => a !== null); + this.detectedHands = hands.length; + if (hands.length > config3.hand.maxDetected) + hands.length = config3.hand.maxDetected; + return hands; + } +}; + +// src/hand/fingerdef.ts +var Finger = { + thumb: 0, + index: 1, + middle: 2, + ring: 3, + pinky: 4, + all: [0, 1, 2, 3, 4], + nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, + pointsMapping: { + 0: [[0, 1], [1, 2], [2, 3], [3, 4]], + 1: [[0, 5], [5, 6], [6, 7], [7, 8]], + 2: [[0, 9], [9, 10], [10, 11], [11, 12]], + 3: [[0, 13], [13, 14], [14, 15], [15, 16]], + 4: [[0, 17], [17, 18], [18, 19], [19, 20]] + }, + getName: (value) => Finger.nameMapping[value], + getPoints: (value) => Finger.pointsMapping[value] +}; +var FingerCurl = { + none: 0, + half: 1, + full: 2, + nameMapping: { 0: "none", 1: "half", 2: "full" }, + getName: (value) => FingerCurl.nameMapping[value] +}; +var FingerDirection = { + verticalUp: 0, + verticalDown: 1, + horizontalLeft: 2, + horizontalRight: 3, + diagonalUpRight: 4, + diagonalUpLeft: 5, + diagonalDownRight: 6, + diagonalDownLeft: 7, + nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, + getName: (value) => FingerDirection.nameMapping[value] +}; +var FingerGesture = class { + constructor(name) { + __publicField(this, "name"); + __publicField(this, "curls"); + __publicField(this, "directions"); + __publicField(this, "weights"); + __publicField(this, "weightsRelative"); + this.name = name; + this.curls = {}; + this.directions = {}; + this.weights = [1, 1, 1, 1, 1]; + this.weightsRelative = [1, 1, 1, 1, 1]; + } + curl(finger, curl, confidence) { + if (typeof this.curls[finger] === "undefined") + this.curls[finger] = []; + this.curls[finger].push([curl, confidence]); + } + direction(finger, position, confidence) { + if (!this.directions[finger]) + this.directions[finger] = []; + this.directions[finger].push([position, confidence]); + } + weight(finger, weight) { + this.weights[finger] = weight; + const total = this.weights.reduce((a, b) => a + b, 0); + this.weightsRelative = this.weights.map((el) => el * 5 / total); + } + matchAgainst(detectedCurls, detectedDirections) { + let confidence = 0; + for (const fingerIdx in detectedCurls) { + const detectedCurl = detectedCurls[fingerIdx]; + const expectedCurls = this.curls[fingerIdx]; + if (typeof expectedCurls === "undefined") { + confidence += this.weightsRelative[fingerIdx]; + continue; + } + for (const [expectedCurl, score] of expectedCurls) { + if (detectedCurl === expectedCurl) { + confidence += score * this.weightsRelative[fingerIdx]; + break; + } + } + } + for (const fingerIdx in detectedDirections) { + const detectedDirection = detectedDirections[fingerIdx]; + const expectedDirections = this.directions[fingerIdx]; + if (typeof expectedDirections === "undefined") { + confidence += this.weightsRelative[fingerIdx]; + continue; + } + for (const [expectedDirection, score] of expectedDirections) { + if (detectedDirection === expectedDirection) { + confidence += score * this.weightsRelative[fingerIdx]; + break; + } + } + } + return confidence / 10; + } +}; + +// src/hand/fingergesture.ts +var { thumb, index, middle, ring, pinky } = Finger; +var { none, half, full } = FingerCurl; +var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; +var ThumbsUp = new FingerGesture("thumbs up"); +ThumbsUp.curl(thumb, none, 1); +ThumbsUp.direction(thumb, verticalUp, 1); +ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); +ThumbsUp.direction(thumb, diagonalUpRight, 0.25); +for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { + ThumbsUp.curl(finger, full, 1); + ThumbsUp.direction(finger, horizontalLeft, 1); + ThumbsUp.direction(finger, horizontalRight, 1); +} +var Victory = new FingerGesture("victory"); +Victory.curl(thumb, half, 0.5); +Victory.curl(thumb, none, 0.5); +Victory.direction(thumb, verticalUp, 1); +Victory.direction(thumb, diagonalUpLeft, 1); +Victory.curl(index, none, 1); +Victory.direction(index, verticalUp, 0.75); +Victory.direction(index, diagonalUpLeft, 1); +Victory.curl(middle, none, 1); +Victory.direction(middle, verticalUp, 1); +Victory.direction(middle, diagonalUpLeft, 0.75); +Victory.curl(ring, full, 1); +Victory.direction(ring, verticalUp, 0.2); +Victory.direction(ring, diagonalUpLeft, 1); +Victory.direction(ring, horizontalLeft, 0.2); +Victory.curl(pinky, full, 1); +Victory.direction(pinky, verticalUp, 0.2); +Victory.direction(pinky, diagonalUpLeft, 1); +Victory.direction(pinky, horizontalLeft, 0.2); +Victory.weight(index, 2); +Victory.weight(middle, 2); +var Point = new FingerGesture("point"); +Point.curl(thumb, full, 1); +Point.curl(index, none, 0.5); +Point.curl(middle, full, 0.5); +Point.curl(ring, full, 0.5); +Point.curl(pinky, full, 0.5); +Point.weight(index, 2); +Point.weight(middle, 2); +var MiddleFinger = new FingerGesture("middle finger"); +MiddleFinger.curl(thumb, none, 1); +MiddleFinger.curl(index, full, 0.5); +MiddleFinger.curl(middle, full, 0.5); +MiddleFinger.curl(ring, full, 0.5); +MiddleFinger.curl(pinky, full, 0.5); +MiddleFinger.weight(index, 2); +MiddleFinger.weight(middle, 2); +var OpenPalm = new FingerGesture("open palm"); +OpenPalm.curl(thumb, none, 0.75); +OpenPalm.curl(index, none, 0.75); +OpenPalm.curl(middle, none, 0.75); +OpenPalm.curl(ring, none, 0.75); +OpenPalm.curl(pinky, none, 0.75); +var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; + +// src/hand/fingerpose.ts +var minConfidence = 0.7; +var options2 = { + HALF_CURL_START_LIMIT: 60, + NO_CURL_START_LIMIT: 130, + DISTANCE_VOTE_POWER: 1.1, + SINGLE_ANGLE_VOTE_POWER: 0.9, + TOTAL_ANGLE_VOTE_POWER: 1.6 +}; +function calculateSlope(point1x, point1y, point2x, point2y) { + const value = (point1y - point2y) / (point1x - point2x); + let slope = Math.atan(value) * 180 / Math.PI; + if (slope <= 0) + slope = -slope; + else if (slope > 0) + slope = 180 - slope; + return slope; +} +function getSlopes(point1, point2) { + if (!point1 || !point2) + return [0, 0]; + const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); + if (point1.length === 2) + return slopeXY; + const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); + return [slopeXY, slopeYZ]; +} +function angleOrientationAt(angle, weightageAt = 1) { + let isVertical = 0; + let isDiagonal = 0; + let isHorizontal = 0; + if (angle >= 75 && angle <= 105) + isVertical = 1 * weightageAt; + else if (angle >= 25 && angle <= 155) + isDiagonal = 1 * weightageAt; + else + isHorizontal = 1 * weightageAt; + return [isVertical, isDiagonal, isHorizontal]; +} +function estimateFingerCurl(startPoint, midPoint, endPoint) { + const start_mid_x_dist = startPoint[0] - midPoint[0]; + const start_end_x_dist = startPoint[0] - endPoint[0]; + const mid_end_x_dist = midPoint[0] - endPoint[0]; + const start_mid_y_dist = startPoint[1] - midPoint[1]; + const start_end_y_dist = startPoint[1] - endPoint[1]; + const mid_end_y_dist = midPoint[1] - endPoint[1]; + const start_mid_z_dist = startPoint[2] - midPoint[2]; + const start_end_z_dist = startPoint[2] - endPoint[2]; + const mid_end_z_dist = midPoint[2] - endPoint[2]; + const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); + const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); + const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); + let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); + if (cos_in > 1) + cos_in = 1; + else if (cos_in < -1) + cos_in = -1; + let angleOfCurve = Math.acos(cos_in); + angleOfCurve = 57.2958 * angleOfCurve % 180; + let fingerCurl; + if (angleOfCurve > options2.NO_CURL_START_LIMIT) + fingerCurl = FingerCurl.none; + else if (angleOfCurve > options2.HALF_CURL_START_LIMIT) + fingerCurl = FingerCurl.half; + else + fingerCurl = FingerCurl.full; + return fingerCurl; +} +function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { + let estimatedDirection; + if (max_dist_x === Math.abs(start_end_x_dist)) { + if (start_end_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } else if (max_dist_x === Math.abs(start_mid_x_dist)) { + if (start_mid_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } else { + if (mid_end_x_dist > 0) + estimatedDirection = FingerDirection.horizontalLeft; + else + estimatedDirection = FingerDirection.horizontalRight; + } + return estimatedDirection; +} +function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { + let estimatedDirection; + if (max_dist_y === Math.abs(start_end_y_dist)) { + if (start_end_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } else if (max_dist_y === Math.abs(start_mid_y_dist)) { + if (start_mid_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } else { + if (mid_end_y_dist < 0) + estimatedDirection = FingerDirection.verticalDown; + else + estimatedDirection = FingerDirection.verticalUp; + } + return estimatedDirection; +} +function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { + let estimatedDirection; + const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); + const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + if (reqd_vertical_direction === FingerDirection.verticalUp) { + if (reqd_horizontal_direction === FingerDirection.horizontalLeft) + estimatedDirection = FingerDirection.diagonalUpLeft; + else + estimatedDirection = FingerDirection.diagonalUpRight; + } else { + if (reqd_horizontal_direction === FingerDirection.horizontalLeft) + estimatedDirection = FingerDirection.diagonalDownLeft; + else + estimatedDirection = FingerDirection.diagonalDownRight; + } + return estimatedDirection; +} +function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { + const start_mid_x_dist = startPoint[0] - midPoint[0]; + const start_end_x_dist = startPoint[0] - endPoint[0]; + const mid_end_x_dist = midPoint[0] - endPoint[0]; + const start_mid_y_dist = startPoint[1] - midPoint[1]; + const start_end_y_dist = startPoint[1] - endPoint[1]; + const mid_end_y_dist = midPoint[1] - endPoint[1]; + const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); + const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); + let voteVertical = 0; + let voteDiagonal = 0; + let voteHorizontal = 0; + const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); + if (start_end_x_y_dist_ratio > 1.5) + voteVertical += options2.DISTANCE_VOTE_POWER; + else if (start_end_x_y_dist_ratio > 0.66) + voteDiagonal += options2.DISTANCE_VOTE_POWER; + else + voteHorizontal += options2.DISTANCE_VOTE_POWER; + const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); + const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); + const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); + const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); + let calc_start_point_x = startPoint[0]; + let calc_start_point_y = startPoint[1]; + let calc_end_point_x = endPoint[0]; + let calc_end_point_y = endPoint[1]; + if (max_dist === start_mid_dist) { + calc_end_point_x = endPoint[0]; + calc_end_point_y = endPoint[1]; + } else if (max_dist === mid_end_dist) { + calc_start_point_x = midPoint[0]; + calc_start_point_y = midPoint[1]; + } + const calcStartPoint = [calc_start_point_x, calc_start_point_y]; + const calcEndPoint = [calc_end_point_x, calc_end_point_y]; + const totalAngle = getSlopes(calcStartPoint, calcEndPoint); + const votes = angleOrientationAt(totalAngle, options2.TOTAL_ANGLE_VOTE_POWER); + voteVertical += votes[0]; + voteDiagonal += votes[1]; + voteHorizontal += votes[2]; + for (const fingerSlope of fingerSlopes) { + const fingerVotes = angleOrientationAt(fingerSlope, options2.SINGLE_ANGLE_VOTE_POWER); + voteVertical += fingerVotes[0]; + voteDiagonal += fingerVotes[1]; + voteHorizontal += fingerVotes[2]; + } + let estimatedDirection; + if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { + estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); + } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { + estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + } else { + estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); + } + return estimatedDirection; +} +function estimate(landmarks) { + const slopesXY = []; + const slopesYZ = []; + const fingerCurls = []; + const fingerDirections = []; + if (!landmarks) + return { curls: fingerCurls, directions: fingerDirections }; + for (const finger of Finger.all) { + const points = Finger.getPoints(finger); + const slopeAtXY = []; + const slopeAtYZ = []; + for (const point2 of points) { + const point1 = landmarks[point2[0]]; + const point22 = landmarks[point2[1]]; + const slopes = getSlopes(point1, point22); + const slopeXY = slopes[0]; + const slopeYZ = slopes[1]; + slopeAtXY.push(slopeXY); + slopeAtYZ.push(slopeYZ); + } + slopesXY.push(slopeAtXY); + slopesYZ.push(slopeAtYZ); + } + for (const finger of Finger.all) { + const pointIndexAt = finger === Finger.thumb ? 1 : 0; + const fingerPointsAt = Finger.getPoints(finger); + const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; + const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; + const endPoint = landmarks[fingerPointsAt[3][1]]; + const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); + const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); + fingerCurls[finger] = fingerCurled; + fingerDirections[finger] = fingerPosition; + } + return { curls: fingerCurls, directions: fingerDirections }; +} +function analyze(keypoints) { + if (!keypoints || keypoints.length === 0) + return null; + const estimatorRes = estimate(keypoints); + const landmarks = {}; + for (const fingerIdx of Finger.all) { + landmarks[Finger.getName(fingerIdx)] = { + curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), + direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) + }; + } + return landmarks; +} +function match(keypoints) { + const poses = []; + if (!keypoints || keypoints.length === 0) + return poses; + const estimatorRes = estimate(keypoints); + for (const gesture2 of fingergesture_default) { + const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); + if (confidence >= minConfidence) + poses.push({ name: gesture2.name, confidence }); + } + return poses; +} + +// src/hand/handpose.ts +var meshAnnotations2 = { + thumb: [1, 2, 3, 4], + index: [5, 6, 7, 8], + middle: [9, 10, 11, 12], + ring: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + palm: [0] +}; +var handDetectorModel; +var handPoseModel; +var handPipeline; +async function predict12(input, config3) { + const predictions = await handPipeline.estimateHands(input, config3); + if (!predictions) + return []; + const hands = []; + for (let i = 0; i < predictions.length; i++) { + const annotations2 = {}; + if (predictions[i].landmarks) { + for (const key of Object.keys(meshAnnotations2)) { + annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); + } + } + const keypoints = predictions[i].landmarks; + let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; + let boxRaw = [0, 0, 0, 0]; + if (keypoints && keypoints.length > 0) { + for (const pt of keypoints) { + if (pt[0] < box[0]) + box[0] = pt[0]; + if (pt[1] < box[1]) + box[1] = pt[1]; + if (pt[0] > box[2]) + box[2] = pt[0]; + if (pt[1] > box[3]) + box[3] = pt[1]; + } + box[2] -= box[0]; + box[3] -= box[1]; + 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)]; + } else { + box = predictions[i].box ? [ + Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), + Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), + Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) + ] : [0, 0, 0, 0]; + boxRaw = [ + predictions[i].box.topLeft[0] / (input.shape[2] || 0), + predictions[i].box.topLeft[1] / (input.shape[1] || 0), + (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), + (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) + ]; + } + const landmarks = analyze(keypoints); + hands.push({ + id: i, + score: Math.round(100 * predictions[i].confidence) / 100, + boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, + fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, + label: "hand", + box, + boxRaw, + keypoints, + annotations: annotations2, + landmarks + }); + } + return hands; +} +async function load13(config3) { + var _a, _b; + if (env.initial) { + handDetectorModel = null; + handPoseModel = null; + } + if (!handDetectorModel || !handPoseModel) { + [handDetectorModel, handPoseModel] = await Promise.all([ + config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null, + config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null + ]); + } else { + if (config3.debug) + log("cached model:", handDetectorModel["modelUrl"]); + if (config3.debug) + log("cached model:", handPoseModel["modelUrl"]); + } + const handDetector = new HandDetector(handDetectorModel); + handPipeline = new HandPipeline(handDetector, handPoseModel); + return [handDetectorModel, handPoseModel]; +} + +// src/hand/handtrack.ts +var models2 = [null, null]; +var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; +var inputSize7 = [[0, 0], [0, 0]]; +var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; +var faceIndex = 4; +var boxExpandFact = 1.6; +var maxDetectorResolution = 512; +var detectorExpandFact = 1.4; +var skipped11 = Number.MAX_SAFE_INTEGER; +var lastTime12 = 0; +var outputSize = [0, 0]; +var cache4 = { + boxes: [], + hands: [] +}; +var fingerMap = { + thumb: [1, 2, 3, 4], + index: [5, 6, 7, 8], + middle: [9, 10, 11, 12], + ring: [13, 14, 15, 16], + pinky: [17, 18, 19, 20], + base: [0], + palm: [0, 17, 13, 9, 5, 1, 0] +}; +async function loadDetect2(config3) { + var _a; + if (env.initial) + models2[0] = null; + if (!models2[0]) { + fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); + models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); + const inputs = Object.values(models2[0].modelSignature["inputs"]); + inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", models2[0]["modelUrl"]); + return models2[0]; +} +async function loadSkeleton(config3) { + var _a; + if (env.initial) + models2[1] = null; + if (!models2[1]) { + models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); + const inputs = Object.values(models2[1].modelSignature["inputs"]); + inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; + inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", models2[1]["modelUrl"]); + return models2[1]; +} +async function detectHands(input, config3) { + const hands = []; + if (!input || !models2[0]) + return hands; + const t = {}; + const ratio = (input.shape[2] || 1) / (input.shape[1] || 1); + const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); + const width = Math.round(height * ratio / 8) * 8; + t.resize = tfjs_esm_exports.image.resizeBilinear(input, [height, width]); + t.cast = tfjs_esm_exports.cast(t.resize, "int32"); + [t.rawScores, t.rawBoxes] = await models2[0].executeAsync(t.cast, modelOutputNodes); + t.boxes = tfjs_esm_exports.squeeze(t.rawBoxes, [0, 2]); + t.scores = tfjs_esm_exports.squeeze(t.rawScores, [0]); + const classScores = tfjs_esm_exports.unstack(t.scores, 1); + tfjs_esm_exports.dispose(classScores[faceIndex]); + classScores.splice(faceIndex, 1); + t.filtered = tfjs_esm_exports.stack(classScores, 1); + tfjs_esm_exports.dispose(classScores); + t.max = tfjs_esm_exports.max(t.filtered, 1); + t.argmax = tfjs_esm_exports.argMax(t.filtered, 1); + let id = 0; + t.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t.boxes, t.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); + const nms = await t.nms.data(); + const scores = await t.max.data(); + const classNum = await t.argmax.data(); + for (const nmsIndex of Array.from(nms)) { + const boxSlice = tfjs_esm_exports.slice(t.boxes, nmsIndex, 1); + const boxYX = await boxSlice.data(); + tfjs_esm_exports.dispose(boxSlice); + const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; + const boxRaw = scale(boxData, detectorExpandFact); + const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; + const score = scores[nmsIndex]; + const label = classes[classNum[nmsIndex]]; + const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; + hands.push(hand3); + } + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + hands.sort((a, b) => b.score - a.score); + if (hands.length > (config3.hand.maxDetected || 1)) + hands.length = config3.hand.maxDetected || 1; + return hands; +} +async function detectFingers(input, h, config3) { + const hand3 = { + id: h.id, + score: Math.round(100 * h.score) / 100, + boxScore: Math.round(100 * h.score) / 100, + fingerScore: 0, + box: h.box, + boxRaw: h.boxRaw, + label: h.label, + keypoints: [], + landmarks: {}, + annotations: {} + }; + if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { + const t = {}; + const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; + t.crop = tfjs_esm_exports.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); + t.div = tfjs_esm_exports.div(t.crop, constants.tf255); + [t.score, t.keypoints] = models2[1].execute(t.div, ["Identity_1", "Identity"]); + const rawScore = (await t.score.data())[0]; + const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; + if (score >= (config3.hand.minConfidence || 0)) { + hand3.fingerScore = score; + t.reshaped = tfjs_esm_exports.reshape(t.keypoints, [-1, 3]); + const coordsData = await t.reshaped.array(); + const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); + const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); + hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); + hand3.landmarks = analyze(hand3.keypoints); + for (const key of Object.keys(fingerMap)) { + hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); + } + } + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + } + return hand3; +} +async function predict13(input, config3) { + var _a, _b; + if (!models2[0] || !models2[1] || !((_a = models2[0]) == null ? void 0 : _a.inputs[0].shape) || !((_b = models2[1]) == null ? void 0 : _b.inputs[0].shape)) + return []; + outputSize = [input.shape[2] || 0, input.shape[1] || 0]; + skipped11++; + const skipTime = (config3.hand.skipTime || 0) > now() - lastTime12; + const skipFrame = skipped11 < (config3.hand.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + return cache4.hands; + } + return new Promise(async (resolve) => { + const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime12; + const skipFrameExtended = skipped11 < 3 * (config3.hand.skipFrames || 0); + if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + } else { + cache4.boxes = await detectHands(input, config3); + lastTime12 = now(); + cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); + skipped11 = 0; + } + const oldCache = [...cache4.boxes]; + cache4.boxes.length = 0; + if (config3.cacheSensitivity > 0) { + for (let i = 0; i < cache4.hands.length; i++) { + const boxKpt = square(cache4.hands[i].keypoints, outputSize); + if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { + const boxScale = scale(boxKpt.box, boxExpandFact); + const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); + cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); + } + } + } + for (let i = 0; i < cache4.hands.length; i++) { + const bbox = calc(cache4.hands[i].keypoints, outputSize); + cache4.hands[i].box = bbox.box; + cache4.hands[i].boxRaw = bbox.boxRaw; + } + resolve(cache4.hands); + }); +} + +// src/face/liveness.ts +var model13; +var cached2 = []; +var skipped12 = Number.MAX_SAFE_INTEGER; +var lastCount8 = 0; +var lastTime13 = 0; +async function load14(config3) { + var _a; + if (env.initial) + model13 = null; + if (!model13) + model13 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); + else if (config3.debug) + log("cached model:", model13["modelUrl"]); + return model13; +} +async function predict14(image24, config3, idx, count2) { + var _a, _b; + if (!model13) + return 0; + const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime13; + const skipFrame = skipped12 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); + if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && cached2[idx]) { + skipped12++; + return cached2[idx]; + } + skipped12 = 0; + return new Promise(async (resolve) => { + const resize = tfjs_esm_exports.image.resizeBilinear(image24, [(model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[2] : 0, (model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[1] : 0], false); + const res = model13 == null ? void 0 : model13.execute(resize); + const num = (await res.data())[0]; + cached2[idx] = Math.round(100 * num) / 100; + lastCount8 = count2; + lastTime13 = now(); + tfjs_esm_exports.dispose([resize, res]); + resolve(cached2[idx]); + }); +} + +// src/body/movenetcoords.ts +var movenetcoords_exports = {}; +__export(movenetcoords_exports, { + connected: () => connected3, + horizontal: () => horizontal, + kpt: () => kpt3, + relative: () => relative, + vertical: () => vertical +}); +var kpt3 = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" +]; +var horizontal = [ + ["leftEye", "rightEye"], + ["leftEar", "rightEar"], + ["leftShoulder", "rightShoulder"], + ["leftElbow", "rightElbow"], + ["leftWrist", "rightWrist"], + ["leftHip", "rightHip"], + ["leftKnee", "rightKnee"], + ["leftAnkle", "rightAnkle"] +]; +var vertical = [ + ["leftKnee", "leftShoulder"], + ["rightKnee", "rightShoulder"], + ["leftAnkle", "leftKnee"], + ["rightAnkle", "rightKnee"] +]; +var relative = [ + [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], + [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] +]; +var connected3 = { + leftLeg: ["leftHip", "leftKnee", "leftAnkle"], + rightLeg: ["rightHip", "rightKnee", "rightAnkle"], + torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], + leftArm: ["leftShoulder", "leftElbow", "leftWrist"], + rightArm: ["rightShoulder", "rightElbow", "rightWrist"], + head: [] +}; + +// src/body/movenetfix.ts +var maxJitter = 5e-3; +var cache5 = { + keypoints: [], + padding: [[0, 0], [0, 0], [0, 0], [0, 0]] +}; +function bodyParts(body4) { + for (const pair of horizontal) { + const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); + const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); + if (body4.keypoints[left] && body4.keypoints[right]) { + if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { + const tmp = body4.keypoints[left]; + body4.keypoints[left] = body4.keypoints[right]; + body4.keypoints[right] = tmp; + } + } + } + for (const pair of vertical) { + const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); + const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); + if (body4.keypoints[lower] && body4.keypoints[higher]) { + if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { + body4.keypoints.splice(lower, 1); + } + } + } + for (const [pair, compare2] of relative) { + const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); + const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); + const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); + const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); + if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) + continue; + const distanceLeft = body4.keypoints[left] ? [ + Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), + Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) + ] : [0, 0]; + const distanceRight = body4.keypoints[right] ? [ + Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), + Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) + ] : [0, 0]; + if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { + const tmp = body4.keypoints[left]; + body4.keypoints[left] = body4.keypoints[right]; + body4.keypoints[right] = tmp; + } + } +} +function jitter(keypoints) { + for (let i = 0; i < keypoints.length; i++) { + if (keypoints[i] && cache5.keypoints[i]) { + const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; + if (diff[0] < maxJitter && diff[1] < maxJitter) { + keypoints[i] = cache5.keypoints[i]; + } else { + cache5.keypoints[i] = keypoints[i]; + } + } else { + cache5.keypoints[i] = keypoints[i]; + } + } + return keypoints; +} +function padInput(input, inputSize10) { + const t = {}; + if (!input.shape || !input.shape[1] || !input.shape[2]) + return input; + cache5.padding = [ + [0, 0], + [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], + [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], + [0, 0] + ]; + t.pad = tfjs_esm_exports.pad(input, cache5.padding); + t.resize = tfjs_esm_exports.image.resizeBilinear(t.pad, [inputSize10, inputSize10]); + const final = tfjs_esm_exports.cast(t.resize, "int32"); + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + return final; +} +function rescaleBody(body4, outputSize2) { + body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 && kpt4.position); + for (const kpt4 of body4.keypoints) { + kpt4.position = [ + kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], + kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] + ]; + kpt4.positionRaw = [ + kpt4.position[0] / outputSize2[0], + kpt4.position[1] / outputSize2[1] + ]; + } + const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); + body4.box = rescaledBoxes.box; + body4.boxRaw = rescaledBoxes.boxRaw; + return body4; +} + +// src/body/movenet.ts +var model14; +var inputSize8 = 0; +var skipped13 = Number.MAX_SAFE_INTEGER; +var cache6 = { + boxes: [], + bodies: [], + last: 0 +}; +async function load15(config3) { + if (env.initial) + model14 = null; + if (!model14) { + fakeOps(["size"], config3); + model14 = await loadModel(config3.body.modelPath); + } else if (config3.debug) + log("cached model:", model14["modelUrl"]); + inputSize8 = model14.inputs[0].shape ? model14.inputs[0].shape[2] : 0; + if (inputSize8 < 64) + inputSize8 = 256; + return model14; +} +async function parseSinglePose(res, config3, image24) { + const kpt4 = res[0][0]; + const keypoints = []; + let score = 0; + for (let id = 0; id < kpt4.length; id++) { + score = kpt4[id][2]; + if (score > config3.body.minConfidence) { + const positionRaw = [kpt4[id][1], kpt4[id][0]]; + keypoints.push({ + score: Math.round(100 * score) / 100, + part: kpt3[id], + positionRaw, + position: [ + Math.round((image24.shape[2] || 0) * positionRaw[0]), + Math.round((image24.shape[1] || 0) * positionRaw[1]) + ] + }); + } + } + score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); + const bodies = []; + const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected3)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[i]); + const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; + bodyParts(body4); + bodies.push(body4); + return bodies; +} +async function parseMultiPose(res, config3, image24) { + const bodies = []; + for (let id = 0; id < res[0].length; id++) { + const kpt4 = res[0][id]; + const totalScore = Math.round(100 * kpt4[51 + 4]) / 100; + if (totalScore > config3.body.minConfidence) { + const keypoints = []; + for (let i = 0; i < 17; i++) { + const score = kpt4[3 * i + 2]; + if (score > config3.body.minConfidence) { + const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; + keypoints.push({ + part: kpt3[i], + score: Math.round(100 * score) / 100, + positionRaw, + position: [Math.round((image24.shape[2] || 0) * positionRaw[0]), Math.round((image24.shape[1] || 0) * positionRaw[1])] + }); + } + } + const newBox = calc(keypoints.map((pt) => pt.position), [image24.shape[2], image24.shape[1]]); + const annotations2 = {}; + for (const [name, indexes] of Object.entries(connected3)) { + const pt = []; + for (let i = 0; i < indexes.length - 1; i++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[i]); + const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); + if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 }; + bodyParts(body4); + bodies.push(body4); + } + } + bodies.sort((a, b) => b.score - a.score); + if (bodies.length > config3.body.maxDetected) + bodies.length = config3.body.maxDetected; + return bodies; +} +async function predict15(input, config3) { + if (!model14 || !(model14 == null ? void 0 : model14.inputs[0].shape)) + return []; + if (!config3.skipAllowed) + cache6.boxes.length = 0; + skipped13++; + const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; + const skipFrame = skipped13 < (config3.body.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame) { + return cache6.bodies; + } + return new Promise(async (resolve) => { + const t = {}; + skipped13 = 0; + t.input = padInput(input, inputSize8); + t.res = model14 == null ? void 0 : model14.execute(t.input); + cache6.last = now(); + const res = await t.res.array(); + cache6.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input) : await parseMultiPose(res, config3, input); + for (const body4 of cache6.bodies) { + rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); + jitter(body4.keypoints); + } + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + resolve(cache6.bodies); + }); +} + +// src/object/nanodet.ts +var model15; +var last9 = []; +var lastTime14 = 0; +var skipped14 = Number.MAX_SAFE_INTEGER; +var inputSize9 = 0; +var scaleBox = 2.5; +async function load16(config3) { + if (!model15 || env.initial) { + model15 = await loadModel(config3.object.modelPath); + const inputs = Object.values(model15.modelSignature["inputs"]); + inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; + } else if (config3.debug) + log("cached model:", model15["modelUrl"]); + return model15; +} +async function process4(res, outputShape, config3) { + let id = 0; + let results = []; + for (const strideSize of [1, 2, 4]) { + tfjs_esm_exports.tidy(async () => { + const baseSize = strideSize * 13; + const scoresT = tfjs_esm_exports.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length)); + const featuresT = tfjs_esm_exports.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length)); + const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); + const boxIdx = await boxesMax.argMax(2).array(); + const scores = await scoresT.array(); + for (let i = 0; i < scoresT.shape[0]; i++) { + for (let j = 0; j < scoresT.shape[1]; j++) { + const score = scores[i][j]; + if (score > (config3.object.minConfidence || 0) && j !== 61) { + const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; + const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; + const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / inputSize9)); + const [x, y] = [ + cx - scaleBox / strideSize * boxOffset[0], + cy - scaleBox / strideSize * boxOffset[1] + ]; + const [w, h] = [ + cx + scaleBox / strideSize * boxOffset[2] - x, + cy + scaleBox / strideSize * boxOffset[3] - y + ]; + let boxRaw = [x, y, w, h]; + boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); + const box = [ + boxRaw[0] * outputShape[0], + boxRaw[1] * outputShape[1], + boxRaw[2] * outputShape[0], + boxRaw[3] * outputShape[1] + ]; + const result = { + id: id++, + score: Math.round(100 * score) / 100, + class: j + 1, + label: labels[j].label, + box: box.map((a) => Math.trunc(a)), + boxRaw + }; + results.push(result); + } + } + } + }); + } + res.forEach((t) => tfjs_esm_exports.dispose(t)); + const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); + const nmsScores = results.map((a) => a.score); + let nmsIdx = []; + if (nmsBoxes && nmsBoxes.length > 0) { + const nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence); + nmsIdx = await nms.data(); + tfjs_esm_exports.dispose(nms); + } + results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); + return results; +} +async function predict16(image24, config3) { + const skipTime = (config3.object.skipTime || 0) > now() - lastTime14; + const skipFrame = skipped14 < (config3.object.skipFrames || 0); + if (config3.skipAllowed && skipTime && skipFrame && last9.length > 0) { + skipped14++; + return last9; + } + skipped14 = 0; + if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) + return last9; + return new Promise(async (resolve) => { + const outputSize2 = [image24.shape[2] || 0, image24.shape[1] || 0]; + const resize = tfjs_esm_exports.image.resizeBilinear(image24, [inputSize9, inputSize9], false); + const norm = tfjs_esm_exports.div(resize, constants.tf255); + const transpose = norm.transpose([0, 3, 1, 2]); + tfjs_esm_exports.dispose(norm); + tfjs_esm_exports.dispose(resize); + let objectT; + if (config3.object.enabled) + objectT = model15.execute(transpose); + lastTime14 = now(); + tfjs_esm_exports.dispose(transpose); + const obj = await process4(objectT, outputSize2, config3); + last9 = obj; + resolve(obj); + }); +} + +// src/body/posenetutils.ts +var partNames = [ + "nose", + "leftEye", + "rightEye", + "leftEar", + "rightEar", + "leftShoulder", + "rightShoulder", + "leftElbow", + "rightElbow", + "leftWrist", + "rightWrist", + "leftHip", + "rightHip", + "leftKnee", + "rightKnee", + "leftAnkle", + "rightAnkle" +]; +var count = partNames.length; +var partIds = partNames.reduce((result, jointName, i) => { + result[jointName] = i; + return result; +}, {}); +var connectedPartNames = [ + ["leftHip", "leftShoulder"], + ["leftElbow", "leftShoulder"], + ["leftElbow", "leftWrist"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["rightHip", "rightShoulder"], + ["rightElbow", "rightShoulder"], + ["rightElbow", "rightWrist"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"], + ["leftShoulder", "rightShoulder"], + ["leftHip", "rightHip"] +]; +var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); +var poseChain = [ + ["nose", "leftEye"], + ["leftEye", "leftEar"], + ["nose", "rightEye"], + ["rightEye", "rightEar"], + ["nose", "leftShoulder"], + ["leftShoulder", "leftElbow"], + ["leftElbow", "leftWrist"], + ["leftShoulder", "leftHip"], + ["leftHip", "leftKnee"], + ["leftKnee", "leftAnkle"], + ["nose", "rightShoulder"], + ["rightShoulder", "rightElbow"], + ["rightElbow", "rightWrist"], + ["rightShoulder", "rightHip"], + ["rightHip", "rightKnee"], + ["rightKnee", "rightAnkle"] +]; +function getBoundingBox(keypoints) { + const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ + maxX: Math.max(maxX, x), + maxY: Math.max(maxY, y), + minX: Math.min(minX, x), + minY: Math.min(minY, y) + }), { + maxX: Number.NEGATIVE_INFINITY, + maxY: Number.NEGATIVE_INFINITY, + minX: Number.POSITIVE_INFINITY, + minY: Number.POSITIVE_INFINITY + }); + return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; +} +function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { + const scaleY = height / inputResolutionHeight; + const scaleX = width / inputResolutionWidth; + const scalePose = (pose, i) => ({ + id: i, + score: pose.score, + boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], + box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], + keypoints: pose.keypoints.map(({ score, part, position }) => ({ + score, + part, + position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], + positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] + })), + annotations: {} + }); + const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); + return scaledPoses; +} +var MaxHeap = class { + constructor(maxSize2, getElementValue) { + __publicField(this, "priorityQueue"); + __publicField(this, "numberOfElements"); + __publicField(this, "getElementValue"); + this.priorityQueue = new Array(maxSize2); + this.numberOfElements = -1; + this.getElementValue = getElementValue; + } + enqueue(x) { + this.priorityQueue[++this.numberOfElements] = x; + this.swim(this.numberOfElements); + } + dequeue() { + const max4 = this.priorityQueue[0]; + this.exchange(0, this.numberOfElements--); + this.sink(0); + this.priorityQueue[this.numberOfElements + 1] = null; + return max4; + } + empty() { + return this.numberOfElements === -1; + } + size() { + return this.numberOfElements + 1; + } + all() { + return this.priorityQueue.slice(0, this.numberOfElements + 1); + } + max() { + return this.priorityQueue[0]; + } + swim(k) { + while (k > 0 && this.less(Math.floor(k / 2), k)) { + this.exchange(k, Math.floor(k / 2)); + k = Math.floor(k / 2); + } + } + sink(k) { + while (2 * k <= this.numberOfElements) { + let j = 2 * k; + if (j < this.numberOfElements && this.less(j, j + 1)) + j++; + if (!this.less(k, j)) + break; + this.exchange(k, j); + k = j; + } + } + getValueAt(i) { + return this.getElementValue(this.priorityQueue[i]); + } + less(i, j) { + return this.getValueAt(i) < this.getValueAt(j); + } + exchange(i, j) { + const t = this.priorityQueue[i]; + this.priorityQueue[i] = this.priorityQueue[j]; + this.priorityQueue[j] = t; + } +}; +function getOffsetPoint(y, x, keypoint, offsets) { + return { + y: offsets.get(y, x, keypoint), + x: offsets.get(y, x, keypoint + count) + }; +} +function getImageCoords(part, outputStride2, offsets) { + const { heatmapY, heatmapX, id: keypoint } = part; + const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); + return { + x: part.heatmapX * outputStride2 + x, + y: part.heatmapY * outputStride2 + y + }; +} +function clamp(a, min2, max4) { + if (a < min2) + return min2; + if (a > max4) + return max4; + return a; +} +function squaredDistance(y1, x1, y2, x2) { + const dy = y2 - y1; + const dx = x2 - x1; + return dy * dy + dx * dx; +} +function addVectors(a, b) { + return { x: a.x + b.x, y: a.y + b.y }; +} + +// src/body/posenet.ts +var model16; +var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"]; +var localMaximumRadius = 1; +var outputStride = 16; +var squaredNmsRadius = 50 ** 2; +function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { + const getDisplacement = (point2) => ({ + y: displacements.get(point2.y, point2.x, edgeId), + x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) + }); + const getStridedIndexNearPoint = (point2, height2, width2) => ({ + y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), + x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) + }); + const [height, width] = scores.shape; + const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); + const displacement = getDisplacement(sourceKeypointIndices); + const displacedPoint = addVectors(sourceKeypoint.position, displacement); + let targetKeypoint = displacedPoint; + for (let i = 0; i < offsetRefineStep; i++) { + const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); + targetKeypoint = addVectors({ x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, { x: offsetPoint.x, y: offsetPoint.y }); + } + const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); + const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); + return { position: targetKeypoint, part: partNames[targetId], score }; +} +function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { + const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); + const edgesFwd = tuples.map(([, childJointId]) => childJointId); + const edgesBwd = tuples.map(([parentJointId]) => parentJointId); + const numParts = scores.shape[2]; + const numEdges = edgesFwd.length; + const keypoints = new Array(numParts); + const rootPoint = getImageCoords(root.part, outputStride, offsets); + keypoints[root.part.id] = { + score: root.score, + part: partNames[root.part.id], + position: rootPoint + }; + for (let edge = numEdges - 1; edge >= 0; --edge) { + const sourceId = edgesFwd[edge]; + const targetId = edgesBwd[edge]; + if (keypoints[sourceId] && !keypoints[targetId]) { + keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); + } + } + for (let edge = 0; edge < numEdges; ++edge) { + const sourceId = edgesBwd[edge]; + const targetId = edgesFwd[edge]; + if (keypoints[sourceId] && !keypoints[targetId]) { + keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); + } + } + return keypoints; +} +function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { + const [height, width] = scores.shape; + let localMaximum = true; + const yStart = Math.max(heatmapY - localMaximumRadius, 0); + const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); + for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { + const xStart = Math.max(heatmapX - localMaximumRadius, 0); + const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); + for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { + if (scores.get(yCurrent, xCurrent, keypointId) > score) { + localMaximum = false; + break; + } + } + if (!localMaximum) + break; + } + return localMaximum; +} +function buildPartWithScoreQueue(minConfidence2, scores) { + const [height, width, numKeypoints] = scores.shape; + const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); + for (let heatmapY = 0; heatmapY < height; ++heatmapY) { + for (let heatmapX = 0; heatmapX < width; ++heatmapX) { + for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { + const score = scores.get(heatmapY, heatmapX, keypointId); + if (score < minConfidence2) + continue; + if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) + queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); + } + } + } + return queue; +} +function withinRadius(poses, { x, y }, keypointId) { + return poses.some(({ keypoints }) => { + var _a; + const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; + if (!correspondingKeypoint) + return false; + return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; + }); +} +function getInstanceScore(existingPoses, keypoints) { + const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { + if (!withinRadius(existingPoses, position, keypointId)) + result += score; + return result; + }, 0); + return notOverlappedKeypointScores / keypoints.length; +} +function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { + const poses = []; + const queue = buildPartWithScoreQueue(minConfidence2, scores); + while (poses.length < maxDetected && !queue.empty()) { + const root = queue.dequeue(); + const rootImageCoords = getImageCoords(root.part, outputStride, offsets); + if (withinRadius(poses, rootImageCoords, root.part.id)) + continue; + let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); + keypoints = keypoints.filter((a) => a.score > minConfidence2); + const score = getInstanceScore(poses, keypoints); + const box = getBoundingBox(keypoints); + if (score > minConfidence2) + poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); + } + return poses; +} +async function predict17(input, config3) { + const res = tfjs_esm_exports.tidy(() => { + if (!model16.inputs[0].shape) + return []; + const resized = tfjs_esm_exports.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]); + const normalized = tfjs_esm_exports.sub(tfjs_esm_exports.div(tfjs_esm_exports.cast(resized, "float32"), 127.5), 1); + const results = model16.execute(normalized, poseNetOutputs); + const results3d = results.map((y) => tfjs_esm_exports.squeeze(y, [0])); + results3d[1] = tfjs_esm_exports.sigmoid(results3d[1]); + return results3d; + }); + const buffers = await Promise.all(res.map((tensor3) => tensor3.buffer())); + for (const t of res) + tfjs_esm_exports.dispose(t); + const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); + if (!model16.inputs[0].shape) + return []; + const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]); + return scaled; +} +async function load17(config3) { + if (!model16 || env.initial) + model16 = await loadModel(config3.body.modelPath); + else if (config3.debug) + log("cached model:", model16["modelUrl"]); + return model16; +} + +// src/segmentation/segmentation.ts +var model17; +var busy = false; +async function load18(config3) { + if (!model17 || env.initial) + model17 = await loadModel(config3.segmentation.modelPath); + else if (config3.debug) + log("cached model:", model17["modelUrl"]); + return model17; +} +async function process5(input, background, config3) { + var _a, _b; + if (busy) + return { data: [], canvas: null, alpha: null }; + busy = true; + if (!model17) + await load18(config3); + const inputImage = await process2(input, config3); + const width = ((_a = inputImage.tensor) == null ? void 0 : _a.shape[2]) || 0; + const height = ((_b = inputImage.tensor) == null ? void 0 : _b.shape[1]) || 0; + if (!inputImage.tensor) + return { data: [], canvas: null, alpha: null }; + const t = {}; + t.resize = tfjs_esm_exports.image.resizeBilinear(inputImage.tensor, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); + tfjs_esm_exports.dispose(inputImage.tensor); + t.norm = tfjs_esm_exports.div(t.resize, constants.tf255); + t.res = model17.execute(t.norm); + t.squeeze = tfjs_esm_exports.squeeze(t.res, 0); + if (t.squeeze.shape[2] === 2) { + t.softmax = tfjs_esm_exports.softmax(t.squeeze); + [t.bg, t.fg] = tfjs_esm_exports.unstack(t.softmax, 2); + t.expand = tfjs_esm_exports.expandDims(t.fg, 2); + t.pad = tfjs_esm_exports.expandDims(t.expand, 0); + t.crop = tfjs_esm_exports.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]); + t.data = tfjs_esm_exports.squeeze(t.crop, 0); + } else { + t.data = tfjs_esm_exports.image.resizeBilinear(t.squeeze, [height, width]); + } + const data = Array.from(await t.data.data()); + if (env.node && !env.Canvas && typeof ImageData === "undefined") { + if (config3.debug) + log("canvas support missing"); + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + return { data, canvas: null, alpha: null }; + } + const alphaCanvas = canvas(width, height); + if (tfjs_esm_exports.browser) + await tfjs_esm_exports.browser.toPixels(t.data, alphaCanvas); + const alphaCtx = alphaCanvas.getContext("2d"); + if (config3.segmentation.blur && config3.segmentation.blur > 0) + alphaCtx.filter = `blur(${config3.segmentation.blur}px)`; + const alphaData = alphaCtx.getImageData(0, 0, width, height); + const compositeCanvas = canvas(width, height); + const compositeCtx = compositeCanvas.getContext("2d"); + if (inputImage.canvas) + compositeCtx.drawImage(inputImage.canvas, 0, 0); + compositeCtx.globalCompositeOperation = "darken"; + if (config3.segmentation.blur && config3.segmentation.blur > 0) + compositeCtx.filter = `blur(${config3.segmentation.blur}px)`; + compositeCtx.drawImage(alphaCanvas, 0, 0); + compositeCtx.globalCompositeOperation = "source-over"; + compositeCtx.filter = "none"; + const compositeData = compositeCtx.getImageData(0, 0, width, height); + for (let i = 0; i < width * height; i++) + compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; + compositeCtx.putImageData(compositeData, 0, 0); + let mergedCanvas = null; + if (background && compositeCanvas) { + mergedCanvas = canvas(width, height); + const bgImage = await process2(background, config3); + tfjs_esm_exports.dispose(bgImage.tensor); + const ctxMerge = mergedCanvas.getContext("2d"); + ctxMerge.drawImage(bgImage.canvas, 0, 0, mergedCanvas.width, mergedCanvas.height); + ctxMerge.drawImage(compositeCanvas, 0, 0); + } + Object.keys(t).forEach((tensor3) => tfjs_esm_exports.dispose(t[tensor3])); + busy = false; + return { data, canvas: compositeCanvas, alpha: alphaCanvas }; +} + +// src/models.ts +var Models = class { + constructor() { + __publicField(this, "ssrnetage", null); + __publicField(this, "gear", null); + __publicField(this, "blazeposedetect", null); + __publicField(this, "blazepose", null); + __publicField(this, "centernet", null); + __publicField(this, "efficientpose", null); + __publicField(this, "mobilefacenet", null); + __publicField(this, "emotion", null); + __publicField(this, "facedetect", null); + __publicField(this, "faceiris", null); + __publicField(this, "facemesh", null); + __publicField(this, "faceres", null); + __publicField(this, "ssrnetgender", null); + __publicField(this, "handpose", null); + __publicField(this, "handskeleton", null); + __publicField(this, "handtrack", null); + __publicField(this, "liveness", null); + __publicField(this, "movenet", null); + __publicField(this, "nanodet", null); + __publicField(this, "posenet", null); + __publicField(this, "segmentation", null); + __publicField(this, "antispoof", null); + } +}; +function reset(instance) { + for (const model18 of Object.keys(instance.models)) + instance.models[model18] = null; +} +async function load19(instance) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C, _D, _E; + if (env.initial) + reset(instance); + if (instance.config.hand.enabled) { + if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect"))) + [instance.models.handpose, instance.models.handskeleton] = await load13(instance.config); + if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect"))) + [instance.models.handpose, instance.models.handskeleton] = await load13(instance.config); + } + if (instance.config.body.enabled && !instance.models.blazepose && ((_f = (_e = instance.config.body) == null ? void 0 : _e.modelPath) == null ? void 0 : _f.includes("blazepose"))) + instance.models.blazepose = loadPose(instance.config); + if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body["detector"] && instance.config.body["detector"]["modelPath"]) + instance.models.blazeposedetect = loadDetect(instance.config); + if (instance.config.body.enabled && !instance.models.efficientpose && ((_h = (_g = instance.config.body) == null ? void 0 : _g.modelPath) == null ? void 0 : _h.includes("efficientpose"))) + instance.models.efficientpose = load7(instance.config); + if (instance.config.body.enabled && !instance.models.movenet && ((_j = (_i = instance.config.body) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("movenet"))) + instance.models.movenet = load15(instance.config); + if (instance.config.body.enabled && !instance.models.posenet && ((_l = (_k = instance.config.body) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("posenet"))) + instance.models.posenet = load17(instance.config); + if (instance.config.face.enabled && !instance.models.facedetect) + instance.models.facedetect = load5(instance.config); + if (instance.config.face.enabled && ((_m = instance.config.face.antispoof) == null ? void 0 : _m.enabled) && !instance.models.antispoof) + instance.models.antispoof = load4(instance.config); + if (instance.config.face.enabled && ((_n = instance.config.face.liveness) == null ? void 0 : _n.enabled) && !instance.models.liveness) + instance.models.liveness = load14(instance.config); + if (instance.config.face.enabled && ((_o = instance.config.face.description) == null ? void 0 : _o.enabled) && !instance.models.faceres) + instance.models.faceres = load12(instance.config); + if (instance.config.face.enabled && ((_p = instance.config.face.emotion) == null ? void 0 : _p.enabled) && !instance.models.emotion) + instance.models.emotion = load8(instance.config); + if (instance.config.face.enabled && ((_q = instance.config.face.iris) == null ? void 0 : _q.enabled) && !((_r = instance.config.face.attention) == null ? void 0 : _r.enabled) && !instance.models.faceiris) + instance.models.faceiris = load10(instance.config); + if (instance.config.face.enabled && ((_s = instance.config.face.mesh) == null ? void 0 : _s.enabled) && !instance.models.facemesh) + instance.models.facemesh = load11(instance.config); + if (instance.config.face.enabled && ((_t = instance.config.face["gear"]) == null ? void 0 : _t.enabled) && !instance.models.gear) + instance.models.gear = load(instance.config); + if (instance.config.face.enabled && ((_u = instance.config.face["ssrnet"]) == null ? void 0 : _u.enabled) && !instance.models.ssrnetage) + instance.models.ssrnetage = load2(instance.config); + if (instance.config.face.enabled && ((_v = instance.config.face["ssrnet"]) == null ? void 0 : _v.enabled) && !instance.models.ssrnetgender) + instance.models.ssrnetgender = load3(instance.config); + if (instance.config.face.enabled && ((_w = instance.config.face["mobilefacenet"]) == null ? void 0 : _w.enabled) && !instance.models.mobilefacenet) + instance.models.mobilefacenet = load9(instance.config); + if (instance.config.hand.enabled && !instance.models.handtrack && ((_y = (_x = instance.config.hand.detector) == null ? void 0 : _x.modelPath) == null ? void 0 : _y.includes("handtrack"))) + instance.models.handtrack = loadDetect2(instance.config); + if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_A = (_z = instance.config.hand.detector) == null ? void 0 : _z.modelPath) == null ? void 0 : _A.includes("handtrack"))) + instance.models.handskeleton = loadSkeleton(instance.config); + if (instance.config.object.enabled && !instance.models.centernet && ((_C = (_B = instance.config.object) == null ? void 0 : _B.modelPath) == null ? void 0 : _C.includes("centernet"))) + instance.models.centernet = load6(instance.config); + if (instance.config.object.enabled && !instance.models.nanodet && ((_E = (_D = instance.config.object) == null ? void 0 : _D.modelPath) == null ? void 0 : _E.includes("nanodet"))) + instance.models.nanodet = load16(instance.config); + if (instance.config.segmentation.enabled && !instance.models.segmentation) + instance.models.segmentation = load18(instance.config); + for await (const model18 of Object.keys(instance.models)) { + if (instance.models[model18] && typeof instance.models[model18] !== "undefined") + instance.models[model18] = await instance.models[model18]; + } +} +async function validate2(instance) { + const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; + for (const defined of Object.keys(instance.models)) { + const model18 = instance.models[defined]; + if (!model18) + continue; + const ops = []; + const executor = model18 == null ? void 0 : model18.executor; + if (executor && executor.graph.nodes) { + for (const kernel of Object.values(executor.graph.nodes)) { + const op = kernel.op.toLowerCase(); + if (!ops.includes(op)) + ops.push(op); + } + } else { + if (!executor && instance.config.debug) + log("model signature not determined:", defined); + } + const missing = []; + for (const op of ops) { + if (!simpleOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { + missing.push(op); + } + } + if (instance.config.debug && missing.length > 0) + log("model validation failed:", defined, missing); + } +} + +// src/tfjs/humangl.ts +var config2 = { + name: "humangl", + priority: 999, + canvas: null, + gl: null, + extensions: [], + webGLattr: { + alpha: false, + antialias: false, + premultipliedAlpha: false, + preserveDrawingBuffer: false, + depth: false, + stencil: false, + failIfMajorPerformanceCaveat: false, + desynchronized: true + } +}; +function extensions() { + const gl = config2.gl; + if (!gl) + return; + config2.extensions = gl.getSupportedExtensions(); +} +async function register(instance) { + var _a; + if (instance.config.backend !== "humangl") + return; + if (config2.name in tfjs_esm_exports.engine().registry && (!config2.gl || !config2.gl.getParameter(config2.gl.VERSION))) { + log("error: humangl backend invalid context"); + reset(instance); + } + if (!tfjs_esm_exports.findBackend(config2.name)) { + try { + config2.canvas = await canvas(100, 100); + } catch (err) { + log("error: cannot create canvas:", err); + return; + } + try { + config2.gl = (_a = config2.canvas) == null ? void 0 : _a.getContext("webgl2", config2.webGLattr); + const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); + if (!glv2) { + log("override: using fallback webgl backend as webgl 2.0 is not detected"); + instance.config.backend = "webgl"; + return; + } + if (config2.canvas) { + config2.canvas.addEventListener("webglcontextlost", async (e) => { + log("error: humangl:", e.type); + log("possible browser memory leak using webgl or conflict with multiple backend registrations"); + instance.emit("error"); + throw new Error("backend error: webgl context lost"); + }); + config2.canvas.addEventListener("webglcontextrestored", (e) => { + log("error: humangl context restored:", e); + }); + config2.canvas.addEventListener("webglcontextcreationerror", (e) => { + log("error: humangl context create:", e); + }); + } + } catch (err) { + log("error: cannot get WebGL context:", err); + return; + } + try { + tfjs_esm_exports.setWebGLContext(2, config2.gl); + } catch (err) { + log("error: cannot set WebGL context:", err); + return; + } + try { + const ctx = new tfjs_esm_exports.GPGPUContext(config2.gl); + tfjs_esm_exports.registerBackend(config2.name, () => new tfjs_esm_exports.MathBackendWebGL(ctx), config2.priority); + } catch (err) { + log("error: cannot register WebGL backend:", err); + return; + } + try { + const kernels = tfjs_esm_exports.getKernelsForBackend("webgl"); + kernels.forEach((kernelConfig) => { + const newKernelConfig = { ...kernelConfig, backendName: config2.name }; + tfjs_esm_exports.registerKernel(newKernelConfig); + }); + } catch (err) { + log("error: cannot update WebGL backend registration:", err); + return; + } + const current = tfjs_esm_exports.backend().getGPGPUContext ? tfjs_esm_exports.backend().getGPGPUContext().gl : null; + if (current) { + log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`); + } else { + log("error: no current gl context:", current, config2.gl); + return; + } + try { + tfjs_esm_exports.ENV.set("WEBGL_VERSION", 2); + } catch (err) { + log("error: cannot set WebGL backend flags:", err); + return; + } + extensions(); + log("backend registered:", config2.name); + } +} + +// src/tfjs/backend.ts +function registerCustomOps() { + if (!env.kernels.includes("mod")) { + const kernelMod = { + kernelName: "Mod", + backendName: tfjs_esm_exports.getBackend(), + kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.sub(op.inputs.a, tfjs_esm_exports.mul(tfjs_esm_exports.div(op.inputs.a, op.inputs.b), op.inputs.b))) + }; + tfjs_esm_exports.registerKernel(kernelMod); + env.kernels.push("mod"); + } + if (!env.kernels.includes("floormod")) { + const kernelMod = { + kernelName: "FloorMod", + backendName: tfjs_esm_exports.getBackend(), + kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tfjs_esm_exports.mod(op.inputs.a, op.inputs.b)) + }; + tfjs_esm_exports.registerKernel(kernelMod); + env.kernels.push("floormod"); + } +} +async function check(instance, force = false) { + instance.state = "backend"; + if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tfjs_esm_exports.getBackend() !== instance.config.backend) { + const timeStamp = now(); + if (instance.config.backend && instance.config.backend.length > 0) { + if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { + if (instance.config.debug) + log("running inside web worker"); + } + if (env.browser && instance.config.backend === "tensorflow") { + if (instance.config.debug) + log("override: backend set to tensorflow while running in browser"); + instance.config.backend = "humangl"; + } + if (env.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) { + if (instance.config.debug) + log(`override: backend set to ${instance.config.backend} while running in nodejs`); + instance.config.backend = "tensorflow"; + } + if (env.browser && instance.config.backend === "webgpu") { + if (typeof navigator === "undefined" || typeof navigator["gpu"] === "undefined") { + log("override: backend set to webgpu but browser does not support webgpu"); + instance.config.backend = "humangl"; + } else { + const adapter = await navigator["gpu"].requestAdapter(); + if (instance.config.debug) + log("enumerated webgpu adapter:", adapter); + } + } + if (instance.config.backend === "humangl") + await register(instance); + const available = Object.keys(tfjs_esm_exports.engine().registryFactory); + if (instance.config.debug) + log("available backends:", available); + if (!available.includes(instance.config.backend)) { + log(`error: backend ${instance.config.backend} not found in registry`); + instance.config.backend = env.node ? "tensorflow" : "webgl"; + if (instance.config.debug) + log(`override: setting backend ${instance.config.backend}`); + } + if (instance.config.debug) + log("setting backend:", instance.config.backend); + if (instance.config.backend === "wasm") { + if (instance.config.debug) + log("wasm path:", instance.config.wasmPath); + if (typeof (tfjs_esm_exports == null ? void 0 : tfjs_esm_exports.setWasmPaths) !== "undefined") + await tfjs_esm_exports.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); + else + throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); + const simd = await tfjs_esm_exports.env().getAsync("WASM_HAS_SIMD_SUPPORT"); + const mt = await tfjs_esm_exports.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); + if (instance.config.debug) + log(`wasm execution: ${simd ? "SIMD" : "no SIMD"} ${mt ? "multithreaded" : "singlethreaded"}`); + if (instance.config.debug && !simd) + log("warning: wasm simd support is not enabled"); + } + try { + await tfjs_esm_exports.setBackend(instance.config.backend); + await tfjs_esm_exports.ready(); + init(); + } catch (err) { + log("error: cannot set backend:", instance.config.backend, err); + return false; + } + } + if (tfjs_esm_exports.getBackend() === "humangl") { + tfjs_esm_exports.ENV.set("CHECK_COMPUTATION_FOR_ERRORS", false); + tfjs_esm_exports.ENV.set("WEBGL_CPU_FORWARD", true); + tfjs_esm_exports.ENV.set("WEBGL_USE_SHAPES_UNIFORMS", true); + tfjs_esm_exports.ENV.set("CPU_HANDOFF_SIZE_THRESHOLD", 256); + if (typeof instance.config["deallocate"] !== "undefined" && instance.config["deallocate"]) { + log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); + tfjs_esm_exports.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); + } + if (tfjs_esm_exports.backend().getGPGPUContext) { + const gl = await tfjs_esm_exports.backend().getGPGPUContext().gl; + if (instance.config.debug) + log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`); + } + } + if (tfjs_esm_exports.getBackend() === "webgpu") { + } + tfjs_esm_exports.enableProdMode(); + await tfjs_esm_exports.ready(); + instance.performance.initBackend = Math.trunc(now() - timeStamp); + instance.config.backend = tfjs_esm_exports.getBackend(); + await env.updateBackend(); + registerCustomOps(); + } + return true; +} +function fakeOps(kernelNames, config3) { + for (const kernelName of kernelNames) { + const kernelConfig = { + kernelName, + backendName: config3.backend, + kernelFunc: () => { + if (config3.debug) + log("kernelFunc", kernelName, config3.backend); + } + }; + tfjs_esm_exports.registerKernel(kernelConfig); + } + env.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); +} + +// src/draw/draw.ts +var draw_exports = {}; +__export(draw_exports, { + all: () => all, + body: () => body, + canvas: () => canvas2, + face: () => face, + gesture: () => gesture, + hand: () => hand, + object: () => object, + options: () => options3, + person: () => person +}); + +// src/draw/primitives.ts +var getCanvasContext = (input) => { + if (!input) + log("draw error: invalid canvas"); + else if (!input.getContext) + log("draw error: canvas context not defined"); + else { + const ctx = input.getContext("2d"); + if (!ctx) + log("draw error: cannot get canvas context"); + else + return ctx; + } + return null; +}; +var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); +var colorDepth = (z, opt2) => { + if (!opt2.useDepth || typeof z === "undefined") + return opt2.color; + const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); + const color = `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt2.alpha})`; + return color; +}; +function point(ctx, x, y, z, localOptions) { + ctx.fillStyle = colorDepth(z, localOptions); + ctx.beginPath(); + ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI); + ctx.fill(); +} +function rect(ctx, x, y, width, height, localOptions) { + ctx.beginPath(); + ctx.lineWidth = localOptions.lineWidth; + if (localOptions.useCurves) { + const cx = (x + x + width) / 2; + const cy = (y + y + height) / 2; + ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); + } else { + ctx.moveTo(x + localOptions.roundRect, y); + ctx.lineTo(x + width - localOptions.roundRect, y); + ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect); + ctx.lineTo(x + width, y + height - localOptions.roundRect); + ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height); + ctx.lineTo(x + localOptions.roundRect, y + height); + ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect); + ctx.lineTo(x, y + localOptions.roundRect); + ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y); + ctx.closePath(); + } + ctx.stroke(); +} +function lines(ctx, points, localOptions) { + if (points.length < 2) + return; + ctx.beginPath(); + ctx.moveTo(points[0][0], points[0][1]); + for (const pt of points) { + ctx.strokeStyle = colorDepth(pt[2], localOptions); + ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); + } + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +function curves(ctx, points, localOptions) { + if (points.length < 2) + return; + ctx.lineWidth = localOptions.lineWidth; + if (!localOptions.useCurves || points.length <= 2) { + lines(ctx, points, localOptions); + return; + } + ctx.moveTo(points[0][0], points[0][1]); + for (let i = 0; i < points.length - 2; i++) { + const xc = (points[i][0] + points[i + 1][0]) / 2; + const yc = (points[i][1] + points[i + 1][1]) / 2; + ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); + } + ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); + ctx.stroke(); + if (localOptions.fillPolygons) { + ctx.closePath(); + ctx.fill(); + } +} +function arrow(ctx, from, to, radius = 5) { + let angle; + let x; + let y; + ctx.beginPath(); + ctx.moveTo(from[0], from[1]); + ctx.lineTo(to[0], to[1]); + angle = Math.atan2(to[1] - from[1], to[0] - from[0]); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.moveTo(x, y); + angle += 1 / 3 * (2 * Math.PI); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y); + angle += 1 / 3 * (2 * Math.PI); + x = radius * Math.cos(angle) + to[0]; + y = radius * Math.sin(angle) + to[1]; + ctx.lineTo(x, y); + ctx.closePath(); + ctx.stroke(); + ctx.fill(); +} + +// src/draw/options.ts +var options3 = { + color: "rgba(173, 216, 230, 0.6)", + labelColor: "rgba(173, 216, 230, 1)", + shadowColor: "black", + alpha: 0.5, + font: 'small-caps 16px "Segoe UI"', + lineHeight: 18, + lineWidth: 4, + pointSize: 2, + roundRect: 8, + drawPoints: false, + drawLabels: true, + drawBoxes: true, + drawAttention: true, + drawGestures: true, + drawPolygons: true, + drawGaze: true, + fillPolygons: false, + useDepth: true, + useCurves: false +}; + +// src/draw/face.ts +var opt; +function drawLabels(f, ctx) { + if (opt.drawLabels) { + const labels2 = []; + labels2.push(`face: ${Math.trunc(100 * f.score)}%`); + if (f.genderScore) + labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}%`); + if (f.age) + labels2.push(`age: ${f.age || ""}`); + if (f.iris) + labels2.push(`distance: ${f.iris}`); + if (f.real) + labels2.push(`real: ${Math.trunc(100 * f.real)}%`); + if (f.live) + labels2.push(`live: ${Math.trunc(100 * f.live)}%`); + if (f.emotion && f.emotion.length > 0) { + const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); + if (emotion.length > 3) + emotion.length = 3; + labels2.push(emotion.join(" ")); + } + if (f.rotation && f.rotation.angle && f.rotation.gaze) { + if (f.rotation.angle.roll) + labels2.push(`roll: ${rad2deg(f.rotation.angle.roll)}\xB0 yaw:${rad2deg(f.rotation.angle.yaw)}\xB0 pitch:${rad2deg(f.rotation.angle.pitch)}\xB0`); + if (f.rotation.gaze.bearing) + labels2.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\xB0`); + } + if (labels2.length === 0) + labels2.push("face"); + ctx.fillStyle = opt.color; + for (let i = labels2.length - 1; i >= 0; i--) { + const x = Math.max(f.box[0], 0); + const y = i * opt.lineHeight + f.box[1]; + if (opt.shadowColor && opt.shadowColor !== "") { + ctx.fillStyle = opt.shadowColor; + ctx.fillText(labels2[i], x + 5, y + 16); + } + ctx.fillStyle = opt.labelColor; + ctx.fillText(labels2[i], x + 4, y + 15); + } + } +} +function drawIrisElipse(f, ctx) { + if (f.annotations && f.annotations["leftEyeIris"] && f.annotations["leftEyeIris"][0]) { + ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations["leftEyeIris"][3][0] - f.annotations["leftEyeIris"][1][0]) / 2; + const sizeY = Math.abs(f.annotations["leftEyeIris"][4][1] - f.annotations["leftEyeIris"][2][1]) / 2; + ctx.ellipse(f.annotations["leftEyeIris"][0][0], f.annotations["leftEyeIris"][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (opt.fillPolygons) { + ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color; + ctx.fill(); + } + } + if (f.annotations && f.annotations["rightEyeIris"] && f.annotations["rightEyeIris"][0]) { + ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color; + ctx.beginPath(); + const sizeX = Math.abs(f.annotations["rightEyeIris"][3][0] - f.annotations["rightEyeIris"][1][0]) / 2; + const sizeY = Math.abs(f.annotations["rightEyeIris"][4][1] - f.annotations["rightEyeIris"][2][1]) / 2; + ctx.ellipse(f.annotations["rightEyeIris"][0][0], f.annotations["rightEyeIris"][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); + ctx.stroke(); + if (opt.fillPolygons) { + ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color; + ctx.fill(); + } + } +} +function drawGazeSpheres(f, ctx) { + var _a; + if (opt.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle) && typeof Path2D !== "undefined") { + ctx.strokeStyle = "pink"; + const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90; + const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90; + const pathV = new Path2D(` + M ${f.box[0] + f.box[2] / 2} ${f.box[1]} C - ${n} ${e.box[1]}, - ${n} ${e.box[1]+e.box[3]}, - ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} - `),a=new Path2D(` - M ${e.box[0]} ${e.box[1]+e.box[3]/2} + ${valX} ${f.box[1]}, + ${valX} ${f.box[1] + f.box[3]}, + ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} + `); + const pathH = new Path2D(` + M ${f.box[0]} ${f.box[1] + f.box[3] / 2} C - ${e.box[0]} ${r}, - ${e.box[0]+e.box[2]} ${r}, - ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} - `);t.stroke(a),t.stroke(s)}}function eA(e,t){var o,n,r,s;if(H.drawGaze&&((n=(o=e.rotation)==null?void 0:o.gaze)==null?void 0:n.strength)&&((s=(r=e.rotation)==null?void 0:r.gaze)==null?void 0:s.bearing)&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let a=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];c2(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[a[0],a[1]],4);let i=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];c2(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[i[0],i[1]],4)}}function tA(e,t){if(H.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let o=0;oe.mesh[r]);x2(t,n,H)}_r(e,t)}}function oA(e,t){if(H.drawPoints&&e.mesh.length>=468)for(let o=0;o0&&(oA(r,n),tA(r,n),$r(r,n),eA(r,n))}}async function Se(e,t,o){var s;let n=K(a0,o);if(!t||!e)return;let r=g0(e);if(!!r){r.lineJoin="round";for(let a=0;a0)for(let a of s.keypoints)r.fillStyle=D0(a[2],n),X0(r,a[0],a[1],0,n);if(n.drawLabels&&s.annotations){let a=(i,l)=>{if(!i||i.length===0||!i[0])return;let x=i[i.length-1][2]||-256;r.fillStyle=D0(x,n),r.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4)};r.font=n.font,a(s.annotations.index,"index"),a(s.annotations.middle,"middle"),a(s.annotations.ring,"ring"),a(s.annotations.pinky,"pinky"),a(s.annotations.thumb,"thumb"),a(s.annotations.palm,"palm")}if(n.drawPolygons&&s.annotations){let a=i=>{if(!(!i||i.length===0||!i[0]))for(let l=0;l0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()}};r.lineWidth=n.lineWidth,a(s.annotations.index),a(s.annotations.middle),a(s.annotations.ring),a(s.annotations.pinky),a(s.annotations.thumb)}}}}async function We(e,t,o){let n=K(a0,o);if(!t||!e)return;let r=g0(e);if(!!r){r.lineJoin="round",r.font=n.font;for(let s of t)if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,W0(r,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels){let a=`${s.label} ${Math.round(100*s.score)}%`;n.shadowColor&&n.shadowColor!==""&&(r.fillStyle=n.shadowColor,r.fillText(a,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),r.fillStyle=n.labelColor,r.fillText(a,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])}r.stroke()}}}async function Oe(e,t,o){let n=K(a0,o);if(!(!t||!e)&&n.drawGestures){let r=g0(e);if(!r)return;r.font=n.font,r.fillStyle=n.color;let s=1;for(let a=0;a1&&l[1].length>0){let x=i[1]>0?`#${i[1]}`:"",d=`${i[0]} ${x}: ${l[1]}`;n.shadowColor&&n.shadowColor!==""&&(r.fillStyle=n.shadowColor,r.fillText(d,8,2+s*n.lineHeight)),r.fillStyle=n.labelColor,r.fillText(d,6,0+s*n.lineHeight),s+=1}}}}var d2=0;async function f2(e,t,o){let n=K(a0,o);if(!t||!e)return;let r=g0(e);if(!!r){r.lineJoin="round",r.font=n.font;for(let s=0;st!=o[r].y>t&&e<(o[r].x-o[s].x)*(t-o[s].y)/(o[r].y-o[s].y)+o[s].x&&(n=!n);return n}async function on(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,o=e.tensor.shape[1]||0,n=await e.tensor.buffer(),r=[];for(let a of M0.silhouette)r.push({x:(e.mesh[a][0]-e.box[0])/e.box[2],y:(e.mesh[a][1]-e.box[1])/e.box[3]});Le&&Le>0&&(r=r.map(a=>({x:a.x>.5?a.x+Le:a.x-Le,y:a.y>.5?a.y+Le:a.y-Le})));for(let a=0;a{let t=(y,c)=>Math.atan2(y[1]-c[1],y[0]-c[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let o=[0,-.1],n=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],a=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(a[0]-s[0])/i[0]-o[0],n*(s[1]-a[1])/i[1]-o[1]],x=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return x=Math.min(x,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:x}},nn=(e,t)=>{let o=m=>{let M=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=M,m[1]/=M,m[2]/=M,m},n=(m,M)=>{let P=m[0]-M[0],u=m[1]-M[1],p=m[2]-M[2];return[P,u,p]},r=(m,M)=>{let P=m[1]*M[2]-m[2]*M[1],u=m[2]*M[0]-m[0]*M[2],p=m[0]*M[1]-m[1]*M[0];return[P,u,p]},s=m=>{let[M,P,u,p,v,w,S,O,C]=m,L,Z,F;return p<1?p>-1?(F=Math.asin(p),Z=Math.atan2(-S,M),L=Math.atan2(-w,v)):(F=-Math.PI/2,Z=-Math.atan2(O,C),L=0):(F=Math.PI/2,Z=Math.atan2(O,C),L=0),isNaN(L)&&(L=0),isNaN(Z)&&(Z=0),isNaN(F)&&(F=0),{pitch:2*-L,yaw:2*-Z,roll:2*-F}},a=e.meshRaw;if(!a||a.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let i=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[a[10],a[152],a[234],a[454]].map(m=>[m[0]*t[0]/i,m[1]*t[1]/i,m[2]]),x=o(n(l[1],l[0])),d=o(n(l[3],l[2])),y=o(r(d,x));d=r(x,y);let c=[d[0],d[1],d[2],x[0],x[1],x[2],y[0],y[1],y[2]],f=s(c),b=a.length===478?sA(e):{bearing:0,strength:0};return{angle:f,matrix:c,gaze:b}};var h2=async(e,t)=>{var f,b,m,M,P,u,p,v,w,S,O,C,L,Z,F,G,Y,z,R0,h0,R,t0;let o=g(),n,r,s,a,i,l,x,d,y=[];e.state="run:face";let c=await U1(t,e.config);if(e.performance.face=T.perfadd?(e.performance.face||0)+Math.trunc(g()-o):Math.trunc(g()-o),!t.shape||t.shape.length!==4)return[];if(!c)return[];for(let E=0;E200?nn(c[E],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?a=(b=e.config.face.emotion)!=null&&b.enabled?wt(c[E].tensor||A.tensor([]),e.config,E,c.length):[]:(e.state="run:emotion",o=g(),a=(m=e.config.face.emotion)!=null&&m.enabled?await wt(c[E].tensor||A.tensor([]),e.config,E,c.length):[],e.performance.emotion=T.perfadd?(e.performance.emotion||0)+Math.trunc(g()-o):Math.trunc(g()-o)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=(M=e.config.face.antispoof)!=null&&M.enabled?At(c[E].tensor||A.tensor([]),e.config,E,c.length):0:(e.state="run:antispoof",o=g(),l=(P=e.config.face.antispoof)!=null&&P.enabled?await At(c[E].tensor||A.tensor([]),e.config,E,c.length):0,e.performance.antispoof=T.perfadd?(e.performance.antispoof||0)+Math.trunc(g()-o):Math.trunc(g()-o)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?x=(u=e.config.face.liveness)!=null&&u.enabled?qt(c[E].tensor||A.tensor([]),e.config,E,c.length):0:(e.state="run:liveness",o=g(),x=(p=e.config.face.liveness)!=null&&p.enabled?await qt(c[E].tensor||A.tensor([]),e.config,E,c.length):0,e.performance.liveness=T.perfadd?(e.performance.antispoof||0)+Math.trunc(g()-o):Math.trunc(g()-o)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(v=e.config.face.gear)!=null&&v.enabled?_5(c[E].tensor||A.tensor([]),e.config,E,c.length):null:(e.state="run:gear",o=g(),r=(w=e.config.face.gear)!=null&&w.enabled?await _5(c[E].tensor||A.tensor([]),e.config,E,c.length):null,e.performance.gear=Math.trunc(g()-o)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(S=e.config.face.ssrnet)!=null&&S.enabled?et(c[E].tensor||A.tensor([]),e.config,E,c.length):null,s=(O=e.config.face.ssrnet)!=null&&O.enabled?nt(c[E].tensor||A.tensor([]),e.config,E,c.length):null):(e.state="run:ssrnet",o=g(),n=(C=e.config.face.ssrnet)!=null&&C.enabled?await et(c[E].tensor||A.tensor([]),e.config,E,c.length):null,s=(L=e.config.face.ssrnet)!=null&&L.enabled?await nt(c[E].tensor||A.tensor([]),e.config,E,c.length):null,e.performance.ssrnet=Math.trunc(g()-o)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=(Z=e.config.face.mobilefacenet)!=null&&Z.enabled?Et(c[E].tensor||A.tensor([]),e.config,E,c.length):null:(e.state="run:mobilefacenet",o=g(),i=(F=e.config.face.mobilefacenet)!=null&&F.enabled?await Et(c[E].tensor||A.tensor([]),e.config,E,c.length):null,e.performance.mobilefacenet=Math.trunc(g()-o)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?d=(G=e.config.face.description)!=null&&G.enabled?Ot(c[E].tensor||A.tensor([]),e.config,E,c.length):null:(e.state="run:description",o=g(),d=(Y=e.config.face.description)!=null&&Y.enabled?await Ot(c[E].tensor||A.tensor([]),e.config,E,c.length):null,e.performance.description=T.perfadd?(e.performance.description||0)+Math.trunc(g()-o):Math.trunc(g()-o)),e.analyze("End Description:"),e.config.async&&([n,s,a,i,d,r,l,x]=await Promise.all([n,s,a,i,d,r,l,x])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((R0=e.config.face.gear)==null?void 0:R0.enabled)&&r&&(d={...d,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((h0=e.config.face.mobilefacenet)==null?void 0:h0.enabled)&&i&&(d.descriptor=i),(R=e.config.face.iris)!=null&&R.enabled;let _=c[E].annotations&&c[E].annotations.leftEyeIris&&c[E].annotations.leftEyeIris[0]&&c[E].annotations.rightEyeIris&&c[E].annotations.rightEyeIris[0]&&c[E].annotations.leftEyeIris.length>0&&c[E].annotations.rightEyeIris.length>0&&c[E].annotations.leftEyeIris[0]!==null&&c[E].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(c[E].annotations.leftEyeIris[3][0]-c[E].annotations.leftEyeIris[1][0]),Math.abs(c[E].annotations.rightEyeIris[4][1]-c[E].annotations.rightEyeIris[2][1]))/t.shape[2]:0,n0=(t0=e.config.face.detector)!=null&&t0.return?A.squeeze(c[E].tensor):null;A.dispose(c[E].tensor),c[E].tensor&&delete c[E].tensor;let W={...c[E],id:E};d!=null&&d.age&&(W.age=d.age),d!=null&&d.gender&&(W.gender=d.gender),d!=null&&d.genderScore&&(W.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(W.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(W.race=d==null?void 0:d.race),a&&(W.emotion=a),l&&(W.real=l),x&&(W.live=x),_&&_!==0&&(W.iris=Math.trunc(500/_/11.7)/100),D&&(W.rotation=D),n0&&(W.tensor=n0),y.push(W),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),y};var rn=e=>{if(!e)return[];let t=[];for(let o=0;ol.part==="leftWrist"),r=e[o].keypoints.find(l=>l.part==="rightWrist"),s=e[o].keypoints.find(l=>l.part==="nose");s&&n&&r&&n.position[1]l.part==="leftShoulder"),i=e[o].keypoints.find(l=>l.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},An=e=>{if(!e)return[];let t=[];for(let o=0;o450){let n=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),r=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));i>10&&t.push({face:o,gesture:`mouth ${Math.trunc(i)}% open`});let l=e[o].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:o,gesture:`head ${l<0?"up":"down"}`})}return t},sn=e=>{if(!e)return[];let t=[];for(let o=0;o.06||c>.06)&&(x=!1),y>c?y>.05&&t.push({iris:o,gesture:"looking right"}):c>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],b=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(b<.01||f<.01||b>.022||f>.022)&&(x=!1),(b<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(b>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),x&&t.push({iris:o,gesture:"looking center"})}return t},an=e=>{if(!e)return[];let t=[];for(let o=0;o0){let r=n.reduce((a,i)=>(a.position[2]||0)<(i.position[2]||0)?a:i);t.push({hand:o,gesture:`${r.name} forward`});let s=n.reduce((a,i)=>a.position[1]((r-1)*j.body[R].box[V]+W)/r),E=e.body[R].boxRaw.map((W,V)=>((r-1)*j.body[R].boxRaw[V]+W)/r),D=e.body[R].keypoints.map((W,V)=>{var O0,L0,Ne,Fe,de,v2,M2,R2,T2;return{score:W.score,part:W.part,position:[j.body[R].keypoints[V]?((r-1)*(j.body[R].keypoints[V].position[0]||0)+(W.position[0]||0))/r:W.position[0],j.body[R].keypoints[V]?((r-1)*(j.body[R].keypoints[V].position[1]||0)+(W.position[1]||0))/r:W.position[1],j.body[R].keypoints[V]?((r-1)*(j.body[R].keypoints[V].position[2]||0)+(W.position[2]||0))/r:W.position[2]],positionRaw:[j.body[R].keypoints[V]?((r-1)*(j.body[R].keypoints[V].positionRaw[0]||0)+(W.positionRaw[0]||0))/r:W.positionRaw[0],j.body[R].keypoints[V]?((r-1)*(j.body[R].keypoints[V].positionRaw[1]||0)+(W.positionRaw[1]||0))/r:W.positionRaw[1],j.body[R].keypoints[V]?((r-1)*(j.body[R].keypoints[V].positionRaw[2]||0)+(W.positionRaw[2]||0))/r:W.positionRaw[2]],distance:[j.body[R].keypoints[V]?((r-1)*(((O0=j.body[R].keypoints[V].distance)==null?void 0:O0[0])||0)+(((L0=W.distance)==null?void 0:L0[0])||0))/r:(Ne=W.distance)==null?void 0:Ne[0],j.body[R].keypoints[V]?((r-1)*(((Fe=j.body[R].keypoints[V].distance)==null?void 0:Fe[1])||0)+(((de=W.distance)==null?void 0:de[1])||0))/r:(v2=W.distance)==null?void 0:v2[1],j.body[R].keypoints[V]?((r-1)*(((M2=j.body[R].keypoints[V].distance)==null?void 0:M2[2])||0)+(((R2=W.distance)==null?void 0:R2[2])||0))/r:(T2=W.distance)==null?void 0:T2[2]]}}),_={},n0={connected:{}};(i=(a=t.body)==null?void 0:a.modelPath)!=null&&i.includes("efficientpose")?n0=u5:(x=(l=t.body)==null?void 0:l.modelPath)!=null&&x.includes("blazepose")?n0=c5:(y=(d=t.body)==null?void 0:d.modelPath)!=null&&y.includes("movenet")&&(n0=Ye);for(let[W,V]of Object.entries(n0.connected)){let O0=[];for(let L0=0;L0de.part===V[L0]),Fe=D.find(de=>de.part===V[L0+1]);Ne&&Fe&&O0.push([Ne.position,Fe.position])}_[W]=O0}j.body[R]={...e.body[R],box:t0,boxRaw:E,keypoints:D,annotations:_}}if(!j.hand||e.hand.length!==j.hand.length)j.hand=JSON.parse(JSON.stringify(e.hand));else for(let R=0;R((r-1)*j.hand[R].box[W]+n0)/r),E=e.hand[R].boxRaw.map((n0,W)=>((r-1)*j.hand[R].boxRaw[W]+n0)/r);j.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(j.hand[R].keypoints=e.hand[R].keypoints);let D=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((n0,W)=>n0.map((V,O0)=>((r-1)*(j.hand[R].keypoints[W][O0]||1)+(V||0))/r)):[],_={};if(Object.keys(j.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)j.hand[R].annotations=e.hand[R].annotations,_=j.hand[R].annotations;else if(e.hand[R].annotations)for(let n0 of Object.keys(e.hand[R].annotations))_[n0]=e.hand[R].annotations[n0]&&e.hand[R].annotations[n0][0]?e.hand[R].annotations[n0].map((W,V)=>W.map((O0,L0)=>((r-1)*j.hand[R].annotations[n0][V][L0]+O0)/r)):null;j.hand[R]={...e.hand[R],box:t0,boxRaw:E,keypoints:D,annotations:_}}if(!j.face||e.face.length!==j.face.length)j.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R((r-1)*j.face[R].box[_]+D)/r),E=e.face[R].boxRaw.map((D,_)=>((r-1)*j.face[R].boxRaw[_]+D)/r);if(e.face[R].rotation){let D={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};D.matrix=(c=e.face[R].rotation)==null?void 0:c.matrix,D.angle={roll:((r-1)*(((b=(f=j.face[R].rotation)==null?void 0:f.angle)==null?void 0:b.roll)||0)+(((M=(m=e.face[R].rotation)==null?void 0:m.angle)==null?void 0:M.roll)||0))/r,yaw:((r-1)*(((u=(P=j.face[R].rotation)==null?void 0:P.angle)==null?void 0:u.yaw)||0)+(((v=(p=e.face[R].rotation)==null?void 0:p.angle)==null?void 0:v.yaw)||0))/r,pitch:((r-1)*(((S=(w=j.face[R].rotation)==null?void 0:w.angle)==null?void 0:S.pitch)||0)+(((C=(O=e.face[R].rotation)==null?void 0:O.angle)==null?void 0:C.pitch)||0))/r},D.gaze={bearing:((r-1)*(((Z=(L=j.face[R].rotation)==null?void 0:L.gaze)==null?void 0:Z.bearing)||0)+(((G=(F=e.face[R].rotation)==null?void 0:F.gaze)==null?void 0:G.bearing)||0))/r,strength:((r-1)*(((z=(Y=j.face[R].rotation)==null?void 0:Y.gaze)==null?void 0:z.strength)||0)+(((h0=(R0=e.face[R].rotation)==null?void 0:R0.gaze)==null?void 0:h0.strength)||0))/r},j.face[R]={...e.face[R],rotation:D,box:t0,boxRaw:E}}j.face[R]={...e.face[R],box:t0,boxRaw:E}}if(!j.object||e.object.length!==j.object.length)j.object=JSON.parse(JSON.stringify(e.object));else for(let R=0;R((r-1)*j.object[R].box[_]+D)/r),E=e.object[R].boxRaw.map((D,_)=>((r-1)*j.object[R].boxRaw[_]+D)/r);j.object[R]={...e.object[R],box:t0,boxRaw:E}}if(e.persons){let R=e.persons;if(!j.persons||R.length!==j.persons.length)j.persons=JSON.parse(JSON.stringify(R));else for(let t0=0;t0((r-1)*j.persons[t0].box[D]+E)/r)}e.gesture&&(j.gesture=e.gesture);let s=g();return b2=T.perfadd?b2+Math.round(s-o):Math.round(s-o),e.performance&&(j.performance={...e.performance,interpolate:b2}),j}var xn={};ne(xn,{distance:()=>$e,match:()=>P2,similarity:()=>g2});function $e(e,t,o={order:2,multiplier:25}){let n=0;for(let r=0;r{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),s=(1-r/100-o)/(n-o);return Math.max(Math.min(s,1),0)};function g2(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let n=$e(e,t,o);return yn(n,o.order||2,o.min||0,o.max||1)}function P2(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let a=0;az.box[0]&&O.box[0]z.box[1]&&O.box[1]+O.box[3]C.body.box[0]&&z.box[0]+z.box[2]C.body.box[1]&&z.box[1]+z.box[3]C.body.box[0]&&z.box[1]+z.box[3]>C.body.box[1]&&z.box[1]+z.box[3]{z&&z.length===4&&(L.push(z[0],z[0]+z[2]),Z.push(z[1],z[1]+z[3]))};F((P=C.face)==null?void 0:P.box),F((u=C.body)==null?void 0:u.box),F((v=(p=C.hands)==null?void 0:p.left)==null?void 0:v.box),F((S=(w=C.hands)==null?void 0:w.right)==null?void 0:S.box);let G=Math.min(...L),Y=Math.min(...Z);C.box=[G,Y,Math.max(...L)-G,Math.max(...Z)-Y],r&&r[1]&&r[2]&&(C.boxRaw=[C.box[0]/r[2],C.box[1]/r[1],C.box[2]/r[2],C.box[3]/r[1]]),a.push(C)}return a}var B5=` + ${f.box[0]} ${valY}, + ${f.box[0] + f.box[2]} ${valY}, + ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} + `); + ctx.stroke(pathH); + ctx.stroke(pathV); + } +} +function drawGazeArrows(f, ctx) { + var _a, _b, _c, _d; + if (opt.drawGaze && ((_b = (_a = f.rotation) == null ? void 0 : _a.gaze) == null ? void 0 : _b.strength) && ((_d = (_c = f.rotation) == null ? void 0 : _c.gaze) == null ? void 0 : _d.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) { + ctx.strokeStyle = "pink"; + ctx.fillStyle = "pink"; + const leftGaze = [ + f.annotations["leftEyeIris"][0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations["leftEyeIris"][0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + arrow(ctx, [f.annotations["leftEyeIris"][0][0], f.annotations["leftEyeIris"][0][1]], [leftGaze[0], leftGaze[1]], 4); + const rightGaze = [ + f.annotations["rightEyeIris"][0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], + f.annotations["rightEyeIris"][0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] + ]; + arrow(ctx, [f.annotations["rightEyeIris"][0][0], f.annotations["rightEyeIris"][0][1]], [rightGaze[0], rightGaze[1]], 4); + } +} +function drawFacePolygons(f, ctx) { + if (opt.drawPolygons && f.mesh.length >= 468) { + ctx.lineWidth = 1; + for (let i = 0; i < TRI468.length / 3; i++) { + const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); + lines(ctx, points, opt); + } + drawIrisElipse(f, ctx); + } +} +function drawFacePoints(f, ctx) { + if (opt.drawPoints && f.mesh.length >= 468) { + for (let i = 0; i < f.mesh.length; i++) { + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt); + if (opt.drawAttention) { + if (attentionDefinitions.lips.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, opt); + if (attentionDefinitions.eyeL.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt); + if (attentionDefinitions.eyeR.includes(i)) + point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt); + } + } + } +} +function drawFaceBoxes(f, ctx) { + if (opt.drawBoxes) { + rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt); + } +} +async function face(inCanvas2, result, drawOptions) { + opt = mergeDeep(options3, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.font = opt.font; + ctx.strokeStyle = opt.color; + ctx.fillStyle = opt.color; + for (const f of result) { + drawFaceBoxes(f, ctx); + drawLabels(f, ctx); + if (f.mesh && f.mesh.length > 0) { + drawFacePoints(f, ctx); + drawFacePolygons(f, ctx); + drawGazeSpheres(f, ctx); + drawGazeArrows(f, ctx); + } + } +} + +// src/draw/body.ts +async function body(inCanvas2, result, drawOptions) { + var _a; + const localOptions = mergeDeep(options3, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + for (let i = 0; i < result.length; i++) { + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + ctx.lineWidth = localOptions.lineWidth; + ctx.font = localOptions.font; + if (localOptions.drawBoxes && result[i].box && ((_a = result[i].box) == null ? void 0 : _a.length) === 4) { + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions); + if (localOptions.drawLabels) { + if (localOptions.shadowColor && localOptions.shadowColor !== "") { + ctx.fillStyle = localOptions.shadowColor; + ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]); + } + ctx.fillStyle = localOptions.labelColor; + ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]); + } + } + if (localOptions.drawPoints && result[i].keypoints) { + for (let pt = 0; pt < result[i].keypoints.length; pt++) { + if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) + continue; + ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions); + point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions); + } + } + if (localOptions.drawLabels && result[i].keypoints) { + ctx.font = localOptions.font; + for (const pt of result[i].keypoints) { + if (!pt.score || pt.score === 0) + continue; + ctx.fillStyle = colorDepth(pt.position[2], localOptions); + ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4); + } + } + if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) { + for (const part of Object.values(result[i].annotations)) { + for (const connected4 of part) + curves(ctx, connected4, localOptions); + } + } + } +} + +// src/draw/hand.ts +async function hand(inCanvas2, result, drawOptions) { + const localOptions = mergeDeep(options3, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions.font; + for (const h of result) { + if (localOptions.drawBoxes) { + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions); + if (localOptions.drawLabels) { + if (localOptions.shadowColor && localOptions.shadowColor !== "") { + ctx.fillStyle = localOptions.shadowColor; + ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); + } + ctx.fillStyle = localOptions.labelColor; + ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); + } + ctx.stroke(); + } + if (localOptions.drawPoints) { + if (h.keypoints && h.keypoints.length > 0) { + for (const pt of h.keypoints) { + ctx.fillStyle = colorDepth(pt[2], localOptions); + point(ctx, pt[0], pt[1], 0, localOptions); + } + } + } + if (localOptions.drawLabels && h.annotations) { + const addHandLabel = (part, title) => { + if (!part || part.length === 0 || !part[0]) + return; + const z = part[part.length - 1][2] || -256; + ctx.fillStyle = colorDepth(z, localOptions); + ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4); + }; + ctx.font = localOptions.font; + addHandLabel(h.annotations["index"], "index"); + addHandLabel(h.annotations["middle"], "middle"); + addHandLabel(h.annotations["ring"], "ring"); + addHandLabel(h.annotations["pinky"], "pinky"); + addHandLabel(h.annotations["thumb"], "thumb"); + addHandLabel(h.annotations["palm"], "palm"); + } + if (localOptions.drawPolygons && h.annotations) { + const addHandLine = (part) => { + if (!part || part.length === 0 || !part[0]) + return; + for (let i = 0; i < part.length; i++) { + ctx.beginPath(); + const z = part[i][2] || 0; + ctx.strokeStyle = colorDepth(i * z, localOptions); + ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); + ctx.lineTo(part[i][0], part[i][1]); + ctx.stroke(); + } + }; + ctx.lineWidth = localOptions.lineWidth; + addHandLine(h.annotations["index"]); + addHandLine(h.annotations["middle"]); + addHandLine(h.annotations["ring"]); + addHandLine(h.annotations["pinky"]); + addHandLine(h.annotations["thumb"]); + } + } +} + +// src/draw/object.ts +async function object(inCanvas2, result, drawOptions) { + const localOptions = mergeDeep(options3, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions.font; + for (const h of result) { + if (localOptions.drawBoxes) { + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions); + if (localOptions.drawLabels) { + const label = `${h.label} ${Math.round(100 * h.score)}%`; + if (localOptions.shadowColor && localOptions.shadowColor !== "") { + ctx.fillStyle = localOptions.shadowColor; + ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); + } + ctx.fillStyle = localOptions.labelColor; + ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); + } + ctx.stroke(); + } + } +} + +// src/draw/gesture.ts +async function gesture(inCanvas2, result, drawOptions) { + const localOptions = mergeDeep(options3, drawOptions); + if (!result || !inCanvas2) + return; + if (localOptions.drawGestures) { + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.font = localOptions.font; + ctx.fillStyle = localOptions.color; + let i = 1; + for (let j = 0; j < result.length; j++) { + let where = []; + let what = []; + [where, what] = Object.entries(result[j]); + if (what.length > 1 && what[1].length > 0) { + const who = where[1] > 0 ? `#${where[1]}` : ""; + const label = `${where[0]} ${who}: ${what[1]}`; + if (localOptions.shadowColor && localOptions.shadowColor !== "") { + ctx.fillStyle = localOptions.shadowColor; + ctx.fillText(label, 8, 2 + i * localOptions.lineHeight); + } + ctx.fillStyle = localOptions.labelColor; + ctx.fillText(label, 6, 0 + i * localOptions.lineHeight); + i += 1; + } + } + } +} + +// src/draw/draw.ts +var drawTime = 0; +async function person(inCanvas2, result, drawOptions) { + const localOptions = mergeDeep(options3, drawOptions); + if (!result || !inCanvas2) + return; + const ctx = getCanvasContext(inCanvas2); + if (!ctx) + return; + ctx.lineJoin = "round"; + ctx.font = localOptions.font; + for (let i = 0; i < result.length; i++) { + if (localOptions.drawBoxes) { + ctx.strokeStyle = localOptions.color; + ctx.fillStyle = localOptions.color; + rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions); + if (localOptions.drawLabels) { + const label = `person #${i}`; + if (localOptions.shadowColor && localOptions.shadowColor !== "") { + ctx.fillStyle = localOptions.shadowColor; + ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]); + } + ctx.fillStyle = localOptions.labelColor; + ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]); + } + ctx.stroke(); + } + } +} +async function canvas2(input, output) { + if (!input || !output) + return; + const ctx = getCanvasContext(output); + if (!ctx) + return; + ctx.drawImage(input, 0, 0); +} +async function all(inCanvas2, result, drawOptions) { + if (!result || !result.performance || !result || !inCanvas2) + return null; + const timeStamp = now(); + const localOptions = mergeDeep(options3, drawOptions); + const promise = Promise.all([ + face(inCanvas2, result.face, localOptions), + body(inCanvas2, result.body, localOptions), + hand(inCanvas2, result.hand, localOptions), + object(inCanvas2, result.object, localOptions), + gesture(inCanvas2, result.gesture, localOptions) + ]); + drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); + result.performance.draw = drawTime; + return promise; +} + +// src/face/mask.ts +var expandFact = 0.1; +var alpha = 0.5; +function insidePoly(x, y, polygon) { + let inside = false; + let j = polygon.length - 1; + for (let i = 0; i < polygon.length; j = i++) { + if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) + inside = !inside; + } + return inside; +} +async function mask(face4) { + if (!face4.tensor) + return face4.tensor; + if (!face4.mesh || face4.mesh.length < 100) + return face4.tensor; + const width = face4.tensor.shape[2] || 0; + const height = face4.tensor.shape[1] || 0; + const buffer = await face4.tensor.buffer(); + let silhouette = []; + for (const pt of meshAnnotations.silhouette) + silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); + if (expandFact && expandFact > 0) + silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); + for (let x = 0; x < width; x++) { + for (let y = 0; y < height; y++) { + const inside = insidePoly(x / width, y / width, silhouette); + if (!inside) { + buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); + buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); + buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); + } + } + } + const output = buffer.toTensor(); + tfjs_esm_exports.dispose(buffer); + return output; +} + +// src/face/angles.ts +var calculateGaze = (face4) => { + const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); + if (!face4.annotations["rightEyeIris"] || !face4.annotations["leftEyeIris"]) + return { bearing: 0, strength: 0 }; + const offsetIris = [0, -0.1]; + const eyeRatio = 1; + const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); + const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; + const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; + const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; + const eyeDiff = [ + (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], + eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] + ]; + let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); + strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); + const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; + return { bearing, strength }; +}; +var calculateFaceAngle = (face4, imageSize) => { + const normalize = (v) => { + const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); + v[0] /= length; + v[1] /= length; + v[2] /= length; + return v; + }; + const subVectors = (a, b) => { + const x = a[0] - b[0]; + const y = a[1] - b[1]; + const z = a[2] - b[2]; + return [x, y, z]; + }; + const crossVectors = (a, b) => { + const x = a[1] * b[2] - a[2] * b[1]; + const y = a[2] * b[0] - a[0] * b[2]; + const z = a[0] * b[1] - a[1] * b[0]; + return [x, y, z]; + }; + const rotationMatrixToEulerAngle = (r) => { + const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; + let thetaX; + let thetaY; + let thetaZ; + if (r10 < 1) { + if (r10 > -1) { + thetaZ = Math.asin(r10); + thetaY = Math.atan2(-r20, r00); + thetaX = Math.atan2(-r12, r11); + } else { + thetaZ = -Math.PI / 2; + thetaY = -Math.atan2(r21, r22); + thetaX = 0; + } + } else { + thetaZ = Math.PI / 2; + thetaY = Math.atan2(r21, r22); + thetaX = 0; + } + if (isNaN(thetaX)) + thetaX = 0; + if (isNaN(thetaY)) + thetaY = 0; + if (isNaN(thetaZ)) + thetaZ = 0; + return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ }; + }; + const mesh = face4.meshRaw; + if (!mesh || mesh.length < 300) + return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; + const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; + const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); + const y_axis = normalize(subVectors(pts[1], pts[0])); + let x_axis = normalize(subVectors(pts[3], pts[2])); + const z_axis = normalize(crossVectors(x_axis, y_axis)); + x_axis = crossVectors(y_axis, z_axis); + const matrix = [ + x_axis[0], + x_axis[1], + x_axis[2], + y_axis[0], + y_axis[1], + y_axis[2], + z_axis[0], + z_axis[1], + z_axis[2] + ]; + const angle = rotationMatrixToEulerAngle(matrix); + const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; + return { angle, matrix, gaze }; +}; + +// src/face/face.ts +var detectFace = async (instance, input) => { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v; + let timeStamp = now(); + let ageRes; + let gearRes; + let genderRes; + let emotionRes; + let mobilefacenetRes; + let antispoofRes; + let livenessRes; + let descRes; + const faceRes = []; + instance.state = "run:face"; + const faces = await predict10(input, instance.config); + instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + if (!input.shape || input.shape.length !== 4) + return []; + if (!faces) + return []; + for (let i = 0; i < faces.length; i++) { + instance.analyze("Get Face"); + if (!faces[i].tensor || faces[i].tensor["isDisposedInternal"]) { + log("Face object is disposed:", faces[i].tensor); + continue; + } + if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { + const masked = await mask(faces[i]); + tfjs_esm_exports.dispose(faces[i].tensor); + faces[i].tensor = masked; + } + const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; + instance.analyze("Start Emotion:"); + if (instance.config.async) { + emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict8(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : []; + } else { + instance.state = "run:emotion"; + timeStamp = now(); + emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict8(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : []; + instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Emotion:"); + instance.analyze("Start AntiSpoof:"); + if (instance.config.async) { + antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict4(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; + } else { + instance.state = "run:antispoof"; + timeStamp = now(); + antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict4(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; + instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End AntiSpoof:"); + instance.analyze("Start Liveness:"); + if (instance.config.async) { + livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict14(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; + } else { + instance.state = "run:liveness"; + timeStamp = now(); + livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict14(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; + instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Liveness:"); + instance.analyze("Start GEAR:"); + if (instance.config.async) { + gearRes = ((_h = instance.config.face["gear"]) == null ? void 0 : _h.enabled) ? predict(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:gear"; + timeStamp = now(); + gearRes = ((_i = instance.config.face["gear"]) == null ? void 0 : _i.enabled) ? await predict(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + instance.performance.gear = Math.trunc(now() - timeStamp); + } + instance.analyze("End GEAR:"); + instance.analyze("Start SSRNet:"); + if (instance.config.async) { + ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict2(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict3(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:ssrnet"; + timeStamp = now(); + ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict2(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict3(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + instance.performance.ssrnet = Math.trunc(now() - timeStamp); + } + instance.analyze("End SSRNet:"); + instance.analyze("Start MobileFaceNet:"); + if (instance.config.async) { + mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict9(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:mobilefacenet"; + timeStamp = now(); + mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict9(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); + } + instance.analyze("End MobileFaceNet:"); + instance.analyze("Start Description:"); + if (instance.config.async) { + descRes = ((_p = instance.config.face.description) == null ? void 0 : _p.enabled) ? predict11(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + } else { + instance.state = "run:description"; + timeStamp = now(); + descRes = ((_q = instance.config.face.description) == null ? void 0 : _q.enabled) ? await predict11(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; + instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + instance.analyze("End Description:"); + if (instance.config.async) { + [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]); + } + instance.analyze("Finish Face:"); + if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { + descRes = { + ...descRes, + age: ageRes.age, + gender: genderRes.gender, + genderScore: genderRes.genderScore + }; + } + if (((_s = instance.config.face["gear"]) == null ? void 0 : _s.enabled) && gearRes) { + descRes = { + ...descRes, + age: gearRes.age, + gender: gearRes.gender, + genderScore: gearRes.genderScore, + race: gearRes.race + }; + } + if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { + descRes.descriptor = mobilefacenetRes; + } + if (!((_u = instance.config.face.iris) == null ? void 0 : _u.enabled)) { + } + const irisSize = faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0] && faces[i].annotations.leftEyeIris.length > 0 && faces[i].annotations.rightEyeIris.length > 0 && faces[i].annotations.leftEyeIris[0] !== null && faces[i].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0; + const tensor3 = ((_v = instance.config.face.detector) == null ? void 0 : _v.return) ? tfjs_esm_exports.squeeze(faces[i].tensor) : null; + tfjs_esm_exports.dispose(faces[i].tensor); + if (faces[i].tensor) + delete faces[i].tensor; + const res = { + ...faces[i], + id: i + }; + if (descRes == null ? void 0 : descRes.age) + res.age = descRes.age; + if (descRes == null ? void 0 : descRes.gender) + res.gender = descRes.gender; + if (descRes == null ? void 0 : descRes.genderScore) + res.genderScore = descRes == null ? void 0 : descRes.genderScore; + if (descRes == null ? void 0 : descRes.descriptor) + res.embedding = descRes == null ? void 0 : descRes.descriptor; + if (descRes == null ? void 0 : descRes.race) + res.race = descRes == null ? void 0 : descRes.race; + if (emotionRes) + res.emotion = emotionRes; + if (antispoofRes) + res.real = antispoofRes; + if (livenessRes) + res.live = livenessRes; + if (irisSize && irisSize !== 0) + res.iris = Math.trunc(500 / irisSize / 11.7) / 100; + if (rotation) + res.rotation = rotation; + if (tensor3) + res.tensor = tensor3; + faceRes.push(res); + instance.analyze("End Face"); + } + instance.analyze("End FaceMesh:"); + if (instance.config.async) { + if (instance.performance.face) + delete instance.performance.face; + if (instance.performance.age) + delete instance.performance.age; + if (instance.performance.gender) + delete instance.performance.gender; + if (instance.performance.emotion) + delete instance.performance.emotion; + } + return faceRes; +}; + +// src/gesture/gesture.ts +var body2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); + const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); + const nose = res[i].keypoints.find((a) => a.part === "nose"); + if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "i give up" }); + else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "raise left hand" }); + else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) + gestures.push({ body: i, gesture: "raise right hand" }); + const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); + const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); + if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { + gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); + } + } + return gestures; +}; +var face2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (res[i].mesh && res[i].mesh.length > 450) { + const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); + const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; + if (Math.abs(zDiff / xDiff) <= 0.15) + gestures.push({ face: i, gesture: "facing center" }); + else + gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); + const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); + if (openLeft < 0.2) + gestures.push({ face: i, gesture: "blink left eye" }); + const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); + if (openRight < 0.2) + gestures.push({ face: i, gesture: "blink right eye" }); + const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); + if (mouthOpen > 10) + gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); + const chinDepth = res[i].mesh[152][2] || 0; + if (Math.abs(chinDepth) > 10) + gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); + } + } + return gestures; +}; +var iris = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) + continue; + const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; + const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; + const areaLeft = Math.abs(sizeXLeft * sizeYLeft); + const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; + const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; + const areaRight = Math.abs(sizeXRight * sizeYRight); + let center = false; + const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); + if (difference < 0.25) { + center = true; + gestures.push({ iris: i, gesture: "facing center" }); + } + const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; + const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; + if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) + center = false; + if (leftIrisCenterX > rightIrisCenterX) { + if (leftIrisCenterX > 0.05) + gestures.push({ iris: i, gesture: "looking right" }); + } else { + if (rightIrisCenterX > 0.05) + gestures.push({ iris: i, gesture: "looking left" }); + } + const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; + const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + center = false; + if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) + gestures.push({ iris: i, gesture: "looking down" }); + if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) + gestures.push({ iris: i, gesture: "looking up" }); + if (center) + gestures.push({ iris: i, gesture: "looking center" }); + } + return gestures; +}; +var hand2 = (res) => { + if (!res) + return []; + const gestures = []; + for (let i = 0; i < res.length; i++) { + const fingers = []; + if (res[i]["annotations"]) { + for (const [finger, pos] of Object.entries(res[i]["annotations"])) { + if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) + fingers.push({ name: finger.toLowerCase(), position: pos[0] }); + } + } + if (fingers && fingers.length > 0) { + const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); + gestures.push({ hand: i, gesture: `${closest.name} forward` }); + const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); + gestures.push({ hand: i, gesture: `${highest.name} up` }); + } + if (res[i]["keypoints"]) { + const poses = match(res[i]["keypoints"]); + for (const pose of poses) + gestures.push({ hand: i, gesture: pose.name }); + } + } + return gestures; +}; + +// src/util/interpolate.ts +var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null }; +var interpolateTime = 0; +function calc2(newResult, config3) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; + const t0 = now(); + if (!newResult) + return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null }; + const elapsed = Date.now() - newResult.timestamp; + const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; + if (newResult.canvas) + bufferedResult.canvas = newResult.canvas; + if (newResult.error) + bufferedResult.error = newResult.error; + if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { + bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); + } else { + for (let i = 0; i < newResult.body.length; i++) { + const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); + const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); + const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { + var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; + return { + score: newKpt.score, + part: newKpt.part, + position: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] + ], + positionRaw: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] + ], + distance: [ + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], + bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] + ] + }; + }); + const annotations2 = {}; + let coords = { connected: {} }; + if ((_b = (_a = config3.body) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("efficientpose")) + coords = efficientposecoords_exports; + else if ((_d = (_c = config3.body) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("blazepose")) + coords = blazeposecoords_exports; + else if ((_f = (_e = config3.body) == null ? void 0 : _e.modelPath) == null ? void 0 : _f.includes("movenet")) + coords = movenetcoords_exports; + for (const [name, indexes] of Object.entries(coords.connected)) { + const pt = []; + for (let j = 0; j < indexes.length - 1; j++) { + const pt0 = keypoints.find((kp) => kp.part === indexes[j]); + const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); + if (pt0 && pt1) + pt.push([pt0.position, pt1.position]); + } + annotations2[name] = pt; + } + bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; + } + } + if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { + bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); + } else { + for (let i = 0; i < newResult.hand.length; i++) { + const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); + if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) + bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; + const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; + let annotations2 = {}; + if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { + bufferedResult.hand[i].annotations = newResult.hand[i].annotations; + annotations2 = bufferedResult.hand[i].annotations; + } else if (newResult.hand[i].annotations) { + for (const key of Object.keys(newResult.hand[i].annotations)) { + annotations2[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0] ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; + } + } + bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; + } + } + if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { + bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); + } else { + for (let i = 0; i < newResult.face.length; i++) { + const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); + if (newResult.face[i].rotation) { + const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; + rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix; + rotation.angle = { + roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor, + yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor, + pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor + }; + rotation.gaze = { + bearing: ((bufferedFactor - 1) * (((_u = (_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.bearing) || 0) + (((_w = (_v = newResult.face[i].rotation) == null ? void 0 : _v.gaze) == null ? void 0 : _w.bearing) || 0)) / bufferedFactor, + strength: ((bufferedFactor - 1) * (((_y = (_x = bufferedResult.face[i].rotation) == null ? void 0 : _x.gaze) == null ? void 0 : _y.strength) || 0) + (((_A = (_z = newResult.face[i].rotation) == null ? void 0 : _z.gaze) == null ? void 0 : _A.strength) || 0)) / bufferedFactor + }; + bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; + } + bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; + } + } + if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { + bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); + } else { + for (let i = 0; i < newResult.object.length; i++) { + const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); + const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); + bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; + } + } + if (newResult.persons) { + const newPersons = newResult.persons; + if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { + bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); + } else { + for (let i = 0; i < newPersons.length; i++) { + bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); + } + } + } + if (newResult.gesture) + bufferedResult.gesture = newResult.gesture; + const t1 = now(); + interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); + if (newResult.performance) + bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; + return bufferedResult; +} + +// src/face/match.ts +var match_exports = {}; +__export(match_exports, { + distance: () => distance, + match: () => match2, + similarity: () => similarity +}); +function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { + let sum3 = 0; + for (let i = 0; i < descriptor1.length; i++) { + const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); + sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; + } + return (options4.multiplier || 20) * sum3; +} +var normalizeDistance = (dist, order, min2, max4) => { + if (dist === 0) + return 1; + const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); + const norm = (1 - root / 100 - min2) / (max4 - min2); + const clamp2 = Math.max(Math.min(norm, 1), 0); + return clamp2; +}; +function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { + const dist = distance(descriptor1, descriptor2, options4); + return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); +} +function match2(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { + if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { + return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; + } + let lowestDistance = Number.MAX_SAFE_INTEGER; + let index2 = -1; + for (let i = 0; i < descriptors.length; i++) { + const res = distance(descriptor, descriptors[i], options4); + if (res < lowestDistance) { + lowestDistance = res; + index2 = i; + } + if (lowestDistance < (options4.threshold || 0)) + break; + } + const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); + return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; +} + +// src/util/persons.ts +function join2(faces, bodies, hands, gestures, shape) { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p; + let id = 0; + const persons = []; + for (const face4 of faces) { + const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; + for (const body4 of bodies) { + if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { + person2.body = body4; + } + } + if (person2.body) { + for (const hand3 of hands) { + if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.left = hand3; + } + if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { + if (person2.hands) + person2.hands.right = hand3; + } + } + } + for (const gesture2 of gestures) { + if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) + (_a = person2.gestures) == null ? void 0 : _a.push(gesture2); + else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) + (_b = person2.gestures) == null ? void 0 : _b.push(gesture2); + else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_c = person2.body) == null ? void 0 : _c.id)) + (_d = person2.gestures) == null ? void 0 : _d.push(gesture2); + else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_f = (_e = person2.hands) == null ? void 0 : _e.left) == null ? void 0 : _f.id)) + (_g = person2.gestures) == null ? void 0 : _g.push(gesture2); + else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_i = (_h = person2.hands) == null ? void 0 : _h.right) == null ? void 0 : _i.id)) + (_j = person2.gestures) == null ? void 0 : _j.push(gesture2); + } + const x = []; + const y = []; + const extractXY = (box) => { + if (box && box.length === 4) { + x.push(box[0], box[0] + box[2]); + y.push(box[1], box[1] + box[3]); + } + }; + extractXY((_k = person2.face) == null ? void 0 : _k.box); + extractXY((_l = person2.body) == null ? void 0 : _l.box); + extractXY((_n = (_m = person2.hands) == null ? void 0 : _m.left) == null ? void 0 : _n.box); + extractXY((_p = (_o = person2.hands) == null ? void 0 : _o.right) == null ? void 0 : _p.box); + const minX = Math.min(...x); + const minY = Math.min(...y); + person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; + if (shape && shape[1] && shape[2]) + person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; + persons.push(person2); + } + return persons; +} + +// src/sample.ts +var face3 = ` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -259,7 +12842,8 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY -euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,H5=` +euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`; +var body3 = ` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA @@ -827,7 +13411,512 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;async function cA(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(a=>a.blob()),o,n;switch(e.config.warmup){case"face":o=await t(B5);break;case"body":case"full":o=await t(H5);break;default:o=null}if(o){let r=await createImageBitmap(o);n=await 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_e,this.draw={options:a0,canvas:(o,n)=>m2(o,n),face:(o,n,r)=>je(o,n,r),body:(o,n,r)=>Se(o,n,r),hand:(o,n,r)=>Ce(o,n,r),gesture:(o,n,r)=>Oe(o,n,r),object:(o,n,r)=>We(o,n,r),person:(o,n,r)=>f2(o,n,r),all:(o,n,r)=>p2(o,n,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=Y1,this.faceUVMap=K1,this.gl=U,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(re)),this.config.backend=t}validate(t){return D5(re,t||this.config)}now(){return g()}image(t,o=!0){return pe(t,this.config,o)}async segmentation(t,o){return Ko(t,o,this.config)}enhance(t){return Wt(t)}compare(t,o){return N2(this.config,t,o)}async init(){await G5(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=g(),n=Object.values(this.models).filter(a=>a).length;t&&(this.config=K(this.config,t)),this.env.initial&&(this.config.debug&&h(`version: ${this.version}`),this.config.debug&&h(`tfjs version: ${this.tf.version["tfjs-core"]}`),await G5(this)||h("error: backend check failed"),await A.ready(),this.env.browser&&(this.config.debug&&h("configuration:",this.config),this.config.debug&&h("environment:",this.env),this.config.debug&&h("tf flags:",this.tf.ENV.flags))),await i2(this),this.env.initial&&this.config.debug&&h("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(a=>a).length!==n&&(await l2(this),this.emit("load"));let s=Math.trunc(g()-o);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return ln(t,this.config)}async warmup(t){let o=g(),n=await dn(this,t),r=g();return this.performance.warmup=Math.trunc(r-o),n}async profile(t,o){let n=await this.tf.profile(()=>this.detect(t,o)),r={};for(let i of n.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs;let s=[];Object.entries(r).forEach(i=>s.push({name:i[0],ms:i[1]})),s.sort((i,l)=>l.ms-i.ms),s.length=20;let a={};for(let i of s)a[i.name]=i.ms;return a}async detect(t,o){return this.state="detect",new Promise(async n=>{var M,P,u,p,v,w,S,O,C,L,Z,F,G,Y,z,R0,h0,R,t0,E,D,_;this.state="config";let r;this.config=K(this.config,o),this.state="check";let s=Ge(this,V5).call(this,t);s&&(h(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:s}));let a=g();await G5(this),await this.load(),r=g(),this.state="image";let i=await pe(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&h("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=g(),this.config.skipAllowed=await I2(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(g()-r):Math.trunc(g()-r),this.analyze("Check Changed:");let l=[],x=[],d=[],y=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?h2(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=g(),l=this.config.face.enabled?await h2(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let c=this.config.body.maxDetected===-1?K(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((M=this.config.body.modelPath)!=null&&M.includes("posenet")?x=this.config.body.enabled?A2(i.tensor,c):[]:(P=this.config.body.modelPath)!=null&&P.includes("blazepose")?x=this.config.body.enabled?ut(i.tensor,c):[]:(u=this.config.body.modelPath)!=null&&u.includes("efficientpose")?x=this.config.body.enabled?Rt(i.tensor,c):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(x=this.config.body.enabled?_t(i.tensor,c):[]),this.performance.body&&delete this.performance.body):(r=g(),(v=this.config.body.modelPath)!=null&&v.includes("posenet")?x=this.config.body.enabled?await A2(i.tensor,c):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?x=this.config.body.enabled?await ut(i.tensor,c):[]:(S=this.config.body.modelPath)!=null&&S.includes("efficientpose")?x=this.config.body.enabled?await Rt(i.tensor,c):[]:(O=this.config.body.modelPath)!=null&&O.includes("movenet")&&(x=this.config.body.enabled?await _t(i.tensor,c):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?K(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((L=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&L.includes("handdetect")?d=this.config.hand.enabled?Bt(i.tensor,f):[]:(F=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&F.includes("handtrack")&&(d=this.config.hand.enabled?Dt(i.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=g(),(Y=(G=this.config.hand.detector)==null?void 0:G.modelPath)!=null&&Y.includes("handdetect")?d=this.config.hand.enabled?await Bt(i.tensor,f):[]:(R0=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&R0.includes("handtrack")&&(d=this.config.hand.enabled?await Dt(i.tensor,f):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((h0=this.config.object.modelPath)!=null&&h0.includes("nanodet")?y=this.config.object.enabled?e2(i.tensor,this.config):[]:(R=this.config.object.modelPath)!=null&&R.includes("centernet")&&(y=this.config.object.enabled?gt(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=g(),(t0=this.config.object.modelPath)!=null&&t0.includes("nanodet")?y=this.config.object.enabled?await e2(i.tensor,this.config):[]:(E=this.config.object.modelPath)!=null&&E.includes("centernet")&&(y=this.config.object.enabled?await gt(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,x,d,y]=await Promise.all([l,x,d,y])),this.state="detect:gesture";let b=[];this.config.gesture.enabled&&(r=g(),b=[...An(l),...rn(x),...an(d),...sn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-r):Math.trunc(g()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-a):Math.trunc(g()-a);let m=((_=(D=this.process)==null?void 0:D.tensor)==null?void 0:_.shape)||[];this.result={face:l,body:x,hand:d,gesture:b,object:y,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return cn(l,x,d,b,m)}},A.dispose(i.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};Ie=new WeakMap,e5=new WeakMap,t5=new WeakMap,V5=new WeakMap;export{fn as Human,fn as default,re as defaults,tn as draw,T as env,xn as match,y2 as models}; +2Q==`; + +// src/warmup.ts +async function warmupBitmap(instance) { + const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); + let blob; + let res; + switch (instance.config.warmup) { + case "face": + blob = await b64toBlob(face3); + break; + case "body": + case "full": + blob = await b64toBlob(body3); + break; + default: + blob = null; + } + if (blob) { + const bitmap = await createImageBitmap(blob); + res = await instance.detect(bitmap, instance.config); + bitmap.close(); + } + return res; +} +async function warmupCanvas(instance) { + return new Promise((resolve) => { + let src; + switch (instance.config.warmup) { + case "face": + src = "data:image/jpeg;base64," + face3; + break; + case "full": + case "body": + src = "data:image/jpeg;base64," + body3; + break; + default: + src = null; + } + let img; + if (typeof Image !== "undefined") + img = new Image(); + else if (env.Image) + img = new env.Image(); + else + return; + img.onload = async () => { + const canvas3 = canvas(img.naturalWidth, img.naturalHeight); + if (!canvas3) { + log("Warmup: Canvas not found"); + resolve(void 0); + } else { + const ctx = canvas3.getContext("2d"); + if (ctx) + ctx.drawImage(img, 0, 0); + const tensor3 = await instance.image(canvas3); + const res = await instance.detect(tensor3.tensor, instance.config); + resolve(res); + } + }; + if (src) + img.src = src; + else + resolve(void 0); + }); +} +async function warmupNode(instance) { + const atob = (str) => Buffer.from(str, "base64"); + let img; + if (instance.config.warmup === "face") + img = atob(face3); + else + img = atob(body3); + let res; + if ("node" in tfjs_esm_exports) { + const data = tfjs_esm_exports["node"].decodeJpeg(img); + const expanded = data.expandDims(0); + instance.tf.dispose(data); + res = await instance.detect(expanded, instance.config); + instance.tf.dispose(expanded); + } else { + if (instance.config.debug) + log("Warmup tfjs-node not loaded"); + } + return res; +} +async function runInference(instance) { + let res; + if (typeof createImageBitmap === "function") + res = await warmupBitmap(instance); + else if (typeof Image !== "undefined" || env.Canvas !== void 0) + res = await warmupCanvas(instance); + else + res = await warmupNode(instance); + return res; +} +async function runCompile(allModels) { + const backendType = tfjs_esm_exports.getBackend(); + const webGLBackend = tfjs_esm_exports.backend(); + if (backendType !== "webgl" && backendType !== "humangl" || (!webGLBackend || !webGLBackend.checkCompileCompletion)) { + return; + } + tfjs_esm_exports.env().set("ENGINE_COMPILE_ONLY", true); + const numTensorsStart = tfjs_esm_exports.engine().state.numTensors; + const compiledModels = []; + for (const [modelName, model18] of Object.entries(allModels).filter(([key, val]) => key !== null && val !== null)) { + const shape = model18.inputs && model18.inputs[0] && model18.inputs[0].shape ? [...model18.inputs[0].shape] : [1, 64, 64, 3]; + const dtype = model18.inputs && model18.inputs[0] && model18.inputs[0].dtype ? model18.inputs[0].dtype : "float32"; + for (let dim = 0; dim < shape.length; dim++) { + if (shape[dim] === -1) + shape[dim] = dim === 0 ? 1 : 64; + } + const tensor3 = tfjs_esm_exports.zeros(shape, dtype); + try { + const res = model18.execute(tensor3); + compiledModels.push(modelName); + if (Array.isArray(res)) + res.forEach((t) => tfjs_esm_exports.dispose(t)); + else + tfjs_esm_exports.dispose(res); + } catch (e) { + log("compile fail model:", modelName); + } + tfjs_esm_exports.dispose(tensor3); + } + const kernels = await webGLBackend.checkCompileCompletionAsync(); + webGLBackend.getUniformLocations(); + log("compile pass models:", compiledModels); + log("compile pass kernels:", kernels.length); + tfjs_esm_exports.env().set("ENGINE_COMPILE_ONLY", false); + const numTensorsEnd = tfjs_esm_exports.engine().state.numTensors; + if (numTensorsEnd - numTensorsStart > 0) + log("tensor leak:", numTensorsEnd - numTensorsStart); +} +async function warmup(instance, userConfig) { + const t0 = now(); + instance.state = "warmup"; + if (userConfig) + instance.config = mergeDeep(instance.config, userConfig); + if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { + return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null }; + } + return new Promise(async (resolve) => { + await runCompile(instance.models); + const res = await runInference(instance); + const t1 = now(); + if (instance.config.debug) + log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); + instance.emit("warmup"); + resolve(res); + }); +} + +// src/human.ts +var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity; +var Human = class { + constructor(userConfig) { + __publicField(this, "version"); + __publicField(this, "config"); + __publicField(this, "result"); + __publicField(this, "state"); + __publicField(this, "process"); + __publicField(this, "tf"); + __publicField(this, "env"); + __publicField(this, "draw"); + __publicField(this, "models"); + __publicField(this, "events"); + __publicField(this, "faceTriangulation"); + __publicField(this, "faceUVMap"); + __publicField(this, "performance"); + __privateAdd(this, _numTensors, void 0); + __privateAdd(this, _analyzeMemoryLeaks, void 0); + __privateAdd(this, _checkSanity, void 0); + __publicField(this, "gl"); + __publicField(this, "analyze", (...msg) => { + if (!__privateGet(this, _analyzeMemoryLeaks)) + return; + const currentTensors = this.tf.engine().state.numTensors; + const previousTensors = __privateGet(this, _numTensors); + __privateSet(this, _numTensors, currentTensors); + const leaked = currentTensors - previousTensors; + if (leaked !== 0) + log(...msg, leaked); + }); + __privateAdd(this, _sanity, (input) => { + if (!__privateGet(this, _checkSanity)) + return null; + if (!input) + return "input is not defined"; + if (this.env.node && !(input instanceof Tensor)) + return "input must be a tensor"; + try { + this.tf.getBackend(); + } catch (e) { + return "backend not loaded"; + } + return null; + }); + __publicField(this, "similarity", similarity); + __publicField(this, "distance", distance); + __publicField(this, "match", match2); + __publicField(this, "emit", (event) => { + var _a; + if (this.events && this.events.dispatchEvent) + (_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event)); + }); + this.env = env; + config.wasmPath = version8["tfjs-core"].includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfjs_esm_exports.version_core}/dist/`; + config.modelBasePath = env.browser ? "../models/" : "file://models/"; + config.backend = env.browser ? "humangl" : "tensorflow"; + this.version = version9; + Object.defineProperty(this, "version", { value: version9 }); + this.config = JSON.parse(JSON.stringify(config)); + Object.seal(this.config); + this.config.cacheModels = typeof indexedDB !== "undefined"; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + setModelLoadOptions(this.config); + this.tf = tfjs_esm_exports; + this.state = "idle"; + __privateSet(this, _numTensors, 0); + __privateSet(this, _analyzeMemoryLeaks, false); + __privateSet(this, _checkSanity, false); + this.performance = {}; + this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; + this.models = new Models(); + this.draw = { + options: options3, + canvas: (input, output) => canvas2(input, output), + face: (output, result, options4) => face(output, result, options4), + body: (output, result, options4) => body(output, result, options4), + hand: (output, result, options4) => hand(output, result, options4), + gesture: (output, result, options4) => gesture(output, result, options4), + object: (output, result, options4) => object(output, result, options4), + person: (output, result, options4) => person(output, result, options4), + all: (output, result, options4) => all(output, result, options4) + }; + this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null }; + this.process = { tensor: null, canvas: null }; + this.faceTriangulation = triangulation; + this.faceUVMap = uvmap; + this.gl = config2; + this.emit("create"); + } + reset() { + const currentBackend = this.config.backend; + this.config = JSON.parse(JSON.stringify(config)); + this.config.backend = currentBackend; + } + validate(userConfig) { + return validate(config, userConfig || this.config); + } + now() { + return now(); + } + image(input, getTensor = true) { + return process2(input, this.config, getTensor); + } + async segmentation(input, background) { + return process5(input, background, this.config); + } + enhance(input) { + return enhance(input); + } + compare(firstImageTensor, secondImageTensor) { + return compare(this.config, firstImageTensor, secondImageTensor); + } + async init() { + await check(this, true); + await this.tf.ready(); + } + async load(userConfig) { + this.state = "load"; + const timeStamp = now(); + const count2 = Object.values(this.models).filter((model18) => model18).length; + if (userConfig) + this.config = mergeDeep(this.config, userConfig); + if (this.env.initial) { + if (this.config.debug) + log(`version: ${this.version}`); + if (this.config.debug) + log(`tfjs version: ${this.tf.version["tfjs-core"]}`); + if (!await check(this)) + log("error: backend check failed"); + await tfjs_esm_exports.ready(); + if (this.env.browser) { + if (this.config.debug) + log("configuration:", this.config); + if (this.config.debug) + log("environment:", this.env); + if (this.config.debug) + log("tf flags:", this.tf.ENV["flags"]); + } + } + await load19(this); + if (this.env.initial && this.config.debug) + log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); + this.env.initial = false; + const loaded = Object.values(this.models).filter((model18) => model18).length; + if (loaded !== count2) { + await validate2(this); + this.emit("load"); + } + const current = Math.trunc(now() - timeStamp); + if (current > (this.performance.loadModels || 0)) + this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; + } + next(result = this.result) { + return calc2(result, this.config); + } + async warmup(userConfig) { + const t0 = now(); + const res = await warmup(this, userConfig); + const t1 = now(); + this.performance.warmup = Math.trunc(t1 - t0); + return res; + } + async profile(input, userConfig) { + const profile = await this.tf.profile(() => this.detect(input, userConfig)); + const kernels = {}; + for (const kernel of profile.kernels) { + if (kernels[kernel.name]) + kernels[kernel.name] += kernel.kernelTimeMs; + else + kernels[kernel.name] = kernel.kernelTimeMs; + } + const kernelArr = []; + Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] })); + kernelArr.sort((a, b) => b.ms - a.ms); + kernelArr.length = 20; + const res = {}; + for (const kernel of kernelArr) + res[kernel.name] = kernel.ms; + return res; + } + async detect(input, userConfig) { + this.state = "detect"; + return new Promise(async (resolve) => { + var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v; + this.state = "config"; + let timeStamp; + this.config = mergeDeep(this.config, userConfig); + this.state = "check"; + const error = __privateGet(this, _sanity).call(this, input); + if (error) { + log(error, input); + this.emit("error"); + resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error }); + } + const timeStart = now(); + await check(this); + await this.load(); + timeStamp = now(); + this.state = "image"; + const img = await process2(input, this.config); + this.process = img; + this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + this.analyze("Get Image:"); + if (!img.tensor) { + if (this.config.debug) + log("could not convert input to tensor"); + this.emit("error"); + resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: "could not convert input to tensor" }); + return; + } + this.emit("image"); + timeStamp = now(); + this.config.skipAllowed = await skip(this.config, img.tensor); + if (!this.performance.totalFrames) + this.performance.totalFrames = 0; + if (!this.performance.cachedFrames) + this.performance.cachedFrames = 0; + this.performance.totalFrames++; + if (this.config.skipAllowed) + this.performance.cachedFrames++; + this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + this.analyze("Check Changed:"); + let faceRes = []; + let bodyRes = []; + let handRes = []; + let objectRes = []; + this.state = "detect:face"; + if (this.config.async) { + faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; + if (this.performance.face) + delete this.performance.face; + } else { + timeStamp = now(); + faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; + this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) + faceRes = await faceRes; + this.analyze("Start Body:"); + this.state = "detect:body"; + const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; + if (this.config.async) { + if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) + bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; + else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) + bodyRes = this.config.body.enabled ? predict5(img.tensor, bodyConfig) : []; + else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) + bodyRes = this.config.body.enabled ? predict7(img.tensor, bodyConfig) : []; + else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) + bodyRes = this.config.body.enabled ? predict15(img.tensor, bodyConfig) : []; + if (this.performance.body) + delete this.performance.body; + } else { + timeStamp = now(); + if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) + bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; + else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) + bodyRes = this.config.body.enabled ? await predict5(img.tensor, bodyConfig) : []; + else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) + bodyRes = this.config.body.enabled ? await predict7(img.tensor, bodyConfig) : []; + else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) + bodyRes = this.config.body.enabled ? await predict15(img.tensor, bodyConfig) : []; + this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Body:"); + this.analyze("Start Hand:"); + this.state = "detect:hand"; + const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; + if (this.config.async) { + if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) + handRes = this.config.hand.enabled ? predict12(img.tensor, handConfig) : []; + else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) + handRes = this.config.hand.enabled ? predict13(img.tensor, handConfig) : []; + if (this.performance.hand) + delete this.performance.hand; + } else { + timeStamp = now(); + if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) + handRes = this.config.hand.enabled ? await predict12(img.tensor, handConfig) : []; + else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) + handRes = this.config.hand.enabled ? await predict13(img.tensor, handConfig) : []; + this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Hand:"); + this.analyze("Start Object:"); + this.state = "detect:object"; + if (this.config.async) { + if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) + objectRes = this.config.object.enabled ? predict16(img.tensor, this.config) : []; + else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) + objectRes = this.config.object.enabled ? predict6(img.tensor, this.config) : []; + if (this.performance.object) + delete this.performance.object; + } else { + timeStamp = now(); + if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) + objectRes = this.config.object.enabled ? await predict16(img.tensor, this.config) : []; + else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) + objectRes = this.config.object.enabled ? await predict6(img.tensor, this.config) : []; + this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + } + this.analyze("End Object:"); + this.state = "detect:await"; + if (this.config.async) + [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); + this.state = "detect:gesture"; + let gestureRes = []; + if (this.config.gesture.enabled) { + timeStamp = now(); + gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris(faceRes)]; + if (!this.config.async) + this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); + else if (this.performance.gesture) + delete this.performance.gesture; + } + this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); + const shape = ((_v = (_u = this.process) == null ? void 0 : _u.tensor) == null ? void 0 : _v.shape) || []; + this.result = { + face: faceRes, + body: bodyRes, + hand: handRes, + gesture: gestureRes, + object: objectRes, + performance: this.performance, + canvas: this.process.canvas, + timestamp: Date.now(), + error: null, + get persons() { + return join2(faceRes, bodyRes, handRes, gestureRes, shape); + } + }; + tfjs_esm_exports.dispose(img.tensor); + this.emit("detect"); + this.state = "idle"; + resolve(this.result); + }); + } +}; +_numTensors = new WeakMap(); +_analyzeMemoryLeaks = new WeakMap(); +_checkSanity = new WeakMap(); +_sanity = new WeakMap(); +export { + Human, + Human as default, + config as defaults, + draw_exports as draw, + env, + match_exports as match, + models_exports as models +}; /** * Human main module * @default Human Library diff --git a/dist/human.esm-nobundle.js.map b/dist/human.esm-nobundle.js.map index 0068e6a3..9a7535ed 100644 --- a/dist/human.esm-nobundle.js.map +++ b/dist/human.esm-nobundle.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["../src/util/util.ts", "../src/config.ts", "tfjs.esm.js", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/tfjs/load.ts", "../src/models.ts", "../src/gear/gear.ts", "../src/tfjs/constants.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/antispoof.ts", "../src/face/facemeshcoords.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/gear/emotion.ts", "../src/face/mobilefacenet.ts", "../src/face/iris.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/face/faceres.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/face/liveness.ts", "../src/body/movenetcoords.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/segmentation.ts", "../src/tfjs/humangl.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/face/mask.ts", "../src/face/angles.ts", "../src/face/face.ts", "../src/gesture/gesture.ts", "../src/util/interpolate.ts", "../src/face/match.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"], - "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array) => data.reduce((acc: Array, val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** blur segmentation output by pixels for more realistic image */\n blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `humangl` for browser and `tensorflow` for nodejs\n */\n backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: true,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: true,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n iouThreshold: 0.1,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-full.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'mb3-centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'selfie.json',\n blur: 8,\n },\n};\n\nexport { config as defaults };\n", "/*\n Human\n homepage: \n author: '\n*/\n\nexport*from\"@tensorflow/tfjs/dist/index.js\";export*from\"@tensorflow/tfjs-backend-webgl/dist/index.js\";var r=\"3.15.0\",e=\"3.15.0\",a=\"3.15.0\",o=\"3.15.0\",t=\"3.15.0\",s=\"3.15.0\",f=\"3.15.0\",v={tfjs:r,\"tfjs-core\":e,\"tfjs-data\":a,\"tfjs-layers\":o,\"tfjs-converter\":t,\"tfjs-backend-webgl\":s,\"tfjs-backend-wasm\":f};import{Tensor as d}from\"@tensorflow/tfjs/dist/index.js\";import{GraphModel as b}from\"@tensorflow/tfjs-converter/dist/index\";export{b as GraphModel,d as Tensor,v as version};\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type) as WebGLShader;\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100);\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture() as WebGLTexture;\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram as GLProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram as GLProgram;\n }\n\n const filter = {\n colorMatrix: (matrix) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const channels = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n const absMax = await Promise.all(max.map((channel) => channel.data()));\n const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type is not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n if ((tensor as Tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n } else {\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx || !fx.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n } else {\n depth = input['data'].length / input['height'] / input['width'];\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array(input['data']['buffer']);\n pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted = tf.cast(pixels, 'float32');\n const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n /** MonkeyPatch for Canvas */\n Canvas: undefined;\n /** MonkeyPatch for Image */\n Image: undefined;\n /** MonkeyPatch for ImageData */\n ImageData: undefined;\n\n constructor() {\n this.browser = typeof navigator !== 'undefined';\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if (typeof navigator !== 'undefined') {\n const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n if (raw && raw[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = navigator.userAgent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n // chrome offscreencanvas gpu memory leak\n /*\n const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n if (verChrome > 92 && verChrome < 96) {\n log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n this.offscreen = false;\n }\n */\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n }\n const c = image.canvas(100, 100);\n const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n this.webgl.supported = typeof ctx !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n // @ts-ignore getGPGPUContext only exists on WebGL backend\n const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n if (gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n }\n }\n // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n async updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n // eslint-disable-next-line global-require\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) {\n cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n }\n if (line.startsWith('flags')) {\n cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n }\n } catch { }\n */\n }\n if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n else this['cpu'] = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n cacheModels: false,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n const modelUrl = join(options.modelBasePath, modelPath || '');\n const modelPathSegments = modelUrl.split('/');\n const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n const cachedModels = await tf.io.listModels(); // list all models already in cache\n const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n let loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n // @ts-ignore private property\n if (options.debug) log('model load handler:', model.handler);\n // @ts-ignore private property\n const artifacts = await model.handler.load(); // load manifest\n model.loadSync(artifacts); // load weights\n if (options.verbose) log('load model:', model['modelUrl']);\n loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (loaded && options.cacheModels && !modelCached) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n ssrnetage: null | GraphModel | Promise = null;\n gear: null | GraphModel | Promise = null;\n blazeposedetect: null | GraphModel | Promise = null;\n blazepose: null | GraphModel | Promise = null;\n centernet: null | GraphModel | Promise = null;\n efficientpose: null | GraphModel | Promise = null;\n mobilefacenet: null | GraphModel | Promise = null;\n emotion: null | GraphModel | Promise = null;\n facedetect: null | GraphModel | Promise = null;\n faceiris: null | GraphModel | Promise = null;\n facemesh: null | GraphModel | Promise = null;\n faceres: null | GraphModel | Promise = null;\n ssrnetgender: null | GraphModel | Promise = null;\n handpose: null | GraphModel | Promise = null;\n handskeleton: null | GraphModel | Promise = null;\n handtrack: null | GraphModel | Promise = null;\n liveness: null | GraphModel | Promise = null;\n movenet: null | GraphModel | Promise = null;\n nanodet: null | GraphModel | Promise = null;\n posenet: null | GraphModel | Promise = null;\n segmentation: null | GraphModel | Promise = null;\n antispoof: null | GraphModel | Promise = null;\n}\n\nexport function reset(instance: Human): void {\n // if (instance.config.debug) log('resetting loaded models');\n for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise {\n if (env.initial) reset(instance);\n if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n }\n if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n // @ts-ignore optional model\n if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n // @ts-ignore optional model\n if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n // @ts-ignore optional model\n if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n // @ts-ignore optional model\n if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n // @ts-ignore optional model\n if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for await (const model of Object.keys(instance.models)) {\n if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n }\n}\n\nexport async function validate(instance: Human): Promise {\n interface Op { name: string, category: string, op: string }\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n for (const defined of Object.keys(instance.models)) {\n const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n if (!model) continue;\n const ops: string[] = [];\n // @ts-ignore // executor is a private method\n const executor = model?.executor;\n if (executor && executor.graph.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) log('model signature not determined:', defined);\n }\n const missing: string[] = [];\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n // log('model validation ops:', defined, ops);\n if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['gear']);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255,\n tf1: 1,\n tf2: 2,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255, 'float32');\n constants.tf1 = tf.scalar(1, 'float32');\n constants.tf2 = tf.scalar(2, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise {\n if (!model) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = Array<[number, number]>;\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf05);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, 0);\n } else {\n t.batch = tf.squeeze(res); // when using tfhub model\n }\n tf.dispose(res);\n t.boxes = decodeBounds(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: Array = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const rawBox = {\n startPoint: [points[0], points[1]] as Point,\n endPoint: [points[2], points[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n const squaredBox = util.squarifyBox(enlargedBox);\n boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n const anchors: Array<{ x: number, y: number }> = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const boxes = tf.stack([xMin, yMin, width, height], 1);\n return boxes;\n });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.argmax = tf.argMax(t.scores);\n const i = (await t.argmax.data())[0] as number;\n const scores = await t.scores.data();\n const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n if (scores[i] >= minScore) {\n const boxes = await t.boxes.array();\n const boxRaw: Box = boxes[i];\n const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n // console.log(box);\n detected.push({ box, boxRaw, score: scores[i] });\n }\n /*\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n const boxes = t.boxes.arraySync();\n const scores = t.scores.dataSync();\n const nms = t.nms.dataSync();\n const detected: Array = [];\n for (const i of Array.from(nms)) {\n const boxRaw: Box = boxes[i];\n const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n detected.push({ box, boxRaw, score: scores[i] });\n }\n */\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array, outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Array, outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise {\n if (env.initial) models.detector = null;\n if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n models.detector = await loadModel(config.body['detector']['modelPath']);\n const inputs = Object.values(models.detector.modelSignature['inputs']);\n inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n await detect.createAnchors();\n return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) models.landmarks = null;\n if (!models.landmarks) {\n models.landmarks = await loadModel(config.body.modelPath);\n const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models.detector) await loadDetect(config);\n if (!models.landmarks) await loadPose(config);\n return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise {\n const t: Record = {};\n if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array, outputSize: [number, number]): Array {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: Array = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: Array = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Array = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array, outputSize: [number, number]): Array {\n for (const b of boxes) {\n b.box = [\n Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n ];\n b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n }\n return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n rescaleBoxes(boxes, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n const t: Record = {};\n /*\n if (config.body['detector'] && config.body['detector']['enabled']) {\n t.detector = await prepareImage(input, 224);\n const boxes = await detectBoxes(t.detector, config, outputSize);\n }\n */\n t.landmarks = await prepareImage(input, 256); // padded and resized\n cache = await detectLandmarks(t.landmarks, config, outputSize);\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n lastTime = now();\n skipped = 0;\n }\n return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = Object.values(model.modelSignature['inputs']);\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: Array = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: Array = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore = (await max.data())[0]; // get highest score\n tf.dispose([reshaped, max]);\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n const y = (await div.data())[0];\n tf.dispose([mod, div]);\n return [x, y, newScore];\n }\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n if (!model?.inputs[0].shape) return null;\n const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = resT.squeeze();\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = squeeze.unstack(2);\n tf.dispose(squeeze);\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body?.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Array = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.emotion?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: Array<{ score: number, emotion: Emotion }> = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise {\n if (!model) return [];\n const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: Array = [];\n if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model?.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n if (!model) {\n if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n return rawCoords;\n }\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import type { Tensor } from '../tfjs/types';\n\nexport const attentionDefinitions = {\n eyeLLower: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n eyeRLower: [263, 249, 390, 373, 374, 380, 381, 382, 362], // 9\n // eslint-disable-next-line max-len\n lips: [185, 96, 90, 181, 84, 17, 314, 405, 320, 307, 409, 40, 39, 73, 37, 0, 267, 269, 270, 409, 40, 88, 178, 178, 87, 14, 268, 402, 318, 324, 409, 80, 41, 38, 87, 12, 268, 303, 318, 324, 185, 95, 80, 81, 85, 16, 315, 404, 319, 325, 409, 40, 39, 73, 72, 0, 302, 303, 270, 408, 185, 88, 88, 81, 82, 15, 316, 403, 319, 324, 409, 80, 41, 38, 87, 12, 268, 303, 318, 324],\n // eslint-disable-next-line max-len\n eyeL: [33, 7, 163, 144, 145, 153, 154, 155, 133, 246, 161, 160, 159, 158, 157, 173, 130, 25, 110, 24, 23, 22, 26, 112, 243, 247, 30, 29, 27, 28, 56, 190, 226, 31, 228, 229, 230, 231, 232, 233, 244, 113, 225, 224, 223, 222, 221, 189, 35, 124, 46, 53, 52, 65, 143, 111, 117, 118, 119, 120, 121, 128, 245, 156, 70, 63, 105, 66, 107, 55, 193],\n // eslint-disable-next-line max-len\n eyeR: [263, 249, 390, 373, 374, 380, 381, 382, 362, 466, 388, 387, 386, 385, 384, 398, 359, 255, 339, 254, 253, 252, 256, 341, 463, 467, 260, 259, 257, 258, 286, 414, 446, 261, 448, 449, 450, 451, 452, 453, 464, 342, 445, 444, 443, 442, 441, 413, 265, 353, 276, 283, 282, 295, 372, 340, 346, 347, 348, 349, 350, 357, 465, 383, 300, 293, 334, 296, 336, 285, 417],\n};\n\n// function used to determine heuristic mapping\n// values in attentionDefinitions are based on top values from 200 iterations\n/*\nconst historyRes: number[][] = [];\n\nconst getMostCommon = (arr): number => {\n const count: Record = {};\n arr.forEach((el) => {\n count[el] = (count[el] || 0) + 1;\n });\n const res: string[] = Object.keys(count).reduce((acc: string[], val, ind) => {\n if (!ind || count[val] > count[acc[0]]) return [val];\n if (count[val] === count[acc[0]]) acc.push(val);\n return acc;\n }, []);\n return parseInt(res[0]);\n};\n\nfunction replaceClosestPoint(rawCoords, newCoords) {\n const currentRes: number[] = [];\n for (let i = 0; i < newCoords.length / 2; i++) {\n let minDist = Number.MAX_VALUE;\n let minDistIdx = -1;\n const pts = rawCoords.map((r) => [r[0], r[1]]);\n for (let j = 0; j < rawCoords.length; j++) {\n const x = pts[j][0] - newCoords[2 * i + 0];\n const y = pts[j][1] - newCoords[2 * i + 1];\n const dist = (x * x) + (y * y);\n if (dist < minDist) {\n minDist = dist;\n minDistIdx = j;\n }\n }\n currentRes.push(minDistIdx);\n rawCoords[minDistIdx] = [newCoords[2 * i + 0], newCoords[2 * i + 1], rawCoords[minDistIdx][2]];\n }\n historyRes.push(currentRes);\n if (historyRes.length % 50 === 0) {\n const invertRes: number[][] = [];\n for (let i = 0; i < currentRes.length; i++) {\n const indexRes = historyRes.map((j) => j[i]);\n invertRes.push(indexRes);\n }\n const mostCommon: number[] = invertRes.map((r) => getMostCommon(r));\n console.log(mostCommon);\n }\n return rawCoords;\n}\n*/\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n irisL: results[3].dataSync() as Float32Array, // 5 x 2d // output_left_iris\n irisR: results[1].dataSync() as Float32Array, // 5 x 2d // output_right_iris\n eyeL: results[0].dataSync() as Float32Array, // 71 x 2d // output_left_eye\n eyeR: results[6].dataSync() as Float32Array, // 71 x 2d // output_right_eye\n lips: results[5].dataSync() as Float32Array, // 80 x 2d // output_lips\n // flag: results[4], // already processed in parent // conv_faceflag\n // mesh: results[2], // already have it in rawCoords // output_mesh_identity\n };\n\n // rawCoords = replaceClosestPoint(rawCoords, t.eyeL);\n // rawCoords = replaceClosestPoint(rawCoords, t.eyeR);\n // rawCoords = replaceClosestPoint(rawCoords, t.lips);\n\n // augment iris: adds additional 5 keypoints per eye\n const irisRDepth = attentionDefinitions.eyeRLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeRLower.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n const irisLDepth = attentionDefinitions.eyeLLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeLLower.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[attentionDefinitions.eyeL[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[attentionDefinitions.eyeL[i]][2]];\n // for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords.push([t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], 0]);\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[attentionDefinitions.eyeR[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[attentionDefinitions.eyeR[i]][2]];\n // for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords.push([t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], 0]);\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[attentionDefinitions.lips[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[attentionDefinitions.lips[i]][2]];\n // for (let i = 0; i < t.lips.length / 2; i++) rawCoords.push([t.lips[2 * i + 0], t.lips[2 * i + 1], 0]);\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array, confidence: number };\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: Array = [];\n const newCache: Array = [];\n let id = 0;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config?.filter?.equalization) {\n const equilized = await histogramEqualization(face.tensor as Tensor);\n tf.dispose(face.tensor);\n face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n ]);\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n const results = model.execute(face.tensor as Tensor) as Array;\n const confidence = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const contourCoords = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const faceConfidence = await confidence.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n } else {\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n newCache.push(calculatedBox);\n }\n tf.dispose([...results, coordsReshaped]);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention?.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n age: number,\n gender: Gender,\n genderScore: number,\n descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const norm = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj = {\n age: 0,\n gender: 'unknown',\n genderScore: 0,\n descriptor: [],\n };\n\n if (config.face.description?.enabled) {\n const enhanced = enhance(image);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description?.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n const age = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 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0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 0.421875, y: 0.890625 },\n { x: 0.421875, y: 0.890625 },\n { x: 0.453125, y: 0.890625 },\n { x: 0.453125, y: 0.890625 },\n { x: 0.484375, y: 0.890625 },\n { x: 0.484375, y: 0.890625 },\n { x: 0.515625, y: 0.890625 },\n { x: 0.515625, y: 0.890625 },\n { x: 0.546875, y: 0.890625 },\n { x: 0.546875, y: 0.890625 },\n { x: 0.578125, y: 0.890625 },\n { x: 0.578125, y: 0.890625 },\n { x: 0.609375, y: 0.890625 },\n { x: 0.609375, y: 0.890625 },\n { x: 0.640625, y: 0.890625 },\n { x: 0.640625, y: 0.890625 },\n { x: 0.671875, y: 0.890625 },\n { x: 0.671875, y: 0.890625 },\n { x: 0.703125, y: 0.890625 },\n { x: 0.703125, y: 0.890625 },\n { x: 0.734375, y: 0.890625 },\n { x: 0.734375, y: 0.890625 },\n { x: 0.765625, y: 0.890625 },\n { x: 0.765625, y: 0.890625 },\n { x: 0.796875, y: 0.890625 },\n { x: 0.796875, y: 0.890625 },\n { x: 0.828125, y: 0.890625 },\n { x: 0.828125, y: 0.890625 },\n { x: 0.859375, y: 0.890625 },\n { x: 0.859375, y: 0.890625 },\n { x: 0.890625, y: 0.890625 },\n { x: 0.890625, y: 0.890625 },\n { x: 0.921875, y: 0.890625 },\n { x: 0.921875, y: 0.890625 },\n { x: 0.953125, y: 0.890625 },\n { x: 0.953125, y: 0.890625 },\n { x: 0.984375, y: 0.890625 },\n { x: 0.984375, y: 0.890625 },\n { x: 0.015625, y: 0.921875 },\n { x: 0.015625, y: 0.921875 },\n { x: 0.046875, y: 0.921875 },\n { x: 0.046875, y: 0.921875 },\n { x: 0.078125, y: 0.921875 },\n { x: 0.078125, y: 0.921875 },\n { x: 0.109375, y: 0.921875 },\n { x: 0.109375, y: 0.921875 },\n { x: 0.140625, y: 0.921875 },\n { x: 0.140625, y: 0.921875 },\n { x: 0.171875, y: 0.921875 },\n { x: 0.171875, y: 0.921875 },\n { x: 0.203125, y: 0.921875 },\n { x: 0.203125, y: 0.921875 },\n { x: 0.234375, y: 0.921875 },\n { x: 0.234375, y: 0.921875 },\n { x: 0.265625, y: 0.921875 },\n { x: 0.265625, y: 0.921875 },\n { x: 0.296875, y: 0.921875 },\n { x: 0.296875, y: 0.921875 },\n { x: 0.328125, y: 0.921875 },\n { x: 0.328125, y: 0.921875 },\n { x: 0.359375, y: 0.921875 },\n { x: 0.359375, y: 0.921875 },\n { x: 0.390625, y: 0.921875 },\n { x: 0.390625, y: 0.921875 },\n { x: 0.421875, y: 0.921875 },\n { x: 0.421875, y: 0.921875 },\n { x: 0.453125, y: 0.921875 },\n { x: 0.453125, y: 0.921875 },\n { x: 0.484375, y: 0.921875 },\n { x: 0.484375, y: 0.921875 },\n { x: 0.515625, y: 0.921875 },\n { x: 0.515625, y: 0.921875 },\n { x: 0.546875, y: 0.921875 },\n { x: 0.546875, y: 0.921875 },\n { x: 0.578125, y: 0.921875 },\n { x: 0.578125, y: 0.921875 },\n { x: 0.609375, y: 0.921875 },\n { x: 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0.5625 },\n { x: 0.0625, y: 0.6875 },\n { x: 0.0625, y: 0.6875 },\n { x: 0.0625, y: 0.6875 },\n { x: 0.0625, y: 0.6875 },\n { x: 0.0625, y: 0.6875 },\n { x: 0.0625, y: 0.6875 },\n { x: 0.1875, y: 0.6875 },\n { x: 0.1875, y: 0.6875 },\n { x: 0.1875, y: 0.6875 },\n { x: 0.1875, y: 0.6875 },\n { x: 0.1875, y: 0.6875 },\n { x: 0.1875, y: 0.6875 },\n { x: 0.3125, y: 0.6875 },\n { x: 0.3125, y: 0.6875 },\n { x: 0.3125, y: 0.6875 },\n { x: 0.3125, y: 0.6875 },\n { x: 0.3125, y: 0.6875 },\n { x: 0.3125, y: 0.6875 },\n { x: 0.4375, y: 0.6875 },\n { x: 0.4375, y: 0.6875 },\n { x: 0.4375, y: 0.6875 },\n { x: 0.4375, y: 0.6875 },\n { x: 0.4375, y: 0.6875 },\n { x: 0.4375, y: 0.6875 },\n { x: 0.5625, y: 0.6875 },\n { x: 0.5625, y: 0.6875 },\n { x: 0.5625, y: 0.6875 },\n { x: 0.5625, y: 0.6875 },\n { x: 0.5625, y: 0.6875 },\n { x: 0.5625, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.6875, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.8125, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.9375, y: 0.6875 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.0625, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.1875, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.3125, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.4375, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.5625, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.6875, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index]);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as Array;\n const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n if (config.skipAllowed) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array;\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: Array = [];\n const slopesYZ: Array = [];\n const fingerCurls: Array = [];\n const fingerDirections: Array = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: Array = [];\n const slopeAtYZ: Array = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: Array<{ name: string, confidence: number }> = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Array;\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n 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)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (env.initial) {\n handDetectorModel = null;\n handPoseModel = null;\n }\n if (!handDetectorModel || !handPoseModel) {\n [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n ]);\n } else {\n if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n }\n const handDetector = new handdetector.HandDetector(handDetectorModel);\n handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: Array,\n hands: Array;\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = Object.values(models[0].modelSignature['inputs']);\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = Object.values(models[1].modelSignature['inputs']);\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Array = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise {\n if (!model) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "export const kpt: Array = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: Array = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: Array,\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: Array): Array {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Array, // unused\n bodies: Array;\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: Array = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: Array = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Array = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n const bodies: Array = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (totalScore > config.body.minConfidence) {\n const keypoints: Array = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Array = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and cached boxes\n /*\n cache.bodies = []; // reset bodies result\n if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n t.cast = tf.cast(t.crop, 'int32');\n // t.input = prepareImage(input);\n t.res = model?.execute(t.cast) as Tensor;\n const res = await t.res.array();\n const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n cache.bodies = cache.bodies.concat(newBodies);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n }\n if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n t.input = prepareImage(input);\n t.res = model?.execute(t.input) as Tensor;\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n cache.boxes.length = 0; // reset cache\n for (let i = 0; i < cache.bodies.length; i++) {\n if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n const cropBox = box.crop(scaledBox);\n cache.boxes.push(cropBox);\n }\n }\n */\n\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? await parseSinglePose(res, config, input)\n : await parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = Object.values(model.modelSignature['inputs']);\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: Array = [];\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n });\n }\n // deallocate tensors\n res.forEach((t) => tf.dispose(t));\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: Array = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = await nms.data();\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const norm = tf.div(resize, constants.tf255);\n const transpose = norm.transpose([0, 3, 1, 2]);\n tf.dispose(norm);\n tf.dispose(resize);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transpose);\n lastTime = now();\n tf.dispose(transpose);\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: Array; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: Array<{ keypoints, box: Box, score: number }> = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Array = model.execute(normalized, poseNetOutputs) as Array;\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n if (busy) return { data: [], canvas: null, alpha: null };\n busy = true;\n if (!model) await load(config);\n const inputImage = await image.process(input, config);\n const width = inputImage.tensor?.shape[2] || 0;\n const height = inputImage.tensor?.shape[1] || 0;\n if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n const t: Record = {};\n\n t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n tf.dispose(inputImage.tensor);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n\n t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n if (t.squeeze.shape[2] === 2) {\n t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n t.expand = tf.expandDims(t.fg, 2);\n t.pad = tf.expandDims(t.expand, 0);\n t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n // otherwise run softmax after unstack and use standard resize\n // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n t.data = tf.squeeze(t.crop, 0);\n } else {\n t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n }\n const data = Array.from(await t.data.data()) as number[];\n\n if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n if (config.debug) log('canvas support missing');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n }\n\n const alphaCanvas = image.canvas(width, height);\n // @ts-ignore browser is not defined in tfjs-node\n if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n const compositeCanvas = image.canvas(width, height);\n const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n compositeCtx.drawImage(alphaCanvas, 0, 0);\n compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n compositeCtx.filter = 'none'; // reset css filter\n const compositeData = compositeCtx.getImageData(0, 0, width, height);\n for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n compositeCtx.putImageData(compositeData, 0, 0);\n\n let mergedCanvas: AnyCanvas | null = null;\n if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n mergedCanvas = image.canvas(width, height);\n const bgImage = await image.process(background, config);\n tf.dispose(bgImage.tensor);\n const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n ctxMerge.drawImage(compositeCanvas, 0, 0);\n }\n\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n busy = false;\n // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null,\n gl: null,\n extensions: [],\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions() as string[];\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n log('error: humangl backend invalid context');\n models.reset(instance);\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = await image.canvas(100, 100);\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', async (e) => {\n log('error: humangl:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n // log('resetting humangl backend');\n // env.initial = true;\n // models.reset(instance);\n // await tf.removeBackend(config.name);\n // await register(instance); // re-register\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('error: humangl context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('error: humangl context create:', e);\n });\n }\n } catch (err) {\n log('error: cannot get WebGL context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n if (current) {\n log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n } else {\n log('error: no current gl context:', current, config.gl);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n log('backend registered:', config.name);\n }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('floormod');\n }\n}\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n // force browser vs node backend\n if (env.browser && instance.config.backend === 'tensorflow') {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'humangl';\n }\n if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'humangl';\n } else {\n const adapter = await navigator['gpu'].requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n }\n }\n\n // check available backends\n if (instance.config.backend === 'humangl') await humangl.register(instance);\n const available = Object.keys(tf.engine().registryFactory);\n if (instance.config.debug) log('available backends:', available);\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n constants.init();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl') {\n tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n tf.ENV.set('WEBGL_CPU_FORWARD', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n if (tf.backend().getGPGPUContext) {\n const gl = await tf.backend().getGPGPUContext().gl;\n if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n }\n\n // wait for ready\n tf.enableProdMode();\n await tf.ready();\n\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend();\n\n await env.updateBackend(); // update env on backend init\n registerCustomOps();\n // await env.updateBackend(); // update env on backend init\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array, config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output);\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result || !result.performance || !result || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d');\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => {\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n const color = `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n return color;\n};\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2], localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n shadowColor: 'black',\n alpha: 0.5,\n font: 'small-caps 16px \"Segoe UI\"',\n lineHeight: 18,\n lineWidth: 4,\n pointSize: 2,\n roundRect: 8,\n drawPoints: false,\n drawLabels: true,\n drawBoxes: true,\n drawAttention: true,\n drawGestures: true,\n drawPolygons: true,\n drawGaze: true,\n fillPolygons: false,\n useDepth: true,\n useCurves: false,\n};\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } from './primitives';\nimport { options } from './options';\nimport { attentionDefinitions } from '../face/attention';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet opt: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (opt.drawLabels) {\n // silly hack since fillText does not suport new line\n const labels:string[] = [];\n labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n if (f.age) labels.push(`age: ${f.age || ''}`);\n if (f.iris) labels.push(`distance: ${f.iris}`);\n if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n labels.push(emotion.join(' '));\n }\n if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n }\n if (labels.length === 0) labels.push('face');\n ctx.fillStyle = opt.color;\n for (let i = labels.length - 1; i >= 0; i--) {\n const x = Math.max(f.box[0], 0);\n const y = i * opt.lineHeight + f.box[1];\n if (opt.shadowColor && opt.shadowColor !== '') {\n ctx.fillStyle = opt.shadowColor;\n ctx.fillText(labels[i], x + 5, y + 16);\n }\n ctx.fillStyle = opt.labelColor;\n ctx.fillText(labels[i], x + 4, y + 15);\n }\n }\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (opt.fillPolygons) {\n ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n ctx.fill();\n }\n }\n if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (opt.fillPolygons) {\n ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (opt.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (opt.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (opt.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, opt);\n }\n drawIrisElipse(f, ctx);\n }\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (opt.drawPoints && f.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt);\n if (opt.drawAttention) {\n if (attentionDefinitions.lips.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, opt);\n if (attentionDefinitions.eyeL.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n if (attentionDefinitions.eyeR.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (opt.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);\n }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n opt = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.font = opt.font;\n ctx.strokeStyle = opt.color;\n ctx.fillStyle = opt.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n ctx.fillStyle = colorDepth(pt.position[2], localOptions);\n ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels) {\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations) {\n const addHandLabel = (part: Array, title: string) => {\n if (!part || part.length === 0 || !part[0]) return;\n const z = part[part.length - 1][2] || -256;\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n };\n ctx.font = localOptions.font;\n addHandLabel(h.annotations['index'], 'index');\n addHandLabel(h.annotations['middle'], 'middle');\n addHandLabel(h.annotations['ring'], 'ring');\n addHandLabel(h.annotations['pinky'], 'pinky');\n addHandLabel(h.annotations['thumb'], 'thumb');\n addHandLabel(h.annotations['palm'], 'palm');\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Array) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations['index']);\n addHandLine(h.annotations['middle']);\n addHandLine(h.annotations['ring']);\n addHandLine(h.annotations['pinky']);\n addHandLine(h.annotations['thumb']);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `${h.label} ${Math.round(100 * h.score)}%`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures) {\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n let where: unknown[] = []; // what&where is a record\n let what: unknown[] = []; // what&where is a record\n [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n const label = `${where[0]} ${who}: ${what[1]}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n i += 1;\n }\n }\n }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: Array<{ x: number, y: number }> = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n tf.dispose(buffer);\n return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (isNaN(thetaX)) thetaX = 0;\n if (isNaN(thetaY)) thetaY = 0;\n if (isNaN(thetaZ)) thetaZ = 0;\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const z_axis = normalize(crossVectors(x_axis, y_axis));\n // adjust x_axis to make sure that all axes are perpendicular to each other\n x_axis = crossVectors(y_axis, z_axis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n x_axis[0], x_axis[1], x_axis[2],\n y_axis[0], y_axis[1], y_axis[2],\n z_axis[0], z_axis[1], z_axis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise => {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: Array = [];\n instance.state = 'run:face';\n\n const faces = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n faces[i].tensor = masked as Tensor;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!instance.config.face.iris?.enabled) {\n // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n }\n const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n : 0; // note: average human iris size is 11.7mm\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array;\n if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: Point }> = [];\n if (res[i]['annotations']) {\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i]['keypoints']) {\n const poses = fingerPose.match(res[i]['keypoints']);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n // curve fitted: buffer = 8 - ln(delay)\n // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n // - at 50ms delay buffer = ~4.1 => 28% towards live data\n // - at 250ms delay buffer = ~2.5 => 40% towards live data\n // - at 500ms delay buffer = ~1.8 => 55% towards live data\n // - at 750ms delay buffer = ~1.4 => 71% towards live data\n // - at 1sec delay buffer = 1 which means live data is used\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part as BodyLandmark,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Array = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n }\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // just copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = distance(descriptor, descriptors[i], options);\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array, bodies: Array, hands: Array, gestures: Array, shape: Array | undefined): Array {\n let id = 0;\n const persons: Array = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face?.box);\n extractXY(person.body?.box);\n extractXY(person.hands?.left?.box);\n extractXY(person.hands?.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else return;\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d');\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas);\n const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res;\n if ('node' in tf) {\n // @ts-ignore tf.node may be undefined\n const data = tf['node'].decodeJpeg(img);\n const expanded = data.expandDims(0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(allModels: Models) {\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !webGLBackend.checkCompileCompletion)) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(allModels).filter(([key, val]) => (key !== null && val !== null))) {\n const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype = (model.inputs && model.inputs[0] && model.inputs[0].dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n // const res = await model.executeAsync(tensor); // fails with current tfjs\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend.checkCompileCompletionAsync();\n webGLBackend.getUniformLocations();\n log('compile pass models:', compiledModels);\n log('compile pass kernels:', kernels.length);\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n }\n return new Promise(async (resolve) => {\n await runCompile(instance.models);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './draw/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n /** Currently loaded models\n * @internal\n * {@link Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n /** WebGL debug info */\n gl: Record;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n this.env = env;\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models();\n // reexport draw methods\n this.draw = {\n options: draw.options as DrawOptions,\n canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n face: (output: AnyCanvas, result: FaceResult[], options?: Partial) => draw.face(output, result, options),\n body: (output: AnyCanvas, result: BodyResult[], options?: Partial) => draw.body(output, result, options),\n hand: (output: AnyCanvas, result: HandResult[], options?: Partial) => draw.hand(output, result, options),\n gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial) => draw.gesture(output, result, options),\n object: (output: AnyCanvas, result: ObjectResult[], options?: Partial) => draw.object(output, result, options),\n person: (output: AnyCanvas, result: PersonResult[], options?: Partial) => draw.person(output, result, options),\n all: (output: AnyCanvas, result: Result, options?: Partial) => draw.all(output, result, options),\n };\n this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n // export access to image processing\n // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // set gl info\n this.gl = humangl.config;\n // include platform info\n this.emit('create');\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n return validate(defaults, userConfig || this.config);\n }\n\n /** Exports face matching methods {@link match#similarity} */\n public similarity = match.similarity;\n /** Exports face matching methods {@link match#distance} */\n public distance = match.distance;\n /** Exports face matching methods {@link match#match} */\n public match = match.match;\n\n /** Utility wrapper for performance.now() */\n now(): number {\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = true) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns processed canvas with body segmentation\n * - Segmentation is not triggered as part of detect process\n * @param input - {@link Input}\n * @param background - {@link Input}\n * - Optional parameter background is used to fill the background with specific input\n * Returns:\n * - `data` as raw data array with per-pixel segmentation values\n * - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n * - `alpha` as grayscale canvas that represents segmentation alpha values\n */\n async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n return segmentation.process(input, background, this.config);\n }\n\n /** Enhance method performs additional enhacements to face image previously detected for futher processing\n *\n * @param input - Tensor as provided in human.result.face[n].tensor\n * @returns Tensor\n */\n // eslint-disable-next-line class-methods-use-this\n enhance(input: Tensor): Tensor | null {\n return faceres.enhance(input);\n }\n\n /** Compare two input tensors for pixel simmilarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n }\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (this.config.debug) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n if (!await backend.check(this)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('environment:', this.env);\n if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n }\n }\n\n await models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n await models.validate(this); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config) as Result;\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise> {\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n else kernels[kernel.name] = kernel.kernelTimeMs;\n }\n const kernelArr: Array<{ name: string, ms: number }> = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n kernelArr.sort((a, b) => b.ms - a.ms); // sort\n kernelArr.length = 20; // crop\n const res: Record = {};\n for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n return res;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n }\n\n const timeStart = now();\n\n // configure backend if needed\n await backend.check(this);\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames as number)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process?.tensor?.shape || [];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], - "mappings": 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+ "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array) => data.reduce((acc: Array, val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** blur segmentation output by pixels for more realistic image */\n blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `humangl` for browser and `tensorflow` for nodejs\n */\n backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: true,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: true,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n iouThreshold: 0.1,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-full.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'mb3-centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'selfie.json',\n blur: 8,\n },\n};\n\nexport { config as defaults };\n", "/*\n Human\n homepage: \n author: '\n*/\n\n// tfjs/tf-browser.ts\nexport * from \"@tensorflow/tfjs/dist/index.js\";\nexport * from \"@tensorflow/tfjs-backend-webgl/dist/index.js\";\n\n// dist/tfjs.version.js\nvar version = \"3.16.0\";\nvar version2 = \"3.16.0\";\nvar version3 = \"3.16.0\";\nvar version4 = \"3.16.0\";\nvar version5 = \"3.16.0\";\nvar version6 = \"3.16.0\";\nvar version7 = \"3.16.0\";\nvar version8 = {\n tfjs: version,\n \"tfjs-core\": version2,\n \"tfjs-data\": version3,\n \"tfjs-layers\": version4,\n \"tfjs-converter\": version5,\n \"tfjs-backend-webgl\": version6,\n \"tfjs-backend-wasm\": version7\n};\n\n// tfjs/tf-browser.ts\nimport { Tensor } from \"@tensorflow/tfjs/dist/index.js\";\nimport { GraphModel } from \"@tensorflow/tfjs-converter/dist/index\";\nexport {\n GraphModel,\n Tensor,\n version8 as version\n};\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type) as WebGLShader;\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100);\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture() as WebGLTexture;\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram as GLProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram as GLProgram;\n }\n\n const filter = {\n colorMatrix: (matrix) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const channels = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n const absMax = await Promise.all(max.map((channel) => channel.data()));\n const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type is not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n if ((tensor as Tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n } else {\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx || !fx.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n } else {\n depth = input['data'].length / input['height'] / input['width'];\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array(input['data']['buffer']);\n pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted = tf.cast(pixels, 'float32');\n const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n /** MonkeyPatch for Canvas */\n Canvas: undefined;\n /** MonkeyPatch for Image */\n Image: undefined;\n /** MonkeyPatch for ImageData */\n ImageData: undefined;\n\n constructor() {\n this.browser = typeof navigator !== 'undefined';\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if (typeof navigator !== 'undefined') {\n const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n if (raw && raw[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = navigator.userAgent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n // chrome offscreencanvas gpu memory leak\n /*\n const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n if (verChrome > 92 && verChrome < 96) {\n log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n this.offscreen = false;\n }\n */\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n }\n const c = image.canvas(100, 100);\n const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n this.webgl.supported = typeof ctx !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n // @ts-ignore getGPGPUContext only exists on WebGL backend\n const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n if (gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n }\n }\n // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n async updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n // eslint-disable-next-line global-require\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) {\n cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n }\n if (line.startsWith('flags')) {\n cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n }\n } catch { }\n */\n }\n if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n else this['cpu'] = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n cacheModels: false,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n const modelUrl = join(options.modelBasePath, modelPath || '');\n const modelPathSegments = modelUrl.split('/');\n const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n const cachedModels = await tf.io.listModels(); // list all models already in cache\n const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n let loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n // @ts-ignore private property\n if (options.debug) log('model load handler:', model.handler);\n // @ts-ignore private property\n const artifacts = await model.handler.load(); // load manifest\n model.loadSync(artifacts); // load weights\n if (options.verbose) log('load model:', model['modelUrl']);\n loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (loaded && options.cacheModels && !modelCached) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n ssrnetage: null | GraphModel | Promise = null;\n gear: null | GraphModel | Promise = null;\n blazeposedetect: null | GraphModel | Promise = null;\n blazepose: null | GraphModel | Promise = null;\n centernet: null | GraphModel | Promise = null;\n efficientpose: null | GraphModel | Promise = null;\n mobilefacenet: null | GraphModel | Promise = null;\n emotion: null | GraphModel | Promise = null;\n facedetect: null | GraphModel | Promise = null;\n faceiris: null | GraphModel | Promise = null;\n facemesh: null | GraphModel | Promise = null;\n faceres: null | GraphModel | Promise = null;\n ssrnetgender: null | GraphModel | Promise = null;\n handpose: null | GraphModel | Promise = null;\n handskeleton: null | GraphModel | Promise = null;\n handtrack: null | GraphModel | Promise = null;\n liveness: null | GraphModel | Promise = null;\n movenet: null | GraphModel | Promise = null;\n nanodet: null | GraphModel | Promise = null;\n posenet: null | GraphModel | Promise = null;\n segmentation: null | GraphModel | Promise = null;\n antispoof: null | GraphModel | Promise = null;\n}\n\nexport function reset(instance: Human): void {\n // if (instance.config.debug) log('resetting loaded models');\n for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise {\n if (env.initial) reset(instance);\n if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n }\n if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n // @ts-ignore optional model\n if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n // @ts-ignore optional model\n if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n // @ts-ignore optional model\n if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n // @ts-ignore optional model\n if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n // @ts-ignore optional model\n if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for await (const model of Object.keys(instance.models)) {\n if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n }\n}\n\nexport async function validate(instance: Human): Promise {\n interface Op { name: string, category: string, op: string }\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n for (const defined of Object.keys(instance.models)) {\n const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n if (!model) continue;\n const ops: string[] = [];\n // @ts-ignore // executor is a private method\n const executor = model?.executor;\n if (executor && executor.graph.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) log('model signature not determined:', defined);\n }\n const missing: string[] = [];\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n // log('model validation ops:', defined, ops);\n if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['gear']);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255,\n tf1: 1,\n tf2: 2,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255, 'float32');\n constants.tf1 = tf.scalar(1, 'float32');\n constants.tf2 = tf.scalar(2, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n t.enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise {\n if (!model) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = Array<[number, number]>;\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors: Array<[number, number]> = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf05);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, 0);\n } else {\n t.batch = tf.squeeze(res); // when using tfhub model\n }\n tf.dispose(res);\n t.boxes = decodeBounds(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: Array = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const rawBox = {\n startPoint: [points[0], points[1]] as Point,\n endPoint: [points[2], points[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n const squaredBox = util.squarifyBox(enlargedBox);\n boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n const anchors: Array<{ x: number, y: number }> = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const boxes = tf.stack([xMin, yMin, width, height], 1);\n return boxes;\n });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.argmax = tf.argMax(t.scores);\n const i = (await t.argmax.data())[0] as number;\n const scores = await t.scores.data();\n const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n if (scores[i] >= minScore) {\n const boxes = await t.boxes.array();\n const boxRaw: Box = boxes[i];\n const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n // console.log(box);\n detected.push({ box, boxRaw, score: scores[i] });\n }\n /*\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n const boxes = t.boxes.arraySync();\n const scores = t.scores.dataSync();\n const nms = t.nms.dataSync();\n const detected: Array = [];\n for (const i of Array.from(nms)) {\n const boxRaw: Box = boxes[i];\n const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n detected.push({ box, boxRaw, score: scores[i] });\n }\n */\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array, outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Array, outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise {\n if (env.initial) models.detector = null;\n if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n models.detector = await loadModel(config.body['detector']['modelPath']);\n const inputs = Object.values(models.detector.modelSignature['inputs']);\n inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n await detect.createAnchors();\n return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) models.landmarks = null;\n if (!models.landmarks) {\n models.landmarks = await loadModel(config.body.modelPath);\n const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models.detector) await loadDetect(config);\n if (!models.landmarks) await loadPose(config);\n return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise {\n const t: Record = {};\n if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array, outputSize: [number, number]): Array {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: Array = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: Array = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Array = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array, outputSize: [number, number]): Array {\n for (const b of boxes) {\n b.box = [\n Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n ];\n b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n }\n return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n rescaleBoxes(boxes, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n const t: Record = {};\n /*\n if (config.body['detector'] && config.body['detector']['enabled']) {\n t.detector = await prepareImage(input, 224);\n const boxes = await detectBoxes(t.detector, config, outputSize);\n }\n */\n t.landmarks = await prepareImage(input, 256); // padded and resized\n cache = await detectLandmarks(t.landmarks, config, outputSize);\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n lastTime = now();\n skipped = 0;\n }\n return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = Object.values(model.modelSignature['inputs']);\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: Array = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: Array = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore = (await max.data())[0]; // get highest score\n tf.dispose([reshaped, max]);\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n const y = (await div.data())[0];\n tf.dispose([mod, div]);\n return [x, y, newScore];\n }\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n if (!model?.inputs[0].shape) return null;\n const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = resT.squeeze();\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = squeeze.unstack(2);\n tf.dispose(squeeze);\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body?.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Array = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.emotion?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: Array<{ score: number, emotion: Emotion }> = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise {\n if (!model) return [];\n const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: Array = [];\n if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model?.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Array = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n if (!model) {\n if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n return rawCoords;\n }\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import type { Tensor } from '../tfjs/types';\n\nexport const attentionDefinitions = {\n eyeLLower: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n eyeRLower: [263, 249, 390, 373, 374, 380, 381, 382, 362], // 9\n // eslint-disable-next-line max-len\n lips: [185, 96, 90, 181, 84, 17, 314, 405, 320, 307, 409, 40, 39, 73, 37, 0, 267, 269, 270, 409, 40, 88, 178, 178, 87, 14, 268, 402, 318, 324, 409, 80, 41, 38, 87, 12, 268, 303, 318, 324, 185, 95, 80, 81, 85, 16, 315, 404, 319, 325, 409, 40, 39, 73, 72, 0, 302, 303, 270, 408, 185, 88, 88, 81, 82, 15, 316, 403, 319, 324, 409, 80, 41, 38, 87, 12, 268, 303, 318, 324],\n // eslint-disable-next-line max-len\n eyeL: [33, 7, 163, 144, 145, 153, 154, 155, 133, 246, 161, 160, 159, 158, 157, 173, 130, 25, 110, 24, 23, 22, 26, 112, 243, 247, 30, 29, 27, 28, 56, 190, 226, 31, 228, 229, 230, 231, 232, 233, 244, 113, 225, 224, 223, 222, 221, 189, 35, 124, 46, 53, 52, 65, 143, 111, 117, 118, 119, 120, 121, 128, 245, 156, 70, 63, 105, 66, 107, 55, 193],\n // eslint-disable-next-line max-len\n eyeR: [263, 249, 390, 373, 374, 380, 381, 382, 362, 466, 388, 387, 386, 385, 384, 398, 359, 255, 339, 254, 253, 252, 256, 341, 463, 467, 260, 259, 257, 258, 286, 414, 446, 261, 448, 449, 450, 451, 452, 453, 464, 342, 445, 444, 443, 442, 441, 413, 265, 353, 276, 283, 282, 295, 372, 340, 346, 347, 348, 349, 350, 357, 465, 383, 300, 293, 334, 296, 336, 285, 417],\n};\n\n// function used to determine heuristic mapping\n// values in attentionDefinitions are based on top values from 200 iterations\n/*\nconst historyRes: number[][] = [];\n\nconst getMostCommon = (arr): number => {\n const count: Record = {};\n arr.forEach((el) => {\n count[el] = (count[el] || 0) + 1;\n });\n const res: string[] = Object.keys(count).reduce((acc: string[], val, ind) => {\n if (!ind || count[val] > count[acc[0]]) return [val];\n if (count[val] === count[acc[0]]) acc.push(val);\n return acc;\n }, []);\n return parseInt(res[0]);\n};\n\nfunction replaceClosestPoint(rawCoords, newCoords) {\n const currentRes: number[] = [];\n for (let i = 0; i < newCoords.length / 2; i++) {\n let minDist = Number.MAX_VALUE;\n let minDistIdx = -1;\n const pts = rawCoords.map((r) => [r[0], r[1]]);\n for (let j = 0; j < rawCoords.length; j++) {\n const x = pts[j][0] - newCoords[2 * i + 0];\n const y = pts[j][1] - newCoords[2 * i + 1];\n const dist = (x * x) + (y * y);\n if (dist < minDist) {\n minDist = dist;\n minDistIdx = j;\n }\n }\n currentRes.push(minDistIdx);\n rawCoords[minDistIdx] = [newCoords[2 * i + 0], newCoords[2 * i + 1], rawCoords[minDistIdx][2]];\n }\n historyRes.push(currentRes);\n if (historyRes.length % 50 === 0) {\n const invertRes: number[][] = [];\n for (let i = 0; i < currentRes.length; i++) {\n const indexRes = historyRes.map((j) => j[i]);\n invertRes.push(indexRes);\n }\n const mostCommon: number[] = invertRes.map((r) => getMostCommon(r));\n console.log(mostCommon);\n }\n return rawCoords;\n}\n*/\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n irisL: results[3].dataSync() as Float32Array, // 5 x 2d // output_left_iris\n irisR: results[1].dataSync() as Float32Array, // 5 x 2d // output_right_iris\n eyeL: results[0].dataSync() as Float32Array, // 71 x 2d // output_left_eye\n eyeR: results[6].dataSync() as Float32Array, // 71 x 2d // output_right_eye\n lips: results[5].dataSync() as Float32Array, // 80 x 2d // output_lips\n // flag: results[4], // already processed in parent // conv_faceflag\n // mesh: results[2], // already have it in rawCoords // output_mesh_identity\n };\n\n // rawCoords = replaceClosestPoint(rawCoords, t.eyeL);\n // rawCoords = replaceClosestPoint(rawCoords, t.eyeR);\n // rawCoords = replaceClosestPoint(rawCoords, t.lips);\n\n // augment iris: adds additional 5 keypoints per eye\n const irisRDepth = attentionDefinitions.eyeRLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeRLower.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n const irisLDepth = attentionDefinitions.eyeLLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeLLower.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[attentionDefinitions.eyeL[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[attentionDefinitions.eyeL[i]][2]];\n // for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords.push([t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], 0]);\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[attentionDefinitions.eyeR[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[attentionDefinitions.eyeR[i]][2]];\n // for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords.push([t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], 0]);\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[attentionDefinitions.lips[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[attentionDefinitions.lips[i]][2]];\n // for (let i = 0; i < t.lips.length / 2; i++) rawCoords.push([t.lips[2 * i + 0], t.lips[2 * i + 1], 0]);\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array, confidence: number };\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: Array = [];\n const newCache: Array = [];\n let id = 0;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config?.filter?.equalization) {\n const equilized = await histogramEqualization(face.tensor as Tensor);\n tf.dispose(face.tensor);\n face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n ]);\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n const results = model.execute(face.tensor as Tensor) as Array;\n const confidence = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const contourCoords = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const faceConfidence = await confidence.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n } else {\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n newCache.push(calculatedBox);\n }\n tf.dispose([...results, coordsReshaped]);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention?.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n age: number,\n gender: Gender,\n genderScore: number,\n descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n const norm = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj = {\n age: 0,\n gender: 'unknown',\n genderScore: 0,\n descriptor: [],\n };\n\n if (config.face.description?.enabled) {\n const enhanced = enhance(image);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description?.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n const age = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: Array = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: Array = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 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0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 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0.65625, y: 0.90625 },\n { x: 0.71875, y: 0.90625 },\n { x: 0.71875, y: 0.90625 },\n { x: 0.78125, y: 0.90625 },\n { x: 0.78125, y: 0.90625 },\n { x: 0.84375, y: 0.90625 },\n { x: 0.84375, y: 0.90625 },\n { x: 0.90625, y: 0.90625 },\n { x: 0.90625, y: 0.90625 },\n { x: 0.96875, y: 0.90625 },\n { x: 0.96875, y: 0.90625 },\n { x: 0.03125, y: 0.96875 },\n { x: 0.03125, y: 0.96875 },\n { x: 0.09375, y: 0.96875 },\n { x: 0.09375, y: 0.96875 },\n { x: 0.15625, y: 0.96875 },\n { x: 0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, 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y: 0.0625 },\n { x: 0.5625, y: 0.0625 },\n { x: 0.5625, y: 0.0625 },\n { x: 0.5625, y: 0.0625 },\n { x: 0.5625, y: 0.0625 },\n { x: 0.6875, y: 0.0625 },\n { x: 0.6875, y: 0.0625 },\n { x: 0.6875, y: 0.0625 },\n { x: 0.6875, y: 0.0625 },\n { x: 0.6875, y: 0.0625 },\n { x: 0.6875, y: 0.0625 },\n { x: 0.8125, y: 0.0625 },\n { x: 0.8125, y: 0.0625 },\n { x: 0.8125, y: 0.0625 },\n { x: 0.8125, y: 0.0625 },\n { x: 0.8125, y: 0.0625 },\n { x: 0.8125, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 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0.3125 },\n { x: 0.8125, y: 0.3125 },\n { x: 0.8125, y: 0.3125 },\n { x: 0.8125, y: 0.3125 },\n { x: 0.8125, y: 0.3125 },\n { x: 0.8125, y: 0.3125 },\n { x: 0.9375, y: 0.3125 },\n { x: 0.9375, y: 0.3125 },\n { x: 0.9375, y: 0.3125 },\n { x: 0.9375, y: 0.3125 },\n { x: 0.9375, y: 0.3125 },\n { x: 0.9375, y: 0.3125 },\n { x: 0.0625, y: 0.4375 },\n { x: 0.0625, y: 0.4375 },\n { x: 0.0625, y: 0.4375 },\n { x: 0.0625, y: 0.4375 },\n { x: 0.0625, y: 0.4375 },\n { x: 0.0625, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.9375, y: 0.4375 },\n { x: 0.9375, y: 0.4375 },\n { x: 0.9375, y: 0.4375 },\n { x: 0.9375, y: 0.4375 },\n { x: 0.9375, y: 0.4375 },\n { x: 0.9375, y: 0.4375 },\n { x: 0.0625, y: 0.5625 },\n { x: 0.0625, y: 0.5625 },\n { x: 0.0625, y: 0.5625 },\n { x: 0.0625, y: 0.5625 },\n { x: 0.0625, y: 0.5625 },\n { x: 0.0625, y: 0.5625 },\n { x: 0.1875, y: 0.5625 },\n { x: 0.1875, y: 0.5625 },\n { x: 0.1875, y: 0.5625 },\n { x: 0.1875, y: 0.5625 },\n { x: 0.1875, y: 0.5625 },\n { x: 0.1875, y: 0.5625 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0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index]);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as Array;\n const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n if (config.skipAllowed) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array;\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: Array = [];\n const slopesYZ: Array = [];\n const fingerCurls: Array = [];\n const fingerDirections: Array = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: Array = [];\n const slopeAtYZ: Array = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: Array<{ name: string, confidence: number }> = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise {\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: Array = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Array;\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n 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)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (env.initial) {\n handDetectorModel = null;\n handPoseModel = null;\n }\n if (!handDetectorModel || !handPoseModel) {\n [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n ]);\n } else {\n if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n }\n const handDetector = new handdetector.HandDetector(handDetectorModel);\n handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: Array,\n hands: Array;\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = Object.values(models[0].modelSignature['inputs']);\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = Object.values(models[1].modelSignature['inputs']);\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Array = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise {\n if (!model) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "export const kpt: Array = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: Array = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: Array,\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: Array): Array {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Array, // unused\n bodies: Array;\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: Array = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: Array = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Array = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n const bodies: Array = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (totalScore > config.body.minConfidence) {\n const keypoints: Array = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Array = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and cached boxes\n /*\n cache.bodies = []; // reset bodies result\n if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n t.cast = tf.cast(t.crop, 'int32');\n // t.input = prepareImage(input);\n t.res = model?.execute(t.cast) as Tensor;\n const res = await t.res.array();\n const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n cache.bodies = cache.bodies.concat(newBodies);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n }\n if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n t.input = prepareImage(input);\n t.res = model?.execute(t.input) as Tensor;\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n cache.boxes.length = 0; // reset cache\n for (let i = 0; i < cache.bodies.length; i++) {\n if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n const cropBox = box.crop(scaledBox);\n cache.boxes.push(cropBox);\n }\n }\n */\n\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? await parseSinglePose(res, config, input)\n : await parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = Object.values(model.modelSignature['inputs']);\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: Array = [];\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n });\n }\n // deallocate tensors\n res.forEach((t) => tf.dispose(t));\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: Array = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = await nms.data();\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise {\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const norm = tf.div(resize, constants.tf255);\n const transpose = norm.transpose([0, 3, 1, 2]);\n tf.dispose(norm);\n tf.dispose(resize);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transpose);\n lastTime = now();\n tf.dispose(transpose);\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: Array; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: Array<{ keypoints, box: Box, score: number }> = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Array = model.execute(normalized, poseNetOutputs) as Array;\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n if (busy) return { data: [], canvas: null, alpha: null };\n busy = true;\n if (!model) await load(config);\n const inputImage = await image.process(input, config);\n const width = inputImage.tensor?.shape[2] || 0;\n const height = inputImage.tensor?.shape[1] || 0;\n if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n const t: Record = {};\n\n t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n tf.dispose(inputImage.tensor);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n\n t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n if (t.squeeze.shape[2] === 2) {\n t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n t.expand = tf.expandDims(t.fg, 2);\n t.pad = tf.expandDims(t.expand, 0);\n t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n // otherwise run softmax after unstack and use standard resize\n // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n t.data = tf.squeeze(t.crop, 0);\n } else {\n t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n }\n const data = Array.from(await t.data.data()) as number[];\n\n if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n if (config.debug) log('canvas support missing');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n }\n\n const alphaCanvas = image.canvas(width, height);\n // @ts-ignore browser is not defined in tfjs-node\n if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n const compositeCanvas = image.canvas(width, height);\n const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n compositeCtx.drawImage(alphaCanvas, 0, 0);\n compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n compositeCtx.filter = 'none'; // reset css filter\n const compositeData = compositeCtx.getImageData(0, 0, width, height);\n for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n compositeCtx.putImageData(compositeData, 0, 0);\n\n let mergedCanvas: AnyCanvas | null = null;\n if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n mergedCanvas = image.canvas(width, height);\n const bgImage = await image.process(background, config);\n tf.dispose(bgImage.tensor);\n const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n ctxMerge.drawImage(compositeCanvas, 0, 0);\n }\n\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n busy = false;\n // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null,\n gl: null,\n extensions: [],\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions() as string[];\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n log('error: humangl backend invalid context');\n models.reset(instance);\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = await image.canvas(100, 100);\n } catch (err) {\n log('error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', async (e) => {\n log('error: humangl:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n // log('resetting humangl backend');\n // env.initial = true;\n // models.reset(instance);\n // await tf.removeBackend(config.name);\n // await register(instance); // re-register\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('error: humangl context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('error: humangl context create:', e);\n });\n }\n } catch (err) {\n log('error: cannot get WebGL context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('error: cannot set WebGL context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('error: cannot register WebGL backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('error: cannot update WebGL backend registration:', err);\n return;\n }\n const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n if (current) {\n log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n } else {\n log('error: no current gl context:', current, config.gl);\n return;\n }\n try {\n tf.ENV.set('WEBGL_VERSION', 2);\n } catch (err) {\n log('error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n log('backend registered:', config.name);\n }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('floormod');\n }\n}\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n // force browser vs node backend\n if (env.browser && instance.config.backend === 'tensorflow') {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'humangl';\n }\n if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'humangl';\n } else {\n const adapter = await navigator['gpu'].requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n }\n }\n\n // check available backends\n if (instance.config.backend === 'humangl') await humangl.register(instance);\n const available = Object.keys(tf.engine().registryFactory);\n if (instance.config.debug) log('available backends:', available);\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n constants.init();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl') {\n tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n tf.ENV.set('WEBGL_CPU_FORWARD', true);\n // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n if (tf.backend().getGPGPUContext) {\n const gl = await tf.backend().getGPGPUContext().gl;\n if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n }\n\n // wait for ready\n tf.enableProdMode();\n await tf.ready();\n\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend();\n\n await env.updateBackend(); // update env on backend init\n registerCustomOps();\n // await env.updateBackend(); // update env on backend init\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array, config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output);\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result || !result.performance || !result || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d');\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => {\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n const color = `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n return color;\n};\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2], localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n shadowColor: 'black',\n alpha: 0.5,\n font: 'small-caps 16px \"Segoe UI\"',\n lineHeight: 18,\n lineWidth: 4,\n pointSize: 2,\n roundRect: 8,\n drawPoints: false,\n drawLabels: true,\n drawBoxes: true,\n drawAttention: true,\n drawGestures: true,\n drawPolygons: true,\n drawGaze: true,\n fillPolygons: false,\n useDepth: true,\n useCurves: false,\n};\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } from './primitives';\nimport { options } from './options';\nimport { attentionDefinitions } from '../face/attention';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet opt: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (opt.drawLabels) {\n // silly hack since fillText does not suport new line\n const labels:string[] = [];\n labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n if (f.age) labels.push(`age: ${f.age || ''}`);\n if (f.iris) labels.push(`distance: ${f.iris}`);\n if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n labels.push(emotion.join(' '));\n }\n if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n }\n if (labels.length === 0) labels.push('face');\n ctx.fillStyle = opt.color;\n for (let i = labels.length - 1; i >= 0; i--) {\n const x = Math.max(f.box[0], 0);\n const y = i * opt.lineHeight + f.box[1];\n if (opt.shadowColor && opt.shadowColor !== '') {\n ctx.fillStyle = opt.shadowColor;\n ctx.fillText(labels[i], x + 5, y + 16);\n }\n ctx.fillStyle = opt.labelColor;\n ctx.fillText(labels[i], x + 4, y + 15);\n }\n }\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (opt.fillPolygons) {\n ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n ctx.fill();\n }\n }\n if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (opt.fillPolygons) {\n ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (opt.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (opt.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (opt.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, opt);\n }\n drawIrisElipse(f, ctx);\n }\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (opt.drawPoints && f.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt);\n if (opt.drawAttention) {\n if (attentionDefinitions.lips.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, opt);\n if (attentionDefinitions.eyeL.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n if (attentionDefinitions.eyeR.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (opt.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);\n }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n opt = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.font = opt.font;\n ctx.strokeStyle = opt.color;\n ctx.fillStyle = opt.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n ctx.fillStyle = colorDepth(pt.position[2], localOptions);\n ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels) {\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations) {\n const addHandLabel = (part: Array, title: string) => {\n if (!part || part.length === 0 || !part[0]) return;\n const z = part[part.length - 1][2] || -256;\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n };\n ctx.font = localOptions.font;\n addHandLabel(h.annotations['index'], 'index');\n addHandLabel(h.annotations['middle'], 'middle');\n addHandLabel(h.annotations['ring'], 'ring');\n addHandLabel(h.annotations['pinky'], 'pinky');\n addHandLabel(h.annotations['thumb'], 'thumb');\n addHandLabel(h.annotations['palm'], 'palm');\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Array) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations['index']);\n addHandLine(h.annotations['middle']);\n addHandLine(h.annotations['ring']);\n addHandLine(h.annotations['pinky']);\n addHandLine(h.annotations['thumb']);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `${h.label} ${Math.round(100 * h.score)}%`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array, drawOptions?: Partial) {\n const localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures) {\n const ctx = getCanvasContext(inCanvas);\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n let where: unknown[] = []; // what&where is a record\n let what: unknown[] = []; // what&where is a record\n [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n const label = `${where[0]} ${who}: ${what[1]}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n i += 1;\n }\n }\n }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: Array<{ x: number, y: number }> = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n tf.dispose(buffer);\n return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n thetaZ = Math.asin(r10);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (isNaN(thetaX)) thetaX = 0;\n if (isNaN(thetaY)) thetaY = 0;\n if (isNaN(thetaZ)) thetaZ = 0;\n return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n };\n\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const z_axis = normalize(crossVectors(x_axis, y_axis));\n // adjust x_axis to make sure that all axes are perpendicular to each other\n x_axis = crossVectors(y_axis, z_axis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n x_axis[0], x_axis[1], x_axis[2],\n y_axis[0], y_axis[1], y_axis[2],\n z_axis[0], z_axis[1], z_axis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise => {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: Array = [];\n instance.state = 'run:face';\n\n const faces = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n faces[i].tensor = masked as Tensor;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!instance.config.face.iris?.enabled) {\n // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n }\n const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n : 0; // note: average human iris size is 11.7mm\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array;\n if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: Array<{ name: string, position: Point }> = [];\n if (res[i]['annotations']) {\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i]['keypoints']) {\n const poses = fingerPose.match(res[i]['keypoints']);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n // curve fitted: buffer = 8 - ln(delay)\n // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n // - at 50ms delay buffer = ~4.1 => 28% towards live data\n // - at 250ms delay buffer = ~2.5 => 40% towards live data\n // - at 500ms delay buffer = ~1.8 => 55% towards live data\n // - at 750ms delay buffer = ~1.4 => 71% towards live data\n // - at 1sec delay buffer = 1 which means live data is used\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part as BodyLandmark,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Array = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n }\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // just copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = distance(descriptor, descriptors[i], options);\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array, bodies: Array, hands: Array, gestures: Array, shape: Array | undefined): Array {\n let id = 0;\n const persons: Array = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face?.box);\n extractXY(person.body?.box);\n extractXY(person.hands?.left?.box);\n extractXY(person.hands?.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else return;\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d');\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas);\n const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res;\n if ('node' in tf) {\n // @ts-ignore tf.node may be undefined\n const data = tf['node'].decodeJpeg(img);\n const expanded = data.expandDims(0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(allModels: Models) {\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !webGLBackend.checkCompileCompletion)) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(allModels).filter(([key, val]) => (key !== null && val !== null))) {\n const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype = (model.inputs && model.inputs[0] && model.inputs[0].dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n // const res = await model.executeAsync(tensor); // fails with current tfjs\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend.checkCompileCompletionAsync();\n webGLBackend.getUniformLocations();\n log('compile pass models:', compiledModels);\n log('compile pass kernels:', kernels.length);\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n }\n return new Promise(async (resolve) => {\n await runCompile(instance.models);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './draw/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n /** Currently loaded models\n * @internal\n * {@link Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n /** WebGL debug info */\n gl: Record;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n this.env = env;\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models();\n // reexport draw methods\n this.draw = {\n options: draw.options as DrawOptions,\n canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n face: (output: AnyCanvas, result: FaceResult[], options?: Partial) => draw.face(output, result, options),\n body: (output: AnyCanvas, result: BodyResult[], options?: Partial) => draw.body(output, result, options),\n hand: (output: AnyCanvas, result: HandResult[], options?: Partial) => draw.hand(output, result, options),\n gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial) => draw.gesture(output, result, options),\n object: (output: AnyCanvas, result: ObjectResult[], options?: Partial) => draw.object(output, result, options),\n person: (output: AnyCanvas, result: PersonResult[], options?: Partial) => draw.person(output, result, options),\n all: (output: AnyCanvas, result: Result, options?: Partial) => draw.all(output, result, options),\n };\n this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n // export access to image processing\n // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // set gl info\n this.gl = humangl.config;\n // include platform info\n this.emit('create');\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n return validate(defaults, userConfig || this.config);\n }\n\n /** Exports face matching methods {@link match#similarity} */\n public similarity = match.similarity;\n /** Exports face matching methods {@link match#distance} */\n public distance = match.distance;\n /** Exports face matching methods {@link match#match} */\n public match = match.match;\n\n /** Utility wrapper for performance.now() */\n now(): number {\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = true) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns processed canvas with body segmentation\n * - Segmentation is not triggered as part of detect process\n * @param input - {@link Input}\n * @param background - {@link Input}\n * - Optional parameter background is used to fill the background with specific input\n * Returns:\n * - `data` as raw data array with per-pixel segmentation values\n * - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n * - `alpha` as grayscale canvas that represents segmentation alpha values\n */\n async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n return segmentation.process(input, background, this.config);\n }\n\n /** Enhance method performs additional enhacements to face image previously detected for futher processing\n *\n * @param input - Tensor as provided in human.result.face[n].tensor\n * @returns Tensor\n */\n // eslint-disable-next-line class-methods-use-this\n enhance(input: Tensor): Tensor | null {\n return faceres.enhance(input);\n }\n\n /** Compare two input tensors for pixel simmilarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n }\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (this.config.debug) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n if (!await backend.check(this)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('environment:', this.env);\n if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n }\n }\n\n await models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n await models.validate(this); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config) as Result;\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise> {\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n else kernels[kernel.name] = kernel.kernelTimeMs;\n }\n const kernelArr: Array<{ name: string, ms: number }> = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n kernelArr.sort((a, b) => b.ms - a.ms); // sort\n kernelArr.length = 20; // crop\n const res: Record = {};\n for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n return res;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n }\n\n const timeStart = now();\n\n // configure backend if needed\n await backend.check(this);\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames as number)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process?.tensor?.shape || [];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], + "mappings": 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"names": [] } diff --git a/dist/human.esm.js b/dist/human.esm.js index 7d98d3bf..8cad14fd 100644 --- a/dist/human.esm.js +++ b/dist/human.esm.js @@ -6,9 +6,9 @@ var __defProp = Object.defineProperty; var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; -var __export = (target, all2) => { - for (var name in all2) - __defProp(target, name, { get: all2[name], enumerable: true }); +var __export = (target, all6) => { + for (var name in all6) + __defProp(target, name, { get: all6[name], enumerable: true }); }; var __publicField = (obj, key, value) => { __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); @@ -35,10 +35,10 @@ var __privateSet = (obj, member, value, setter) => { // src/util/util.ts function log(...msg) { - const dt2 = new Date(); - const ts2 = `${dt2.getHours().toString().padStart(2, "0")}:${dt2.getMinutes().toString().padStart(2, "0")}:${dt2.getSeconds().toString().padStart(2, "0")}.${dt2.getMilliseconds().toString().padStart(3, "0")}`; + const dt = new Date(); + const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; if (msg) - console.log(ts2, "Human:", ...msg); + console.log(ts, "Human:", ...msg); } function join(folder, file) { const separator = folder.endsWith("/") ? "" : "/"; @@ -219,3500 +219,5788 @@ var config = { // dist/tfjs.esm.js var tfjs_esm_exports = {}; __export(tfjs_esm_exports, { - Abs: () => uo, - Acos: () => rl, - Acosh: () => al, - AdadeltaOptimizer: () => gb, - AdagradOptimizer: () => bb, - AdamOptimizer: () => yb, - AdamaxOptimizer: () => vb, - Add: () => Sr, - AddN: () => Ia, - All: () => il, - Any: () => ol, - ArgMax: () => Sa, - ArgMin: () => ul, - Asin: () => ll, - Asinh: () => cl, - Atan: () => dl, - Atan2: () => hl, - Atanh: () => pl, - AvgPool: () => Ca, - AvgPool3D: () => Kd, - AvgPool3DGrad: () => lg, - AvgPoolGrad: () => ug, - BackendWasm: () => Pde, - BatchMatMul: () => Na, - BatchToSpaceND: () => lo, - Bincount: () => cg, - BroadcastArgs: () => dg, - BroadcastTo: () => P$, - Callback: () => OW, - CallbackList: () => QL, - Cast: () => Ta, - Ceil: () => $a, - ClipByValue: () => Cr, - Complex: () => Xd, - ComplexAbs: () => Yd, - Concat: () => co, - Conv2D: () => _a, - Conv2DBackpropFilter: () => pg, - Conv2DBackpropInput: () => Aa, - Conv3D: () => Qd, - Conv3DBackpropFilterV2: () => hg, - Conv3DBackpropInputV2: () => fg, - Cos: () => Ea, - Cosh: () => Ra, - CropAndResize: () => ho, - Cumprod: () => po, - Cumsum: () => Da, - CustomCallback: () => eB, - DataStorage: () => Hd, - DenseBincount: () => mg, - DepthToSpace: () => fo, - DepthwiseConv2dNative: () => Fa, - DepthwiseConv2dNativeBackpropFilter: () => gg, - DepthwiseConv2dNativeBackpropInput: () => bg, - Diag: () => yg, - Dilation2D: () => Zd, - Dilation2DBackpropFilter: () => Zf, - Dilation2DBackpropInput: () => Qf, - ENV: () => sk, - EarlyStopping: () => PW, - Einsum: () => Jd, - Elu: () => Pa, - EluGrad: () => vg, - Environment: () => A$, - Equal: () => mo, - Erf: () => fl, - Exp: () => za, - ExpandDims: () => go, - Expm1: () => bo, - FFT: () => xg, - Fill: () => ml, - FlipLeftRight: () => yo, - Floor: () => Ma, - FloorDiv: () => La, - FromPixels: () => fd, - FusedBatchNorm: () => Ba, - FusedConv2D: () => ia, - FusedDepthwiseConv2D: () => oa, - GPGPUContext: () => Xf, - GatherNd: () => xo, - GatherV2: () => vo, - GraphModel: () => V4, - Greater: () => wo, - GreaterEqual: () => Va, - History: () => JL, - IFFT: () => wg, - Identity: () => Wa, - Imag: () => ep, - InputSpec: () => Dt, - IsFinite: () => gl, - IsInf: () => bl, - IsNan: () => yl, - KernelBackend: () => sl, - LRN: () => np, - LRNGrad: () => Ig, - LayerVariable: () => pz, - LayersModel: () => hr, - LeakyRelu: () => Ua, - Less: () => ko, - LessEqual: () => Io, - LinSpace: () => kg, - Log: () => Ga, - Log1p: () => vl, - LogSoftmax: () => z$, - LogicalAnd: () => So, - LogicalNot: () => xl, - LogicalOr: () => tp, - MathBackendCPU: () => G0, - MathBackendWebGL: () => U1, - Max: () => Ha, - MaxPool: () => ja, - MaxPool3D: () => sp, - MaxPool3DGrad: () => Cg, - MaxPoolGrad: () => Sg, - MaxPoolWithArgmax: () => Ng, - Maximum: () => qa, - Mean: () => Ka, - Min: () => Xa, - Minimum: () => Ya, - MirrorPad: () => Qa, - Mod: () => wl, - MomentumOptimizer: () => xb, - Multinomial: () => Tg, - Multiply: () => Za, - Neg: () => Co, - NonMaxSuppressionV3: () => To, - NonMaxSuppressionV4: () => kl, - NonMaxSuppressionV5: () => $o, - NotEqual: () => No, - OP_SCOPE_SUFFIX: () => m_, - OneHot: () => Ao, - OnesLike: () => _o, - Optimizer: () => Ar, - OptimizerConstructors: () => Ur, - Pack: () => Eo, - PadV2: () => Ja, - Pool: () => Wde, - Pow: () => ei, - Prelu: () => ti, - Prod: () => ni, - RMSPropOptimizer: () => wb, - RNN: () => Er, - Range: () => Il, - Rank: () => i_, - Real: () => rp, - RealDiv: () => Oa, - Reciprocal: () => Sl, - Reduction: () => pO, - Relu: () => si, - Relu6: () => ai, - Reshape: () => Ro, - ResizeBilinear: () => ri, - ResizeBilinearGrad: () => _g, - ResizeNearestNeighbor: () => Cl, - ResizeNearestNeighborGrad: () => $g, - Reverse: () => Do, - RotateWithOffset: () => jo, - Round: () => Fo, - Rsqrt: () => ii, - SGDOptimizer: () => Cp, - ScatterNd: () => Oo, - Select: () => Po, - Selu: () => Nl, - Sequential: () => Hb, - Sigmoid: () => ui, - Sign: () => Tl, - Sin: () => oi, - Sinh: () => Mo, - Slice: () => zo, - Softmax: () => di, - Softplus: () => $l, - SpaceToBatchND: () => Lo, - SparseFillEmptyRows: () => ap, - SparseReshape: () => _l, - SparseSegmentMean: () => ip, - SparseSegmentSum: () => op, - SparseToDense: () => up, - SplitV: () => Bo, - Sqrt: () => li, - Square: () => Al, - SquaredDifference: () => pi, - Step: () => gi, - StridedSlice: () => Vo, - StringNGrams: () => lp, - StringSplit: () => Ag, - StringToHashBucketFast: () => Eg, - Sub: () => hi, - Sum: () => ci, - SymbolicTensor: () => $s, - Tan: () => Wo, - Tanh: () => fi, - Tensor: () => et, - TensorBuffer: () => Vt, - Tile: () => Nr, - TopK: () => Uo, - Transform: () => Go, - Transpose: () => mi, - Unique: () => Rg, - Unpack: () => Ho, - UnsortedSegmentSum: () => cp, - Variable: () => gd, - ZerosLike: () => qo, - _FusedMatMul: () => aa, - abs: () => Mt, - acos: () => YA, - acosh: () => ZA, - add: () => ie, - addN: () => eE, - all: () => Yk, - any: () => hm, - argMax: () => qu, - argMin: () => aE, - asin: () => oE, - asinh: () => lE, - atan: () => dE, - atan2: () => hE, - atanh: () => mE, - avgPool: () => qg, - avgPool3d: () => eI, - backend: () => UA, - backend_util: () => S, - basicLSTMCell: () => ipe, - batchNorm: () => Ku, - batchNorm2d: () => FE, - batchNorm3d: () => PE, - batchNorm4d: () => ME, - batchToSpaceND: () => jg, - bincount: () => tI, - booleanMaskAsync: () => Fpe, - broadcastArgs: () => VE, - broadcastTo: () => nd, - broadcast_util: () => Ko, - browser: () => Ak, - buffer: () => De, - callbacks: () => Gpe, - cast: () => ce, - ceil: () => GE, - clipByValue: () => Vn, - clone: () => cr, - complex: () => ua, - concat: () => Ft, - concat1d: () => jE, - concat2d: () => XE, - concat3d: () => QE, - concat4d: () => JE, - constraints: () => $L, - conv1d: () => nI, - conv2d: () => da, - conv2dTranspose: () => sI, - conv3d: () => rI, - conv3dTranspose: () => oR, - copyRegisteredKernels: () => Hde, - cos: () => Xg, - cosh: () => iI, - cosineWindow: () => EI, - cumprod: () => mm, - cumsum: () => oI, - customGrad: () => js, - data: () => W4, - denseBincount: () => hR, - deprecationWarn: () => Kk, - depthToSpace: () => mR, - depthwiseConv2d: () => mp, - deregisterOp: () => qpe, - device_util: () => hp, - diag: () => ope, - dilation2d: () => vR, - disableDeprecationWarnings: () => Xde, - dispose: () => Re, - disposeVariables: () => Yde, - div: () => xe, - divNoNan: () => SR, - dot: () => upe, - dropout: () => oF, - einsum: () => TR, - elu: () => gp, - enableDebugMode: () => Kde, - enableProdMode: () => jde, - enclosingPowerOfTwo: () => uF, - engine: () => Ss, - env: () => X, - equal: () => Xn, - erf: () => AR, - exp: () => Yn, - expandDims: () => Pn, - expm1: () => FR, - eye: () => uI, - fft: () => db, - fill: () => Pl, - findBackend: () => spe, - findBackendFactory: () => rpe, - floor: () => bp, - floorDiv: () => Xk, - forceHalfFloat: () => e8, - fused: () => fa, - gather: () => Xu, - gatherND: () => rF, - gather_util: () => Rk, - getBackend: () => tpe, - getGradient: () => rx, - getKernel: () => Jf, - getKernelsForBackend: () => em, - getThreadsCount: () => nhe, - gpgpu_util: () => WK, - grad: () => dpe, - grads: () => ppe, - greater: () => Un, - greaterEqual: () => Xo, - ifft: () => Id, - imag: () => Yg, - image: () => jn, - inTopKAsync: () => Ppe, - initializers: () => DL, - input: () => HB, - io: () => En, - irfft: () => SI, - isFinite: () => lpe, - isInf: () => cpe, - isNaN: () => HR, - keep: () => Ht, - kernel_impls: () => xs, - layers: () => XL, - leakyRelu: () => Qg, - less: () => lI, - lessEqual: () => Yo, - linalg: () => qO, - linspace: () => XR, - loadGraphModel: () => jpe, - loadLayersModel: () => Wpe, - localResponseNormalization: () => QR, - log: () => Qn, - log1p: () => Zg, - logSigmoid: () => mpe, - logSoftmax: () => cI, - logSumExp: () => dD, - logicalAnd: () => Ds, - logicalNot: () => tb, - logicalOr: () => fI, - logicalXor: () => gpe, - losses: () => Lpe, - matMul: () => We, - math: () => pA, - max: () => As, - maxPool: () => nb, - maxPool3d: () => mI, - maxPoolWithArgmax: () => vD, - maximum: () => _r, - mean: () => St, - memory: () => pm, - meshgrid: () => bpe, - metrics: () => fW, - min: () => gm, - minimum: () => vp, - mirrorPad: () => CD, - mod: () => TD, - model: () => Bpe, - models: () => AW, - moments: () => sb, - movingAverage: () => Ope, - mul: () => V, - multiRNNCell: () => ype, - multinomial: () => RD, - neg: () => kt, - nextFrame: () => LI, - norm: () => _I, - notEqual: () => Yu, - oneHot: () => vd, - ones: () => Mn, - onesLike: () => Zn, - op: () => L, - outerProduct: () => vpe, - pad: () => bi, - pad1d: () => xpe, - pad2d: () => wpe, - pad3d: () => kpe, - pad4d: () => Ipe, - pool: () => Spe, - pow: () => ha, - prelu: () => ab, - print: () => X_, - prod: () => gI, - profile: () => Qde, - rand: () => Cpe, - randomGamma: () => Npe, - randomNormal: () => JD, - randomUniform: () => Ml, - range: () => Qu, - ready: () => epe, - real: () => wd, - reciprocal: () => s3, - registerBackend: () => fp, - registerCallbackConstructor: () => Upe, - registerGradient: () => L$, - registerKernel: () => El, - registerOp: () => Hpe, - regularizers: () => EW, - relu: () => Ys, - relu6: () => bI, - removeBackend: () => npe, - reshape: () => G, - reverse: () => Jn, - reverse1d: () => Tpe, - reverse2d: () => $pe, - reverse3d: () => _pe, - reverse4d: () => Ape, - rfft: () => pb, - round: () => yI, - rsqrt: () => vI, - scalar: () => we, - scatterND: () => eF, - scatter_util: () => Fk, - selu: () => xI, - separableConv2d: () => m3, - sequential: () => Vpe, - serialization: () => ae, - setBackend: () => Jde, - setPlatform: () => ape, - setThreadsCount: () => the, - setWasmPath: () => Jpe, - setWasmPaths: () => ehe, - setWebGLContext: () => F5, - setdiff1dAsync: () => b3, - shared: () => ev, - sigmoid: () => qs, - sign: () => v3, - signal: () => Mpe, - sin: () => wI, - sinh: () => kI, - slice: () => qe, - slice1d: () => ub, - slice2d: () => II, - slice3d: () => lb, - slice4d: () => kd, - slice_util: () => wt, - softmax: () => cb, - softplus: () => zl, - spaceToBatchND: () => rb, - sparse: () => Wc, - sparseToDense: () => AI, - spectral: () => zpe, - split: () => Bn, - sqrt: () => dn, - square: () => ct, - squaredDifference: () => CI, - squeeze: () => gr, - stack: () => es, - step: () => xp, - stridedSlice: () => M3, - string: () => Bf, - sub: () => ge, - sum: () => ve, - sumOutType: () => pp, - tan: () => B3, - tanh: () => ju, - tensor: () => fs, - tensor1d: () => Zt, - tensor2d: () => Yi, - tensor3d: () => yA, - tensor4d: () => Epe, - tensor5d: () => Rpe, - tensor6d: () => Dpe, - tensor_util: () => _s, - test_util: () => OA, - tidy: () => j, - tile: () => ps, - time: () => Zde, - topk: () => W3, - train: () => Mi, - transpose: () => Ge, - truncatedNormal: () => hb, - unique: () => mx, - unregisterGradient: () => Gde, - unregisterKernel: () => Ude, - unsortedSegmentSum: () => q3, - unstack: () => Fs, - upcastType: () => cn, - util: () => w, - valueAndGrad: () => hpe, - valueAndGrads: () => fpe, - variable: () => K3, - variableGrads: () => eD, - version: () => rhe, - version_converter: () => Kpe, - version_core: () => qde, - version_cpu: () => Xpe, - version_layers: () => hS, - version_wasm: () => she, - version_webgl: () => Ype, - webgl: () => Qpe, - webgl_util: () => D5, - webgpu: () => yoe, - where: () => vn, - whereAsync: () => TI, - zeros: () => $t, - zerosLike: () => je + Abs: () => Abs, + Acos: () => Acos, + Acosh: () => Acosh, + AdadeltaOptimizer: () => AdadeltaOptimizer, + AdagradOptimizer: () => AdagradOptimizer, + AdamOptimizer: () => AdamOptimizer, + AdamaxOptimizer: () => AdamaxOptimizer, + Add: () => Add, + AddN: () => AddN, + All: () => All, + Any: () => Any, + ArgMax: () => ArgMax, + ArgMin: () => ArgMin, + Asin: () => Asin, + Asinh: () => Asinh, + Atan: () => Atan, + Atan2: () => Atan2, + Atanh: () => Atanh, + AvgPool: () => AvgPool, + AvgPool3D: () => AvgPool3D, + AvgPool3DGrad: () => AvgPool3DGrad, + AvgPoolGrad: () => AvgPoolGrad, + BackendWasm: () => BackendWasm, + BatchMatMul: () => BatchMatMul, + BatchToSpaceND: () => BatchToSpaceND, + Bincount: () => Bincount, + BroadcastArgs: () => BroadcastArgs, + BroadcastTo: () => BroadcastTo, + Callback: () => Callback, + CallbackList: () => CallbackList, + Cast: () => Cast, + Ceil: () => Ceil, + ClipByValue: () => ClipByValue, + Complex: () => Complex, + ComplexAbs: () => ComplexAbs, + Concat: () => Concat, + Conv2D: () => Conv2D, + Conv2DBackpropFilter: () => Conv2DBackpropFilter, + Conv2DBackpropInput: () => Conv2DBackpropInput, + Conv3D: () => Conv3D, + Conv3DBackpropFilterV2: () => Conv3DBackpropFilterV2, + Conv3DBackpropInputV2: () => Conv3DBackpropInputV2, + Cos: () => Cos, + Cosh: () => Cosh, + CropAndResize: () => CropAndResize, + Cumprod: () => Cumprod, + Cumsum: () => Cumsum, + CustomCallback: () => CustomCallback, + DataStorage: () => DataStorage, + DenseBincount: () => DenseBincount, + DepthToSpace: () => DepthToSpace, + DepthwiseConv2dNative: () => DepthwiseConv2dNative, + DepthwiseConv2dNativeBackpropFilter: () => DepthwiseConv2dNativeBackpropFilter, + DepthwiseConv2dNativeBackpropInput: () => DepthwiseConv2dNativeBackpropInput, + Diag: () => Diag, + Dilation2D: () => Dilation2D, + Dilation2DBackpropFilter: () => Dilation2DBackpropFilter, + Dilation2DBackpropInput: () => Dilation2DBackpropInput, + ENV: () => ENV, + EarlyStopping: () => EarlyStopping, + Einsum: () => Einsum, + Elu: () => Elu, + EluGrad: () => EluGrad, + Environment: () => Environment, + Equal: () => Equal, + Erf: () => Erf, + Exp: () => Exp, + ExpandDims: () => ExpandDims, + Expm1: () => Expm1, + FFT: () => FFT, + Fill: () => Fill, + FlipLeftRight: () => FlipLeftRight, + Floor: () => Floor, + FloorDiv: () => FloorDiv, + FromPixels: () => FromPixels, + FusedBatchNorm: () => FusedBatchNorm, + FusedConv2D: () => FusedConv2D, + FusedDepthwiseConv2D: () => FusedDepthwiseConv2D, + GPGPUContext: () => GPGPUContext, + GatherNd: () => GatherNd, + GatherV2: () => GatherV2, + GraphModel: () => GraphModel, + Greater: () => Greater, + GreaterEqual: () => GreaterEqual, + History: () => History, + IFFT: () => IFFT, + Identity: () => Identity, + Imag: () => Imag, + InputSpec: () => InputSpec, + IsFinite: () => IsFinite, + IsInf: () => IsInf, + IsNan: () => IsNan, + KernelBackend: () => KernelBackend, + LRN: () => LRN, + LRNGrad: () => LRNGrad, + LayerVariable: () => LayerVariable, + LayersModel: () => LayersModel, + LeakyRelu: () => LeakyRelu, + Less: () => Less, + LessEqual: () => LessEqual, + LinSpace: () => LinSpace, + Log: () => Log, + Log1p: () => Log1p, + LogSoftmax: () => LogSoftmax, + LogicalAnd: () => LogicalAnd, + LogicalNot: () => LogicalNot, + LogicalOr: () => LogicalOr, + MathBackendCPU: () => MathBackendCPU, + MathBackendWebGL: () => MathBackendWebGL, + Max: () => Max, + MaxPool: () => MaxPool, + MaxPool3D: () => MaxPool3D, + MaxPool3DGrad: () => MaxPool3DGrad, + MaxPoolGrad: () => MaxPoolGrad, + MaxPoolWithArgmax: () => MaxPoolWithArgmax, + Maximum: () => Maximum, + Mean: () => Mean, + Min: () => Min, + Minimum: () => Minimum, + MirrorPad: () => MirrorPad, + Mod: () => Mod, + MomentumOptimizer: () => MomentumOptimizer, + Multinomial: () => Multinomial, + Multiply: () => Multiply, + Neg: () => Neg, + NonMaxSuppressionV3: () => NonMaxSuppressionV3, + NonMaxSuppressionV4: () => NonMaxSuppressionV4, + NonMaxSuppressionV5: () => NonMaxSuppressionV5, + NotEqual: () => NotEqual, + OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX, + OneHot: () => OneHot, + OnesLike: () => OnesLike, + Optimizer: () => Optimizer, + OptimizerConstructors: () => OptimizerConstructors, + Pack: () => Pack, + PadV2: () => PadV2, + Pool: () => Pool, + Pow: () => Pow, + Prelu: () => Prelu, + Prod: () => Prod, + RMSPropOptimizer: () => RMSPropOptimizer, + RNN: () => RNN, + Range: () => Range, + Rank: () => Rank, + Real: () => Real, + RealDiv: () => RealDiv, + Reciprocal: () => Reciprocal, + Reduction: () => Reduction, + Relu: () => Relu, + Relu6: () => Relu6, + Reshape: () => Reshape, + ResizeBilinear: () => ResizeBilinear, + ResizeBilinearGrad: () => ResizeBilinearGrad, + ResizeNearestNeighbor: () => ResizeNearestNeighbor, + ResizeNearestNeighborGrad: () => ResizeNearestNeighborGrad, + Reverse: () => Reverse, + RotateWithOffset: () => RotateWithOffset, + Round: () => Round, + Rsqrt: () => Rsqrt, + SGDOptimizer: () => SGDOptimizer, + ScatterNd: () => ScatterNd, + Select: () => Select, + Selu: () => Selu, + Sequential: () => Sequential, + Sigmoid: () => Sigmoid, + Sign: () => Sign, + Sin: () => Sin, + Sinh: () => Sinh, + Slice: () => Slice, + Softmax: () => Softmax, + Softplus: () => Softplus, + SpaceToBatchND: () => SpaceToBatchND, + SparseFillEmptyRows: () => SparseFillEmptyRows, + SparseReshape: () => SparseReshape, + SparseSegmentMean: () => SparseSegmentMean, + SparseSegmentSum: () => SparseSegmentSum, + SparseToDense: () => SparseToDense, + SplitV: () => SplitV, + Sqrt: () => Sqrt, + Square: () => Square, + SquaredDifference: () => SquaredDifference, + Step: () => Step, + StridedSlice: () => StridedSlice, + StringNGrams: () => StringNGrams, + StringSplit: () => StringSplit, + StringToHashBucketFast: () => StringToHashBucketFast, + Sub: () => Sub, + Sum: () => Sum, + SymbolicTensor: () => SymbolicTensor, + Tan: () => Tan, + Tanh: () => Tanh, + Tensor: () => Tensor, + TensorBuffer: () => TensorBuffer, + Tile: () => Tile, + TopK: () => TopK, + Transform: () => Transform, + Transpose: () => Transpose, + Unique: () => Unique, + Unpack: () => Unpack, + UnsortedSegmentSum: () => UnsortedSegmentSum, + Variable: () => Variable, + ZerosLike: () => ZerosLike, + _FusedMatMul: () => _FusedMatMul, + abs: () => abs, + acos: () => acos, + acosh: () => acosh, + add: () => add2, + addN: () => addN, + all: () => all, + any: () => any, + argMax: () => argMax, + argMin: () => argMin, + asin: () => asin, + asinh: () => asinh, + atan: () => atan, + atan2: () => atan2, + atanh: () => atanh, + avgPool: () => avgPool, + avgPool3d: () => avgPool3d, + backend: () => backend, + backend_util: () => backend_util_exports, + basicLSTMCell: () => basicLSTMCell, + batchNorm: () => batchNorm, + batchNorm2d: () => batchNorm2d, + batchNorm3d: () => batchNorm3d, + batchNorm4d: () => batchNorm4d, + batchToSpaceND: () => batchToSpaceND, + bincount: () => bincount, + booleanMaskAsync: () => booleanMaskAsync, + broadcastArgs: () => broadcastArgs, + broadcastTo: () => broadcastTo, + broadcast_util: () => broadcast_util_exports, + browser: () => browser_exports, + buffer: () => buffer, + callbacks: () => callbacks, + cast: () => cast, + ceil: () => ceil, + clipByValue: () => clipByValue, + clone: () => clone, + complex: () => complex, + concat: () => concat, + concat1d: () => concat1d, + concat2d: () => concat2d, + concat3d: () => concat3d, + concat4d: () => concat4d, + constraints: () => exports_constraints_exports, + conv1d: () => conv1d, + conv2d: () => conv2d, + conv2dTranspose: () => conv2dTranspose, + conv3d: () => conv3d, + conv3dTranspose: () => conv3dTranspose, + copyRegisteredKernels: () => copyRegisteredKernels, + cos: () => cos, + cosh: () => cosh, + cosineWindow: () => cosineWindow, + cumprod: () => cumprod, + cumsum: () => cumsum, + customGrad: () => customGrad, + data: () => src_exports2, + denseBincount: () => denseBincount, + deprecationWarn: () => deprecationWarn, + depthToSpace: () => depthToSpace, + depthwiseConv2d: () => depthwiseConv2d, + deregisterOp: () => deregisterOp, + device_util: () => device_util_exports, + diag: () => diag, + dilation2d: () => dilation2d, + disableDeprecationWarnings: () => disableDeprecationWarnings, + dispose: () => dispose, + disposeVariables: () => disposeVariables, + div: () => div, + divNoNan: () => divNoNan, + dot: () => dot, + dropout: () => dropout, + einsum: () => einsum, + elu: () => elu, + enableDebugMode: () => enableDebugMode, + enableProdMode: () => enableProdMode, + enclosingPowerOfTwo: () => enclosingPowerOfTwo, + engine: () => engine, + env: () => env, + equal: () => equal, + erf: () => erf, + exp: () => exp, + expandDims: () => expandDims, + expm1: () => expm1, + eye: () => eye, + fft: () => fft, + fill: () => fill, + findBackend: () => findBackend, + findBackendFactory: () => findBackendFactory, + floor: () => floor, + floorDiv: () => floorDiv, + forceHalfFloat: () => forceHalfFloat, + fused: () => fused_ops_exports, + gather: () => gather, + gatherND: () => gatherND, + gather_util: () => gather_nd_util_exports, + getBackend: () => getBackend, + getGradient: () => getGradient, + getKernel: () => getKernel, + getKernelsForBackend: () => getKernelsForBackend, + getThreadsCount: () => getThreadsCount, + gpgpu_util: () => gpgpu_util_exports, + grad: () => grad, + grads: () => grads, + greater: () => greater, + greaterEqual: () => greaterEqual, + ifft: () => ifft, + imag: () => imag, + image: () => image, + inTopKAsync: () => inTopKAsync, + initializers: () => exports_initializers_exports, + input: () => input, + io: () => io_exports, + irfft: () => irfft, + isFinite: () => isFinite2, + isInf: () => isInf, + isNaN: () => isNaN2, + keep: () => keep, + kernel_impls: () => kernel_impls_exports, + layers: () => exports_layers_exports, + leakyRelu: () => leakyRelu, + less: () => less, + lessEqual: () => lessEqual, + linalg: () => linalg, + linspace: () => linspace, + loadGraphModel: () => loadGraphModel, + loadLayersModel: () => loadLayersModel, + localResponseNormalization: () => localResponseNormalization, + log: () => log22, + log1p: () => log1p, + logSigmoid: () => logSigmoid, + logSoftmax: () => logSoftmax, + logSumExp: () => logSumExp, + logicalAnd: () => logicalAnd, + logicalNot: () => logicalNot, + logicalOr: () => logicalOr, + logicalXor: () => logicalXor, + losses: () => losses, + matMul: () => matMul, + math: () => math_exports, + max: () => max, + maxPool: () => maxPool, + maxPool3d: () => maxPool3d, + maxPoolWithArgmax: () => maxPoolWithArgmax, + maximum: () => maximum, + mean: () => mean, + memory: () => memory, + meshgrid: () => meshgrid, + metrics: () => exports_metrics_exports, + min: () => min, + minimum: () => minimum, + mirrorPad: () => mirrorPad, + mod: () => mod, + model: () => model, + models: () => exports_models_exports, + moments: () => moments, + movingAverage: () => movingAverage, + mul: () => mul, + multiRNNCell: () => multiRNNCell, + multinomial: () => multinomial, + neg: () => neg, + nextFrame: () => nextFrame, + norm: () => norm, + notEqual: () => notEqual, + oneHot: () => oneHot, + ones: () => ones2, + onesLike: () => onesLike, + op: () => op, + outerProduct: () => outerProduct, + pad: () => pad, + pad1d: () => pad1d, + pad2d: () => pad2d, + pad3d: () => pad3d, + pad4d: () => pad4d, + pool: () => pool, + pow: () => pow, + prelu: () => prelu, + print: () => print, + prod: () => prod, + profile: () => profile, + rand: () => rand, + randomGamma: () => randomGamma, + randomNormal: () => randomNormal, + randomUniform: () => randomUniform, + range: () => range, + ready: () => ready, + real: () => real, + reciprocal: () => reciprocal, + registerBackend: () => registerBackend, + registerCallbackConstructor: () => registerCallbackConstructor, + registerGradient: () => registerGradient, + registerKernel: () => registerKernel, + registerOp: () => registerOp, + regularizers: () => exports_regularizers_exports, + relu: () => relu, + relu6: () => relu6, + removeBackend: () => removeBackend, + reshape: () => reshape, + reverse: () => reverse, + reverse1d: () => reverse1d, + reverse2d: () => reverse2d, + reverse3d: () => reverse3d, + reverse4d: () => reverse4d, + rfft: () => rfft, + round: () => round2, + rsqrt: () => rsqrt, + scalar: () => scalar, + scatterND: () => scatterND, + scatter_util: () => scatter_nd_util_exports, + selu: () => selu, + separableConv2d: () => separableConv2d, + sequential: () => sequential, + serialization: () => serialization_exports, + setBackend: () => setBackend, + setPlatform: () => setPlatform, + setThreadsCount: () => setThreadsCount, + setWasmPath: () => setWasmPath, + setWasmPaths: () => setWasmPaths, + setWebGLContext: () => setWebGLContext, + setdiff1dAsync: () => setdiff1dAsync, + shared: () => shared_exports, + sigmoid: () => sigmoid, + sign: () => sign, + signal: () => signal, + sin: () => sin, + sinh: () => sinh, + slice: () => slice, + slice1d: () => slice1d, + slice2d: () => slice2d, + slice3d: () => slice3d, + slice4d: () => slice4d, + slice_util: () => slice_util_exports, + softmax: () => softmax, + softplus: () => softplus, + spaceToBatchND: () => spaceToBatchND, + sparse: () => sparse, + sparseToDense: () => sparseToDense, + spectral: () => spectral, + split: () => split, + sqrt: () => sqrt, + square: () => square, + squaredDifference: () => squaredDifference, + squeeze: () => squeeze, + stack: () => stack, + step: () => step, + stridedSlice: () => stridedSlice, + string: () => string, + sub: () => sub, + sum: () => sum2, + sumOutType: () => sumOutType, + tan: () => tan, + tanh: () => tanh2, + tensor: () => tensor, + tensor1d: () => tensor1d, + tensor2d: () => tensor2d, + tensor3d: () => tensor3d, + tensor4d: () => tensor4d, + tensor5d: () => tensor5d, + tensor6d: () => tensor6d, + tensor_util: () => tensor_util_exports, + test_util: () => test_util_exports, + tidy: () => tidy, + tile: () => tile, + time: () => time, + topk: () => topk, + train: () => train, + transpose: () => transpose, + truncatedNormal: () => truncatedNormal, + unique: () => unique, + unregisterGradient: () => unregisterGradient, + unregisterKernel: () => unregisterKernel, + unsortedSegmentSum: () => unsortedSegmentSum, + unstack: () => unstack, + upcastType: () => upcastType, + util: () => util_exports, + valueAndGrad: () => valueAndGrad, + valueAndGrads: () => valueAndGrads, + variable: () => variable, + variableGrads: () => variableGrads, + version: () => version8, + version_converter: () => version3, + version_core: () => version, + version_cpu: () => version5, + version_layers: () => version2, + version_wasm: () => version7, + version_webgl: () => version6, + webgl: () => webgl, + webgl_util: () => webgl_util_exports, + webgpu: () => webgpu_exports, + where: () => where, + whereAsync: () => whereAsync, + zeros: () => zeros, + zerosLike: () => zerosLike }); -var HT = Object.create; -var Ud = Object.defineProperty; -var qT = Object.getOwnPropertyDescriptor; -var Hw = Object.getOwnPropertyNames; -var jT = Object.getPrototypeOf; -var KT = Object.prototype.hasOwnProperty; -var XT = (e) => Ud(e, "__esModule", { value: true }); -var zt = (e, t) => function() { - return t || (0, e[Hw(e)[0]])((t = { exports: {} }).exports, t), t.exports; +var __create = Object.create; +var __defProp2 = Object.defineProperty; +var __getOwnPropDesc = Object.getOwnPropertyDescriptor; +var __getOwnPropNames = Object.getOwnPropertyNames; +var __getProtoOf = Object.getPrototypeOf; +var __hasOwnProp = Object.prototype.hasOwnProperty; +var __markAsModule = (target) => __defProp2(target, "__esModule", { value: true }); +var __commonJS = (cb, mod4) => function __require() { + return mod4 || (0, cb[__getOwnPropNames(cb)[0]])((mod4 = { exports: {} }).exports, mod4), mod4.exports; }; -var Ae = (e, t) => { - for (var n in t) - Ud(e, n, { get: t[n], enumerable: true }); +var __export2 = (target, all52) => { + for (var name in all52) + __defProp2(target, name, { get: all52[name], enumerable: true }); }; -var YT = (e, t, n, s) => { - if (t && typeof t == "object" || typeof t == "function") - for (let r of Hw(t)) - !KT.call(e, r) && (n || r !== "default") && Ud(e, r, { get: () => t[r], enumerable: !(s = qT(t, r)) || s.enumerable }); - return e; +var __reExport = (target, module, copyDefault, desc) => { + if (module && typeof module === "object" || typeof module === "function") { + for (let key of __getOwnPropNames(module)) + if (!__hasOwnProp.call(target, key) && (copyDefault || key !== "default")) + __defProp2(target, key, { get: () => module[key], enumerable: !(desc = __getOwnPropDesc(module, key)) || desc.enumerable }); + } + return target; }; -var wa = (e, t) => YT(XT(Ud(e != null ? 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(z = $, $ = false) : $ = !!$, z = z || 10, z < 2 || 36 < z) - throw RangeError("radix"); - var W; - if ((W = F.indexOf("-")) > 0) - throw Error("interior hyphen"); - if (W === 0) - return p(F.substring(1), $, z).neg(); - for (var q = u(c(z, 8)), K = v, Y = 0; Y < F.length; Y += 8) { - var Z = Math.min(8, F.length - Y), te = parseInt(F.substring(Y, Y + Z), z); - if (Z < 8) { - var ee = u(c(z, Z)); - K = K.mul(ee).add(u(te)); - } else - K = K.mul(q), K = K.add(u(te)); + function Long2(low, high, unsigned) { + this.low = low | 0; + this.high = high | 0; + this.unsigned = !!unsigned; } - return K.unsigned = $, K; - } - s.fromString = p; - function d(F, $) { - return typeof F == "number" ? u(F, $) : typeof F == "string" ? p(F, $) : l(F.low, F.high, typeof $ == "boolean" ? $ : F.unsigned); - } - s.fromValue = d; - var h = 1 << 16, f = 1 << 24, m = h * h, g = m * m, b = g / 2, y = o(f), v = o(0); - s.ZERO = v; - var x = o(0, true); - s.UZERO = x; - var k = o(1); - s.ONE = k; - var T = o(1, true); - s.UONE = T; - var N = o(-1); - s.NEG_ONE = N; - var E = l(-1, 2147483647, false); - s.MAX_VALUE = E; - var A = l(-1, -1, true); - s.MAX_UNSIGNED_VALUE = A; - var P = l(0, -2147483648, false); - s.MIN_VALUE = P; - var R = s.prototype; - R.toInt = function() { - return this.unsigned ? this.low >>> 0 : this.low; - }, R.toNumber = function() { - return this.unsigned ? (this.high >>> 0) * m + (this.low >>> 0) : this.high * m + (this.low >>> 0); - }, R.toString = function($) { - if ($ = $ || 10, $ < 2 || 36 < $) - throw RangeError("radix"); - if (this.isZero()) - return "0"; - if (this.isNegative()) - if (this.eq(P)) { - var z = u($), W = this.div(z), q = W.mul(z).sub(this); - return W.toString($) + q.toInt().toString($); - } else - return "-" + this.neg().toString($); - for (var K = u(c($, 6), this.unsigned), Y = this, Z = ""; ; ) { - var te = Y.div(K), ee = Y.sub(te.mul(K)).toInt() >>> 0, se = ee.toString($); - if (Y = te, Y.isZero()) - return se + Z; - for (; se.length < 6; ) - se = "0" + se; - Z = "" + se + Z; + Long2.prototype.__isLong__; + Object.defineProperty(Long2.prototype, "__isLong__", { value: true }); + function isLong(obj) { + return (obj && obj["__isLong__"]) === true; } - }, R.getHighBits = function() { - return this.high; - }, R.getHighBitsUnsigned = function() { - return this.high >>> 0; - }, R.getLowBits = function() { - return this.low; - }, R.getLowBitsUnsigned = function() { - return this.low >>> 0; - }, R.getNumBitsAbs = function() { - if (this.isNegative()) - return this.eq(P) ? 64 : this.neg().getNumBitsAbs(); - for (var $ = this.high != 0 ? this.high : this.low, z = 31; z > 0 && ($ & 1 << z) == 0; z--) - ; - return this.high != 0 ? z + 33 : z + 1; - }, R.isZero = function() { - return this.high === 0 && this.low === 0; - }, R.eqz = R.isZero, R.isNegative = function() { - return !this.unsigned && this.high < 0; - }, R.isPositive = function() { - return this.unsigned || this.high >= 0; - }, R.isOdd = function() { - return (this.low & 1) === 1; - }, R.isEven = function() { - return (this.low & 1) === 0; - }, R.equals = function($) { - return r($) || ($ = d($)), this.unsigned !== $.unsigned && this.high >>> 31 === 1 && $.high >>> 31 === 1 ? false : this.high === $.high && this.low === $.low; - }, R.eq = R.equals, R.notEquals = function($) { - return !this.eq($); - }, R.neq = R.notEquals, R.ne = R.notEquals, R.lessThan = function($) { - return this.comp($) < 0; - }, R.lt = R.lessThan, R.lessThanOrEqual = function($) { - return this.comp($) <= 0; - }, R.lte = R.lessThanOrEqual, R.le = R.lessThanOrEqual, R.greaterThan = function($) { - return this.comp($) > 0; - }, R.gt = R.greaterThan, R.greaterThanOrEqual = function($) { - return this.comp($) >= 0; - }, R.gte = R.greaterThanOrEqual, R.ge = R.greaterThanOrEqual, R.compare = function($) { - if (r($) || ($ = d($)), this.eq($)) - return 0; - var z = this.isNegative(), W = $.isNegative(); - return z && !W ? -1 : !z && W ? 1 : this.unsigned ? $.high >>> 0 > this.high >>> 0 || $.high === this.high && $.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub($).isNegative() ? -1 : 1; - }, R.comp = R.compare, R.negate = function() { - return !this.unsigned && this.eq(P) ? P : this.not().add(k); - }, R.neg = R.negate, R.add = function($) { - r($) || ($ = d($)); - var z = this.high >>> 16, W = this.high & 65535, q = this.low >>> 16, K = this.low & 65535, Y = $.high >>> 16, Z = $.high & 65535, te = $.low >>> 16, ee = $.low & 65535, se = 0, ne = 0, oe = 0, re = 0; - return re += K + ee, oe += re >>> 16, re &= 65535, oe += q + te, ne += oe >>> 16, oe &= 65535, ne += W + Z, se += ne >>> 16, ne &= 65535, se += z + Y, se &= 65535, l(oe << 16 | re, se << 16 | ne, this.unsigned); - }, R.subtract = function($) { - return r($) || ($ = d($)), this.add($.neg()); - }, R.sub = R.subtract, R.multiply = function($) { - if (this.isZero()) - return v; - if (r($) || ($ = d($)), n) { - var z = n.mul(this.low, this.high, $.low, $.high); - return l(z, n.get_high(), this.unsigned); + Long2.isLong = isLong; + var INT_CACHE = {}; + var UINT_CACHE = {}; + function fromInt(value, unsigned) { + var obj, cachedObj, cache7; + if (unsigned) { + value >>>= 0; + if (cache7 = 0 <= value && value < 256) { + cachedObj = UINT_CACHE[value]; + if (cachedObj) + return cachedObj; + } + obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true); + if (cache7) + UINT_CACHE[value] = obj; + return obj; + } else { + value |= 0; + if (cache7 = -128 <= value && value < 128) { + cachedObj = INT_CACHE[value]; + if (cachedObj) + return cachedObj; + } + obj = fromBits(value, value < 0 ? -1 : 0, false); + if (cache7) + INT_CACHE[value] = obj; + return obj; + } } - if ($.isZero()) - return v; - if (this.eq(P)) - return $.isOdd() ? P : v; - if ($.eq(P)) - return this.isOdd() ? P : v; - if (this.isNegative()) - return $.isNegative() ? this.neg().mul($.neg()) : this.neg().mul($).neg(); - if ($.isNegative()) - return this.mul($.neg()).neg(); - if (this.lt(y) && $.lt(y)) - return u(this.toNumber() * $.toNumber(), this.unsigned); - var W = this.high >>> 16, q = this.high & 65535, K = this.low >>> 16, Y = this.low & 65535, Z = $.high >>> 16, te = $.high & 65535, ee = $.low >>> 16, se = $.low & 65535, ne = 0, oe = 0, re = 0, le = 0; - return le += Y * se, re += le >>> 16, le &= 65535, re += K * se, oe += re >>> 16, re &= 65535, re += Y * ee, oe += re >>> 16, re &= 65535, oe += q * se, ne += oe >>> 16, oe &= 65535, oe += K * ee, ne += oe >>> 16, oe &= 65535, oe += Y * te, ne += oe >>> 16, oe &= 65535, ne += W * se + q * ee + K * te + Y * Z, ne &= 65535, l(re << 16 | le, ne << 16 | oe, this.unsigned); - }, R.mul = R.multiply, R.divide = function($) { - if (r($) || ($ = d($)), $.isZero()) - throw Error("division by zero"); - if (n) { - if (!this.unsigned && this.high === -2147483648 && $.low === -1 && $.high === -1) - return this; - var z = (this.unsigned ? n.div_u : n.div_s)(this.low, this.high, $.low, $.high); - return l(z, n.get_high(), this.unsigned); + Long2.fromInt = fromInt; + function fromNumber(value, unsigned) { + if (isNaN(value)) + return unsigned ? UZERO : ZERO; + if (unsigned) { + if (value < 0) + return UZERO; + if (value >= TWO_PWR_64_DBL) + return MAX_UNSIGNED_VALUE; + } else { + if (value <= -TWO_PWR_63_DBL) + return MIN_VALUE; + if (value + 1 >= TWO_PWR_63_DBL) + return MAX_VALUE; + } + if (value < 0) + return fromNumber(-value, unsigned).neg(); + return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned); } - if (this.isZero()) - return this.unsigned ? x : v; - var W, q, K; - if (this.unsigned) { - if ($.unsigned || ($ = $.toUnsigned()), $.gt(this)) - return x; - if ($.gt(this.shru(1))) - return T; - K = x; - } else { - if (this.eq(P)) { - if ($.eq(k) || $.eq(N)) - return P; - if ($.eq(P)) - return k; - var Y = this.shr(1); - return W = Y.div($).shl(1), W.eq(v) ? $.isNegative() ? k : N : (q = this.sub($.mul(W)), K = W.add(q.div($)), K); - } else if ($.eq(P)) - return this.unsigned ? x : v; + Long2.fromNumber = fromNumber; + function fromBits(lowBits, highBits, unsigned) { + return new Long2(lowBits, highBits, unsigned); + } + Long2.fromBits = fromBits; + var pow_dbl = Math.pow; + function fromString(str, unsigned, radix) { + if (str.length === 0) + throw Error("empty string"); + if (str === "NaN" || str === "Infinity" || str === "+Infinity" || str === "-Infinity") + return ZERO; + if (typeof unsigned === "number") { + radix = unsigned, unsigned = false; + } else { + unsigned = !!unsigned; + } + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError("radix"); + var p2; + if ((p2 = str.indexOf("-")) > 0) + throw Error("interior hyphen"); + else if (p2 === 0) { + return fromString(str.substring(1), unsigned, radix).neg(); + } + var radixToPower = fromNumber(pow_dbl(radix, 8)); + var result = ZERO; + for (var i = 0; i < str.length; i += 8) { + var size2 = Math.min(8, str.length - i), value = parseInt(str.substring(i, i + size2), radix); + if (size2 < 8) { + var power = fromNumber(pow_dbl(radix, size2)); + result = result.mul(power).add(fromNumber(value)); + } else { + result = result.mul(radixToPower); + result = result.add(fromNumber(value)); + } + } + result.unsigned = unsigned; + return result; + } + Long2.fromString = fromString; + function fromValue(val, unsigned) { + if (typeof val === "number") + return fromNumber(val, unsigned); + if (typeof val === "string") + return fromString(val, unsigned); + return fromBits(val.low, val.high, typeof unsigned === "boolean" ? unsigned : val.unsigned); + } + Long2.fromValue = fromValue; + var TWO_PWR_16_DBL = 1 << 16; + var TWO_PWR_24_DBL = 1 << 24; + var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL; + var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL; + var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2; + var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL); + var ZERO = fromInt(0); + Long2.ZERO = ZERO; + var UZERO = fromInt(0, true); + Long2.UZERO = UZERO; + var ONE = fromInt(1); + Long2.ONE = ONE; + var UONE = fromInt(1, true); + Long2.UONE = UONE; + var NEG_ONE = fromInt(-1); + Long2.NEG_ONE = NEG_ONE; + var MAX_VALUE = fromBits(4294967295 | 0, 2147483647 | 0, false); + Long2.MAX_VALUE = MAX_VALUE; + var MAX_UNSIGNED_VALUE = fromBits(4294967295 | 0, 4294967295 | 0, true); + Long2.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE; + var MIN_VALUE = fromBits(0, 2147483648 | 0, false); + Long2.MIN_VALUE = MIN_VALUE; + var LongPrototype = Long2.prototype; + LongPrototype.toInt = function toInt() { + return this.unsigned ? this.low >>> 0 : this.low; + }; + LongPrototype.toNumber = function toNumber() { + if (this.unsigned) + return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0); + return this.high * TWO_PWR_32_DBL + (this.low >>> 0); + }; + LongPrototype.toString = function toString(radix) { + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError("radix"); + if (this.isZero()) + return "0"; + if (this.isNegative()) { + if (this.eq(MIN_VALUE)) { + var radixLong = fromNumber(radix), div3 = this.div(radixLong), rem1 = div3.mul(radixLong).sub(this); + return div3.toString(radix) + rem1.toInt().toString(radix); + } else + return "-" + this.neg().toString(radix); + } + var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this; + var result = ""; + while (true) { + var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix); + rem = remDiv; + if (rem.isZero()) + return digits + result; + else { + while (digits.length < 6) + digits = "0" + digits; + result = "" + digits + result; + } + } + }; + LongPrototype.getHighBits = function getHighBits() { + return this.high; + }; + LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() { + return this.high >>> 0; + }; + LongPrototype.getLowBits = function getLowBits() { + return this.low; + }; + LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() { + return this.low >>> 0; + }; + LongPrototype.getNumBitsAbs = function getNumBitsAbs() { if (this.isNegative()) - return $.isNegative() ? this.neg().div($.neg()) : this.neg().div($).neg(); - if ($.isNegative()) - return this.div($.neg()).neg(); - K = v; - } - for (q = this; q.gte($); ) { - W = Math.max(1, Math.floor(q.toNumber() / $.toNumber())); - for (var Z = Math.ceil(Math.log(W) / Math.LN2), te = Z <= 48 ? 1 : c(2, Z - 48), ee = u(W), se = ee.mul($); se.isNegative() || se.gt(q); ) - W -= te, ee = u(W, this.unsigned), se = ee.mul($); - ee.isZero() && (ee = k), K = K.add(ee), q = q.sub(se); - } - return K; - }, R.div = R.divide, R.modulo = function($) { - if (r($) || ($ = d($)), n) { - var z = (this.unsigned ? n.rem_u : n.rem_s)(this.low, this.high, $.low, $.high); - return l(z, n.get_high(), this.unsigned); - } - return this.sub(this.div($).mul($)); - }, R.mod = R.modulo, R.rem = R.modulo, R.not = function() { - return l(~this.low, ~this.high, this.unsigned); - }, R.and = function($) { - return r($) || ($ = d($)), l(this.low & $.low, this.high & $.high, this.unsigned); - }, R.or = function($) { - return r($) || ($ = d($)), l(this.low | $.low, this.high | $.high, this.unsigned); - }, R.xor = function($) { - return r($) || ($ = d($)), l(this.low ^ $.low, this.high ^ $.high, this.unsigned); - }, R.shiftLeft = function($) { - return r($) && ($ = $.toInt()), ($ &= 63) === 0 ? this : $ < 32 ? l(this.low << $, this.high << $ | this.low >>> 32 - $, this.unsigned) : l(0, this.low << $ - 32, this.unsigned); - }, R.shl = R.shiftLeft, R.shiftRight = function($) { - return r($) && ($ = $.toInt()), ($ &= 63) === 0 ? this : $ < 32 ? l(this.low >>> $ | this.high << 32 - $, this.high >> $, this.unsigned) : l(this.high >> $ - 32, this.high >= 0 ? 0 : -1, this.unsigned); - }, R.shr = R.shiftRight, R.shiftRightUnsigned = function($) { - if (r($) && ($ = $.toInt()), $ &= 63, $ === 0) - return this; - var z = this.high; - if ($ < 32) { - var W = this.low; - return l(W >>> $ | z << 32 - $, z >>> $, this.unsigned); - } else - return $ === 32 ? l(z, 0, this.unsigned) : l(z >>> $ - 32, 0, this.unsigned); - }, R.shru = R.shiftRightUnsigned, R.shr_u = R.shiftRightUnsigned, R.toSigned = function() { - return this.unsigned ? l(this.low, this.high, false) : this; - }, R.toUnsigned = function() { - return this.unsigned ? this : l(this.low, this.high, true); - }, R.toBytes = function($) { - return $ ? this.toBytesLE() : this.toBytesBE(); - }, R.toBytesLE = function() { - var $ = this.high, z = this.low; - return [z & 255, z >>> 8 & 255, z >>> 16 & 255, z >>> 24, $ & 255, $ >>> 8 & 255, $ >>> 16 & 255, $ >>> 24]; - }, R.toBytesBE = function() { - var $ = this.high, z = this.low; - return [$ >>> 24, $ >>> 16 & 255, $ >>> 8 & 255, $ & 255, z >>> 24, z >>> 16 & 255, z >>> 8 & 255, z & 255]; - }, s.fromBytes = function($, z, W) { - return W ? s.fromBytesLE($, z) : s.fromBytesBE($, z); - }, s.fromBytesLE = function($, z) { - return new s($[0] | $[1] << 8 | $[2] << 16 | $[3] << 24, $[4] | $[5] << 8 | $[6] << 16 | $[7] << 24, z); - }, s.fromBytesBE = function($, z) { - return new s($[4] << 24 | $[5] << 16 | $[6] << 8 | $[7], $[0] << 24 | $[1] << 16 | $[2] << 8 | $[3], z); - }; -} }); -var ZT = zt({ "(disabled):src/node_modules/node-fetch/browser.js"() { -} }); -var JT = zt({ "(disabled):util"() { -} }); -var e$ = zt({ "src/node_modules/seedrandom/lib/alea.js"(e, t) { - (function(n, s, r) { - function a(l) { - var c = this, p = u(); - c.next = function() { - var d = 2091639 * c.s0 + c.c * 23283064365386963e-26; - return c.s0 = c.s1, c.s1 = c.s2, c.s2 = d - (c.c = d | 0); - }, c.c = 1, c.s0 = p(" "), c.s1 = p(" "), c.s2 = p(" "), c.s0 -= p(l), c.s0 < 0 && (c.s0 += 1), c.s1 -= p(l), c.s1 < 0 && (c.s1 += 1), c.s2 -= p(l), c.s2 < 0 && (c.s2 += 1), p = null; - } - function i(l, c) { - return c.c = l.c, c.s0 = l.s0, c.s1 = l.s1, c.s2 = l.s2, c; - } - function o(l, c) { - var p = new a(l), d = c && c.state, h = p.next; - return h.int32 = function() { - return p.next() * 4294967296 | 0; - }, h.double = function() { - return h() + (h() * 2097152 | 0) * 11102230246251565e-32; - }, h.quick = h, d && (typeof d == "object" && i(d, p), h.state = function() { - return i(p, {}); - }), h; - } - function u() { - var l = 4022871197, c = function(p) { - p = String(p); - for (var d = 0; d < p.length; d++) { - l += p.charCodeAt(d); - var h = 0.02519603282416938 * l; - l = h >>> 0, h -= l, h *= l, l = h >>> 0, h -= l, l += h * 4294967296; - } - return (l >>> 0) * 23283064365386963e-26; - }; - return c; - } - s && s.exports ? s.exports = o : r && r.amd ? r(function() { - return o; - }) : this.alea = o; - })(e, typeof t == "object" && t, typeof define == "function" && define); -} }); -var t$ = zt({ "src/node_modules/seedrandom/lib/xor128.js"(e, t) { - (function(n, s, r) { - function a(u) { - var l = this, c = ""; - l.x = 0, l.y = 0, l.z = 0, l.w = 0, l.next = function() { - var d = l.x ^ l.x << 11; - return l.x = l.y, l.y = l.z, l.z = l.w, l.w ^= l.w >>> 19 ^ d ^ d >>> 8; - }, u === (u | 0) ? l.x = u : c += u; - for (var p = 0; p < c.length + 64; p++) - l.x ^= c.charCodeAt(p) | 0, l.next(); - } - function i(u, l) { - return l.x = u.x, l.y = u.y, l.z = u.z, l.w = u.w, l; - } - function o(u, l) { - var c = new a(u), p = l && l.state, d = function() { - return (c.next() >>> 0) / 4294967296; - }; - return d.double = function() { - do - var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21); - while (m === 0); - return m; - }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && i(p, c), d.state = function() { - return i(c, {}); - }), d; - } - s && s.exports ? s.exports = o : r && r.amd ? r(function() { - return o; - }) : this.xor128 = o; - })(e, typeof t == "object" && t, typeof define == "function" && define); -} }); -var n$ = zt({ "src/node_modules/seedrandom/lib/xorwow.js"(e, t) { - (function(n, s, r) { - function a(u) { - var l = this, c = ""; - l.next = function() { - var d = l.x ^ l.x >>> 2; - return l.x = l.y, l.y = l.z, l.z = l.w, l.w = l.v, (l.d = l.d + 362437 | 0) + (l.v = l.v ^ l.v << 4 ^ (d ^ d << 1)) | 0; - }, l.x = 0, l.y = 0, l.z = 0, l.w = 0, l.v = 0, u === (u | 0) ? l.x = u : c += u; - for (var p = 0; p < c.length + 64; p++) - l.x ^= c.charCodeAt(p) | 0, p == c.length && (l.d = l.x << 10 ^ l.x >>> 4), l.next(); - } - function i(u, l) { - return l.x = u.x, l.y = u.y, l.z = u.z, l.w = u.w, l.v = u.v, l.d = u.d, l; - } - function o(u, l) { - var c = new a(u), p = l && l.state, d = function() { - return (c.next() >>> 0) / 4294967296; - }; - return d.double = function() { - do - var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21); - while (m === 0); - return m; - }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && i(p, c), d.state = function() { - return i(c, {}); - }), d; - } - s && s.exports ? s.exports = o : r && r.amd ? r(function() { - return o; - }) : this.xorwow = o; - })(e, typeof t == "object" && t, typeof define == "function" && define); -} }); -var s$ = zt({ "src/node_modules/seedrandom/lib/xorshift7.js"(e, t) { - (function(n, s, r) { - function a(u) { - var l = this; - l.next = function() { - var p = l.x, d = l.i, h, f, m; - return h = p[d], h ^= h >>> 7, f = h ^ h << 24, h = p[d + 1 & 7], f ^= h ^ h >>> 10, h = p[d + 3 & 7], f ^= h ^ h >>> 3, h = p[d + 4 & 7], f ^= h ^ h << 7, h = p[d + 7 & 7], h = h ^ h << 13, f ^= h ^ h << 9, p[d] = f, l.i = d + 1 & 7, f; - }; - function c(p, d) { - var h, f, m = []; - if (d === (d | 0)) - f = m[0] = d; + return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs(); + var val = this.high != 0 ? this.high : this.low; + for (var bit = 31; bit > 0; bit--) + if ((val & 1 << bit) != 0) + break; + return this.high != 0 ? bit + 33 : bit + 1; + }; + LongPrototype.isZero = function isZero() { + return this.high === 0 && this.low === 0; + }; + LongPrototype.eqz = LongPrototype.isZero; + LongPrototype.isNegative = function isNegative() { + return !this.unsigned && this.high < 0; + }; + LongPrototype.isPositive = function isPositive() { + return this.unsigned || this.high >= 0; + }; + LongPrototype.isOdd = function isOdd() { + return (this.low & 1) === 1; + }; + LongPrototype.isEven = function isEven2() { + return (this.low & 1) === 0; + }; + LongPrototype.equals = function equals(other) { + if (!isLong(other)) + other = fromValue(other); + if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1) + return false; + return this.high === other.high && this.low === other.low; + }; + LongPrototype.eq = LongPrototype.equals; + LongPrototype.notEquals = function notEquals(other) { + return !this.eq(other); + }; + LongPrototype.neq = LongPrototype.notEquals; + LongPrototype.ne = LongPrototype.notEquals; + LongPrototype.lessThan = function lessThan(other) { + return this.comp(other) < 0; + }; + LongPrototype.lt = LongPrototype.lessThan; + LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) { + return this.comp(other) <= 0; + }; + LongPrototype.lte = LongPrototype.lessThanOrEqual; + LongPrototype.le = LongPrototype.lessThanOrEqual; + LongPrototype.greaterThan = function greaterThan(other) { + return this.comp(other) > 0; + }; + LongPrototype.gt = LongPrototype.greaterThan; + LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) { + return this.comp(other) >= 0; + }; + LongPrototype.gte = LongPrototype.greaterThanOrEqual; + LongPrototype.ge = LongPrototype.greaterThanOrEqual; + LongPrototype.compare = function compare2(other) { + if (!isLong(other)) + other = fromValue(other); + if (this.eq(other)) + return 0; + var thisNeg = this.isNegative(), otherNeg = other.isNegative(); + if (thisNeg && !otherNeg) + return -1; + if (!thisNeg && otherNeg) + return 1; + if (!this.unsigned) + return this.sub(other).isNegative() ? -1 : 1; + return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1; + }; + LongPrototype.comp = LongPrototype.compare; + LongPrototype.negate = function negate() { + if (!this.unsigned && this.eq(MIN_VALUE)) + return MIN_VALUE; + return this.not().add(ONE); + }; + LongPrototype.neg = LongPrototype.negate; + LongPrototype.add = function add5(addend) { + if (!isLong(addend)) + addend = fromValue(addend); + var a48 = this.high >>> 16; + var a32 = this.high & 65535; + var a16 = this.low >>> 16; + var a00 = this.low & 65535; + var b48 = addend.high >>> 16; + var b32 = addend.high & 65535; + var b16 = addend.low >>> 16; + var b00 = addend.low & 65535; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 + b00; + c16 += c00 >>> 16; + c00 &= 65535; + c16 += a16 + b16; + c32 += c16 >>> 16; + c16 &= 65535; + c32 += a32 + b32; + c48 += c32 >>> 16; + c32 &= 65535; + c48 += a48 + b48; + c48 &= 65535; + return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned); + }; + LongPrototype.subtract = function subtract(subtrahend) { + if (!isLong(subtrahend)) + subtrahend = fromValue(subtrahend); + return this.add(subtrahend.neg()); + }; + LongPrototype.sub = LongPrototype.subtract; + LongPrototype.multiply = function multiply4(multiplier) { + if (this.isZero()) + return ZERO; + if (!isLong(multiplier)) + multiplier = fromValue(multiplier); + if (wasm) { + var low = wasm.mul(this.low, this.high, multiplier.low, multiplier.high); + return fromBits(low, wasm.get_high(), this.unsigned); + } + if (multiplier.isZero()) + return ZERO; + if (this.eq(MIN_VALUE)) + return multiplier.isOdd() ? MIN_VALUE : ZERO; + if (multiplier.eq(MIN_VALUE)) + return this.isOdd() ? MIN_VALUE : ZERO; + if (this.isNegative()) { + if (multiplier.isNegative()) + return this.neg().mul(multiplier.neg()); else - for (d = "" + d, h = 0; h < d.length; ++h) - m[h & 7] = m[h & 7] << 15 ^ d.charCodeAt(h) + m[h + 1 & 7] << 13; - for (; m.length < 8; ) - m.push(0); - for (h = 0; h < 8 && m[h] === 0; ++h) - ; - for (h == 8 ? f = m[7] = -1 : f = m[h], p.x = m, p.i = 0, h = 256; h > 0; --h) - p.next(); - } - c(l, u); - } - function i(u, l) { - return l.x = u.x.slice(), l.i = u.i, l; - } - function o(u, l) { - u == null && (u = +new Date()); - var c = new a(u), p = l && l.state, d = function() { - return (c.next() >>> 0) / 4294967296; - }; - return d.double = function() { - do - var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21); - while (m === 0); - return m; - }, d.int32 = c.next, d.quick = d, p && (p.x && i(p, c), d.state = function() { - return i(c, {}); - }), d; - } - s && s.exports ? s.exports = o : r && r.amd ? r(function() { - return o; - }) : this.xorshift7 = o; - })(e, typeof t == "object" && t, typeof define == "function" && define); -} }); -var r$ = zt({ "src/node_modules/seedrandom/lib/xor4096.js"(e, t) { - (function(n, s, r) { - function a(u) { - var l = this; - l.next = function() { - var p = l.w, d = l.X, h = l.i, f, m; - return l.w = p = p + 1640531527 | 0, m = d[h + 34 & 127], f = d[h = h + 1 & 127], m ^= m << 13, f ^= f << 17, m ^= m >>> 15, f ^= f >>> 12, m = d[h] = m ^ f, l.i = h, m + (p ^ p >>> 16) | 0; - }; - function c(p, d) { - var h, f, m, g, b, y = [], v = 128; - for (d === (d | 0) ? (f = d, d = null) : (d = d + "\0", f = 0, v = Math.max(v, d.length)), m = 0, g = -32; g < v; ++g) - d && (f ^= d.charCodeAt((g + 32) % d.length)), g === 0 && (b = f), f ^= f << 10, f ^= f >>> 15, f ^= f << 4, f ^= f >>> 13, g >= 0 && (b = b + 1640531527 | 0, h = y[g & 127] ^= f + b, m = h == 0 ? m + 1 : 0); - for (m >= 128 && (y[(d && d.length || 0) & 127] = -1), m = 127, g = 4 * 128; g > 0; --g) - f = y[m + 34 & 127], h = y[m = m + 1 & 127], f ^= f << 13, h ^= h << 17, f ^= f >>> 15, h ^= h >>> 12, y[m] = f ^ h; - p.w = b, p.X = y, p.i = m; - } - c(l, u); - } - function i(u, l) { - return l.i = u.i, l.w = u.w, l.X = u.X.slice(), l; - } - function o(u, l) { - u == null && (u = +new Date()); - var c = new a(u), p = l && l.state, d = function() { - return (c.next() >>> 0) / 4294967296; - }; - return d.double = function() { - do - var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21); - while (m === 0); - return m; - }, d.int32 = c.next, d.quick = d, p && (p.X && i(p, c), d.state = function() { - return i(c, {}); - }), d; - } - s && s.exports ? s.exports = o : r && r.amd ? r(function() { - return o; - }) : this.xor4096 = o; - })(e, typeof t == "object" && t, typeof define == "function" && define); -} }); -var a$ = zt({ "src/node_modules/seedrandom/lib/tychei.js"(e, t) { - (function(n, s, r) { - function a(u) { - var l = this, c = ""; - l.next = function() { - var d = l.b, h = l.c, f = l.d, m = l.a; - return d = d << 25 ^ d >>> 7 ^ h, h = h - f | 0, f = f << 24 ^ f >>> 8 ^ m, m = m - d | 0, l.b = d = d << 20 ^ d >>> 12 ^ h, l.c = h = h - f | 0, l.d = f << 16 ^ h >>> 16 ^ m, l.a = m - d | 0; - }, l.a = 0, l.b = 0, l.c = -1640531527, l.d = 1367130551, u === Math.floor(u) ? (l.a = u / 4294967296 | 0, l.b = u | 0) : c += u; - for (var p = 0; p < c.length + 20; p++) - l.b ^= c.charCodeAt(p) | 0, l.next(); - } - function i(u, l) { - return l.a = u.a, l.b = u.b, l.c = u.c, l.d = u.d, l; - } - function o(u, l) { - var c = new a(u), p = l && l.state, d = function() { - return (c.next() >>> 0) / 4294967296; - }; - return d.double = function() { - do - var h = c.next() >>> 11, f = (c.next() >>> 0) / 4294967296, m = (h + f) / (1 << 21); - while (m === 0); - return m; - }, d.int32 = c.next, d.quick = d, p && (typeof p == "object" && i(p, c), d.state = function() { - return i(c, {}); - }), d; - } - s && s.exports ? s.exports = o : r && r.amd ? r(function() { - return o; - }) : this.tychei = o; - })(e, typeof t == "object" && t, typeof define == "function" && define); -} }); -var i$ = zt({ "(disabled):crypto"() { -} }); -var o$ = zt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) { - (function(n, s, r) { - var a = 256, i = 6, o = 52, u = "random", l = r.pow(a, i), c = r.pow(2, o), p = c * 2, d = a - 1, h; - function f(k, T, N) { - var E = []; - T = T == true ? { entropy: true } : T || {}; - var A = y(b(T.entropy ? [k, x(s)] : k == null ? v() : k, 3), E), P = new m(E), R = function() { - for (var F = P.g(i), $ = l, z = 0; F < c; ) - F = (F + z) * a, $ *= a, z = P.g(1); - for (; F >= p; ) - F /= 2, $ /= 2, z >>>= 1; - return (F + z) / $; - }; - return R.int32 = function() { - return P.g(4) | 0; - }, R.quick = function() { - return P.g(4) / 4294967296; - }, R.double = R, y(x(P.S), s), (T.pass || N || function(F, $, z, W) { - return W && (W.S && g(W, P), F.state = function() { - return g(P, {}); - }), z ? (r[u] = F, $) : F; - })(R, A, "global" in T ? T.global : this == r, T.state); - } - function m(k) { - var T, N = k.length, E = this, A = 0, P = E.i = E.j = 0, R = E.S = []; - for (N || (k = [N++]); A < a; ) - R[A] = A++; - for (A = 0; A < a; A++) - R[A] = R[P = d & P + k[A % N] + (T = R[A])], R[P] = T; - (E.g = function(F) { - for (var $, z = 0, W = E.i, q = E.j, K = E.S; F--; ) - $ = K[W = d & W + 1], z = z * a + K[d & (K[W] = K[q = d & q + $]) + (K[q] = $)]; - return E.i = W, E.j = q, z; - })(a); - } - function g(k, T) { - return T.i = k.i, T.j = k.j, T.S = k.S.slice(), T; - } - function b(k, T) { - var N = [], E = typeof k, A; - if (T && E == "object") - for (A in k) - try { - N.push(b(k[A], T - 1)); - } catch (P) { - } - return N.length ? N : E == "string" ? k : k + "\0"; - } - function y(k, T) { - for (var N = k + "", E, A = 0; A < N.length; ) - T[d & A] = d & (E ^= T[d & A] * 19) + N.charCodeAt(A++); - return x(T); - } - function v() { - try { - var k; - return h && (k = h.randomBytes) ? k = k(a) : (k = new Uint8Array(a), (n.crypto || n.msCrypto).getRandomValues(k)), x(k); - } catch (E) { - var T = n.navigator, N = T && T.plugins; - return [+new Date(), n, N, n.screen, x(s)]; - } - } - function x(k) { - return String.fromCharCode.apply(0, k); - } - if (y(r.random(), s), typeof t == "object" && t.exports) { - t.exports = f; - try { - h = i$(); - } catch (k) { - } - } else - typeof define == "function" && define.amd ? define(function() { - return f; - }) : r["seed" + u] = f; - })(typeof self != "undefined" ? self : e, [], Math); -} }); -var Gd = zt({ "src/node_modules/seedrandom/index.js"(e, t) { - var n = e$(), s = t$(), r = n$(), a = s$(), i = r$(), o = a$(), u = o$(); - u.alea = n, u.xor128 = s, u.xorwow = r, u.xorshift7 = a, u.xor4096 = i, u.tychei = o, t.exports = u; -} }); -var qw = zt({ "(disabled):src/node_modules/string_decoder/index.js"() { -} }); -var sg = zt({ "(disabled):fs"() { -} }); -var dd = zt({ "(disabled):path"() { -} }); -var u$ = zt({ "(disabled):worker_threads"() { -} }); -var l$ = zt({ "(disabled):perf_hooks"() { -} }); -var c$ = zt({ "(disabled):os"() { -} }); -var d$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e, t) { - var n = (() => { - var s = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; - return typeof __filename != "undefined" && (s = s || __filename), function(r) { - r = r || {}; - function a() { - return Ce.buffer != nn && rs(Ce.buffer), ic; - } - function i() { - return Ce.buffer != nn && rs(Ce.buffer), oc; - } - function o() { - return Ce.buffer != nn && rs(Ce.buffer), fu; - } - function u() { - return Ce.buffer != nn && rs(Ce.buffer), uc; - } - function l() { - return Ce.buffer != nn && rs(Ce.buffer), lc; - } - function c() { - return Ce.buffer != nn && rs(Ce.buffer), cc; - } - function p() { - return Ce.buffer != nn && rs(Ce.buffer), dc; - } - var d = typeof r != "undefined" ? r : {}, h, f; - d.ready = new Promise(function(C, D) { - h = C, f = D; - }); - var m; - typeof process != "undefined" && process.listeners && (m = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }); - var g = Object.assign({}, d), b = [], y = "./this.program", v = (C, D) => { - throw D; - }, x = typeof window == "object", k = typeof importScripts == "function", T = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", N = d.ENVIRONMENT_IS_PTHREAD || false, E = ""; - function A(C) { - return d.locateFile ? d.locateFile(C, E) : E + C; - } - var P, R, F, $; - function z(C) { - if (C instanceof Iu) - return; - ee("exiting due to exception: " + C); - } - var W, q, K; - if (T) { - k ? E = dd().dirname(E) + "/" : E = __dirname + "/", K = () => { - q || (W = sg(), q = dd()); - }, P = function(B, Q) { - return K(), B = q.normalize(B), W.readFileSync(B, Q ? void 0 : "utf8"); - }, F = (D) => { - var B = P(D, true); - return B.buffer || (B = new Uint8Array(B)), B; - }, R = (D, B, Q) => { - K(), D = q.normalize(D), W.readFile(D, function(ue, pe) { - ue ? Q(ue) : B(pe.buffer); - }); - }, process.argv.length > 1 && (y = process.argv[1].replace(/\\/g, "/")), b = process.argv.slice(2), process.on("uncaughtException", function(D) { - if (!(D instanceof Iu)) - throw D; - }), process.on("unhandledRejection", function(D) { - throw D; - }), v = (D, B) => { - if (zr()) - throw process.exitCode = D, B; - z(B), process.exit(D); - }, d.inspect = function() { - return "[Emscripten Module object]"; - }; - let C; - try { - C = u$(); - } catch (D) { - throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'), D; + return this.neg().mul(multiplier).neg(); + } else if (multiplier.isNegative()) + return this.mul(multiplier.neg()).neg(); + if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24)) + return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned); + var a48 = this.high >>> 16; + var a32 = this.high & 65535; + var a16 = this.low >>> 16; + var a00 = this.low & 65535; + var b48 = multiplier.high >>> 16; + var b32 = multiplier.high & 65535; + var b16 = multiplier.low >>> 16; + var b00 = multiplier.low & 65535; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 * b00; + c16 += c00 >>> 16; + c00 &= 65535; + c16 += a16 * b00; + c32 += c16 >>> 16; + c16 &= 65535; + c16 += a00 * b16; + c32 += c16 >>> 16; + c16 &= 65535; + c32 += a32 * b00; + c48 += c32 >>> 16; + c32 &= 65535; + c32 += a16 * b16; + c48 += c32 >>> 16; + c32 &= 65535; + c32 += a00 * b32; + c48 += c32 >>> 16; + c32 &= 65535; + c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48; + c48 &= 65535; + return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned); + }; + LongPrototype.mul = LongPrototype.multiply; + LongPrototype.divide = function divide(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + if (divisor.isZero()) + throw Error("division by zero"); + if (wasm) { + if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) { + return this; } - global.Worker = C.Worker; - } else - (x || k) && (k ? E = self.location.href : typeof document != "undefined" && document.currentScript && (E = document.currentScript.src), typeof s != "undefined" && s && (E = s), E.indexOf("blob:") !== 0 ? E = E.substr(0, E.replace(/[?#].*/, "").lastIndexOf("/") + 1) : E = "", T || (P = (C) => { - var D = new XMLHttpRequest(); - return D.open("GET", C, false), D.send(null), D.responseText; - }, k && (F = (C) => { - var D = new XMLHttpRequest(); - return D.open("GET", C, false), D.responseType = "arraybuffer", D.send(null), new Uint8Array(D.response); - }), R = (C, D, B) => { - var Q = new XMLHttpRequest(); - Q.open("GET", C, true), Q.responseType = "arraybuffer", Q.onload = () => { - if (Q.status == 200 || Q.status == 0 && Q.response) { - D(Q.response); + var low = (this.unsigned ? wasm.div_u : wasm.div_s)(this.low, this.high, divisor.low, divisor.high); + return fromBits(low, wasm.get_high(), this.unsigned); + } + if (this.isZero()) + return this.unsigned ? UZERO : ZERO; + var approx, rem, res; + if (!this.unsigned) { + if (this.eq(MIN_VALUE)) { + if (divisor.eq(ONE) || divisor.eq(NEG_ONE)) + return MIN_VALUE; + else if (divisor.eq(MIN_VALUE)) + return ONE; + else { + var halfThis = this.shr(1); + approx = halfThis.div(divisor).shl(1); + if (approx.eq(ZERO)) { + return divisor.isNegative() ? ONE : NEG_ONE; + } else { + rem = this.sub(divisor.mul(approx)); + res = approx.add(rem.div(divisor)); + return res; + } + } + } else if (divisor.eq(MIN_VALUE)) + return this.unsigned ? UZERO : ZERO; + if (this.isNegative()) { + if (divisor.isNegative()) + return this.neg().div(divisor.neg()); + return this.neg().div(divisor).neg(); + } else if (divisor.isNegative()) + return this.div(divisor.neg()).neg(); + res = ZERO; + } else { + if (!divisor.unsigned) + divisor = divisor.toUnsigned(); + if (divisor.gt(this)) + return UZERO; + if (divisor.gt(this.shru(1))) + return UONE; + res = UZERO; + } + rem = this; + while (rem.gte(divisor)) { + approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber())); + var log222 = Math.ceil(Math.log(approx) / Math.LN2), delta = log222 <= 48 ? 1 : pow_dbl(2, log222 - 48), approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor); + while (approxRem.isNegative() || approxRem.gt(rem)) { + approx -= delta; + approxRes = fromNumber(approx, this.unsigned); + approxRem = approxRes.mul(divisor); + } + if (approxRes.isZero()) + approxRes = ONE; + res = res.add(approxRes); + rem = rem.sub(approxRem); + } + return res; + }; + LongPrototype.div = LongPrototype.divide; + LongPrototype.modulo = function modulo(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + if (wasm) { + var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(this.low, this.high, divisor.low, divisor.high); + return fromBits(low, wasm.get_high(), this.unsigned); + } + return this.sub(this.div(divisor).mul(divisor)); + }; + LongPrototype.mod = LongPrototype.modulo; + LongPrototype.rem = LongPrototype.modulo; + LongPrototype.not = function not() { + return fromBits(~this.low, ~this.high, this.unsigned); + }; + LongPrototype.and = function and(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low & other.low, this.high & other.high, this.unsigned); + }; + LongPrototype.or = function or(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low | other.low, this.high | other.high, this.unsigned); + }; + LongPrototype.xor = function xor(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned); + }; + LongPrototype.shiftLeft = function shiftLeft(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + if ((numBits &= 63) === 0) + return this; + else if (numBits < 32) + return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned); + else + return fromBits(0, this.low << numBits - 32, this.unsigned); + }; + LongPrototype.shl = LongPrototype.shiftLeft; + LongPrototype.shiftRight = function shiftRight(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + if ((numBits &= 63) === 0) + return this; + else if (numBits < 32) + return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned); + else + return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned); + }; + LongPrototype.shr = LongPrototype.shiftRight; + LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) { + if (isLong(numBits)) + numBits = numBits.toInt(); + numBits &= 63; + if (numBits === 0) + return this; + else { + var high = this.high; + if (numBits < 32) { + var low = this.low; + return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned); + } else if (numBits === 32) + return fromBits(high, 0, this.unsigned); + else + return fromBits(high >>> numBits - 32, 0, this.unsigned); + } + }; + LongPrototype.shru = LongPrototype.shiftRightUnsigned; + LongPrototype.shr_u = LongPrototype.shiftRightUnsigned; + LongPrototype.toSigned = function toSigned() { + if (!this.unsigned) + return this; + return fromBits(this.low, this.high, false); + }; + LongPrototype.toUnsigned = function toUnsigned() { + if (this.unsigned) + return this; + return fromBits(this.low, this.high, true); + }; + LongPrototype.toBytes = function toBytes(le) { + return le ? this.toBytesLE() : this.toBytesBE(); + }; + LongPrototype.toBytesLE = function toBytesLE() { + var hi = this.high, lo = this.low; + return [ + lo & 255, + lo >>> 8 & 255, + lo >>> 16 & 255, + lo >>> 24, + hi & 255, + hi >>> 8 & 255, + hi >>> 16 & 255, + hi >>> 24 + ]; + }; + LongPrototype.toBytesBE = function toBytesBE() { + var hi = this.high, lo = this.low; + return [ + hi >>> 24, + hi >>> 16 & 255, + hi >>> 8 & 255, + hi & 255, + lo >>> 24, + lo >>> 16 & 255, + lo >>> 8 & 255, + lo & 255 + ]; + }; + Long2.fromBytes = function fromBytes(bytes, unsigned, le) { + return le ? Long2.fromBytesLE(bytes, unsigned) : Long2.fromBytesBE(bytes, unsigned); + }; + Long2.fromBytesLE = function fromBytesLE(bytes, unsigned) { + return new Long2(bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24, bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24, unsigned); + }; + Long2.fromBytesBE = function fromBytesBE(bytes, unsigned) { + return new Long2(bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7], bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3], unsigned); + }; + } +}); +var require_browser = __commonJS({ + "(disabled):src/node_modules/node-fetch/browser.js"() { + } +}); +var require_util = __commonJS({ + "(disabled):util"() { + } +}); +var require_alea = __commonJS({ + "src/node_modules/seedrandom/lib/alea.js"(exports, module) { + (function(global2, module2, define2) { + function Alea(seed) { + var me = this, mash = Mash(); + me.next = function() { + var t = 2091639 * me.s0 + me.c * 23283064365386963e-26; + me.s0 = me.s1; + me.s1 = me.s2; + return me.s2 = t - (me.c = t | 0); + }; + me.c = 1; + me.s0 = mash(" "); + me.s1 = mash(" "); + me.s2 = mash(" "); + me.s0 -= mash(seed); + if (me.s0 < 0) { + me.s0 += 1; + } + me.s1 -= mash(seed); + if (me.s1 < 0) { + me.s1 += 1; + } + me.s2 -= mash(seed); + if (me.s2 < 0) { + me.s2 += 1; + } + mash = null; + } + function copy2(f, t) { + t.c = f.c; + t.s0 = f.s0; + t.s1 = f.s1; + t.s2 = f.s2; + return t; + } + function impl(seed, opts) { + var xg = new Alea(seed), state = opts && opts.state, prng = xg.next; + prng.int32 = function() { + return xg.next() * 4294967296 | 0; + }; + prng.double = function() { + return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32; + }; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy2(state, xg); + prng.state = function() { + return copy2(xg, {}); + }; + } + return prng; + } + function Mash() { + var n = 4022871197; + var mash = function(data) { + data = String(data); + for (var i = 0; i < data.length; i++) { + n += data.charCodeAt(i); + var h = 0.02519603282416938 * n; + n = h >>> 0; + h -= n; + h *= n; + n = h >>> 0; + h -= n; + n += h * 4294967296; + } + return (n >>> 0) * 23283064365386963e-26; + }; + return mash; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.alea = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xor128 = __commonJS({ + "src/node_modules/seedrandom/lib/xor128.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.next = function() { + var t = me.x ^ me.x << 11; + me.x = me.y; + me.y = me.z; + me.z = me.w; + return me.w ^= me.w >>> 19 ^ t ^ t >>> 8; + }; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy2(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy2(state, xg); + prng.state = function() { + return copy2(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor128 = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xorwow = __commonJS({ + "src/node_modules/seedrandom/lib/xorwow.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var t = me.x ^ me.x >>> 2; + me.x = me.y; + me.y = me.z; + me.z = me.w; + me.w = me.v; + return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0; + }; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + me.v = 0; + if (seed === (seed | 0)) { + me.x = seed; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 64; k++) { + me.x ^= strseed.charCodeAt(k) | 0; + if (k == strseed.length) { + me.d = me.x << 10 ^ me.x >>> 4; + } + me.next(); + } + } + function copy2(f, t) { + t.x = f.x; + t.y = f.y; + t.z = f.z; + t.w = f.w; + t.v = f.v; + t.d = f.d; + return t; + } + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy2(state, xg); + prng.state = function() { + return copy2(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorwow = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xorshift7 = __commonJS({ + "src/node_modules/seedrandom/lib/xorshift7.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var X = me.x, i = me.i, t, v, w; + t = X[i]; + t ^= t >>> 7; + v = t ^ t << 24; + t = X[i + 1 & 7]; + v ^= t ^ t >>> 10; + t = X[i + 3 & 7]; + v ^= t ^ t >>> 3; + t = X[i + 4 & 7]; + v ^= t ^ t << 7; + t = X[i + 7 & 7]; + t = t ^ t << 13; + v ^= t ^ t << 9; + X[i] = v; + me.i = i + 1 & 7; + return v; + }; + function init22(me2, seed2) { + var j, w, X = []; + if (seed2 === (seed2 | 0)) { + w = X[0] = seed2; + } else { + seed2 = "" + seed2; + for (j = 0; j < seed2.length; ++j) { + X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13; + } + } + while (X.length < 8) + X.push(0); + for (j = 0; j < 8 && X[j] === 0; ++j) + ; + if (j == 8) + w = X[7] = -1; + else + w = X[j]; + me2.x = X; + me2.i = 0; + for (j = 256; j > 0; --j) { + me2.next(); + } + } + init22(me, seed); + } + function copy2(f, t) { + t.x = f.x.slice(); + t.i = f.i; + return t; + } + function impl(seed, opts) { + if (seed == null) + seed = +new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.x) + copy2(state, xg); + prng.state = function() { + return copy2(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xorshift7 = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_xor4096 = __commonJS({ + "src/node_modules/seedrandom/lib/xor4096.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this; + me.next = function() { + var w = me.w, X = me.X, i = me.i, t, v; + me.w = w = w + 1640531527 | 0; + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + v = X[i] = v ^ t; + me.i = i; + return v + (w ^ w >>> 16) | 0; + }; + function init22(me2, seed2) { + var t, v, i, j, w, X = [], limit = 128; + if (seed2 === (seed2 | 0)) { + v = seed2; + seed2 = null; + } else { + seed2 = seed2 + "\0"; + v = 0; + limit = Math.max(limit, seed2.length); + } + for (i = 0, j = -32; j < limit; ++j) { + if (seed2) + v ^= seed2.charCodeAt((j + 32) % seed2.length); + if (j === 0) + w = v; + v ^= v << 10; + v ^= v >>> 15; + v ^= v << 4; + v ^= v >>> 13; + if (j >= 0) { + w = w + 1640531527 | 0; + t = X[j & 127] ^= v + w; + i = t == 0 ? i + 1 : 0; + } + } + if (i >= 128) { + X[(seed2 && seed2.length || 0) & 127] = -1; + } + i = 127; + for (j = 4 * 128; j > 0; --j) { + v = X[i + 34 & 127]; + t = X[i = i + 1 & 127]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + X[i] = v ^ t; + } + me2.w = w; + me2.X = X; + me2.i = i; + } + init22(me, seed); + } + function copy2(f, t) { + t.i = f.i; + t.w = f.w; + t.X = f.X.slice(); + return t; + } + ; + function impl(seed, opts) { + if (seed == null) + seed = +new Date(); + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (state.X) + copy2(state, xg); + prng.state = function() { + return copy2(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.xor4096 = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_tychei = __commonJS({ + "src/node_modules/seedrandom/lib/tychei.js"(exports, module) { + (function(global2, module2, define2) { + function XorGen(seed) { + var me = this, strseed = ""; + me.next = function() { + var b = me.b, c = me.c, d = me.d, a = me.a; + b = b << 25 ^ b >>> 7 ^ c; + c = c - d | 0; + d = d << 24 ^ d >>> 8 ^ a; + a = a - b | 0; + me.b = b = b << 20 ^ b >>> 12 ^ c; + me.c = c = c - d | 0; + me.d = d << 16 ^ c >>> 16 ^ a; + return me.a = a - b | 0; + }; + me.a = 0; + me.b = 0; + me.c = 2654435769 | 0; + me.d = 1367130551; + if (seed === Math.floor(seed)) { + me.a = seed / 4294967296 | 0; + me.b = seed | 0; + } else { + strseed += seed; + } + for (var k = 0; k < strseed.length + 20; k++) { + me.b ^= strseed.charCodeAt(k) | 0; + me.next(); + } + } + function copy2(f, t) { + t.a = f.a; + t.b = f.b; + t.c = f.c; + t.d = f.d; + return t; + } + ; + function impl(seed, opts) { + var xg = new XorGen(seed), state = opts && opts.state, prng = function() { + return (xg.next() >>> 0) / 4294967296; + }; + prng.double = function() { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21); + } while (result === 0); + return result; + }; + prng.int32 = xg.next; + prng.quick = prng; + if (state) { + if (typeof state == "object") + copy2(state, xg); + prng.state = function() { + return copy2(xg, {}); + }; + } + return prng; + } + if (module2 && module2.exports) { + module2.exports = impl; + } else if (define2 && define2.amd) { + define2(function() { + return impl; + }); + } else { + this.tychei = impl; + } + })(exports, typeof module == "object" && module, typeof define == "function" && define); + } +}); +var require_crypto = __commonJS({ + "(disabled):crypto"() { + } +}); +var require_seedrandom = __commonJS({ + "src/node_modules/seedrandom/seedrandom.js"(exports, module) { + (function(global2, pool3, math) { + var width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask2 = width - 1, nodecrypto; + function seedrandom5(seed, options4, callback) { + var key = []; + options4 = options4 == true ? { entropy: true } : options4 || {}; + var shortseed = mixkey(flatten4(options4.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key); + var arc4 = new ARC4(key); + var prng = function() { + var n = arc4.g(chunks), d = startdenom, x = 0; + while (n < significance) { + n = (n + x) * width; + d *= width; + x = arc4.g(1); + } + while (n >= overflow) { + n /= 2; + d /= 2; + x >>>= 1; + } + return (n + x) / d; + }; + prng.int32 = function() { + return arc4.g(4) | 0; + }; + prng.quick = function() { + return arc4.g(4) / 4294967296; + }; + prng.double = prng; + mixkey(tostring(arc4.S), pool3); + return (options4.pass || callback || function(prng2, seed2, is_math_call, state) { + if (state) { + if (state.S) { + copy2(state, arc4); + } + prng2.state = function() { + return copy2(arc4, {}); + }; + } + if (is_math_call) { + math[rngname] = prng2; + return seed2; + } else + return prng2; + })(prng, shortseed, "global" in options4 ? options4.global : this == math, options4.state); + } + function ARC4(key) { + var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = []; + if (!keylen) { + key = [keylen++]; + } + while (i < width) { + s[i] = i++; + } + for (i = 0; i < width; i++) { + s[i] = s[j = mask2 & j + key[i % keylen] + (t = s[i])]; + s[j] = t; + } + (me.g = function(count22) { + var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S; + while (count22--) { + t2 = s2[i2 = mask2 & i2 + 1]; + r = r * width + s2[mask2 & (s2[i2] = s2[j2 = mask2 & j2 + t2]) + (s2[j2] = t2)]; + } + me.i = i2; + me.j = j2; + return r; + })(width); + } + function copy2(f, t) { + t.i = f.i; + t.j = f.j; + t.S = f.S.slice(); + return t; + } + ; + function flatten4(obj, depth) { + var result = [], typ = typeof obj, prop; + if (depth && typ == "object") { + for (prop in obj) { + try { + result.push(flatten4(obj[prop], depth - 1)); + } catch (e) { + } + } + } + return result.length ? result : typ == "string" ? obj : obj + "\0"; + } + function mixkey(seed, key) { + var stringseed = seed + "", smear, j = 0; + while (j < stringseed.length) { + key[mask2 & j] = mask2 & (smear ^= key[mask2 & j] * 19) + stringseed.charCodeAt(j++); + } + return tostring(key); + } + function autoseed() { + try { + var out; + if (nodecrypto && (out = nodecrypto.randomBytes)) { + out = out(width); + } else { + out = new Uint8Array(width); + (global2.crypto || global2.msCrypto).getRandomValues(out); + } + return tostring(out); + } catch (e) { + var browser = global2.navigator, plugins = browser && browser.plugins; + return [+new Date(), global2, plugins, global2.screen, tostring(pool3)]; + } + } + function tostring(a) { + return String.fromCharCode.apply(0, a); + } + mixkey(math.random(), pool3); + if (typeof module == "object" && module.exports) { + module.exports = seedrandom5; + try { + nodecrypto = require_crypto(); + } catch (ex) { + } + } else if (typeof define == "function" && define.amd) { + define(function() { + return seedrandom5; + }); + } else { + math["seed" + rngname] = seedrandom5; + } + })(typeof self !== "undefined" ? self : exports, [], Math); + } +}); +var require_seedrandom2 = __commonJS({ + "src/node_modules/seedrandom/index.js"(exports, module) { + var alea5 = require_alea(); + var xor128 = require_xor128(); + var xorwow = require_xorwow(); + var xorshift7 = require_xorshift7(); + var xor4096 = require_xor4096(); + var tychei = require_tychei(); + var sr = require_seedrandom(); + sr.alea = alea5; + sr.xor128 = xor128; + sr.xorwow = xorwow; + sr.xorshift7 = xorshift7; + sr.xor4096 = xor4096; + sr.tychei = tychei; + module.exports = sr; + } +}); +var require_string_decoder = __commonJS({ + "(disabled):src/node_modules/string_decoder/index.js"() { + } +}); +var require_fs = __commonJS({ + "(disabled):fs"() { + } +}); +var require_path = __commonJS({ + "(disabled):path"() { + } +}); +var require_worker_threads = __commonJS({ + "(disabled):worker_threads"() { + } +}); +var require_perf_hooks = __commonJS({ + "(disabled):perf_hooks"() { + } +}); +var require_os = __commonJS({ + "(disabled):os"() { + } +}); +var require_tfjs_backend_wasm_threaded_simd = __commonJS({ + "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(exports, module) { + var WasmBackendModuleThreadedSimd2 = (() => { + var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; + if (typeof __filename !== "undefined") + _scriptDir = _scriptDir || __filename; + return function(WasmBackendModuleThreadedSimd3) { + WasmBackendModuleThreadedSimd3 = WasmBackendModuleThreadedSimd3 || {}; + function GROWABLE_HEAP_I8() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP8; + } + function GROWABLE_HEAP_U8() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPU8; + } + function GROWABLE_HEAP_I16() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP16; + } + function GROWABLE_HEAP_U16() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPU16; + } + function GROWABLE_HEAP_I32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAP32; + } + function GROWABLE_HEAP_F32() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPF32; + } + function GROWABLE_HEAP_F64() { + if (wasmMemory.buffer != buffer2) { + updateGlobalBufferAndViews(wasmMemory.buffer); + } + return HEAPF64; + } + var Module = typeof WasmBackendModuleThreadedSimd3 !== "undefined" ? WasmBackendModuleThreadedSimd3 : {}; + var readyPromiseResolve, readyPromiseReject; + Module["ready"] = new Promise(function(resolve, reject) { + readyPromiseResolve = resolve; + readyPromiseReject = reject; + }); + var beforeListeners; + if (typeof process !== "undefined" && process.listeners) { + beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }; + } + var moduleOverrides = Object.assign({}, Module); + var arguments_ = []; + var thisProgram = "./this.program"; + var quit_ = (status, toThrow) => { + throw toThrow; + }; + var ENVIRONMENT_IS_WEB = typeof window === "object"; + var ENVIRONMENT_IS_WORKER = typeof importScripts === "function"; + var ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string"; + var ENVIRONMENT_IS_PTHREAD = Module["ENVIRONMENT_IS_PTHREAD"] || false; + var scriptDirectory = ""; + function locateFile(path) { + if (Module["locateFile"]) { + return Module["locateFile"](path, scriptDirectory); + } + return scriptDirectory + path; + } + var read_, readAsync, readBinary, setWindowTitle; + function logExceptionOnExit(e) { + if (e instanceof ExitStatus) + return; + let toLog = e; + err("exiting due to exception: " + toLog); + } + var fs; + var nodePath; + var requireNodeFS; + if (ENVIRONMENT_IS_NODE) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = require_path().dirname(scriptDirectory) + "/"; + } else { + scriptDirectory = __dirname + "/"; + } + requireNodeFS = () => { + if (!nodePath) { + fs = require_fs(); + nodePath = require_path(); + } + }; + read_ = function shell_read(filename, binary) { + requireNodeFS(); + filename = nodePath["normalize"](filename); + return fs.readFileSync(filename, binary ? void 0 : "utf8"); + }; + readBinary = (filename) => { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + return ret; + }; + readAsync = (filename, onload, onerror) => { + requireNodeFS(); + filename = nodePath["normalize"](filename); + fs.readFile(filename, function(err2, data) { + if (err2) + onerror(err2); + else + onload(data.buffer); + }); + }; + if (process["argv"].length > 1) { + thisProgram = process["argv"][1].replace(/\\/g, "/"); + } + arguments_ = process["argv"].slice(2); + process["on"]("uncaughtException", function(ex) { + if (!(ex instanceof ExitStatus)) { + throw ex; + } + }); + process["on"]("unhandledRejection", function(reason) { + throw reason; + }); + quit_ = (status, toThrow) => { + if (keepRuntimeAlive()) { + process["exitCode"] = status; + throw toThrow; + } + logExceptionOnExit(toThrow); + process["exit"](status); + }; + Module["inspect"] = function() { + return "[Emscripten Module object]"; + }; + let nodeWorkerThreads; + try { + nodeWorkerThreads = require_worker_threads(); + } catch (e) { + console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'); + throw e; + } + global.Worker = nodeWorkerThreads.Worker; + } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = self.location.href; + } else if (typeof document !== "undefined" && document.currentScript) { + scriptDirectory = document.currentScript.src; + } + if (typeof _scriptDir !== "undefined" && _scriptDir) { + scriptDirectory = _scriptDir; + } + if (scriptDirectory.indexOf("blob:") !== 0) { + scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1); + } else { + scriptDirectory = ""; + } + if (!ENVIRONMENT_IS_NODE) { + read_ = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.send(null); + return xhr.responseText; + }; + if (ENVIRONMENT_IS_WORKER) { + readBinary = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.responseType = "arraybuffer"; + xhr.send(null); + return new Uint8Array(xhr.response); + }; + } + readAsync = (url, onload, onerror) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, true); + xhr.responseType = "arraybuffer"; + xhr.onload = () => { + if (xhr.status == 200 || xhr.status == 0 && xhr.response) { + onload(xhr.response); + return; + } + onerror(); + }; + xhr.onerror = onerror; + xhr.send(null); + }; + } + setWindowTitle = (title) => document.title = title; + } else { + } + if (ENVIRONMENT_IS_NODE) { + if (typeof performance === "undefined") { + global.performance = require_perf_hooks().performance; + } + } + var defaultPrint = console.log.bind(console); + var defaultPrintErr = console.warn.bind(console); + if (ENVIRONMENT_IS_NODE) { + requireNodeFS(); + defaultPrint = (str) => fs.writeSync(1, str + "\n"); + defaultPrintErr = (str) => fs.writeSync(2, str + "\n"); + } + var out = Module["print"] || defaultPrint; + var err = Module["printErr"] || defaultPrintErr; + Object.assign(Module, moduleOverrides); + moduleOverrides = null; + if (Module["arguments"]) + arguments_ = Module["arguments"]; + if (Module["thisProgram"]) + thisProgram = Module["thisProgram"]; + if (Module["quit"]) + quit_ = Module["quit"]; + var POINTER_SIZE = 4; + function warnOnce(text) { + if (!warnOnce.shown) + warnOnce.shown = {}; + if (!warnOnce.shown[text]) { + warnOnce.shown[text] = 1; + err(text); + } + } + function convertJsFunctionToWasm(func2, sig) { + if (typeof WebAssembly.Function === "function") { + var typeNames = { "i": "i32", "j": "i64", "f": "f32", "d": "f64" }; + var type = { parameters: [], results: sig[0] == "v" ? [] : [typeNames[sig[0]]] }; + for (var i = 1; i < sig.length; ++i) { + type.parameters.push(typeNames[sig[i]]); + } + return new WebAssembly.Function(type, func2); + } + var typeSection = [1, 0, 1, 96]; + var sigRet = sig.slice(0, 1); + var sigParam = sig.slice(1); + var typeCodes = { "i": 127, "j": 126, "f": 125, "d": 124 }; + typeSection.push(sigParam.length); + for (var i = 0; i < sigParam.length; ++i) { + typeSection.push(typeCodes[sigParam[i]]); + } + if (sigRet == "v") { + typeSection.push(0); + } else { + typeSection = typeSection.concat([1, typeCodes[sigRet]]); + } + typeSection[1] = typeSection.length - 2; + var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])); + var module2 = new WebAssembly.Module(bytes); + var instance = new WebAssembly.Instance(module2, { "e": { "f": func2 } }); + var wrappedFunc = instance.exports["f"]; + return wrappedFunc; + } + var freeTableIndexes = []; + var functionsInTableMap; + function getEmptyTableSlot() { + if (freeTableIndexes.length) { + return freeTableIndexes.pop(); + } + try { + wasmTable.grow(1); + } catch (err2) { + if (!(err2 instanceof RangeError)) { + throw err2; + } + throw "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH."; + } + return wasmTable.length - 1; + } + function updateTableMap(offset, count22) { + for (var i = offset; i < offset + count22; i++) { + var item = getWasmTableEntry(i); + if (item) { + functionsInTableMap.set(item, i); + } + } + } + var tempRet0 = 0; + var setTempRet0 = (value) => { + tempRet0 = value; + }; + var Atomics_load = Atomics.load; + var Atomics_store = Atomics.store; + var Atomics_compareExchange = Atomics.compareExchange; + var wasmBinary; + if (Module["wasmBinary"]) + wasmBinary = Module["wasmBinary"]; + var noExitRuntime = Module["noExitRuntime"] || true; + if (typeof WebAssembly !== "object") { + abort("no native wasm support detected"); + } + var wasmMemory; + var wasmModule; + var ABORT = false; + var EXITSTATUS; + function assert3(condition, text) { + if (!condition) { + abort(text); + } + } + function getCFunc(ident) { + var func2 = Module["_" + ident]; + return func2; + } + function ccall(ident, returnType, argTypes, args, opts) { + var toC = { "string": function(str) { + var ret2 = 0; + if (str !== null && str !== void 0 && str !== 0) { + var len = (str.length << 2) + 1; + ret2 = stackAlloc(len); + stringToUTF8(str, ret2, len); + } + return ret2; + }, "array": function(arr) { + var ret2 = stackAlloc(arr.length); + writeArrayToMemory(arr, ret2); + return ret2; + } }; + function convertReturnValue(ret2) { + if (returnType === "string") + return UTF8ToString(ret2); + if (returnType === "boolean") + return Boolean(ret2); + return ret2; + } + var func2 = getCFunc(ident); + var cArgs = []; + var stack2 = 0; + if (args) { + for (var i = 0; i < args.length; i++) { + var converter = toC[argTypes[i]]; + if (converter) { + if (stack2 === 0) + stack2 = stackSave(); + cArgs[i] = converter(args[i]); + } else { + cArgs[i] = args[i]; + } + } + } + var ret = func2.apply(null, cArgs); + function onDone(ret2) { + if (stack2 !== 0) + stackRestore(stack2); + return convertReturnValue(ret2); + } + ret = onDone(ret); + return ret; + } + function cwrap(ident, returnType, argTypes, opts) { + argTypes = argTypes || []; + var numericArgs = argTypes.every(function(type) { + return type === "number"; + }); + var numericRet = returnType !== "string"; + if (numericRet && numericArgs && !opts) { + return getCFunc(ident); + } + return function() { + return ccall(ident, returnType, argTypes, arguments, opts); + }; + } + var ALLOC_STACK = 1; + function TextDecoderWrapper(encoding) { + var textDecoder = new TextDecoder(encoding); + this.decode = (data) => { + if (data.buffer instanceof SharedArrayBuffer) { + data = new Uint8Array(data); + } + return textDecoder.decode.call(textDecoder, data); + }; + } + var UTF8Decoder = typeof TextDecoder !== "undefined" ? new TextDecoderWrapper("utf8") : void 0; + function UTF8ArrayToString(heap, idx, maxBytesToRead) { + var endIdx = idx + maxBytesToRead; + var endPtr = idx; + while (heap[endPtr] && !(endPtr >= endIdx)) + ++endPtr; + if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) { + return UTF8Decoder.decode(heap.subarray(idx, endPtr)); + } else { + var str = ""; + while (idx < endPtr) { + var u0 = heap[idx++]; + if (!(u0 & 128)) { + str += String.fromCharCode(u0); + continue; + } + var u1 = heap[idx++] & 63; + if ((u0 & 224) == 192) { + str += String.fromCharCode((u0 & 31) << 6 | u1); + continue; + } + var u2 = heap[idx++] & 63; + if ((u0 & 240) == 224) { + u0 = (u0 & 15) << 12 | u1 << 6 | u2; + } else { + u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63; + } + if (u0 < 65536) { + str += String.fromCharCode(u0); + } else { + var ch = u0 - 65536; + str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023); + } + } + } + return str; + } + function UTF8ToString(ptr, maxBytesToRead) { + return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : ""; + } + function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) { + if (!(maxBytesToWrite > 0)) + return 0; + var startIdx = outIdx; + var endIdx = outIdx + maxBytesToWrite - 1; + for (var i = 0; i < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 55296 && u <= 57343) { + var u1 = str.charCodeAt(++i); + u = 65536 + ((u & 1023) << 10) | u1 & 1023; + } + if (u <= 127) { + if (outIdx >= endIdx) + break; + heap[outIdx++] = u; + } else if (u <= 2047) { + if (outIdx + 1 >= endIdx) + break; + heap[outIdx++] = 192 | u >> 6; + heap[outIdx++] = 128 | u & 63; + } else if (u <= 65535) { + if (outIdx + 2 >= endIdx) + break; + heap[outIdx++] = 224 | u >> 12; + heap[outIdx++] = 128 | u >> 6 & 63; + heap[outIdx++] = 128 | u & 63; + } else { + if (outIdx + 3 >= endIdx) + break; + heap[outIdx++] = 240 | u >> 18; + heap[outIdx++] = 128 | u >> 12 & 63; + heap[outIdx++] = 128 | u >> 6 & 63; + heap[outIdx++] = 128 | u & 63; + } + } + heap[outIdx] = 0; + return outIdx - startIdx; + } + function stringToUTF8(str, outPtr, maxBytesToWrite) { + return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite); + } + function lengthBytesUTF8(str) { + var len = 0; + for (var i = 0; i < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 55296 && u <= 57343) + u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023; + if (u <= 127) + ++len; + else if (u <= 2047) + len += 2; + else if (u <= 65535) + len += 3; + else + len += 4; + } + return len; + } + var UTF16Decoder = typeof TextDecoder !== "undefined" ? new TextDecoderWrapper("utf-16le") : void 0; + function writeArrayToMemory(array2, buffer3) { + GROWABLE_HEAP_I8().set(array2, buffer3); + } + function writeAsciiToMemory(str, buffer3, dontAddNull) { + for (var i = 0; i < str.length; ++i) { + GROWABLE_HEAP_I8()[buffer3++ >> 0] = str.charCodeAt(i); + } + if (!dontAddNull) + GROWABLE_HEAP_I8()[buffer3 >> 0] = 0; + } + function alignUp(x, multiple) { + if (x % multiple > 0) { + x += multiple - x % multiple; + } + return x; + } + var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64; + if (ENVIRONMENT_IS_PTHREAD) { + buffer2 = Module["buffer"]; + } + function updateGlobalBufferAndViews(buf) { + buffer2 = buf; + Module["HEAP8"] = HEAP8 = new Int8Array(buf); + Module["HEAP16"] = HEAP16 = new Int16Array(buf); + Module["HEAP32"] = HEAP32 = new Int32Array(buf); + Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf); + Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf); + Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf); + Module["HEAPF32"] = HEAPF32 = new Float32Array(buf); + Module["HEAPF64"] = HEAPF64 = new Float64Array(buf); + } + var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216; + if (ENVIRONMENT_IS_PTHREAD) { + wasmMemory = Module["wasmMemory"]; + buffer2 = Module["buffer"]; + } else { + if (Module["wasmMemory"]) { + wasmMemory = Module["wasmMemory"]; + } else { + wasmMemory = new WebAssembly.Memory({ "initial": INITIAL_MEMORY / 65536, "maximum": 2147483648 / 65536, "shared": true }); + if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) { + err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"); + if (ENVIRONMENT_IS_NODE) { + console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"); + } + throw Error("bad memory"); + } + } + } + if (wasmMemory) { + buffer2 = wasmMemory.buffer; + } + INITIAL_MEMORY = buffer2.byteLength; + updateGlobalBufferAndViews(buffer2); + var wasmTable; + var __ATPRERUN__ = []; + var __ATINIT__ = []; + var __ATEXIT__ = []; + var __ATPOSTRUN__ = []; + var runtimeInitialized = false; + var runtimeExited = false; + var runtimeKeepaliveCounter = 0; + function keepRuntimeAlive() { + return noExitRuntime || runtimeKeepaliveCounter > 0; + } + function preRun() { + if (Module["preRun"]) { + if (typeof Module["preRun"] == "function") + Module["preRun"] = [Module["preRun"]]; + while (Module["preRun"].length) { + addOnPreRun(Module["preRun"].shift()); + } + } + callRuntimeCallbacks(__ATPRERUN__); + } + function initRuntime() { + runtimeInitialized = true; + if (ENVIRONMENT_IS_PTHREAD) + return; + callRuntimeCallbacks(__ATINIT__); + } + function exitRuntime() { + if (ENVIRONMENT_IS_PTHREAD) + return; + PThread.terminateAllThreads(); + runtimeExited = true; + } + function postRun() { + if (ENVIRONMENT_IS_PTHREAD) + return; + if (Module["postRun"]) { + if (typeof Module["postRun"] == "function") + Module["postRun"] = [Module["postRun"]]; + while (Module["postRun"].length) { + addOnPostRun(Module["postRun"].shift()); + } + } + callRuntimeCallbacks(__ATPOSTRUN__); + } + function addOnPreRun(cb) { + __ATPRERUN__.unshift(cb); + } + function addOnInit(cb) { + __ATINIT__.unshift(cb); + } + function addOnPostRun(cb) { + __ATPOSTRUN__.unshift(cb); + } + var runDependencies = 0; + var runDependencyWatcher = null; + var dependenciesFulfilled = null; + function addRunDependency(id) { + runDependencies++; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + } + function removeRunDependency(id) { + runDependencies--; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + if (runDependencies == 0) { + if (runDependencyWatcher !== null) { + clearInterval(runDependencyWatcher); + runDependencyWatcher = null; + } + if (dependenciesFulfilled) { + var callback = dependenciesFulfilled; + dependenciesFulfilled = null; + callback(); + } + } + } + Module["preloadedImages"] = {}; + Module["preloadedAudios"] = {}; + function abort(what) { + if (ENVIRONMENT_IS_PTHREAD) { + postMessage({ "cmd": "onAbort", "arg": what }); + } else { + if (Module["onAbort"]) { + Module["onAbort"](what); + } + } + what = "Aborted(" + what + ")"; + err(what); + ABORT = true; + EXITSTATUS = 1; + what += ". Build with -s ASSERTIONS=1 for more info."; + var e = new WebAssembly.RuntimeError(what); + readyPromiseReject(e); + throw e; + } + var dataURIPrefix = "data:application/octet-stream;base64,"; + function isDataURI(filename) { + return filename.startsWith(dataURIPrefix); + } + function isFileURI(filename) { + return filename.startsWith("file://"); + } + var wasmBinaryFile; + wasmBinaryFile = "tfjs-backend-wasm-threaded-simd.wasm"; + if (!isDataURI(wasmBinaryFile)) { + wasmBinaryFile = locateFile(wasmBinaryFile); + } + function getBinary(file) { + try { + if (file == wasmBinaryFile && wasmBinary) { + return new Uint8Array(wasmBinary); + } + if (readBinary) { + return readBinary(file); + } else { + throw "both async and sync fetching of the wasm failed"; + } + } catch (err2) { + abort(err2); + } + } + function getBinaryPromise() { + if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) { + if (typeof fetch === "function" && !isFileURI(wasmBinaryFile)) { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + if (!response["ok"]) { + throw "failed to load wasm binary file at '" + wasmBinaryFile + "'"; + } + return response["arrayBuffer"](); + }).catch(function() { + return getBinary(wasmBinaryFile); + }); + } else { + if (readAsync) { + return new Promise(function(resolve, reject) { + readAsync(wasmBinaryFile, function(response) { + resolve(new Uint8Array(response)); + }, reject); + }); + } + } + } + return Promise.resolve().then(function() { + return getBinary(wasmBinaryFile); + }); + } + function createWasm() { + var info = { "env": asmLibraryArg, "wasi_snapshot_preview1": asmLibraryArg }; + function receiveInstance(instance, module2) { + var exports3 = instance.exports; + Module["asm"] = exports3; + registerTlsInit(Module["asm"]["emscripten_tls_init"]); + wasmTable = Module["asm"]["__indirect_function_table"]; + addOnInit(Module["asm"]["__wasm_call_ctors"]); + wasmModule = module2; + if (!ENVIRONMENT_IS_PTHREAD) { + var numWorkersToLoad = PThread.unusedWorkers.length; + PThread.unusedWorkers.forEach(function(w) { + PThread.loadWasmModuleToWorker(w, function() { + if (!--numWorkersToLoad) + removeRunDependency("wasm-instantiate"); + }); + }); + } + } + if (!ENVIRONMENT_IS_PTHREAD) { + addRunDependency("wasm-instantiate"); + } + function receiveInstantiationResult(result) { + receiveInstance(result["instance"], result["module"]); + } + function instantiateArrayBuffer(receiver) { + return getBinaryPromise().then(function(binary) { + return WebAssembly.instantiate(binary, info); + }).then(function(instance) { + return instance; + }).then(receiver, function(reason) { + err("failed to asynchronously prepare wasm: " + reason); + abort(reason); + }); + } + function instantiateAsync() { + if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + var result = WebAssembly.instantiateStreaming(response, info); + return result.then(receiveInstantiationResult, function(reason) { + err("wasm streaming compile failed: " + reason); + err("falling back to ArrayBuffer instantiation"); + return instantiateArrayBuffer(receiveInstantiationResult); + }); + }); + } else { + return instantiateArrayBuffer(receiveInstantiationResult); + } + } + if (Module["instantiateWasm"]) { + try { + var exports2 = Module["instantiateWasm"](info, receiveInstance); + return exports2; + } catch (e) { + err("Module.instantiateWasm callback failed with error: " + e); + return false; + } + } + instantiateAsync().catch(readyPromiseReject); + return {}; + } + var tempDouble; + var tempI64; + var ASM_CONSTS = {}; + function callRuntimeCallbacks(callbacks2) { + while (callbacks2.length > 0) { + var callback = callbacks2.shift(); + if (typeof callback == "function") { + callback(Module); + continue; + } + var func2 = callback.func; + if (typeof func2 === "number") { + if (callback.arg === void 0) { + getWasmTableEntry(func2)(); + } else { + getWasmTableEntry(func2)(callback.arg); + } + } else { + func2(callback.arg === void 0 ? null : callback.arg); + } + } + } + function withStackSave(f) { + var stack2 = stackSave(); + var ret = f(); + stackRestore(stack2); + return ret; + } + function demangle(func2) { + return func2; + } + function demangleAll(text) { + var regex = /\b_Z[\w\d_]+/g; + return text.replace(regex, function(x) { + var y = demangle(x); + return x === y ? x : y + " [" + x + "]"; + }); + } + function killThread(pthread_ptr) { + GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0; + var pthread = PThread.pthreads[pthread_ptr]; + delete PThread.pthreads[pthread_ptr]; + pthread.worker.terminate(); + __emscripten_thread_free_data(pthread_ptr); + PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker), 1); + pthread.worker.pthread = void 0; + } + function cancelThread(pthread_ptr) { + var pthread = PThread.pthreads[pthread_ptr]; + pthread.worker.postMessage({ "cmd": "cancel" }); + } + function cleanupThread(pthread_ptr) { + var pthread = PThread.pthreads[pthread_ptr]; + if (pthread) { + GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0; + var worker = pthread.worker; + PThread.returnWorkerToPool(worker); + } + } + function _exit(status) { + exit(status); + } + function handleException(e) { + if (e instanceof ExitStatus || e == "unwind") { + return EXITSTATUS; + } + quit_(1, e); + } + var PThread = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() { + if (ENVIRONMENT_IS_PTHREAD) { + PThread.initWorker(); + } else { + PThread.initMainThread(); + } + }, initMainThread: function() { + var pthreadPoolSize = 8; + for (var i = 0; i < pthreadPoolSize; ++i) { + PThread.allocateUnusedWorker(); + } + }, initWorker: function() { + noExitRuntime = false; + }, pthreads: {}, setExitStatus: function(status) { + EXITSTATUS = status; + }, terminateAllThreads: function() { + for (var t in PThread.pthreads) { + var pthread = PThread.pthreads[t]; + if (pthread && pthread.worker) { + PThread.returnWorkerToPool(pthread.worker); + } + } + for (var i = 0; i < PThread.unusedWorkers.length; ++i) { + var worker = PThread.unusedWorkers[i]; + worker.terminate(); + } + PThread.unusedWorkers = []; + }, returnWorkerToPool: function(worker) { + PThread.runWithoutMainThreadQueuedCalls(function() { + delete PThread.pthreads[worker.pthread.threadInfoStruct]; + PThread.unusedWorkers.push(worker); + PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1); + __emscripten_thread_free_data(worker.pthread.threadInfoStruct); + worker.pthread = void 0; + }); + }, runWithoutMainThreadQueuedCalls: function(func2) { + GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 0; + try { + func2(); + } finally { + GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 1; + } + }, receiveObjectTransfer: function(data) { + }, threadInit: function() { + for (var i in PThread.tlsInitFunctions) { + PThread.tlsInitFunctions[i](); + } + }, loadWasmModuleToWorker: function(worker, onFinishedLoading) { + worker.onmessage = (e) => { + var d = e["data"]; + var cmd = d["cmd"]; + if (worker.pthread) + PThread.currentProxiedOperationCallerThread = worker.pthread.threadInfoStruct; + if (d["targetThread"] && d["targetThread"] != _pthread_self()) { + var thread = PThread.pthreads[d.targetThread]; + if (thread) { + thread.worker.postMessage(d, d["transferList"]); + } else { + err('Internal error! Worker sent a message "' + cmd + '" to target pthread ' + d["targetThread"] + ", but that thread no longer exists!"); + } + PThread.currentProxiedOperationCallerThread = void 0; return; } - B(); - }, Q.onerror = B, Q.send(null); - }), $ = (C) => document.title = C); - T && typeof performance == "undefined" && (global.performance = l$().performance); - var Y = console.log.bind(console), Z = console.warn.bind(console); - T && (K(), Y = (C) => W.writeSync(1, C + ` -`), Z = (C) => W.writeSync(2, C + ` -`)); - var te = d.print || Y, ee = d.printErr || Z; - Object.assign(d, g), g = null, d.arguments && (b = d.arguments), d.thisProgram && (y = d.thisProgram), d.quit && (v = d.quit); - var se = 4; - function ne(C) { - ne.shown || (ne.shown = {}), ne.shown[C] || (ne.shown[C] = 1, ee(C)); - } - function oe(C, D) { - if (typeof WebAssembly.Function == "function") { - for (var B = { i: "i32", j: "i64", f: "f32", d: "f64" }, Q = { parameters: [], results: D[0] == "v" ? [] : [B[D[0]]] }, ue = 1; ue < D.length; ++ue) - Q.parameters.push(B[D[ue]]); - return new WebAssembly.Function(Q, C); - } - var pe = [1, 0, 1, 96], ye = D.slice(0, 1), Te = D.slice(1), bt = { i: 127, j: 126, f: 125, d: 124 }; - pe.push(Te.length); - for (var ue = 0; ue < Te.length; ++ue) - pe.push(bt[Te[ue]]); - ye == "v" ? pe.push(0) : pe = pe.concat([1, bt[ye]]), pe[1] = pe.length - 2; - var us = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(pe, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), ls = new WebAssembly.Module(us), Bc = new WebAssembly.Instance(ls, { e: { f: C } }), Su = Bc.exports.f; - return Su; - } - var re = [], le; - function me() { - if (re.length) - return re.pop(); - try { - Fn.grow(1); - } catch (C) { - throw C instanceof RangeError ? "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH." : C; - } - return Fn.length - 1; - } - function ke(C, D) { - for (var B = C; B < C + D; B++) { - var Q = Ai(B); - Q && le.set(Q, B); - } - } - var Se = 0, Ee = (C) => { - Se = C; - }, Pe = Atomics.load, Xe = Atomics.store, Je = Atomics.compareExchange, Ye; - d.wasmBinary && (Ye = d.wasmBinary); - var tt = d.noExitRuntime || true; - typeof WebAssembly != "object" && Ti("no native wasm support detected"); - var Ce, ut, at = false, Jt; - function Nt(C, D) { - C || Ti(D); - } - function Cn(C) { - var D = d["_" + C]; - return D; - } - function Et(C, D, B, Q, ue) { - var pe = { string: function($n) { - var zi = 0; - if ($n != null && $n !== 0) { - var nx = ($n.length << 2) + 1; - zi = Pi(nx), Ls($n, zi, nx); + if (cmd === "processQueuedMainThreadWork") { + _emscripten_main_thread_process_queued_calls(); + } else if (cmd === "spawnThread") { + spawnThread(d); + } else if (cmd === "cleanupThread") { + cleanupThread(d["thread"]); + } else if (cmd === "killThread") { + killThread(d["thread"]); + } else if (cmd === "cancelThread") { + cancelThread(d["thread"]); + } else if (cmd === "loaded") { + worker.loaded = true; + if (onFinishedLoading) + onFinishedLoading(worker); + if (worker.runPthread) { + worker.runPthread(); + delete worker.runPthread; + } + } else if (cmd === "print") { + out("Thread " + d["threadId"] + ": " + d["text"]); + } else if (cmd === "printErr") { + err("Thread " + d["threadId"] + ": " + d["text"]); + } else if (cmd === "alert") { + alert("Thread " + d["threadId"] + ": " + d["text"]); + } else if (d.target === "setimmediate") { + worker.postMessage(d); + } else if (cmd === "onAbort") { + if (Module["onAbort"]) { + Module["onAbort"](d["arg"]); + } + } else { + err("worker sent an unknown command " + cmd); + } + PThread.currentProxiedOperationCallerThread = void 0; + }; + worker.onerror = (e) => { + var message = "worker sent an error!"; + err(message + " " + e.filename + ":" + e.lineno + ": " + e.message); + throw e; + }; + if (ENVIRONMENT_IS_NODE) { + worker.on("message", function(data) { + worker.onmessage({ data }); + }); + worker.on("error", function(e) { + worker.onerror(e); + }); + worker.on("detachedExit", function() { + }); } - return zi; - }, array: function($n) { - var zi = Pi($n.length); - return Bs($n, zi), zi; + worker.postMessage({ "cmd": "load", "urlOrBlob": Module["mainScriptUrlOrBlob"] || _scriptDir, "wasmMemory": wasmMemory, "wasmModule": wasmModule }); + }, allocateUnusedWorker: function() { + var pthreadMainJs = locateFile("tfjs-backend-wasm-threaded-simd.worker.js"); + PThread.unusedWorkers.push(new Worker(pthreadMainJs)); + }, getNewWorker: function() { + if (PThread.unusedWorkers.length == 0) { + PThread.allocateUnusedWorker(); + PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]); + } + return PThread.unusedWorkers.pop(); } }; - function ye($n) { - return D === "string" ? tn($n) : D === "boolean" ? Boolean($n) : $n; + function establishStackSpace() { + var pthread_ptr = _pthread_self(); + var stackTop = GROWABLE_HEAP_I32()[pthread_ptr + 44 >> 2]; + var stackSize = GROWABLE_HEAP_I32()[pthread_ptr + 48 >> 2]; + var stackMax = stackTop - stackSize; + _emscripten_stack_set_limits(stackTop, stackMax); + stackRestore(stackTop); } - var Te = Cn(C), bt = [], us = 0; - if (Q) - for (var ls = 0; ls < Q.length; ls++) { - var Bc = pe[B[ls]]; - Bc ? (us === 0 && (us = Df()), bt[ls] = Bc(Q[ls])) : bt[ls] = Q[ls]; - } - var Su = Te.apply(null, bt); - function GT($n) { - return us !== 0 && Pc(us), ye($n); - } - return Su = GT(Su), Su; - } - function en(C, D, B, Q) { - B = B || []; - var ue = B.every(function(ye) { - return ye === "number"; - }), pe = D !== "string"; - return pe && ue && !Q ? Cn(C) : function() { - return Et(C, D, B, arguments, Q); - }; - } - var Nn = 1; - function Tn(C) { - var D = new TextDecoder(C); - this.decode = (B) => (B.buffer instanceof SharedArrayBuffer && (B = new Uint8Array(B)), D.decode.call(D, B)); - } - var Yt = typeof TextDecoder != "undefined" ? new Tn("utf8") : void 0; - function Dn(C, D, B) { - for (var Q = D + B, ue = D; C[ue] && !(ue >= Q); ) - ++ue; - if (ue - D > 16 && C.subarray && Yt) - return Yt.decode(C.subarray(D, ue)); - for (var pe = ""; D < ue; ) { - var ye = C[D++]; - if (!(ye & 128)) { - pe += String.fromCharCode(ye); - continue; - } - var Te = C[D++] & 63; - if ((ye & 224) == 192) { - pe += String.fromCharCode((ye & 31) << 6 | Te); - continue; - } - var bt = C[D++] & 63; - if ((ye & 240) == 224 ? ye = (ye & 15) << 12 | Te << 6 | bt : ye = (ye & 7) << 18 | Te << 12 | bt << 6 | C[D++] & 63, ye < 65536) - pe += String.fromCharCode(ye); - else { - var us = ye - 65536; - pe += String.fromCharCode(55296 | us >> 10, 56320 | us & 1023); - } - } - return pe; - } - function tn(C, D) { - return C ? Dn(i(), C, D) : ""; - } - function Ms(C, D, B, Q) { - if (!(Q > 0)) - return 0; - for (var ue = B, pe = B + Q - 1, ye = 0; ye < C.length; ++ye) { - var Te = C.charCodeAt(ye); - if (Te >= 55296 && Te <= 57343) { - var bt = C.charCodeAt(++ye); - Te = 65536 + ((Te & 1023) << 10) | bt & 1023; - } - if (Te <= 127) { - if (B >= pe) - break; - D[B++] = Te; - } else if (Te <= 2047) { - if (B + 1 >= pe) - break; - D[B++] = 192 | Te >> 6, D[B++] = 128 | Te & 63; - } else if (Te <= 65535) { - if (B + 2 >= pe) - break; - D[B++] = 224 | Te >> 12, D[B++] = 128 | Te >> 6 & 63, D[B++] = 128 | Te & 63; - } else { - if (B + 3 >= pe) - break; - D[B++] = 240 | Te >> 18, D[B++] = 128 | Te >> 12 & 63, D[B++] = 128 | Te >> 6 & 63, D[B++] = 128 | Te & 63; - } - } - return D[B] = 0, B - ue; - } - function Ls(C, D, B) { - return Ms(C, i(), D, B); - } - function Si(C) { - for (var D = 0, B = 0; B < C.length; ++B) { - var Q = C.charCodeAt(B); - Q >= 55296 && Q <= 57343 && (Q = 65536 + ((Q & 1023) << 10) | C.charCodeAt(++B) & 1023), Q <= 127 ? ++D : Q <= 2047 ? D += 2 : Q <= 65535 ? D += 3 : D += 4; - } - return D; - } - var er = typeof TextDecoder != "undefined" ? new Tn("utf-16le") : void 0; - function Bs(C, D) { - a().set(C, D); - } - function hu(C, D, B) { - for (var Q = 0; Q < C.length; ++Q) - a()[D++ >> 0] = C.charCodeAt(Q); - B || (a()[D >> 0] = 0); - } - function Ci(C, D) { - return C % D > 0 && (C += D - C % D), C; - } - var nn, ic, oc, fu, uc, lc, Pv, cc, dc; - N && (nn = d.buffer); - function rs(C) { - nn = C, d.HEAP8 = ic = new Int8Array(C), d.HEAP16 = fu = new Int16Array(C), d.HEAP32 = lc = new Int32Array(C), d.HEAPU8 = oc = new Uint8Array(C), d.HEAPU16 = uc = new Uint16Array(C), d.HEAPU32 = Pv = new Uint32Array(C), d.HEAPF32 = cc = new Float32Array(C), d.HEAPF64 = dc = new Float64Array(C); - } - var pc = d.INITIAL_MEMORY || 16777216; - if (N) - Ce = d.wasmMemory, nn = d.buffer; - else if (d.wasmMemory) - Ce = d.wasmMemory; - else if (Ce = new WebAssembly.Memory({ initial: pc / 65536, maximum: 32768, shared: true }), !(Ce.buffer instanceof SharedArrayBuffer)) - throw ee("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"), T && console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"), Error("bad memory"); - Ce && (nn = Ce.buffer), pc = nn.byteLength, rs(nn); - var Fn, Ni = [], tr = [], sh = [], hc = [], Pr = false, rh = false, fc = 0; - function zr() { - return tt || fc > 0; - } - function sn() { - if (d.preRun) - for (typeof d.preRun == "function" && (d.preRun = [d.preRun]); d.preRun.length; ) - zv(d.preRun.shift()); - vc(Ni); - } - function mu() { - Pr = true, !N && vc(tr); - } - function ah() { - N || (_e.terminateAllThreads(), rh = true); - } - function ih() { - if (!N) { - if (d.postRun) - for (typeof d.postRun == "function" && (d.postRun = [d.postRun]); d.postRun.length; ) - gu(d.postRun.shift()); - vc(hc); - } - } - function zv(C) { - Ni.unshift(C); - } - function Mv(C) { - tr.unshift(C); - } - function gu(C) { - hc.unshift(C); - } - var nr = 0, mc = null, as = null; - function bu(C) { - nr++, d.monitorRunDependencies && d.monitorRunDependencies(nr); - } - function Lv(C) { - if (nr--, d.monitorRunDependencies && d.monitorRunDependencies(nr), nr == 0 && (mc !== null && (clearInterval(mc), mc = null), as)) { - var D = as; - as = null, D(); - } - } - d.preloadedImages = {}, d.preloadedAudios = {}; - function Ti(C) { - N ? postMessage({ cmd: "onAbort", arg: C }) : d.onAbort && d.onAbort(C), C = "Aborted(" + C + ")", ee(C), at = true, Jt = 1, C += ". Build with -s ASSERTIONS=1 for more info."; - var D = new WebAssembly.RuntimeError(C); - throw f(D), D; - } - var oh = "data:application/octet-stream;base64,"; - function gc(C) { - return C.startsWith(oh); - } - function bc(C) { - return C.startsWith("file://"); - } - var rn; - rn = "tfjs-backend-wasm-threaded-simd.wasm", gc(rn) || (rn = A(rn)); - function yc(C) { - try { - if (C == rn && Ye) - return new Uint8Array(Ye); - if (F) - return F(C); - throw "both async and sync fetching of the wasm failed"; - } catch (D) { - Ti(D); - } - } - function $i() { - if (!Ye && (x || k)) { - if (typeof fetch == "function" && !bc(rn)) - return fetch(rn, { credentials: "same-origin" }).then(function(C) { - if (!C.ok) - throw "failed to load wasm binary file at '" + rn + "'"; - return C.arrayBuffer(); - }).catch(function() { - return yc(rn); - }); - if (R) - return new Promise(function(C, D) { - R(rn, function(B) { - C(new Uint8Array(B)); - }, D); - }); - } - return Promise.resolve().then(function() { - return yc(rn); - }); - } - function uh() { - var C = { env: Ec, wasi_snapshot_preview1: Ec }; - function D(ye, Te) { - var bt = ye.exports; - if (d.asm = bt, mh(d.asm.emscripten_tls_init), Fn = d.asm.__indirect_function_table, Mv(d.asm.__wasm_call_ctors), ut = Te, !N) { - var us = _e.unusedWorkers.length; - _e.unusedWorkers.forEach(function(ls) { - _e.loadWasmModuleToWorker(ls, function() { - --us || Lv("wasm-instantiate"); - }); - }); - } - } - N || bu("wasm-instantiate"); - function B(ye) { - D(ye.instance, ye.module); - } - function Q(ye) { - return $i().then(function(Te) { - return WebAssembly.instantiate(Te, C); - }).then(function(Te) { - return Te; - }).then(ye, function(Te) { - ee("failed to asynchronously prepare wasm: " + Te), Ti(Te); - }); - } - function ue() { - return !Ye && typeof WebAssembly.instantiateStreaming == "function" && !gc(rn) && !bc(rn) && typeof fetch == "function" ? fetch(rn, { credentials: "same-origin" }).then(function(ye) { - var Te = WebAssembly.instantiateStreaming(ye, C); - return Te.then(B, function(bt) { - return ee("wasm streaming compile failed: " + bt), ee("falling back to ArrayBuffer instantiation"), Q(B); - }); - }) : Q(B); - } - if (d.instantiateWasm) + Module["establishStackSpace"] = establishStackSpace; + function exitOnMainThread(returnCode) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(1, 0, returnCode); try { - var pe = d.instantiateWasm(C, D); - return pe; - } catch (ye) { - return ee("Module.instantiateWasm callback failed with error: " + ye), false; + _exit(returnCode); + } catch (e) { + handleException(e); } - return ue().catch(f), {}; - } - var Bv, Vv, lh = {}; - function vc(C) { - for (; C.length > 0; ) { - var D = C.shift(); - if (typeof D == "function") { - D(d); - continue; + } + var wasmTableMirror = []; + function getWasmTableEntry(funcPtr) { + var func2 = wasmTableMirror[funcPtr]; + if (!func2) { + if (funcPtr >= wasmTableMirror.length) + wasmTableMirror.length = funcPtr + 1; + wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr); } - var B = D.func; - typeof B == "number" ? D.arg === void 0 ? Ai(B)() : Ai(B)(D.arg) : B(D.arg === void 0 ? null : D.arg); + return func2; } - } - function _i(C) { - var D = Df(), B = C(); - return Pc(D), B; - } - function ZN(C) { - return C; - } - function Wv(C) { - var D = /\b_Z[\w\d_]+/g; - return C.replace(D, function(B) { - var Q = B; - return B === Q ? B : Q + " [" + B + "]"; - }); - } - function ch(C) { - l()[C >> 2] = 0; - var D = _e.pthreads[C]; - delete _e.pthreads[C], D.worker.terminate(), Rf(C), _e.runningWorkers.splice(_e.runningWorkers.indexOf(D.worker), 1), D.worker.pthread = void 0; - } - function dh(C) { - var D = _e.pthreads[C]; - D.worker.postMessage({ cmd: "cancel" }); - } - function xc(C) { - var D = _e.pthreads[C]; - if (D) { - l()[C >> 2] = 0; - var B = D.worker; - _e.returnWorkerToPool(B); + function invokeEntryPoint(ptr, arg) { + return getWasmTableEntry(ptr)(arg); } - } - function wc(C) { - VT(C); - } - function ph(C) { - if (C instanceof Iu || C == "unwind") - return Jt; - v(1, C); - } - var _e = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() { - N ? _e.initWorker() : _e.initMainThread(); - }, initMainThread: function() { - for (var C = 8, D = 0; D < C; ++D) - _e.allocateUnusedWorker(); - }, initWorker: function() { - tt = false; - }, pthreads: {}, setExitStatus: function(C) { - Jt = C; - }, terminateAllThreads: function() { - for (var C in _e.pthreads) { - var D = _e.pthreads[C]; - D && D.worker && _e.returnWorkerToPool(D.worker); - } - for (var B = 0; B < _e.unusedWorkers.length; ++B) { - var Q = _e.unusedWorkers[B]; - Q.terminate(); - } - _e.unusedWorkers = []; - }, returnWorkerToPool: function(C) { - _e.runWithoutMainThreadQueuedCalls(function() { - delete _e.pthreads[C.pthread.threadInfoStruct], _e.unusedWorkers.push(C), _e.runningWorkers.splice(_e.runningWorkers.indexOf(C), 1), Rf(C.pthread.threadInfoStruct), C.pthread = void 0; - }); - }, runWithoutMainThreadQueuedCalls: function(C) { - l()[tx >> 2] = 0; - try { - C(); - } finally { - l()[tx >> 2] = 1; - } - }, receiveObjectTransfer: function(C) { - }, threadInit: function() { - for (var C in _e.tlsInitFunctions) - _e.tlsInitFunctions[C](); - }, loadWasmModuleToWorker: function(C, D) { - C.onmessage = (B) => { - var Q = B.data, ue = Q.cmd; - if (C.pthread && (_e.currentProxiedOperationCallerThread = C.pthread.threadInfoStruct), Q.targetThread && Q.targetThread != Oc()) { - var pe = _e.pthreads[Q.targetThread]; - pe ? pe.worker.postMessage(Q, Q.transferList) : ee('Internal error! Worker sent a message "' + ue + '" to target pthread ' + Q.targetThread + ", but that thread no longer exists!"), _e.currentProxiedOperationCallerThread = void 0; - return; + Module["invokeEntryPoint"] = invokeEntryPoint; + function jsStackTrace() { + var error = new Error(); + if (!error.stack) { + try { + throw new Error(); + } catch (e) { + error = e; + } + if (!error.stack) { + return "(no stack trace available)"; + } } - ue === "processQueuedMainThreadWork" ? Yv() : ue === "spawnThread" ? Ic(Q) : ue === "cleanupThread" ? xc(Q.thread) : ue === "killThread" ? ch(Q.thread) : ue === "cancelThread" ? dh(Q.thread) : ue === "loaded" ? (C.loaded = true, D && D(C), C.runPthread && (C.runPthread(), delete C.runPthread)) : ue === "print" ? te("Thread " + Q.threadId + ": " + Q.text) : ue === "printErr" ? ee("Thread " + Q.threadId + ": " + Q.text) : ue === "alert" ? alert("Thread " + Q.threadId + ": " + Q.text) : Q.target === "setimmediate" ? 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Lr = () => { - var C = process.hrtime(); - return C[0] * 1e3 + C[1] / 1e6; - } : N ? Lr = () => performance.now() - d.__performance_now_clock_drift : Lr = () => performance.now(); - var gh = true; - function bh(C) { - return l()[Xv() >> 2] = C, C; - } - function yh(C, D) { - var B; - if (C === 0) - B = Date.now(); - else if ((C === 1 || C === 4) && gh) - B = Lr(); - else - return bh(28), -1; - return l()[D >> 2] = B / 1e3 | 0, l()[D + 4 >> 2] = B % 1e3 * 1e3 * 1e3 | 0, 0; - } - function vh(C, D) { - return yh(C, D); - } - function xh(C) { - Qv(C, !k, 1, !x), _e.threadInit(); - } - function wh(C) { - N ? postMessage({ cmd: "cleanupThread", thread: C }) : xc(C); - } - function Ic(C) { - var D = _e.getNewWorker(); - if (!D) - return 6; - _e.runningWorkers.push(D); - var B = _e.pthreads[C.pthread_ptr] = { worker: D, threadInfoStruct: C.pthread_ptr }; - D.pthread = B; - var Q = { cmd: "run", start_routine: C.startRoutine, arg: C.arg, threadInfoStruct: C.pthread_ptr }; - return D.runPthread = () => { - Q.time = performance.now(), D.postMessage(Q, C.transferList); - }, D.loaded && (D.runPthread(), delete D.runPthread), 0; - } - function kh(C, D, B, Q) { - if (typeof SharedArrayBuffer == "undefined") - return ee("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6; - var ue = [], pe = 0; - if (N && (ue.length === 0 || pe)) - return Zv(687865856, C, D, B, Q); - if (pe) - return pe; - var ye = { startRoutine: B, pthread_ptr: C, arg: Q, transferList: ue }; - return N ? (ye.cmd = "spawnThread", postMessage(ye, ue), 0) : Ic(ye); - } - function Ih() { - return 2097152; - } - function Sh(C, D) { - if (C == D) - postMessage({ cmd: "processQueuedMainThreadWork" }); - else if (N) - postMessage({ targetThread: C, cmd: "processThreadQueue" }); - else { - var B = _e.pthreads[C], Q = B && B.worker; - if (!Q) - return; - Q.postMessage({ cmd: "processThreadQueue" }); + function setWasmTableEntry(idx, func2) { + wasmTable.set(idx, func2); + wasmTableMirror[idx] = func2; } - return 1; - } - function Ch() { - Ti(""); - } - function Nh() { - T || k || ne("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread"); - } - function Sc() { - return 2147483648; - } - function Th(C, D, B) { - i().copyWithin(C, D, D + B); - } - function $h() { - return T ? c$().cpus().length : navigator.hardwareConcurrency; - } - function Br(C, D) { - var B = arguments.length - 2, Q = arguments; - return _i(function() { - for (var ue = B, pe = Pi(ue * 8), ye = pe >> 3, Te = 0; Te < B; Te++) { - var bt = Q[2 + Te]; - p()[ye + Te] = bt; - } - return Jv(C, ue, pe, D); - }); - } - var yu = []; - function _h(C, D, B) { - yu.length = D; - for (var Q = B >> 3, ue = 0; ue < D; ue++) - yu[ue] = p()[Q + ue]; - var pe = C < 0, ye = pe ? lh[-C - 1] : Kh[C]; - return ye.apply(null, yu); - } - function Ah(C) { - try { - return Ce.grow(C - nn.byteLength + 65535 >>> 16), rs(Ce.buffer), 1; - } catch (D) { - } - } - function Eh(C) { - var D = i().length; - if (C = C >>> 0, C <= D) - return false; - var B = Sc(); - if (C > B) - return false; - for (var Q = 1; Q <= 4; Q *= 2) { - var ue = D * (1 + 0.2 / Q); - ue = Math.min(ue, C + 100663296); - var pe = Math.min(B, Ci(Math.max(C, ue), 65536)), ye = Ah(pe); - if (ye) - return true; - } - return false; - } - var Be = { inEventHandler: 0, removeAllEventListeners: function() { - for (var C = Be.eventHandlers.length - 1; C >= 0; --C) - Be._removeHandler(C); - Be.eventHandlers = [], Be.deferredCalls = []; - }, registerRemoveEventListeners: function() { - Be.removeEventListenersRegistered || (sh.push(Be.removeAllEventListeners), Be.removeEventListenersRegistered = true); - }, deferredCalls: [], deferCall: function(C, D, B) { - function Q(ye, Te) { - if (ye.length != Te.length) - return false; - for (var bt in ye) - if (ye[bt] != Te[bt]) - return false; - return true; - } - for (var ue in Be.deferredCalls) { - var pe = Be.deferredCalls[ue]; - if (pe.targetFunction == C && Q(pe.argsList, B)) - return; - } - Be.deferredCalls.push({ targetFunction: C, precedence: D, argsList: B }), Be.deferredCalls.sort(function(ye, Te) { - return ye.precedence < Te.precedence; - }); - }, removeDeferredCalls: function(C) { - for (var D = 0; D < Be.deferredCalls.length; ++D) - Be.deferredCalls[D].targetFunction == C && (Be.deferredCalls.splice(D, 1), --D); - }, canPerformEventHandlerRequests: function() { - return Be.inEventHandler && Be.currentEventHandler.allowsDeferredCalls; - }, runDeferredCalls: function() { - if (!!Be.canPerformEventHandlerRequests()) - for (var C = 0; C < Be.deferredCalls.length; ++C) { - var D = Be.deferredCalls[C]; - Be.deferredCalls.splice(C, 1), --C, D.targetFunction.apply(null, D.argsList); - } - }, eventHandlers: [], removeAllHandlersOnTarget: function(C, D) { - for (var B = 0; B < Be.eventHandlers.length; ++B) - Be.eventHandlers[B].target == C && (!D || D == Be.eventHandlers[B].eventTypeString) && Be._removeHandler(B--); - }, _removeHandler: function(C) { - var D = Be.eventHandlers[C]; - D.target.removeEventListener(D.eventTypeString, D.eventListenerFunc, D.useCapture), Be.eventHandlers.splice(C, 1); - }, registerOrRemoveHandler: function(C) { - var D = function(ue) { - ++Be.inEventHandler, Be.currentEventHandler = C, Be.runDeferredCalls(), C.handlerFunc(ue), Be.runDeferredCalls(), --Be.inEventHandler; - }; - if (C.callbackfunc) - C.eventListenerFunc = D, C.target.addEventListener(C.eventTypeString, D, C.useCapture), Be.eventHandlers.push(C), Be.registerRemoveEventListeners(); - else - for (var B = 0; B < Be.eventHandlers.length; ++B) - Be.eventHandlers[B].target == C.target && Be.eventHandlers[B].eventTypeString == C.eventTypeString && Be._removeHandler(B--); - }, queueEventHandlerOnThread_iiii: function(C, D, B, Q, ue) { - _i(function() { - var pe = Pi(12); - l()[pe >> 2] = B, l()[pe + 4 >> 2] = Q, l()[pe + 8 >> 2] = ue, Ef(C, 637534208, D, Q, pe); - }); - }, getTargetThreadForEventCallback: function(C) { - switch (C) { - case 1: - return 0; - case 2: - return _e.currentProxiedOperationCallerThread; - default: - return C; - } - }, getNodeNameForTarget: function(C) { - return C ? C == window ? "#window" : C == screen ? "#screen" : C && C.nodeName ? 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Br(2, 1, C, D, B) : Cc(C, D, B); - } - function Mh(C, D, B) { - var Q = vu(C); - return Q ? Cc(C, D, B) : Nc(C, D, B); - } - function Lh() { - throw "unwind"; - } - function Bh(C) { - var D = C.getExtension("ANGLE_instanced_arrays"); - if (D) - return C.vertexAttribDivisor = function(B, Q) { - D.vertexAttribDivisorANGLE(B, Q); - }, C.drawArraysInstanced = function(B, Q, ue, pe) { - D.drawArraysInstancedANGLE(B, Q, ue, pe); - }, C.drawElementsInstanced = function(B, Q, ue, pe, ye) { - D.drawElementsInstancedANGLE(B, Q, ue, pe, ye); - }, 1; - } - function Vh(C) { - var D = C.getExtension("OES_vertex_array_object"); - if (D) - return C.createVertexArray = function() { - return D.createVertexArrayOES(); - }, C.deleteVertexArray = function(B) { - D.deleteVertexArrayOES(B); - }, C.bindVertexArray = function(B) { - D.bindVertexArrayOES(B); - }, C.isVertexArray = function(B) { - return D.isVertexArrayOES(B); - }, 1; - } - function Wh(C) { - var D = C.getExtension("WEBGL_draw_buffers"); - if (D) - return C.drawBuffers = function(B, Q) { - D.drawBuffersWEBGL(B, Q); - }, 1; - } - function Uh(C) { - return !!(C.multiDrawWebgl = C.getExtension("WEBGL_multi_draw")); - } - var gt = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function(D) { - gt.lastError || (gt.lastError = D); - }, getNewId: function(C) { - for (var D = gt.counter++, B = C.length; B < D; B++) - C[B] = null; - return D; - }, getSource: function(C, D, B, Q) { - for (var ue = "", pe = 0; pe < D; ++pe) { - var ye = Q ? l()[Q + pe * 4 >> 2] : -1; - ue += tn(l()[B + pe * 4 >> 2], ye < 0 ? void 0 : ye); + _emscripten_get_now = () => performance.now(); + var _emscripten_get_now_is_monotonic = true; + function setErrNo(value) { + GROWABLE_HEAP_I32()[___errno_location() >> 2] = value; + return value; } - return ue; - }, createContext: function(C, D) { - C.getContextSafariWebGL2Fixed || (C.getContextSafariWebGL2Fixed = C.getContext, C.getContext = function(ue, pe) { - var ye = C.getContextSafariWebGL2Fixed(ue, pe); - return ue == "webgl" == ye instanceof WebGLRenderingContext ? ye : null; - }); - var B = C.getContext("webgl", D); - if (!B) + function _clock_gettime(clk_id, tp) { + var now22; + if (clk_id === 0) { + now22 = Date.now(); + } else if ((clk_id === 1 || clk_id === 4) && _emscripten_get_now_is_monotonic) { + now22 = _emscripten_get_now(); + } else { + setErrNo(28); + return -1; + } + GROWABLE_HEAP_I32()[tp >> 2] = now22 / 1e3 | 0; + GROWABLE_HEAP_I32()[tp + 4 >> 2] = now22 % 1e3 * 1e3 * 1e3 | 0; return 0; - var Q = gt.registerContext(B, D); - return Q; - }, registerContext: function(C, D) { - var B = Af(8); - l()[B + 4 >> 2] = Oc(); - var Q = { handle: B, attributes: D, version: D.majorVersion, GLctx: C }; - return C.canvas && (C.canvas.GLctxObject = Q), gt.contexts[B] = Q, (typeof D.enableExtensionsByDefault == "undefined" || D.enableExtensionsByDefault) && gt.initExtensions(Q), B; - }, makeContextCurrent: function(C) { - return gt.currentContext = gt.contexts[C], d.ctx = Ac = gt.currentContext && gt.currentContext.GLctx, !(C && !Ac); - }, getContext: function(C) { - return gt.contexts[C]; - }, deleteContext: function(C) { - gt.currentContext === gt.contexts[C] && (gt.currentContext = null), typeof Be == "object" && Be.removeAllHandlersOnTarget(gt.contexts[C].GLctx.canvas), gt.contexts[C] && gt.contexts[C].GLctx.canvas && (gt.contexts[C].GLctx.canvas.GLctxObject = void 0), Kv(gt.contexts[C].handle), gt.contexts[C] = null; - }, initExtensions: function(C) { - if (C || (C = gt.currentContext), !C.initExtensionsDone) { - C.initExtensionsDone = true; - var D = C.GLctx; - Bh(D), Vh(D), Wh(D), D.disjointTimerQueryExt = D.getExtension("EXT_disjoint_timer_query"), Uh(D); - var B = D.getSupportedExtensions() || []; - B.forEach(function(Q) { - !Q.includes("lose_context") && !Q.includes("debug") && D.getExtension(Q); + } + function ___clock_gettime(a0, a12) { + return _clock_gettime(a0, a12); + } + function ___emscripten_init_main_thread_js(tb) { + __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1, !ENVIRONMENT_IS_WEB); + PThread.threadInit(); + } + function ___emscripten_thread_cleanup(thread) { + if (!ENVIRONMENT_IS_PTHREAD) + cleanupThread(thread); + else + postMessage({ "cmd": "cleanupThread", "thread": thread }); + } + function spawnThread(threadParams) { + var worker = PThread.getNewWorker(); + if (!worker) { + return 6; + } + PThread.runningWorkers.push(worker); + var pthread = PThread.pthreads[threadParams.pthread_ptr] = { worker, threadInfoStruct: threadParams.pthread_ptr }; + worker.pthread = pthread; + var msg = { "cmd": "run", "start_routine": threadParams.startRoutine, "arg": threadParams.arg, "threadInfoStruct": threadParams.pthread_ptr }; + worker.runPthread = () => { + msg.time = performance.now(); + worker.postMessage(msg, threadParams.transferList); + }; + if (worker.loaded) { + worker.runPthread(); + delete worker.runPthread; + } + return 0; + } + function ___pthread_create_js(pthread_ptr, attr, start_routine, arg) { + if (typeof SharedArrayBuffer === "undefined") { + err("Current environment does not support SharedArrayBuffer, pthreads are not available!"); + return 6; + } + var transferList = []; + var error = 0; + if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) { + return _emscripten_sync_run_in_main_thread_4(687865856, pthread_ptr, attr, start_routine, arg); + } + if (error) + return error; + var threadParams = { startRoutine: start_routine, pthread_ptr, arg, transferList }; + if (ENVIRONMENT_IS_PTHREAD) { + threadParams.cmd = "spawnThread"; + postMessage(threadParams, transferList); + return 0; + } + return spawnThread(threadParams); + } + function __emscripten_default_pthread_stack_size() { + return 2097152; + } + function __emscripten_notify_thread_queue(targetThreadId, mainThreadId) { + if (targetThreadId == mainThreadId) { + postMessage({ "cmd": "processQueuedMainThreadWork" }); + } else if (ENVIRONMENT_IS_PTHREAD) { + postMessage({ "targetThread": targetThreadId, "cmd": "processThreadQueue" }); + } else { + var pthread = PThread.pthreads[targetThreadId]; + var worker = pthread && pthread.worker; + if (!worker) { + return; + } + worker.postMessage({ "cmd": "processThreadQueue" }); + } + return 1; + } + function _abort() { + abort(""); + } + function _emscripten_check_blocking_allowed() { + if (ENVIRONMENT_IS_NODE) + return; + if (ENVIRONMENT_IS_WORKER) + return; + warnOnce("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread"); + } + function _emscripten_get_heap_max() { + return 2147483648; + } + function _emscripten_memcpy_big(dest, src, num) { + GROWABLE_HEAP_U8().copyWithin(dest, src, src + num); + } + function _emscripten_num_logical_cores() { + if (ENVIRONMENT_IS_NODE) + return require_os().cpus().length; + return navigator["hardwareConcurrency"]; + } + function _emscripten_proxy_to_main_thread_js(index2, sync) { + var numCallArgs = arguments.length - 2; + var outerArgs = arguments; + return withStackSave(function() { + var serializedNumCallArgs = numCallArgs; + var args = stackAlloc(serializedNumCallArgs * 8); + var b = args >> 3; + for (var i = 0; i < numCallArgs; i++) { + var arg = outerArgs[2 + i]; + GROWABLE_HEAP_F64()[b + i] = arg; + } + return _emscripten_run_in_main_runtime_thread_js(index2, serializedNumCallArgs, args, sync); }); } - } }, Gh = ["default", "low-power", "high-performance"]; - function Hh(C, D) { - var B = D >> 2, Q = l()[B + 6], ue = { alpha: !!l()[B + 0], depth: !!l()[B + 1], stencil: !!l()[B + 2], antialias: !!l()[B + 3], premultipliedAlpha: !!l()[B + 4], preserveDrawingBuffer: !!l()[B + 5], powerPreference: Gh[Q], failIfMajorPerformanceCaveat: !!l()[B + 7], majorVersion: l()[B + 8], minorVersion: l()[B + 9], enableExtensionsByDefault: l()[B + 10], explicitSwapControl: l()[B + 11], proxyContextToMainThread: l()[B + 12], renderViaOffscreenBackBuffer: l()[B + 13] }, pe = vu(C); - if (!pe || ue.explicitSwapControl) - return 0; - var ye = gt.createContext(pe, ue); - return ye; - } - function qh(C, D) { - return Hh(C, D); - } - var Ei = { mappings: {}, buffers: [null, [], []], printChar: function(C, D) { - var B = Ei.buffers[C]; - D === 0 || D === 10 ? ((C === 1 ? te : ee)(Dn(B, 0)), B.length = 0) : B.push(D); - }, varargs: void 0, get: function() { - Ei.varargs += 4; - var C = l()[Ei.varargs - 4 >> 2]; - return C; - }, getStr: function(C) { - var D = tn(C); - return D; - }, get64: function(C, D) { - return C; - } }; - function Tc(C) { - return N ? Br(3, 1, C) : 0; - } - function $c(C, D, B, Q, ue) { - if (N) - return Br(4, 1, C, D, B, Q, ue); - } - function _c(C, D, B, Q) { - if (N) - return Br(5, 1, C, D, B, Q); - for (var ue = 0, pe = 0; pe < B; pe++) { - var ye = l()[D >> 2], Te = l()[D + 4 >> 2]; - D += 8; - for (var bt = 0; bt < Te; bt++) - Ei.printChar(C, i()[ye + bt]); - ue += Te; + var _emscripten_receive_on_main_thread_js_callArgs = []; + function _emscripten_receive_on_main_thread_js(index2, numCallArgs, args) { + _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs; + var b = args >> 3; + for (var i = 0; i < numCallArgs; i++) { + _emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i]; + } + var isEmAsmConst = index2 < 0; + var func2 = !isEmAsmConst ? proxiedFunctionTable[index2] : ASM_CONSTS[-index2 - 1]; + return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs); } - return l()[Q >> 2] = ue, 0; - } - function jh(C) { - Ee(C); - } - _e.init(); - var Ac, Kh = [null, kc, Nc, Tc, $c, _c], Hv = false, Ec = { __clock_gettime: vh, __emscripten_init_main_thread_js: xh, __emscripten_thread_cleanup: wh, __pthread_create_js: kh, _emscripten_default_pthread_stack_size: Ih, _emscripten_notify_thread_queue: Sh, abort: Ch, emscripten_check_blocking_allowed: Nh, emscripten_get_heap_max: Sc, emscripten_get_now: Lr, emscripten_memcpy_big: Th, emscripten_num_logical_cores: $h, emscripten_receive_on_main_thread_js: _h, emscripten_resize_heap: Eh, emscripten_set_canvas_element_size: Mh, emscripten_unwind_to_js_event_loop: Lh, emscripten_webgl_create_context: qh, exit: wc, fd_close: Tc, fd_seek: $c, fd_write: _c, memory: Ce || d.wasmMemory, setTempRet0: jh }, qv = uh(), Xh = d.___wasm_call_ctors = function() { - return (Xh = d.___wasm_call_ctors = d.asm.__wasm_call_ctors).apply(null, arguments); - }, Yh = d._init = function() { - return (Yh = d._init = d.asm.init).apply(null, arguments); - }, Qh = d._init_with_threads_count = function() { - return (Qh = d._init_with_threads_count = d.asm.init_with_threads_count).apply(null, arguments); - }, Zh = d._get_threads_count = function() { - return (Zh = d._get_threads_count = d.asm.get_threads_count).apply(null, arguments); - }, Jh = d._register_tensor = function() { - return (Jh = d._register_tensor = d.asm.register_tensor).apply(null, arguments); - }, ef = d._dispose_data = function() { - return (ef = d._dispose_data = d.asm.dispose_data).apply(null, arguments); - }, tf = d._dispose = function() { - return (tf = d._dispose = d.asm.dispose).apply(null, arguments); - }, nf = d._Abs = function() { - return (nf = d._Abs = d.asm.Abs).apply(null, arguments); - }, sf = d._Add = function() { - return (sf = d._Add = d.asm.Add).apply(null, arguments); - }, rf = d._AddN = function() { - return (rf = d._AddN = d.asm.AddN).apply(null, arguments); - }, af = d._All = function() { - return (af = d._All = d.asm.All).apply(null, arguments); - }, of = d._Any = function() { - return (of = d._Any = d.asm.Any).apply(null, arguments); - }, uf = d._ArgMax = function() { - return (uf = d._ArgMax = d.asm.ArgMax).apply(null, arguments); - }, lf = d._AvgPool = function() { - return (lf = d._AvgPool = d.asm.AvgPool).apply(null, arguments); - }, cf = d._BatchMatMul = function() { - return (cf = d._BatchMatMul = d.asm.BatchMatMul).apply(null, arguments); - }, df = d._Ceil = function() { - return (df = d._Ceil = d.asm.Ceil).apply(null, arguments); - }, pf = d._ClipByValue = function() { - return (pf = d._ClipByValue = d.asm.ClipByValue).apply(null, arguments); - }, hf = d._Conv2D = function() { - return (hf = d._Conv2D = d.asm.Conv2D).apply(null, arguments); - }, ff = d._Conv2DBackpropInput = function() { - return (ff = d._Conv2DBackpropInput = d.asm.Conv2DBackpropInput).apply(null, arguments); - }, mf = d._Cos = function() { - return (mf = d._Cos = d.asm.Cos).apply(null, arguments); - }, gf = d._Cosh = function() { - return (gf = d._Cosh = d.asm.Cosh).apply(null, arguments); - }, bf = d._CropAndResize = function() { - return (bf = d._CropAndResize = d.asm.CropAndResize).apply(null, arguments); - }, yf = d._Cumprod = function() { - return (yf = d._Cumprod = d.asm.Cumprod).apply(null, arguments); - }, vf = d._Cumsum = function() { - return (vf = d._Cumsum = d.asm.Cumsum).apply(null, arguments); - }, xf = d._DepthToSpace = function() { - return (xf = d._DepthToSpace = d.asm.DepthToSpace).apply(null, arguments); - }, wf = d._DepthwiseConv2dNative = function() { - return (wf = d._DepthwiseConv2dNative = d.asm.DepthwiseConv2dNative).apply(null, arguments); - }, kf = d._Elu = function() { - return (kf = d._Elu = d.asm.Elu).apply(null, arguments); - }, If = d._Equal = function() { - return (If = d._Equal = d.asm.Equal).apply(null, arguments); - }, Sf = d._Exp = function() { - return (Sf = d._Exp = d.asm.Exp).apply(null, arguments); - }, Cf = d._FlipLeftRight = function() { - return (Cf = d._FlipLeftRight = d.asm.FlipLeftRight).apply(null, arguments); - }, Rc = d._Floor = function() { - return (Rc = d._Floor = d.asm.Floor).apply(null, arguments); - }, Dc = d._FloorDiv = function() { - return (Dc = d._FloorDiv = d.asm.FloorDiv).apply(null, arguments); - }, xu = d._FusedBatchNorm = function() { - return (xu = d._FusedBatchNorm = d.asm.FusedBatchNorm).apply(null, arguments); - }, Nf = d._FusedConv2D = function() { - return (Nf = d._FusedConv2D = d.asm.FusedConv2D).apply(null, arguments); - }, Tf = d._FusedDepthwiseConv2D = function() { - return (Tf = d._FusedDepthwiseConv2D = d.asm.FusedDepthwiseConv2D).apply(null, arguments); - }, Ri = d._Gather = function() { - return (Ri = d._Gather = d.asm.Gather).apply(null, arguments); - }, wu = d._GatherNd = function() { - return (wu = d._GatherNd = d.asm.GatherNd).apply(null, arguments); - }, ku = d._Greater = function() { - return (ku = d._Greater = d.asm.Greater).apply(null, arguments); - }, jv = d._GreaterEqual = function() { - return (jv = d._GreaterEqual = d.asm.GreaterEqual).apply(null, arguments); - }, Di = d._LeakyRelu = function() { - return (Di = d._LeakyRelu = d.asm.LeakyRelu).apply(null, arguments); - }, Fi = d._Less = function() { - return (Fi = d._Less = d.asm.Less).apply(null, arguments); - }, $f = d._LessEqual = function() { - return ($f = d._LessEqual = d.asm.LessEqual).apply(null, arguments); - }, H = d._Log = function() { - return (H = d._Log = d.asm.Log).apply(null, arguments); - }, J = d._LogicalAnd = function() { - return (J = d._LogicalAnd = d.asm.LogicalAnd).apply(null, arguments); - }, de = d._Max = function() { - return (de = d._Max = d.asm.Max).apply(null, arguments); - }, Ie = d._MaxPool = function() { - return (Ie = d._MaxPool = d.asm.MaxPool).apply(null, arguments); - }, Qe = d._Maximum = function() { - return (Qe = d._Maximum = d.asm.Maximum).apply(null, arguments); - }, Ze = d._Mean = function() { - return (Ze = d._Mean = d.asm.Mean).apply(null, arguments); - }, Ve = d._Min = function() { - return (Ve = d._Min = d.asm.Min).apply(null, arguments); - }, ze = d._Minimum = function() { - return (ze = d._Minimum = d.asm.Minimum).apply(null, arguments); - }, Tt = d._MirrorPad = function() { - return (Tt = d._MirrorPad = d.asm.MirrorPad).apply(null, arguments); - }, is = d._Multiply = function() { - return (is = d._Multiply = d.asm.Multiply).apply(null, arguments); - }, os = d._Neg = function() { - return (os = d._Neg = d.asm.Neg).apply(null, arguments); - }, Oi = d._NonMaxSuppressionV3 = function() { - return (Oi = d._NonMaxSuppressionV3 = d.asm.NonMaxSuppressionV3).apply(null, arguments); - }, Vr = d._NonMaxSuppressionV4 = function() { - return (Vr = d._NonMaxSuppressionV4 = d.asm.NonMaxSuppressionV4).apply(null, arguments); - }, _f = d._NonMaxSuppressionV5 = function() { - return (_f = d._NonMaxSuppressionV5 = d.asm.NonMaxSuppressionV5).apply(null, arguments); - }, an = d._NotEqual = function() { - return (an = d._NotEqual = d.asm.NotEqual).apply(null, arguments); - }, sr = d._OneHot = function() { - return (sr = d._OneHot = d.asm.OneHot).apply(null, arguments); - }, Fc = d._PadV2 = function() { - return (Fc = d._PadV2 = d.asm.PadV2).apply(null, arguments); - }, JN = d._Pow = function() { - return (JN = d._Pow = d.asm.Pow).apply(null, arguments); - }, eT = d._Prelu = function() { - return (eT = d._Prelu = d.asm.Prelu).apply(null, arguments); - }, tT = d._Prod = function() { - return (tT = d._Prod = d.asm.Prod).apply(null, arguments); - }, nT = d._RealDiv = function() { - return (nT = d._RealDiv = d.asm.RealDiv).apply(null, arguments); - }, sT = d._Relu = function() { - return (sT = d._Relu = d.asm.Relu).apply(null, arguments); - }, rT = d._Relu6 = function() { - return (rT = d._Relu6 = d.asm.Relu6).apply(null, arguments); - }, aT = d._ResizeBilinear = function() { - return (aT = d._ResizeBilinear = d.asm.ResizeBilinear).apply(null, arguments); - }, iT = d._Reverse = function() { - return (iT = d._Reverse = d.asm.Reverse).apply(null, arguments); - }, oT = d._RotateWithOffset = function() { - return (oT = d._RotateWithOffset = d.asm.RotateWithOffset).apply(null, arguments); - }, uT = d._Round = function() { - return (uT = d._Round = d.asm.Round).apply(null, arguments); - }, lT = d._Rsqrt = function() { - return (lT = d._Rsqrt = d.asm.Rsqrt).apply(null, arguments); - }, cT = d._ScatterNd = function() { - return (cT = d._ScatterNd = d.asm.ScatterNd).apply(null, arguments); - }, dT = d._SelectV2 = function() { - return (dT = d._SelectV2 = d.asm.SelectV2).apply(null, arguments); - }, pT = d._Sigmoid = function() { - return (pT = d._Sigmoid = d.asm.Sigmoid).apply(null, arguments); - }, hT = d._Sin = function() { - return (hT = d._Sin = d.asm.Sin).apply(null, arguments); - }, fT = d._Softmax = function() { - return (fT = d._Softmax = d.asm.Softmax).apply(null, arguments); - }, mT = d._SparseFillEmptyRows = function() { - return (mT = d._SparseFillEmptyRows = d.asm.SparseFillEmptyRows).apply(null, arguments); - }, gT = d._SparseReshape = function() { - return (gT = d._SparseReshape = d.asm.SparseReshape).apply(null, arguments); - }, bT = d._SparseSegmentReduction = function() { - return (bT = d._SparseSegmentReduction = d.asm.SparseSegmentReduction).apply(null, arguments); - }, yT = d._Sqrt = function() { - return (yT = d._Sqrt = d.asm.Sqrt).apply(null, arguments); - }, vT = d._Square = function() { - return (vT = d._Square = d.asm.Square).apply(null, arguments); - }, xT = d._SquaredDifference = function() { - return (xT = d._SquaredDifference = d.asm.SquaredDifference).apply(null, arguments); - }, wT = d._Step = function() { - return (wT = d._Step = d.asm.Step).apply(null, arguments); - }, kT = d._StridedSlice = function() { - return (kT = d._StridedSlice = d.asm.StridedSlice).apply(null, arguments); - }, IT = d._Sub = function() { - return (IT = d._Sub = d.asm.Sub).apply(null, arguments); - }, ST = d._Sum = function() { - return (ST = d._Sum = d.asm.Sum).apply(null, arguments); - }, CT = d._Tan = function() { - return (CT = d._Tan = d.asm.Tan).apply(null, arguments); - }, NT = d._Tanh = function() { - return (NT = d._Tanh = d.asm.Tanh).apply(null, arguments); - }, TT = d._Tile = function() { - return (TT = d._Tile = d.asm.Tile).apply(null, arguments); - }, $T = d._TopK = function() { - return ($T = d._TopK = d.asm.TopK).apply(null, arguments); - }, _T = d._Transform = function() { - return (_T = d._Transform = d.asm.Transform).apply(null, arguments); - }, AT = d._Transpose = function() { - return (AT = d._Transpose = d.asm.Transpose).apply(null, arguments); - }, ET = d.__FusedMatMul = function() { - return (ET = d.__FusedMatMul = d.asm._FusedMatMul).apply(null, arguments); - }, Af = d._malloc = function() { - return (Af = d._malloc = d.asm.malloc).apply(null, arguments); - }, Kv = d._free = function() { - return (Kv = d._free = d.asm.free).apply(null, arguments); - }, RT = d._emscripten_tls_init = function() { - return (RT = d._emscripten_tls_init = d.asm.emscripten_tls_init).apply(null, arguments); - }, Xv = d.___errno_location = function() { - return (Xv = d.___errno_location = d.asm.__errno_location).apply(null, arguments); - }, Oc = d._pthread_self = function() { - return (Oc = d._pthread_self = d.asm.pthread_self).apply(null, arguments); - }, Yv = d._emscripten_main_thread_process_queued_calls = function() { - return (Yv = d._emscripten_main_thread_process_queued_calls = d.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments); - }, DT = d.__emscripten_thread_crashed = function() { - return (DT = d.__emscripten_thread_crashed = d.asm._emscripten_thread_crashed).apply(null, arguments); - }, Qv = d.__emscripten_thread_init = function() { - return (Qv = d.__emscripten_thread_init = d.asm._emscripten_thread_init).apply(null, arguments); - }, FT = d._emscripten_current_thread_process_queued_calls = function() { - return (FT = d._emscripten_current_thread_process_queued_calls = d.asm.emscripten_current_thread_process_queued_calls).apply(null, arguments); - }, OT = d._emscripten_main_browser_thread_id = function() { - return (OT = d._emscripten_main_browser_thread_id = d.asm.emscripten_main_browser_thread_id).apply(null, arguments); - }, PT = d._emscripten_sync_run_in_main_thread_2 = function() { - return (PT = d._emscripten_sync_run_in_main_thread_2 = d.asm.emscripten_sync_run_in_main_thread_2).apply(null, arguments); - }, Zv = d._emscripten_sync_run_in_main_thread_4 = function() { - return (Zv = d._emscripten_sync_run_in_main_thread_4 = d.asm.emscripten_sync_run_in_main_thread_4).apply(null, arguments); - }, Jv = d._emscripten_run_in_main_runtime_thread_js = function() { - return (Jv = d._emscripten_run_in_main_runtime_thread_js = d.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments); - }, Ef = d._emscripten_dispatch_to_thread_ = function() { - return (Ef = d._emscripten_dispatch_to_thread_ = d.asm.emscripten_dispatch_to_thread_).apply(null, arguments); - }, Rf = d.__emscripten_thread_free_data = function() { - return (Rf = d.__emscripten_thread_free_data = d.asm._emscripten_thread_free_data).apply(null, arguments); - }, zT = d.__emscripten_thread_exit = function() { - return (zT = d.__emscripten_thread_exit = d.asm._emscripten_thread_exit).apply(null, arguments); - }, MT = d._memalign = function() { - return (MT = d._memalign = d.asm.memalign).apply(null, arguments); - }, ex = d._emscripten_stack_set_limits = function() { - return (ex = d._emscripten_stack_set_limits = d.asm.emscripten_stack_set_limits).apply(null, arguments); - }, Df = d.stackSave = function() { - return (Df = d.stackSave = d.asm.stackSave).apply(null, arguments); - }, Pc = d.stackRestore = function() { - return (Pc = d.stackRestore = d.asm.stackRestore).apply(null, arguments); - }, Pi = d.stackAlloc = function() { - return (Pi = d.stackAlloc = d.asm.stackAlloc).apply(null, arguments); - }, LT = d.dynCall_iijjiiii = function() { - return (LT = d.dynCall_iijjiiii = d.asm.dynCall_iijjiiii).apply(null, arguments); - }, BT = d.dynCall_jiji = function() { - return (BT = d.dynCall_jiji = d.asm.dynCall_jiji).apply(null, arguments); - }, tx = d.__emscripten_allow_main_runtime_queued_calls = 21456; - d.cwrap = en, d.keepRuntimeAlive = zr, d.PThread = _e, d.PThread = _e, d.wasmMemory = Ce, d.ExitStatus = Iu; - var zc; - function Iu(C) { - this.name = "ExitStatus", this.message = "Program terminated with exit(" + C + ")", this.status = C; - } - as = function C() { - zc || Ff(), zc || (as = C); - }; - function Ff(C) { - if (C = C || b, nr > 0) - return; - if (N) { - h(d), mu(), postMessage({ cmd: "loaded" }); - return; + function emscripten_realloc_buffer(size2) { + try { + wasmMemory.grow(size2 - buffer2.byteLength + 65535 >>> 16); + updateGlobalBufferAndViews(wasmMemory.buffer); + return 1; + } catch (e) { + } } - if (sn(), nr > 0) - return; - function D() { - zc || (zc = true, d.calledRun = true, !at && (mu(), h(d), d.onRuntimeInitialized && d.onRuntimeInitialized(), ih())); + function _emscripten_resize_heap(requestedSize) { + var oldSize = GROWABLE_HEAP_U8().length; + requestedSize = requestedSize >>> 0; + if (requestedSize <= oldSize) { + return false; + } + var maxHeapSize = _emscripten_get_heap_max(); + if (requestedSize > maxHeapSize) { + return false; + } + for (var cutDown = 1; cutDown <= 4; cutDown *= 2) { + var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown); + overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296); + var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536)); + var replacement = emscripten_realloc_buffer(newSize); + if (replacement) { + return true; + } + } + return false; } - d.setStatus ? (d.setStatus("Running..."), setTimeout(function() { - setTimeout(function() { - d.setStatus(""); - }, 1), D(); - }, 1)) : D(); - } - d.run = Ff; - function VT(C, D) { - if (Jt = C, !D && N) - throw kc(C), "unwind"; - zr() || ah(), WT(C); - } - function WT(C) { - Jt = C, zr() || (_e.terminateAllThreads(), d.onExit && d.onExit(C), at = true), v(C, new Iu(C)); - } - if (d.preInit) - for (typeof d.preInit == "function" && (d.preInit = [d.preInit]); d.preInit.length > 0; ) - d.preInit.pop()(); - Ff(); - var Mc; - m && (Mc = { uncaughtException: process.listeners("uncaughtException").filter(function(C) { - return !m.uncaughtException.indexOf(C) > -1; - }), unhandledRejection: process.listeners("unhandledRejection").filter(function(C) { - return !m.unhandledRejection.indexOf(C) > -1; - }) }); - var Lc; - if (typeof WasmBackendModule != "undefined") - Lc = WasmBackendModule; - else if (typeof r != "undefined") - Lc = r; - else - throw new Error("Could not find wasm module in post.js"); - if (Mc) { - var UT = Lc._dispose; - Lc._dispose = function() { - UT(), Mc.uncaughtException.forEach(function(C) { - process.removeListener("uncaughtException", C); - }), Mc.unhandledRejection.forEach(function(C) { - process.removeListener("unhandledRejection", C); + var JSEvents = { inEventHandler: 0, removeAllEventListeners: function() { + for (var i = JSEvents.eventHandlers.length - 1; i >= 0; --i) { + JSEvents._removeHandler(i); + } + JSEvents.eventHandlers = []; + JSEvents.deferredCalls = []; + }, registerRemoveEventListeners: function() { + if (!JSEvents.removeEventListenersRegistered) { + __ATEXIT__.push(JSEvents.removeAllEventListeners); + JSEvents.removeEventListenersRegistered = true; + } + }, deferredCalls: [], deferCall: function(targetFunction, precedence, argsList) { + function arraysHaveEqualContent(arrA, arrB) { + if (arrA.length != arrB.length) + return false; + for (var i2 in arrA) { + if (arrA[i2] != arrB[i2]) + return false; + } + return true; + } + for (var i in JSEvents.deferredCalls) { + var call = JSEvents.deferredCalls[i]; + if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) { + return; + } + } + JSEvents.deferredCalls.push({ targetFunction, precedence, argsList }); + JSEvents.deferredCalls.sort(function(x, y) { + return x.precedence < y.precedence; }); - }; - } - return r.ready; - }; - })(); - typeof e == "object" && typeof t == "object" ? t.exports = n : typeof define == "function" && define.amd ? define([], function() { - return n; - }) : typeof e == "object" && (e.WasmBackendModuleThreadedSimd = n); -} }); -var p$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) { - var n = (() => { - var s = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; - return typeof __filename != "undefined" && (s = s || __filename), function(r) { - r = r || {}; - var a = typeof r != "undefined" ? r : {}, i, o; - a.ready = new Promise(function(H, J) { - i = H, o = J; - }); - var u; - typeof process != "undefined" && process.listeners && (u = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }); - var l = Object.assign({}, a), c = [], p = "./this.program", d = (H, J) => { - throw J; - }, h = typeof window == "object", f = typeof importScripts == "function", m = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string", g = ""; - function b(H) { - return a.locateFile ? a.locateFile(H, g) : g + H; - } - var y, v, x, k; - function T(H) { - if (H instanceof wu) - return; - R("exiting due to exception: " + H); - } - var N, E, A; - m ? (f ? g = dd().dirname(g) + "/" : g = __dirname + "/", A = () => { - E || (N = sg(), E = dd()); - }, y = function(J, de) { - return A(), J = E.normalize(J), N.readFileSync(J, de ? void 0 : "utf8"); - }, x = (H) => { - var J = y(H, true); - return J.buffer || (J = new Uint8Array(J)), J; - }, v = (H, J, de) => { - A(), H = E.normalize(H), N.readFile(H, function(Ie, Qe) { - Ie ? de(Ie) : J(Qe.buffer); - }); - }, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), c = process.argv.slice(2), process.on("uncaughtException", function(H) { - if (!(H instanceof wu)) - throw H; - }), process.on("unhandledRejection", function(H) { - throw H; - }), d = (H, J) => { - if (fu()) - throw process.exitCode = H, J; - T(J), process.exit(H); - }, a.inspect = function() { - return "[Emscripten Module object]"; - }) : (h || f) && (f ? g = self.location.href : typeof document != "undefined" && document.currentScript && (g = document.currentScript.src), s && (g = s), g.indexOf("blob:") !== 0 ? g = g.substr(0, g.replace(/[?#].*/, "").lastIndexOf("/") + 1) : g = "", y = (H) => { - var J = new XMLHttpRequest(); - return J.open("GET", H, false), J.send(null), J.responseText; - }, f && (x = (H) => { - var J = new XMLHttpRequest(); - return J.open("GET", H, false), J.responseType = "arraybuffer", J.send(null), new Uint8Array(J.response); - }), v = (H, J, de) => { - var Ie = new XMLHttpRequest(); - Ie.open("GET", H, true), Ie.responseType = "arraybuffer", Ie.onload = () => { - if (Ie.status == 200 || Ie.status == 0 && Ie.response) { - J(Ie.response); + }, removeDeferredCalls: function(targetFunction) { + for (var i = 0; i < JSEvents.deferredCalls.length; ++i) { + if (JSEvents.deferredCalls[i].targetFunction == targetFunction) { + JSEvents.deferredCalls.splice(i, 1); + --i; + } + } + }, canPerformEventHandlerRequests: function() { + return JSEvents.inEventHandler && JSEvents.currentEventHandler.allowsDeferredCalls; + }, runDeferredCalls: function() { + if (!JSEvents.canPerformEventHandlerRequests()) { return; } - de(); - }, Ie.onerror = de, Ie.send(null); - }, k = (H) => document.title = H); - var P = a.print || console.log.bind(console), R = a.printErr || console.warn.bind(console); - Object.assign(a, l), l = null, a.arguments && (c = a.arguments), a.thisProgram && (p = a.thisProgram), a.quit && (d = a.quit); - var F = 4; - function $(H) { - $.shown || ($.shown = {}), $.shown[H] || ($.shown[H] = 1, R(H)); - } - function z(H, J) { - if (typeof WebAssembly.Function == "function") { - for (var de = { i: "i32", j: "i64", f: "f32", d: "f64" }, Ie = { parameters: [], results: J[0] == "v" ? [] : [de[J[0]]] }, Qe = 1; Qe < J.length; ++Qe) - Ie.parameters.push(de[J[Qe]]); - return new WebAssembly.Function(Ie, H); - } - var Ze = [1, 0, 1, 96], Ve = J.slice(0, 1), ze = J.slice(1), Tt = { i: 127, j: 126, f: 125, d: 124 }; - Ze.push(ze.length); - for (var Qe = 0; Qe < ze.length; ++Qe) - Ze.push(Tt[ze[Qe]]); - Ve == "v" ? Ze.push(0) : Ze = Ze.concat([1, Tt[Ve]]), Ze[1] = Ze.length - 2; - var is = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(Ze, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), os = new WebAssembly.Module(is), Oi = new WebAssembly.Instance(os, { e: { f: H } }), Vr = Oi.exports.f; - return Vr; - } - var W = [], q; - function K() { - if (W.length) - return W.pop(); - try { - er.grow(1); - } catch (H) { - throw H instanceof RangeError ? "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH." : H; - } - return er.length - 1; - } - function Y(H, J) { - for (var de = H; de < H + J; de++) { - var Ie = bu(de); - Ie && q.set(Ie, de); - } - } - var Z = 0, te = (H) => { - Z = H; - }, ee; - a.wasmBinary && (ee = a.wasmBinary); - var se = a.noExitRuntime || true; - typeof WebAssembly != "object" && Pr("no native wasm support detected"); - var ne, oe = false, re; - function le(H, J) { - H || Pr(J); - } - function me(H) { - var J = a["_" + H]; - return J; - } - function ke(H, J, de, Ie, Qe) { - var Ze = { string: function(an) { - var sr = 0; - if (an != null && an !== 0) { - var Fc = (an.length << 2) + 1; - sr = xu(Fc), tt(an, sr, Fc); + for (var i = 0; i < JSEvents.deferredCalls.length; ++i) { + var call = JSEvents.deferredCalls[i]; + JSEvents.deferredCalls.splice(i, 1); + --i; + call.targetFunction.apply(null, call.argsList); } - return sr; - }, array: function(an) { - var sr = xu(an.length); - return at(an, sr), sr; - } }; - function Ve(an) { - return J === "string" ? Je(an) : J === "boolean" ? Boolean(an) : an; - } - var ze = me(H), Tt = [], is = 0; - if (Ie) - for (var os = 0; os < Ie.length; os++) { - var Oi = Ze[de[os]]; - Oi ? (is === 0 && (is = Rc()), Tt[os] = Oi(Ie[os])) : Tt[os] = Ie[os]; + }, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) { + for (var i = 0; i < JSEvents.eventHandlers.length; ++i) { + if (JSEvents.eventHandlers[i].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i].eventTypeString)) { + JSEvents._removeHandler(i--); + } } - var Vr = ze.apply(null, Tt); - function _f(an) { - return is !== 0 && Dc(is), Ve(an); - } - return Vr = _f(Vr), Vr; - } - function Se(H, J, de, Ie) { - de = de || []; - var Qe = de.every(function(Ve) { - return Ve === "number"; - }), Ze = J !== "string"; - return Ze && Qe && !Ie ? me(H) : function() { - return ke(H, J, de, arguments, Ie); - }; - } - var Ee = 1, Pe = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0; - function Xe(H, J, de) { - for (var Ie = J + de, Qe = J; H[Qe] && !(Qe >= Ie); ) - ++Qe; - if (Qe - J > 16 && H.subarray && Pe) - return Pe.decode(H.subarray(J, Qe)); - for (var Ze = ""; J < Qe; ) { - var Ve = H[J++]; - if (!(Ve & 128)) { - Ze += String.fromCharCode(Ve); - continue; - } - var ze = H[J++] & 63; - if ((Ve & 224) == 192) { - Ze += String.fromCharCode((Ve & 31) << 6 | ze); - continue; - } - var Tt = H[J++] & 63; - if ((Ve & 240) == 224 ? Ve = (Ve & 15) << 12 | ze << 6 | Tt : Ve = (Ve & 7) << 18 | ze << 12 | Tt << 6 | H[J++] & 63, Ve < 65536) - Ze += String.fromCharCode(Ve); - else { - var is = Ve - 65536; - Ze += String.fromCharCode(55296 | is >> 10, 56320 | is & 1023); - } - } - return Ze; - } - function Je(H, J) { - return H ? Xe(en, H, J) : ""; - } - function Ye(H, J, de, Ie) { - if (!(Ie > 0)) - return 0; - for (var Qe = de, Ze = de + Ie - 1, Ve = 0; Ve < H.length; ++Ve) { - var ze = H.charCodeAt(Ve); - if (ze >= 55296 && ze <= 57343) { - var Tt = H.charCodeAt(++Ve); - ze = 65536 + ((ze & 1023) << 10) | Tt & 1023; - } - if (ze <= 127) { - if (de >= Ze) - break; - J[de++] = ze; - } else if (ze <= 2047) { - if (de + 1 >= Ze) - break; - J[de++] = 192 | ze >> 6, J[de++] = 128 | ze & 63; - } else if (ze <= 65535) { - if (de + 2 >= Ze) - break; - J[de++] = 224 | ze >> 12, J[de++] = 128 | ze >> 6 & 63, J[de++] = 128 | ze & 63; + }, _removeHandler: function(i) { + var h = JSEvents.eventHandlers[i]; + h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture); + JSEvents.eventHandlers.splice(i, 1); + }, registerOrRemoveHandler: function(eventHandler) { + var jsEventHandler = function jsEventHandler2(event) { + ++JSEvents.inEventHandler; + JSEvents.currentEventHandler = eventHandler; + JSEvents.runDeferredCalls(); + eventHandler.handlerFunc(event); + JSEvents.runDeferredCalls(); + --JSEvents.inEventHandler; + }; + if (eventHandler.callbackfunc) { + eventHandler.eventListenerFunc = jsEventHandler; + eventHandler.target.addEventListener(eventHandler.eventTypeString, jsEventHandler, eventHandler.useCapture); + JSEvents.eventHandlers.push(eventHandler); + JSEvents.registerRemoveEventListeners(); } else { - if (de + 3 >= Ze) - break; - J[de++] = 240 | ze >> 18, J[de++] = 128 | ze >> 12 & 63, J[de++] = 128 | ze >> 6 & 63, J[de++] = 128 | ze & 63; + for (var i = 0; i < JSEvents.eventHandlers.length; ++i) { + if (JSEvents.eventHandlers[i].target == eventHandler.target && JSEvents.eventHandlers[i].eventTypeString == eventHandler.eventTypeString) { + JSEvents._removeHandler(i--); + } + } } + }, queueEventHandlerOnThread_iiii: function(targetThread, eventHandlerFunc, eventTypeId, eventData, userData) { + withStackSave(function() { + var varargs = stackAlloc(12); + GROWABLE_HEAP_I32()[varargs >> 2] = eventTypeId; + GROWABLE_HEAP_I32()[varargs + 4 >> 2] = eventData; + GROWABLE_HEAP_I32()[varargs + 8 >> 2] = userData; + _emscripten_dispatch_to_thread_(targetThread, 637534208, eventHandlerFunc, eventData, varargs); + }); + }, getTargetThreadForEventCallback: function(targetThread) { + switch (targetThread) { + case 1: + return 0; + case 2: + return PThread.currentProxiedOperationCallerThread; + default: + return targetThread; + } + }, getNodeNameForTarget: function(target) { + if (!target) + return ""; + if (target == window) + return "#window"; + if (target == screen) + return "#screen"; + return target && target.nodeName ? target.nodeName : ""; + }, fullscreenEnabled: function() { + return document.fullscreenEnabled || document.webkitFullscreenEnabled; + } }; + function stringToNewUTF8(jsString) { + var length = lengthBytesUTF8(jsString) + 1; + var cString = _malloc(length); + stringToUTF8(jsString, cString, length); + return cString; } - return J[de] = 0, de - Qe; - } - function tt(H, J, de) { - return Ye(H, en, J, de); - } - function Ce(H) { - for (var J = 0, de = 0; de < H.length; ++de) { - var Ie = H.charCodeAt(de); - Ie >= 55296 && Ie <= 57343 && (Ie = 65536 + ((Ie & 1023) << 10) | H.charCodeAt(++de) & 1023), Ie <= 127 ? ++J : Ie <= 2047 ? J += 2 : Ie <= 65535 ? J += 3 : J += 4; - } - return J; - } - var ut = typeof TextDecoder != "undefined" ? new TextDecoder("utf-16le") : void 0; - function at(H, J) { - Et.set(H, J); - } - function Jt(H, J, de) { - for (var Ie = 0; Ie < H.length; ++Ie) - Et[J++ >> 0] = H.charCodeAt(Ie); - de || (Et[J >> 0] = 0); - } - function Nt(H, J) { - return H % J > 0 && (H += J - H % J), H; - } - var Cn, Et, en, Nn, Tn, Yt, Dn, tn, Ms; - function Ls(H) { - Cn = H, a.HEAP8 = Et = new Int8Array(H), a.HEAP16 = Nn = new Int16Array(H), a.HEAP32 = Yt = new Int32Array(H), a.HEAPU8 = en = new Uint8Array(H), a.HEAPU16 = Tn = new Uint16Array(H), a.HEAPU32 = Dn = new Uint32Array(H), a.HEAPF32 = tn = new Float32Array(H), a.HEAPF64 = Ms = new Float64Array(H); - } - var Si = a.INITIAL_MEMORY || 16777216, er, Bs = [], hu = [], Ci = [], nn = false, ic = false, oc = 0; - function fu() { - return se || oc > 0; - } - function uc() { - if (a.preRun) - for (typeof a.preRun == "function" && (a.preRun = [a.preRun]); a.preRun.length; ) - dc(a.preRun.shift()); - gu(Bs); - } - function lc() { - nn = true, gu(hu); - } - function Pv() { - ic = true; - } - function cc() { - if (a.postRun) - for (typeof a.postRun == "function" && (a.postRun = [a.postRun]); a.postRun.length; ) - pc(a.postRun.shift()); - gu(Ci); - } - function dc(H) { - Bs.unshift(H); - } - function rs(H) { - hu.unshift(H); - } - function pc(H) { - Ci.unshift(H); - } - var Fn = 0, Ni = null, tr = null; - function sh(H) { - Fn++, a.monitorRunDependencies && a.monitorRunDependencies(Fn); - } - function hc(H) { - if (Fn--, a.monitorRunDependencies && a.monitorRunDependencies(Fn), Fn == 0 && (Ni !== null && (clearInterval(Ni), Ni = null), tr)) { - var J = tr; - tr = null, J(); - } - } - a.preloadedImages = {}, a.preloadedAudios = {}; - function Pr(H) { - a.onAbort && a.onAbort(H), H = "Aborted(" + H + ")", R(H), oe = true, re = 1, H += ". Build with -s ASSERTIONS=1 for more info."; - var J = new WebAssembly.RuntimeError(H); - throw o(J), J; - } - var rh = "data:application/octet-stream;base64,"; - function fc(H) { - return H.startsWith(rh); - } - function zr(H) { - return H.startsWith("file://"); - } - var sn; - sn = "tfjs-backend-wasm.wasm", fc(sn) || (sn = b(sn)); - function mu(H) { - try { - if (H == sn && ee) - return new Uint8Array(ee); - if (x) - return x(H); - throw "both async and sync fetching of the wasm failed"; - } catch (J) { - Pr(J); - } - } - function ah() { - if (!ee && (h || f)) { - if (typeof fetch == "function" && !zr(sn)) - return fetch(sn, { credentials: "same-origin" }).then(function(H) { - if (!H.ok) - throw "failed to load wasm binary file at '" + sn + "'"; - return H.arrayBuffer(); - }).catch(function() { - return mu(sn); - }); - if (v) - return new Promise(function(H, J) { - v(sn, function(de) { - H(new Uint8Array(de)); - }, J); - }); - } - return Promise.resolve().then(function() { - return mu(sn); - }); - } - function ih() { - var H = { env: _i, wasi_snapshot_preview1: _i }; - function J(Ve, ze) { - var Tt = Ve.exports; - a.asm = Tt, ne = a.asm.memory, Ls(ne.buffer), er = a.asm.__indirect_function_table, rs(a.asm.__wasm_call_ctors), hc("wasm-instantiate"); - } - sh("wasm-instantiate"); - function de(Ve) { - J(Ve.instance); - } - function Ie(Ve) { - return ah().then(function(ze) { - return WebAssembly.instantiate(ze, H); - }).then(function(ze) { - return ze; - }).then(Ve, function(ze) { - R("failed to asynchronously prepare wasm: " + ze), Pr(ze); + function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height) { + withStackSave(function() { + var varargs = stackAlloc(12); + var targetCanvasPtr = 0; + if (targetCanvas) { + targetCanvasPtr = stringToNewUTF8(targetCanvas); + } + GROWABLE_HEAP_I32()[varargs >> 2] = targetCanvasPtr; + GROWABLE_HEAP_I32()[varargs + 4 >> 2] = width; + GROWABLE_HEAP_I32()[varargs + 8 >> 2] = height; + _emscripten_dispatch_to_thread_(targetThread, 657457152, 0, targetCanvasPtr, varargs); }); } - function Qe() { - return !ee && typeof WebAssembly.instantiateStreaming == "function" && !fc(sn) && !zr(sn) && typeof fetch == "function" ? fetch(sn, { credentials: "same-origin" }).then(function(Ve) { - var ze = WebAssembly.instantiateStreaming(Ve, H); - return ze.then(de, function(Tt) { - return R("wasm streaming compile failed: " + Tt), R("falling back to ArrayBuffer instantiation"), Ie(de); - }); - }) : Ie(de); + function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, targetCanvas, width, height) { + targetCanvas = targetCanvas ? UTF8ToString(targetCanvas) : ""; + _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height); } - if (a.instantiateWasm) - try { - var Ze = a.instantiateWasm(H, J); - return Ze; - } catch (Ve) { - return R("Module.instantiateWasm callback failed with error: " + Ve), false; + function maybeCStringToJsString(cString) { + return cString > 2 ? UTF8ToString(cString) : cString; + } + var specialHTMLTargets = [0, typeof document !== "undefined" ? document : 0, typeof window !== "undefined" ? window : 0]; + function findEventTarget(target) { + target = maybeCStringToJsString(target); + var domElement = specialHTMLTargets[target] || (typeof document !== "undefined" ? document.querySelector(target) : void 0); + return domElement; + } + function findCanvasEventTarget(target) { + return findEventTarget(target); + } + function _emscripten_set_canvas_element_size_calling_thread(target, width, height) { + var canvas3 = findCanvasEventTarget(target); + if (!canvas3) + return -4; + if (canvas3.canvasSharedPtr) { + GROWABLE_HEAP_I32()[canvas3.canvasSharedPtr >> 2] = width; + GROWABLE_HEAP_I32()[canvas3.canvasSharedPtr + 4 >> 2] = height; } - return Qe().catch(o), {}; - } - var zv, Mv; - function gu(H) { - for (; H.length > 0; ) { - var J = H.shift(); - if (typeof J == "function") { - J(a); - continue; + if (canvas3.offscreenCanvas || !canvas3.controlTransferredOffscreen) { + if (canvas3.offscreenCanvas) + canvas3 = canvas3.offscreenCanvas; + var autoResizeViewport = false; + if (canvas3.GLctxObject && canvas3.GLctxObject.GLctx) { + var prevViewport = canvas3.GLctxObject.GLctx.getParameter(2978); + autoResizeViewport = prevViewport[0] === 0 && prevViewport[1] === 0 && prevViewport[2] === canvas3.width && prevViewport[3] === canvas3.height; + } + canvas3.width = width; + canvas3.height = height; + if (autoResizeViewport) { + canvas3.GLctxObject.GLctx.viewport(0, 0, width, height); + } + } else if (canvas3.canvasSharedPtr) { + var targetThread = GROWABLE_HEAP_I32()[canvas3.canvasSharedPtr + 8 >> 2]; + _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, target, width, height); + return 1; + } else { + return -4; } - var de = J.func; - typeof de == "number" ? J.arg === void 0 ? bu(de)() : bu(de)(J.arg) : de(J.arg === void 0 ? null : J.arg); + return 0; } - } - function nr(H) { - return H; - } - function mc(H) { - var J = /\b_Z[\w\d_]+/g; - return H.replace(J, function(de) { - var Ie = de; - return de === Ie ? de : Ie + " [" + de + "]"; - }); - } - var as = []; - function bu(H) { - var J = as[H]; - return J || (H >= as.length && (as.length = H + 1), as[H] = J = er.get(H)), J; - } - function Lv() { - var H = new Error(); - if (!H.stack) { - try { - throw new Error(); - } catch (J) { - H = J; + function _emscripten_set_canvas_element_size_main_thread(target, width, height) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(2, 1, target, width, height); + return _emscripten_set_canvas_element_size_calling_thread(target, width, height); + } + function _emscripten_set_canvas_element_size(target, width, height) { + var canvas3 = findCanvasEventTarget(target); + if (canvas3) { + return _emscripten_set_canvas_element_size_calling_thread(target, width, height); + } else { + return _emscripten_set_canvas_element_size_main_thread(target, width, height); } - if (!H.stack) - return "(no stack trace available)"; } - return H.stack.toString(); - } - function Ti(H, J) { - er.set(H, J), as[H] = J; - } - function oh() { - Pr(""); - } - function gc(H, J, de) { - en.copyWithin(H, J, J + de); - } - function bc() { - return 2147483648; - } - function rn(H) { - try { - return ne.grow(H - Cn.byteLength + 65535 >>> 16), Ls(ne.buffer), 1; - } catch (J) { + function _emscripten_unwind_to_js_event_loop() { + throw "unwind"; } - } - function yc(H) { - var J = en.length; - H = H >>> 0; - var de = bc(); - if (H > de) - return false; - for (var Ie = 1; Ie <= 4; Ie *= 2) { - var Qe = J * (1 + 0.2 / Ie); - Qe = Math.min(Qe, H + 100663296); - var Ze = Math.min(de, Nt(Math.max(H, Qe), 65536)), Ve = rn(Ze); - if (Ve) - return true; + function __webgl_enable_ANGLE_instanced_arrays(ctx) { + var ext = ctx.getExtension("ANGLE_instanced_arrays"); + if (ext) { + ctx["vertexAttribDivisor"] = function(index2, divisor) { + ext["vertexAttribDivisorANGLE"](index2, divisor); + }; + ctx["drawArraysInstanced"] = function(mode, first, count22, primcount) { + ext["drawArraysInstancedANGLE"](mode, first, count22, primcount); + }; + ctx["drawElementsInstanced"] = function(mode, count22, type, indices, primcount) { + ext["drawElementsInstancedANGLE"](mode, count22, type, indices, primcount); + }; + return 1; + } } - return false; - } - var $i = { mappings: {}, buffers: [null, [], []], printChar: function(H, J) { - var de = $i.buffers[H]; - J === 0 || J === 10 ? ((H === 1 ? P : R)(Xe(de, 0)), de.length = 0) : de.push(J); - }, varargs: void 0, get: function() { - $i.varargs += 4; - var H = Yt[$i.varargs - 4 >> 2]; - return H; - }, getStr: function(H) { - var J = Je(H); - return J; - }, get64: function(H, J) { - return H; - } }; - function uh(H) { - return 0; - } - function Bv(H, J, de, Ie, Qe) { - } - function Vv(H, J, de, Ie) { - for (var Qe = 0, Ze = 0; Ze < de; Ze++) { - var Ve = Yt[J >> 2], ze = Yt[J + 4 >> 2]; - J += 8; - for (var Tt = 0; Tt < ze; Tt++) - $i.printChar(H, en[Ve + Tt]); - Qe += ze; + function __webgl_enable_OES_vertex_array_object(ctx) { + var ext = ctx.getExtension("OES_vertex_array_object"); + if (ext) { + ctx["createVertexArray"] = function() { + return ext["createVertexArrayOES"](); + }; + ctx["deleteVertexArray"] = function(vao) { + ext["deleteVertexArrayOES"](vao); + }; + ctx["bindVertexArray"] = function(vao) { + ext["bindVertexArrayOES"](vao); + }; + ctx["isVertexArray"] = function(vao) { + return ext["isVertexArrayOES"](vao); + }; + return 1; + } } - return Yt[Ie >> 2] = Qe, 0; - } - function lh(H) { - te(H); - } - var vc = false, _i = { abort: oh, emscripten_memcpy_big: gc, emscripten_resize_heap: yc, fd_close: uh, fd_seek: Bv, fd_write: Vv, setTempRet0: lh }, ZN = ih(), Wv = a.___wasm_call_ctors = function() { - return (Wv = a.___wasm_call_ctors = a.asm.__wasm_call_ctors).apply(null, arguments); - }, ch = a._init = function() { - return (ch = a._init = a.asm.init).apply(null, arguments); - }, dh = a._init_with_threads_count = function() { - return (dh = a._init_with_threads_count = a.asm.init_with_threads_count).apply(null, arguments); - }, xc = a._get_threads_count = function() { - return (xc = a._get_threads_count = a.asm.get_threads_count).apply(null, arguments); - }, wc = a._register_tensor = function() { - return (wc = a._register_tensor = a.asm.register_tensor).apply(null, arguments); - }, ph = a._dispose_data = function() { - return (ph = a._dispose_data = a.asm.dispose_data).apply(null, arguments); - }, _e = a._dispose = function() { - return (_e = a._dispose = a.asm.dispose).apply(null, arguments); - }, hh = a._Abs = function() { - return (hh = a._Abs = a.asm.Abs).apply(null, arguments); - }, kc = a._Add = function() { - return (kc = a._Add = a.asm.Add).apply(null, arguments); - }, Mr = a._AddN = function() { - return (Mr = a._AddN = a.asm.AddN).apply(null, arguments); - }, Ai = a._All = function() { - return (Ai = a._All = a.asm.All).apply(null, arguments); - }, fh = a._Any = function() { - return (fh = a._Any = a.asm.Any).apply(null, arguments); - }, Uv = a._ArgMax = function() { - return (Uv = a._ArgMax = a.asm.ArgMax).apply(null, arguments); - }, mh = a._AvgPool = function() { - return (mh = a._AvgPool = a.asm.AvgPool).apply(null, arguments); - }, Gv = a._BatchMatMul = function() { - return (Gv = a._BatchMatMul = a.asm.BatchMatMul).apply(null, arguments); - }, Lr = a._Ceil = function() { - return (Lr = a._Ceil = a.asm.Ceil).apply(null, arguments); - }, gh = a._ClipByValue = function() { - return (gh = a._ClipByValue = a.asm.ClipByValue).apply(null, arguments); - }, bh = a._Conv2D = function() { - return (bh = a._Conv2D = a.asm.Conv2D).apply(null, arguments); - }, yh = a._Conv2DBackpropInput = function() { - return (yh = a._Conv2DBackpropInput = a.asm.Conv2DBackpropInput).apply(null, arguments); - }, vh = a._Cos = function() { - return (vh = a._Cos = a.asm.Cos).apply(null, arguments); - }, xh = a._Cosh = function() { - return (xh = a._Cosh = a.asm.Cosh).apply(null, arguments); - }, wh = a._CropAndResize = function() { - return (wh = a._CropAndResize = a.asm.CropAndResize).apply(null, arguments); - }, Ic = a._Cumprod = function() { - return (Ic = a._Cumprod = a.asm.Cumprod).apply(null, arguments); - }, kh = a._Cumsum = function() { - return (kh = a._Cumsum = a.asm.Cumsum).apply(null, arguments); - }, Ih = a._DepthToSpace = function() { - return (Ih = a._DepthToSpace = a.asm.DepthToSpace).apply(null, arguments); - }, Sh = a._DepthwiseConv2dNative = function() { - return (Sh = a._DepthwiseConv2dNative = a.asm.DepthwiseConv2dNative).apply(null, arguments); - }, Ch = a._Elu = function() { - return (Ch = a._Elu = a.asm.Elu).apply(null, arguments); - }, Nh = a._Equal = function() { - return (Nh = a._Equal = a.asm.Equal).apply(null, arguments); - }, Sc = a._Exp = function() { - return (Sc = a._Exp = a.asm.Exp).apply(null, arguments); - }, Th = a._FlipLeftRight = function() { - return (Th = a._FlipLeftRight = a.asm.FlipLeftRight).apply(null, arguments); - }, $h = a._Floor = function() { - return ($h = a._Floor = a.asm.Floor).apply(null, arguments); - }, Br = a._FloorDiv = function() { - return (Br = a._FloorDiv = a.asm.FloorDiv).apply(null, arguments); - }, yu = a._FusedBatchNorm = function() { - return (yu = a._FusedBatchNorm = a.asm.FusedBatchNorm).apply(null, arguments); - }, _h = a._FusedConv2D = function() { - return (_h = a._FusedConv2D = a.asm.FusedConv2D).apply(null, arguments); - }, Ah = a._FusedDepthwiseConv2D = function() { - return (Ah = a._FusedDepthwiseConv2D = a.asm.FusedDepthwiseConv2D).apply(null, arguments); - }, Eh = a._Gather = function() { - return (Eh = a._Gather = a.asm.Gather).apply(null, arguments); - }, Be = a._GatherNd = function() { - return (Be = a._GatherNd = a.asm.GatherNd).apply(null, arguments); - }, Rh = a._Greater = function() { - return (Rh = a._Greater = a.asm.Greater).apply(null, arguments); - }, Dh = a._GreaterEqual = function() { - return (Dh = a._GreaterEqual = a.asm.GreaterEqual).apply(null, arguments); - }, Fh = a._LeakyRelu = function() { - return (Fh = a._LeakyRelu = a.asm.LeakyRelu).apply(null, arguments); - }, Oh = a._Less = function() { - return (Oh = a._Less = a.asm.Less).apply(null, arguments); - }, Ph = a._LessEqual = function() { - return (Ph = a._LessEqual = a.asm.LessEqual).apply(null, arguments); - }, zh = a._Log = function() { - return (zh = a._Log = a.asm.Log).apply(null, arguments); - }, vu = a._LogicalAnd = function() { - return (vu = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments); - }, Cc = a._Max = function() { - return (Cc = a._Max = a.asm.Max).apply(null, arguments); - }, Nc = a._MaxPool = function() { - return (Nc = a._MaxPool = a.asm.MaxPool).apply(null, arguments); - }, Mh = a._Maximum = function() { - return (Mh = a._Maximum = a.asm.Maximum).apply(null, arguments); - }, Lh = a._Mean = function() { - return (Lh = a._Mean = a.asm.Mean).apply(null, arguments); - }, Bh = a._Min = function() { - return (Bh = a._Min = a.asm.Min).apply(null, arguments); - }, Vh = a._Minimum = function() { - return (Vh = a._Minimum = a.asm.Minimum).apply(null, arguments); - }, Wh = a._MirrorPad = function() { - return (Wh = a._MirrorPad = a.asm.MirrorPad).apply(null, arguments); - }, Uh = a._Multiply = function() { - return (Uh = a._Multiply = a.asm.Multiply).apply(null, arguments); - }, gt = a._Neg = function() { - return (gt = a._Neg = a.asm.Neg).apply(null, arguments); - }, Gh = a._NonMaxSuppressionV3 = function() { - return (Gh = a._NonMaxSuppressionV3 = a.asm.NonMaxSuppressionV3).apply(null, arguments); - }, Hh = a._NonMaxSuppressionV4 = function() { - return (Hh = a._NonMaxSuppressionV4 = a.asm.NonMaxSuppressionV4).apply(null, arguments); - }, qh = a._NonMaxSuppressionV5 = function() { - return (qh = a._NonMaxSuppressionV5 = a.asm.NonMaxSuppressionV5).apply(null, arguments); - }, Ei = a._NotEqual = function() { - return (Ei = a._NotEqual = a.asm.NotEqual).apply(null, arguments); - }, Tc = a._OneHot = function() { - return (Tc = a._OneHot = a.asm.OneHot).apply(null, arguments); - }, $c = a._PadV2 = function() { - return ($c = a._PadV2 = a.asm.PadV2).apply(null, arguments); - }, _c = a._Pow = function() { - return (_c = a._Pow = a.asm.Pow).apply(null, arguments); - }, jh = a._Prelu = function() { - return (jh = a._Prelu = a.asm.Prelu).apply(null, arguments); - }, Ac = a._Prod = function() { - return (Ac = a._Prod = a.asm.Prod).apply(null, arguments); - }, Kh = a._RealDiv = function() { - return (Kh = a._RealDiv = a.asm.RealDiv).apply(null, arguments); - }, Hv = a._Relu = function() { - return (Hv = a._Relu = a.asm.Relu).apply(null, arguments); - }, Ec = a._Relu6 = function() { - return (Ec = a._Relu6 = a.asm.Relu6).apply(null, arguments); - }, qv = a._ResizeBilinear = function() { - return (qv = a._ResizeBilinear = a.asm.ResizeBilinear).apply(null, arguments); - }, Xh = a._Reverse = function() { - return (Xh = a._Reverse = a.asm.Reverse).apply(null, arguments); - }, Yh = a._RotateWithOffset = function() { - return (Yh = a._RotateWithOffset = a.asm.RotateWithOffset).apply(null, arguments); - }, Qh = a._Round = function() { - return (Qh = a._Round = a.asm.Round).apply(null, arguments); - }, Zh = a._Rsqrt = function() { - return (Zh = a._Rsqrt = a.asm.Rsqrt).apply(null, arguments); - }, Jh = a._ScatterNd = function() { - return (Jh = a._ScatterNd = a.asm.ScatterNd).apply(null, arguments); - }, ef = a._SelectV2 = function() { - return (ef = a._SelectV2 = a.asm.SelectV2).apply(null, arguments); - }, tf = a._Sigmoid = function() { - return (tf = a._Sigmoid = a.asm.Sigmoid).apply(null, arguments); - }, nf = a._Sin = function() { - return (nf = a._Sin = a.asm.Sin).apply(null, arguments); - }, sf = a._Softmax = function() { - return (sf = a._Softmax = a.asm.Softmax).apply(null, arguments); - }, rf = a._SparseFillEmptyRows = function() { - return (rf = a._SparseFillEmptyRows = a.asm.SparseFillEmptyRows).apply(null, arguments); - }, af = a._SparseReshape = function() { - return (af = a._SparseReshape = a.asm.SparseReshape).apply(null, arguments); - }, of = a._SparseSegmentReduction = function() { - return (of = a._SparseSegmentReduction = a.asm.SparseSegmentReduction).apply(null, arguments); - }, uf = a._Sqrt = function() { - return (uf = a._Sqrt = a.asm.Sqrt).apply(null, arguments); - }, lf = a._Square = function() { - return (lf = a._Square = a.asm.Square).apply(null, arguments); - }, cf = a._SquaredDifference = function() { - return (cf = a._SquaredDifference = a.asm.SquaredDifference).apply(null, arguments); - }, df = a._Step = function() { - return (df = a._Step = a.asm.Step).apply(null, arguments); - }, pf = a._StridedSlice = function() { - return (pf = a._StridedSlice = a.asm.StridedSlice).apply(null, arguments); - }, hf = a._Sub = function() { - return (hf = a._Sub = a.asm.Sub).apply(null, arguments); - }, ff = a._Sum = function() { - return (ff = a._Sum = a.asm.Sum).apply(null, arguments); - }, mf = a._Tan = function() { - return (mf = a._Tan = a.asm.Tan).apply(null, arguments); - }, gf = a._Tanh = function() { - return (gf = a._Tanh = a.asm.Tanh).apply(null, arguments); - }, bf = a._Tile = function() { - return (bf = a._Tile = a.asm.Tile).apply(null, arguments); - }, yf = a._TopK = function() { - return (yf = a._TopK = a.asm.TopK).apply(null, arguments); - }, vf = a._Transform = function() { - return (vf = a._Transform = a.asm.Transform).apply(null, arguments); - }, xf = a._Transpose = function() { - return (xf = a._Transpose = a.asm.Transpose).apply(null, arguments); - }, wf = a.__FusedMatMul = function() { - return (wf = a.__FusedMatMul = a.asm._FusedMatMul).apply(null, arguments); - }, kf = a._malloc = function() { - return (kf = a._malloc = a.asm.malloc).apply(null, arguments); - }, If = a._free = function() { - return (If = a._free = a.asm.free).apply(null, arguments); - }, Sf = a.___errno_location = function() { - return (Sf = a.___errno_location = a.asm.__errno_location).apply(null, arguments); - }, Cf = a._emscripten_main_thread_process_queued_calls = function() { - return (Cf = a._emscripten_main_thread_process_queued_calls = a.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments); - }, Rc = a.stackSave = function() { - return (Rc = a.stackSave = a.asm.stackSave).apply(null, arguments); - }, Dc = a.stackRestore = function() { - return (Dc = a.stackRestore = a.asm.stackRestore).apply(null, arguments); - }, xu = a.stackAlloc = function() { - return (xu = a.stackAlloc = a.asm.stackAlloc).apply(null, arguments); - }, Nf = a.dynCall_iijjiiii = function() { - return (Nf = a.dynCall_iijjiiii = a.asm.dynCall_iijjiiii).apply(null, arguments); - }, Tf = a.dynCall_jiji = function() { - return (Tf = a.dynCall_jiji = a.asm.dynCall_jiji).apply(null, arguments); - }; - a.cwrap = Se; - var Ri; - function wu(H) { - this.name = "ExitStatus", this.message = "Program terminated with exit(" + H + ")", this.status = H; - } - tr = function H() { - Ri || ku(), Ri || (tr = H); - }; - function ku(H) { - if (H = H || c, Fn > 0 || (uc(), Fn > 0)) - return; - function J() { - Ri || (Ri = true, a.calledRun = true, !oe && (lc(), i(a), a.onRuntimeInitialized && a.onRuntimeInitialized(), cc())); + function __webgl_enable_WEBGL_draw_buffers(ctx) { + var ext = ctx.getExtension("WEBGL_draw_buffers"); + if (ext) { + ctx["drawBuffers"] = function(n, bufs) { + ext["drawBuffersWEBGL"](n, bufs); + }; + return 1; + } } - a.setStatus ? (a.setStatus("Running..."), setTimeout(function() { - setTimeout(function() { - a.setStatus(""); - }, 1), J(); - }, 1)) : J(); - } - a.run = ku; - function jv(H) { - re = H, fu() || (a.onExit && a.onExit(H), oe = true), d(H, new wu(H)); - } - if (a.preInit) - for (typeof a.preInit == "function" && (a.preInit = [a.preInit]); a.preInit.length > 0; ) - a.preInit.pop()(); - ku(); - var Di; - u && (Di = { uncaughtException: process.listeners("uncaughtException").filter(function(H) { - return !u.uncaughtException.indexOf(H) > -1; - }), unhandledRejection: process.listeners("unhandledRejection").filter(function(H) { - return !u.unhandledRejection.indexOf(H) > -1; - }) }); - var Fi; - if (typeof r != "undefined") - Fi = r; - else if (typeof WasmBackendModuleThreadedSimd != "undefined") - Fi = WasmBackendModuleThreadedSimd; - else - throw new Error("Could not find wasm module in post.js"); - if (Di) { - var $f = Fi._dispose; - Fi._dispose = function() { - $f(), Di.uncaughtException.forEach(function(H) { - process.removeListener("uncaughtException", H); - }), Di.unhandledRejection.forEach(function(H) { - process.removeListener("unhandledRejection", H); + function __webgl_enable_WEBGL_multi_draw(ctx) { + return !!(ctx.multiDrawWebgl = ctx.getExtension("WEBGL_multi_draw")); + } + var GL = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function recordError(errorCode) { + if (!GL.lastError) { + GL.lastError = errorCode; + } + }, getNewId: function(table) { + var ret = GL.counter++; + for (var i = table.length; i < ret; i++) { + table[i] = null; + } + return ret; + }, getSource: function(shader, count22, string2, length) { + var source = ""; + for (var i = 0; i < count22; ++i) { + var len = length ? GROWABLE_HEAP_I32()[length + i * 4 >> 2] : -1; + source += UTF8ToString(GROWABLE_HEAP_I32()[string2 + i * 4 >> 2], len < 0 ? void 0 : len); + } + return source; + }, createContext: function(canvas3, webGLContextAttributes) { + if (!canvas3.getContextSafariWebGL2Fixed) { + canvas3.getContextSafariWebGL2Fixed = canvas3.getContext; + canvas3.getContext = function(ver, attrs) { + var gl = canvas3.getContextSafariWebGL2Fixed(ver, attrs); + return ver == "webgl" == gl instanceof WebGLRenderingContext ? gl : null; + }; + } + var ctx = canvas3.getContext("webgl", webGLContextAttributes); + if (!ctx) + return 0; + var handle = GL.registerContext(ctx, webGLContextAttributes); + return handle; + }, registerContext: function(ctx, webGLContextAttributes) { + var handle = _malloc(8); + GROWABLE_HEAP_I32()[handle + 4 >> 2] = _pthread_self(); + var context = { handle, attributes: webGLContextAttributes, version: webGLContextAttributes.majorVersion, GLctx: ctx }; + if (ctx.canvas) + ctx.canvas.GLctxObject = context; + GL.contexts[handle] = context; + if (typeof webGLContextAttributes.enableExtensionsByDefault === "undefined" || webGLContextAttributes.enableExtensionsByDefault) { + GL.initExtensions(context); + } + return handle; + }, makeContextCurrent: function(contextHandle) { + GL.currentContext = GL.contexts[contextHandle]; + Module.ctx = GLctx = GL.currentContext && GL.currentContext.GLctx; + return !(contextHandle && !GLctx); + }, getContext: function(contextHandle) { + return GL.contexts[contextHandle]; + }, deleteContext: function(contextHandle) { + if (GL.currentContext === GL.contexts[contextHandle]) + GL.currentContext = null; + if (typeof JSEvents === "object") + JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas); + if (GL.contexts[contextHandle] && GL.contexts[contextHandle].GLctx.canvas) + GL.contexts[contextHandle].GLctx.canvas.GLctxObject = void 0; + _free(GL.contexts[contextHandle].handle); + GL.contexts[contextHandle] = null; + }, initExtensions: function(context) { + if (!context) + context = GL.currentContext; + if (context.initExtensionsDone) + return; + context.initExtensionsDone = true; + var GLctx2 = context.GLctx; + __webgl_enable_ANGLE_instanced_arrays(GLctx2); + __webgl_enable_OES_vertex_array_object(GLctx2); + __webgl_enable_WEBGL_draw_buffers(GLctx2); + { + GLctx2.disjointTimerQueryExt = GLctx2.getExtension("EXT_disjoint_timer_query"); + } + __webgl_enable_WEBGL_multi_draw(GLctx2); + var exts = GLctx2.getSupportedExtensions() || []; + exts.forEach(function(ext) { + if (!ext.includes("lose_context") && !ext.includes("debug")) { + GLctx2.getExtension(ext); + } }); + } }; + var __emscripten_webgl_power_preferences = ["default", "low-power", "high-performance"]; + function _emscripten_webgl_do_create_context(target, attributes) { + var a = attributes >> 2; + var powerPreference = GROWABLE_HEAP_I32()[a + (24 >> 2)]; + var contextAttributes = { "alpha": !!GROWABLE_HEAP_I32()[a + (0 >> 2)], "depth": !!GROWABLE_HEAP_I32()[a + (4 >> 2)], "stencil": !!GROWABLE_HEAP_I32()[a + (8 >> 2)], "antialias": !!GROWABLE_HEAP_I32()[a + (12 >> 2)], "premultipliedAlpha": !!GROWABLE_HEAP_I32()[a + (16 >> 2)], "preserveDrawingBuffer": !!GROWABLE_HEAP_I32()[a + (20 >> 2)], "powerPreference": __emscripten_webgl_power_preferences[powerPreference], "failIfMajorPerformanceCaveat": !!GROWABLE_HEAP_I32()[a + (28 >> 2)], majorVersion: GROWABLE_HEAP_I32()[a + (32 >> 2)], minorVersion: GROWABLE_HEAP_I32()[a + (36 >> 2)], enableExtensionsByDefault: GROWABLE_HEAP_I32()[a + (40 >> 2)], explicitSwapControl: GROWABLE_HEAP_I32()[a + (44 >> 2)], proxyContextToMainThread: GROWABLE_HEAP_I32()[a + (48 >> 2)], renderViaOffscreenBackBuffer: GROWABLE_HEAP_I32()[a + (52 >> 2)] }; + var canvas3 = findCanvasEventTarget(target); + if (!canvas3) { + return 0; + } + if (contextAttributes.explicitSwapControl) { + return 0; + } + var contextHandle = GL.createContext(canvas3, contextAttributes); + return contextHandle; + } + function _emscripten_webgl_create_context(a0, a12) { + return _emscripten_webgl_do_create_context(a0, a12); + } + var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) { + var buffer3 = SYSCALLS.buffers[stream]; + if (curr === 0 || curr === 10) { + (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0)); + buffer3.length = 0; + } else { + buffer3.push(curr); + } + }, varargs: void 0, get: function() { + SYSCALLS.varargs += 4; + var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >> 2]; + return ret; + }, getStr: function(ptr) { + var ret = UTF8ToString(ptr); + return ret; + }, get64: function(low, high) { + return low; + } }; + function _fd_close(fd) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(3, 1, fd); + return 0; + } + function _fd_seek(fd, offset_low, offset_high, whence, newOffset) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(4, 1, fd, offset_low, offset_high, whence, newOffset); + } + function _fd_write(fd, iov, iovcnt, pnum) { + if (ENVIRONMENT_IS_PTHREAD) + return _emscripten_proxy_to_main_thread_js(5, 1, fd, iov, iovcnt, pnum); + var num = 0; + for (var i = 0; i < iovcnt; i++) { + var ptr = GROWABLE_HEAP_I32()[iov >> 2]; + var len = GROWABLE_HEAP_I32()[iov + 4 >> 2]; + iov += 8; + for (var j = 0; j < len; j++) { + SYSCALLS.printChar(fd, GROWABLE_HEAP_U8()[ptr + j]); + } + num += len; + } + GROWABLE_HEAP_I32()[pnum >> 2] = num; + return 0; + } + function _setTempRet0(val) { + setTempRet0(val); + } + PThread.init(); + var GLctx; + var proxiedFunctionTable = [null, exitOnMainThread, _emscripten_set_canvas_element_size_main_thread, _fd_close, _fd_seek, _fd_write]; + var ASSERTIONS = false; + var asmLibraryArg = { "__clock_gettime": ___clock_gettime, "__emscripten_init_main_thread_js": ___emscripten_init_main_thread_js, "__emscripten_thread_cleanup": ___emscripten_thread_cleanup, "__pthread_create_js": ___pthread_create_js, "_emscripten_default_pthread_stack_size": __emscripten_default_pthread_stack_size, "_emscripten_notify_thread_queue": __emscripten_notify_thread_queue, "abort": _abort, "emscripten_check_blocking_allowed": _emscripten_check_blocking_allowed, "emscripten_get_heap_max": _emscripten_get_heap_max, "emscripten_get_now": _emscripten_get_now, "emscripten_memcpy_big": _emscripten_memcpy_big, "emscripten_num_logical_cores": _emscripten_num_logical_cores, "emscripten_receive_on_main_thread_js": _emscripten_receive_on_main_thread_js, "emscripten_resize_heap": _emscripten_resize_heap, "emscripten_set_canvas_element_size": _emscripten_set_canvas_element_size, "emscripten_unwind_to_js_event_loop": _emscripten_unwind_to_js_event_loop, "emscripten_webgl_create_context": _emscripten_webgl_create_context, "exit": _exit, "fd_close": _fd_close, "fd_seek": _fd_seek, "fd_write": _fd_write, "memory": wasmMemory || Module["wasmMemory"], "setTempRet0": _setTempRet0 }; + var asm = createWasm(); + var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() { + return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["__wasm_call_ctors"]).apply(null, arguments); }; - } - return r.ready; - }; - })(); - typeof e == "object" && typeof t == "object" ? t.exports = n : typeof define == "function" && define.amd ? define([], function() { - return n; - }) : typeof e == "object" && (e.WasmBackendModule = n); -} }); -var h$ = 1e-7; -var f$ = 1e-4; -var Hd = class { - constructor(e, t) { - this.backend = e, this.dataMover = t, this.data = /* @__PURE__ */ new WeakMap(), this.dataIdsCount = 0; + var _init = Module["_init"] = function() { + return (_init = Module["_init"] = Module["asm"]["init"]).apply(null, arguments); + }; + var _init_with_threads_count = Module["_init_with_threads_count"] = function() { + return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["init_with_threads_count"]).apply(null, arguments); + }; + var _get_threads_count = Module["_get_threads_count"] = function() { + return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["get_threads_count"]).apply(null, arguments); + }; + var _register_tensor = Module["_register_tensor"] = function() { + return (_register_tensor = Module["_register_tensor"] = Module["asm"]["register_tensor"]).apply(null, arguments); + }; + var _dispose_data = Module["_dispose_data"] = function() { + return (_dispose_data = Module["_dispose_data"] = Module["asm"]["dispose_data"]).apply(null, arguments); + }; + var _dispose = Module["_dispose"] = function() { + return (_dispose = Module["_dispose"] = Module["asm"]["dispose"]).apply(null, arguments); + }; + var _Abs = Module["_Abs"] = function() { + return (_Abs = Module["_Abs"] = Module["asm"]["Abs"]).apply(null, arguments); + }; + var _Add = Module["_Add"] = function() { + return (_Add = Module["_Add"] = Module["asm"]["Add"]).apply(null, arguments); + }; + var _AddN = Module["_AddN"] = function() { + return (_AddN = Module["_AddN"] = Module["asm"]["AddN"]).apply(null, arguments); + }; + var _All = Module["_All"] = function() { + return (_All = Module["_All"] = Module["asm"]["All"]).apply(null, arguments); + }; + var _Any = Module["_Any"] = function() { + return (_Any = Module["_Any"] = Module["asm"]["Any"]).apply(null, arguments); + }; + var _ArgMax = Module["_ArgMax"] = function() { + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["ArgMax"]).apply(null, arguments); + }; + var _AvgPool = Module["_AvgPool"] = function() { + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["AvgPool"]).apply(null, arguments); + }; + var _BatchMatMul = Module["_BatchMatMul"] = function() { + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["BatchMatMul"]).apply(null, arguments); + }; + var _Ceil = Module["_Ceil"] = function() { + return (_Ceil = Module["_Ceil"] = Module["asm"]["Ceil"]).apply(null, arguments); + }; + var _ClipByValue = Module["_ClipByValue"] = function() { + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["ClipByValue"]).apply(null, arguments); + }; + var _Conv2D2 = Module["_Conv2D"] = function() { + return (_Conv2D2 = Module["_Conv2D"] = Module["asm"]["Conv2D"]).apply(null, arguments); + }; + var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["Conv2DBackpropInput"]).apply(null, arguments); + }; + var _Cos = Module["_Cos"] = function() { + return (_Cos = Module["_Cos"] = Module["asm"]["Cos"]).apply(null, arguments); + }; + var _Cosh = Module["_Cosh"] = function() { + return (_Cosh = Module["_Cosh"] = Module["asm"]["Cosh"]).apply(null, arguments); + }; + var _CropAndResize = Module["_CropAndResize"] = function() { + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["CropAndResize"]).apply(null, arguments); + }; + var _Cumprod = Module["_Cumprod"] = function() { + return (_Cumprod = Module["_Cumprod"] = Module["asm"]["Cumprod"]).apply(null, arguments); + }; + var _Cumsum = Module["_Cumsum"] = function() { + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Cumsum"]).apply(null, arguments); + }; + var _DepthToSpace = Module["_DepthToSpace"] = function() { + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["DepthToSpace"]).apply(null, arguments); + }; + var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["DepthwiseConv2dNative"]).apply(null, arguments); + }; + var _Elu = Module["_Elu"] = function() { + return (_Elu = Module["_Elu"] = Module["asm"]["Elu"]).apply(null, arguments); + }; + var _Equal = Module["_Equal"] = function() { + return (_Equal = Module["_Equal"] = Module["asm"]["Equal"]).apply(null, arguments); + }; + var _Exp = Module["_Exp"] = function() { + return (_Exp = Module["_Exp"] = Module["asm"]["Exp"]).apply(null, arguments); + }; + var _FlipLeftRight = Module["_FlipLeftRight"] = function() { + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["FlipLeftRight"]).apply(null, arguments); + }; + var _Floor = Module["_Floor"] = function() { + return (_Floor = Module["_Floor"] = Module["asm"]["Floor"]).apply(null, arguments); + }; + var _FloorDiv = Module["_FloorDiv"] = function() { + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["FloorDiv"]).apply(null, arguments); + }; + var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["FusedBatchNorm"]).apply(null, arguments); + }; + var _FusedConv2D = Module["_FusedConv2D"] = function() { + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["FusedConv2D"]).apply(null, arguments); + }; + var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["FusedDepthwiseConv2D"]).apply(null, arguments); + }; + var _Gather = Module["_Gather"] = function() { + return (_Gather = Module["_Gather"] = Module["asm"]["Gather"]).apply(null, arguments); + }; + var _GatherNd = Module["_GatherNd"] = function() { + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["GatherNd"]).apply(null, arguments); + }; + var _Greater = Module["_Greater"] = function() { + return (_Greater = Module["_Greater"] = Module["asm"]["Greater"]).apply(null, arguments); + }; + var _GreaterEqual = Module["_GreaterEqual"] = function() { + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["GreaterEqual"]).apply(null, arguments); + }; + var _LeakyRelu = Module["_LeakyRelu"] = function() { + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["LeakyRelu"]).apply(null, arguments); + }; + var _Less = Module["_Less"] = function() { + return (_Less = Module["_Less"] = Module["asm"]["Less"]).apply(null, arguments); + }; + var _LessEqual = Module["_LessEqual"] = function() { + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["LessEqual"]).apply(null, arguments); + }; + var _Log = Module["_Log"] = function() { + return (_Log = Module["_Log"] = Module["asm"]["Log"]).apply(null, arguments); + }; + var _LogicalAnd = Module["_LogicalAnd"] = function() { + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["LogicalAnd"]).apply(null, arguments); + }; + var _Max = Module["_Max"] = function() { + return (_Max = Module["_Max"] = Module["asm"]["Max"]).apply(null, arguments); + }; + var _MaxPool = Module["_MaxPool"] = function() { + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["MaxPool"]).apply(null, arguments); + }; + var _Maximum = Module["_Maximum"] = function() { + return (_Maximum = Module["_Maximum"] = Module["asm"]["Maximum"]).apply(null, arguments); + }; + var _Mean = Module["_Mean"] = function() { + return (_Mean = Module["_Mean"] = Module["asm"]["Mean"]).apply(null, arguments); + }; + var _Min = Module["_Min"] = function() { + return (_Min = Module["_Min"] = Module["asm"]["Min"]).apply(null, arguments); + }; + var _Minimum = Module["_Minimum"] = function() { + return (_Minimum = Module["_Minimum"] = Module["asm"]["Minimum"]).apply(null, arguments); + }; + var _MirrorPad = Module["_MirrorPad"] = function() { + return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["MirrorPad"]).apply(null, arguments); + }; + var _Multiply = Module["_Multiply"] = function() { + return (_Multiply = Module["_Multiply"] = Module["asm"]["Multiply"]).apply(null, arguments); + }; + var _Neg = Module["_Neg"] = function() { + return (_Neg = Module["_Neg"] = Module["asm"]["Neg"]).apply(null, arguments); + }; + var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["NonMaxSuppressionV3"]).apply(null, arguments); + }; + var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["NonMaxSuppressionV4"]).apply(null, arguments); + }; + var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["NonMaxSuppressionV5"]).apply(null, arguments); + }; + var _NotEqual = Module["_NotEqual"] = function() { + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["NotEqual"]).apply(null, arguments); + }; + var _OneHot = Module["_OneHot"] = function() { + return (_OneHot = Module["_OneHot"] = Module["asm"]["OneHot"]).apply(null, arguments); + }; + var _PadV2 = Module["_PadV2"] = function() { + return (_PadV2 = Module["_PadV2"] = Module["asm"]["PadV2"]).apply(null, arguments); + }; + var _Pow = Module["_Pow"] = function() { + return (_Pow = Module["_Pow"] = Module["asm"]["Pow"]).apply(null, arguments); + }; + var _Prelu = Module["_Prelu"] = function() { + return (_Prelu = Module["_Prelu"] = Module["asm"]["Prelu"]).apply(null, arguments); + }; + var _Prod = Module["_Prod"] = function() { + return (_Prod = Module["_Prod"] = Module["asm"]["Prod"]).apply(null, arguments); + }; + var _RealDiv = Module["_RealDiv"] = function() { + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["RealDiv"]).apply(null, arguments); + }; + var _Relu = Module["_Relu"] = function() { + return (_Relu = Module["_Relu"] = Module["asm"]["Relu"]).apply(null, arguments); + }; + var _Relu6 = Module["_Relu6"] = function() { + return (_Relu6 = Module["_Relu6"] = Module["asm"]["Relu6"]).apply(null, arguments); + }; + var _ResizeBilinear = Module["_ResizeBilinear"] = function() { + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ResizeBilinear"]).apply(null, arguments); + }; + var _Reverse = Module["_Reverse"] = function() { + return (_Reverse = Module["_Reverse"] = Module["asm"]["Reverse"]).apply(null, arguments); + }; + var _RotateWithOffset = Module["_RotateWithOffset"] = function() { + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["RotateWithOffset"]).apply(null, arguments); + }; + var _Round = Module["_Round"] = function() { + return (_Round = Module["_Round"] = Module["asm"]["Round"]).apply(null, arguments); + }; + var _Rsqrt = Module["_Rsqrt"] = function() { + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Rsqrt"]).apply(null, arguments); + }; + var _ScatterNd = Module["_ScatterNd"] = function() { + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["ScatterNd"]).apply(null, arguments); + }; + var _SelectV2 = Module["_SelectV2"] = function() { + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["SelectV2"]).apply(null, arguments); + }; + var _Sigmoid = Module["_Sigmoid"] = function() { + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sigmoid"]).apply(null, arguments); + }; + var _Sin = Module["_Sin"] = function() { + return (_Sin = Module["_Sin"] = Module["asm"]["Sin"]).apply(null, arguments); + }; + var _Softmax = Module["_Softmax"] = function() { + return (_Softmax = Module["_Softmax"] = Module["asm"]["Softmax"]).apply(null, arguments); + }; + var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() { + return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["SparseFillEmptyRows"]).apply(null, arguments); + }; + var _SparseReshape = Module["_SparseReshape"] = function() { + return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["SparseReshape"]).apply(null, arguments); + }; + var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() { + return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["SparseSegmentReduction"]).apply(null, arguments); + }; + var _Sqrt = Module["_Sqrt"] = function() { + return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sqrt"]).apply(null, arguments); + }; + var _Square = Module["_Square"] = function() { + return (_Square = Module["_Square"] = Module["asm"]["Square"]).apply(null, arguments); + }; + var _SquaredDifference = Module["_SquaredDifference"] = function() { + return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["SquaredDifference"]).apply(null, arguments); + }; + var _Step = Module["_Step"] = function() { + return (_Step = Module["_Step"] = Module["asm"]["Step"]).apply(null, arguments); + }; + var _StridedSlice = Module["_StridedSlice"] = function() { + return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["StridedSlice"]).apply(null, arguments); + }; + var _Sub = Module["_Sub"] = function() { + return (_Sub = Module["_Sub"] = Module["asm"]["Sub"]).apply(null, arguments); + }; + var _Sum = Module["_Sum"] = function() { + return (_Sum = Module["_Sum"] = Module["asm"]["Sum"]).apply(null, arguments); + }; + var _Tan = Module["_Tan"] = function() { + return (_Tan = Module["_Tan"] = Module["asm"]["Tan"]).apply(null, arguments); + }; + var _Tanh = Module["_Tanh"] = function() { + return (_Tanh = Module["_Tanh"] = Module["asm"]["Tanh"]).apply(null, arguments); + }; + var _Tile = Module["_Tile"] = function() { + return (_Tile = Module["_Tile"] = Module["asm"]["Tile"]).apply(null, arguments); + }; + var _TopK = Module["_TopK"] = function() { + return (_TopK = Module["_TopK"] = Module["asm"]["TopK"]).apply(null, arguments); + }; + var _Transform = Module["_Transform"] = function() { + return (_Transform = Module["_Transform"] = Module["asm"]["Transform"]).apply(null, arguments); + }; + var _Transpose = Module["_Transpose"] = function() { + return (_Transpose = Module["_Transpose"] = Module["asm"]["Transpose"]).apply(null, arguments); + }; + var __FusedMatMul = Module["__FusedMatMul"] = function() { + return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["_FusedMatMul"]).apply(null, arguments); + }; + var _malloc = Module["_malloc"] = function() { + return (_malloc = Module["_malloc"] = Module["asm"]["malloc"]).apply(null, arguments); + }; + var _free = Module["_free"] = function() { + return (_free = Module["_free"] = Module["asm"]["free"]).apply(null, arguments); + }; + var _emscripten_tls_init = Module["_emscripten_tls_init"] = function() { + return (_emscripten_tls_init = Module["_emscripten_tls_init"] = Module["asm"]["emscripten_tls_init"]).apply(null, arguments); + }; + var ___errno_location = Module["___errno_location"] = function() { + return (___errno_location = Module["___errno_location"] = Module["asm"]["__errno_location"]).apply(null, arguments); + }; + var _pthread_self = Module["_pthread_self"] = function() { + return (_pthread_self = Module["_pthread_self"] = Module["asm"]["pthread_self"]).apply(null, arguments); + }; + var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() { + return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["emscripten_main_thread_process_queued_calls"]).apply(null, arguments); + }; + var __emscripten_thread_crashed = Module["__emscripten_thread_crashed"] = function() { + return (__emscripten_thread_crashed = Module["__emscripten_thread_crashed"] = Module["asm"]["_emscripten_thread_crashed"]).apply(null, arguments); + }; + var __emscripten_thread_init = Module["__emscripten_thread_init"] = function() { + return (__emscripten_thread_init = Module["__emscripten_thread_init"] = Module["asm"]["_emscripten_thread_init"]).apply(null, arguments); + }; + var _emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = function() { + return (_emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = Module["asm"]["emscripten_current_thread_process_queued_calls"]).apply(null, arguments); + }; + var _emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = function() { + return (_emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = Module["asm"]["emscripten_main_browser_thread_id"]).apply(null, arguments); + }; + var _emscripten_sync_run_in_main_thread_2 = Module["_emscripten_sync_run_in_main_thread_2"] = function() { + return (_emscripten_sync_run_in_main_thread_2 = Module["_emscripten_sync_run_in_main_thread_2"] = Module["asm"]["emscripten_sync_run_in_main_thread_2"]).apply(null, arguments); + }; + var _emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = function() { + return (_emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = Module["asm"]["emscripten_sync_run_in_main_thread_4"]).apply(null, arguments); + }; + var _emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = function() { + return (_emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = Module["asm"]["emscripten_run_in_main_runtime_thread_js"]).apply(null, arguments); + }; + var _emscripten_dispatch_to_thread_ = Module["_emscripten_dispatch_to_thread_"] = function() { + return (_emscripten_dispatch_to_thread_ = Module["_emscripten_dispatch_to_thread_"] = Module["asm"]["emscripten_dispatch_to_thread_"]).apply(null, arguments); + }; + var __emscripten_thread_free_data = Module["__emscripten_thread_free_data"] = function() { + return (__emscripten_thread_free_data = Module["__emscripten_thread_free_data"] = Module["asm"]["_emscripten_thread_free_data"]).apply(null, arguments); + }; + var __emscripten_thread_exit = Module["__emscripten_thread_exit"] = function() { + return (__emscripten_thread_exit = Module["__emscripten_thread_exit"] = Module["asm"]["_emscripten_thread_exit"]).apply(null, arguments); + }; + var _memalign = Module["_memalign"] = function() { + return (_memalign = Module["_memalign"] = Module["asm"]["memalign"]).apply(null, arguments); + }; + var _emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = function() { + return (_emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = Module["asm"]["emscripten_stack_set_limits"]).apply(null, arguments); + }; + var stackSave = Module["stackSave"] = function() { + return (stackSave = Module["stackSave"] = Module["asm"]["stackSave"]).apply(null, arguments); + }; + var stackRestore = Module["stackRestore"] = function() { + return (stackRestore = Module["stackRestore"] = Module["asm"]["stackRestore"]).apply(null, arguments); + }; + var stackAlloc = Module["stackAlloc"] = function() { + return (stackAlloc = Module["stackAlloc"] = Module["asm"]["stackAlloc"]).apply(null, arguments); + }; + var dynCall_iijjiiii = Module["dynCall_iijjiiii"] = function() { + return (dynCall_iijjiiii = Module["dynCall_iijjiiii"] = Module["asm"]["dynCall_iijjiiii"]).apply(null, arguments); + }; + var dynCall_jiji = Module["dynCall_jiji"] = function() { + return (dynCall_jiji = Module["dynCall_jiji"] = Module["asm"]["dynCall_jiji"]).apply(null, arguments); + }; + var __emscripten_allow_main_runtime_queued_calls = Module["__emscripten_allow_main_runtime_queued_calls"] = 21456; + Module["cwrap"] = cwrap; + Module["keepRuntimeAlive"] = keepRuntimeAlive; + Module["PThread"] = PThread; + Module["PThread"] = PThread; + Module["wasmMemory"] = wasmMemory; + Module["ExitStatus"] = ExitStatus; + var calledRun; + function ExitStatus(status) { + this.name = "ExitStatus"; + this.message = "Program terminated with exit(" + status + ")"; + this.status = status; + } + dependenciesFulfilled = function runCaller() { + if (!calledRun) + run(); + if (!calledRun) + dependenciesFulfilled = runCaller; + }; + function run(args) { + args = args || arguments_; + if (runDependencies > 0) { + return; + } + if (ENVIRONMENT_IS_PTHREAD) { + readyPromiseResolve(Module); + initRuntime(); + postMessage({ "cmd": "loaded" }); + return; + } + preRun(); + if (runDependencies > 0) { + return; + } + function doRun() { + if (calledRun) + return; + calledRun = true; + Module["calledRun"] = true; + if (ABORT) + return; + initRuntime(); + readyPromiseResolve(Module); + if (Module["onRuntimeInitialized"]) + Module["onRuntimeInitialized"](); + postRun(); + } + if (Module["setStatus"]) { + Module["setStatus"]("Running..."); + setTimeout(function() { + setTimeout(function() { + Module["setStatus"](""); + }, 1); + doRun(); + }, 1); + } else { + doRun(); + } + } + Module["run"] = run; + function exit(status, implicit) { + EXITSTATUS = status; + if (!implicit) { + if (ENVIRONMENT_IS_PTHREAD) { + exitOnMainThread(status); + throw "unwind"; + } else { + } + } + if (keepRuntimeAlive()) { + } else { + exitRuntime(); + } + procExit(status); + } + function procExit(code) { + EXITSTATUS = code; + if (!keepRuntimeAlive()) { + PThread.terminateAllThreads(); + if (Module["onExit"]) + Module["onExit"](code); + ABORT = true; + } + quit_(code, new ExitStatus(code)); + } + if (Module["preInit"]) { + if (typeof Module["preInit"] == "function") + Module["preInit"] = [Module["preInit"]]; + while (Module["preInit"].length > 0) { + Module["preInit"].pop()(); + } + } + run(); + var listenersAdded; + if (beforeListeners) { + listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) { + return !beforeListeners.uncaughtException.indexOf(listener) > -1; + }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) { + return !beforeListeners.unhandledRejection.indexOf(listener) > -1; + }) }; + } + var actualModule; + if (typeof WasmBackendModule !== "undefined") { + actualModule = WasmBackendModule; + } else if (typeof WasmBackendModuleThreadedSimd3 !== "undefined") { + actualModule = WasmBackendModuleThreadedSimd3; + } else { + throw new Error("Could not find wasm module in post.js"); + } + if (listenersAdded) { + var tmpDispose = actualModule["_dispose"]; + actualModule["_dispose"] = function() { + tmpDispose(); + listenersAdded.uncaughtException.forEach(function(listener) { + process.removeListener("uncaughtException", listener); + }); + listenersAdded.unhandledRejection.forEach(function(listener) { + process.removeListener("unhandledRejection", listener); + }); + }; + } + return WasmBackendModuleThreadedSimd3.ready; + }; + })(); + if (typeof exports === "object" && typeof module === "object") + module.exports = WasmBackendModuleThreadedSimd2; + else if (typeof define === "function" && define["amd"]) + define([], function() { + return WasmBackendModuleThreadedSimd2; + }); + else if (typeof exports === "object") + exports["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd2; } - get(e) { - return this.data.has(e) || this.dataMover.moveData(this.backend, e), this.data.get(e); +}); +var require_tfjs_backend_wasm = __commonJS({ + "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(exports, module) { + var WasmBackendModule2 = (() => { + var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0; + if (typeof __filename !== "undefined") + _scriptDir = _scriptDir || __filename; + return function(WasmBackendModule3) { + WasmBackendModule3 = WasmBackendModule3 || {}; + var Module = typeof WasmBackendModule3 !== "undefined" ? WasmBackendModule3 : {}; + var readyPromiseResolve, readyPromiseReject; + Module["ready"] = new Promise(function(resolve, reject) { + readyPromiseResolve = resolve; + readyPromiseReject = reject; + }); + var beforeListeners; + if (typeof process !== "undefined" && process.listeners) { + beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") }; + } + var moduleOverrides = Object.assign({}, Module); + var arguments_ = []; + var thisProgram = "./this.program"; + var quit_ = (status, toThrow) => { + throw toThrow; + }; + var ENVIRONMENT_IS_WEB = typeof window === "object"; + var ENVIRONMENT_IS_WORKER = typeof importScripts === "function"; + var ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string"; + var scriptDirectory = ""; + function locateFile(path) { + if (Module["locateFile"]) { + return Module["locateFile"](path, scriptDirectory); + } + return scriptDirectory + path; + } + var read_, readAsync, readBinary, setWindowTitle; + function logExceptionOnExit(e) { + if (e instanceof ExitStatus) + return; + let toLog = e; + err("exiting due to exception: " + toLog); + } + var fs; + var nodePath; + var requireNodeFS; + if (ENVIRONMENT_IS_NODE) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = require_path().dirname(scriptDirectory) + "/"; + } else { + scriptDirectory = __dirname + "/"; + } + requireNodeFS = () => { + if (!nodePath) { + fs = require_fs(); + nodePath = require_path(); + } + }; + read_ = function shell_read(filename, binary) { + requireNodeFS(); + filename = nodePath["normalize"](filename); + return fs.readFileSync(filename, binary ? void 0 : "utf8"); + }; + readBinary = (filename) => { + var ret = read_(filename, true); + if (!ret.buffer) { + ret = new Uint8Array(ret); + } + return ret; + }; + readAsync = (filename, onload, onerror) => { + requireNodeFS(); + filename = nodePath["normalize"](filename); + fs.readFile(filename, function(err2, data) { + if (err2) + onerror(err2); + else + onload(data.buffer); + }); + }; + if (process["argv"].length > 1) { + thisProgram = process["argv"][1].replace(/\\/g, "/"); + } + arguments_ = process["argv"].slice(2); + process["on"]("uncaughtException", function(ex) { + if (!(ex instanceof ExitStatus)) { + throw ex; + } + }); + process["on"]("unhandledRejection", function(reason) { + throw reason; + }); + quit_ = (status, toThrow) => { + if (keepRuntimeAlive()) { + process["exitCode"] = status; + throw toThrow; + } + logExceptionOnExit(toThrow); + process["exit"](status); + }; + Module["inspect"] = function() { + return "[Emscripten Module object]"; + }; + } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) { + if (ENVIRONMENT_IS_WORKER) { + scriptDirectory = self.location.href; + } else if (typeof document !== "undefined" && document.currentScript) { + scriptDirectory = document.currentScript.src; + } + if (_scriptDir) { + scriptDirectory = _scriptDir; + } + if (scriptDirectory.indexOf("blob:") !== 0) { + scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1); + } else { + scriptDirectory = ""; + } + { + read_ = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.send(null); + return xhr.responseText; + }; + if (ENVIRONMENT_IS_WORKER) { + readBinary = (url) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, false); + xhr.responseType = "arraybuffer"; + xhr.send(null); + return new Uint8Array(xhr.response); + }; + } + readAsync = (url, onload, onerror) => { + var xhr = new XMLHttpRequest(); + xhr.open("GET", url, true); + xhr.responseType = "arraybuffer"; + xhr.onload = () => { + if (xhr.status == 200 || xhr.status == 0 && xhr.response) { + onload(xhr.response); + return; + } + onerror(); + }; + xhr.onerror = onerror; + xhr.send(null); + }; + } + setWindowTitle = (title) => document.title = title; + } else { + } + var out = Module["print"] || console.log.bind(console); + var err = Module["printErr"] || console.warn.bind(console); + Object.assign(Module, moduleOverrides); + moduleOverrides = null; + if (Module["arguments"]) + arguments_ = Module["arguments"]; + if (Module["thisProgram"]) + thisProgram = Module["thisProgram"]; + if (Module["quit"]) + quit_ = Module["quit"]; + var POINTER_SIZE = 4; + function warnOnce(text) { + if (!warnOnce.shown) + warnOnce.shown = {}; + if (!warnOnce.shown[text]) { + warnOnce.shown[text] = 1; + err(text); + } + } + function convertJsFunctionToWasm(func2, sig) { + if (typeof WebAssembly.Function === "function") { + var typeNames = { "i": "i32", "j": "i64", "f": "f32", "d": "f64" }; + var type = { parameters: [], results: sig[0] == "v" ? [] : [typeNames[sig[0]]] }; + for (var i = 1; i < sig.length; ++i) { + type.parameters.push(typeNames[sig[i]]); + } + return new WebAssembly.Function(type, func2); + } + var typeSection = [1, 0, 1, 96]; + var sigRet = sig.slice(0, 1); + var sigParam = sig.slice(1); + var typeCodes = { "i": 127, "j": 126, "f": 125, "d": 124 }; + typeSection.push(sigParam.length); + for (var i = 0; i < sigParam.length; ++i) { + typeSection.push(typeCodes[sigParam[i]]); + } + if (sigRet == "v") { + typeSection.push(0); + } else { + typeSection = typeSection.concat([1, typeCodes[sigRet]]); + } + typeSection[1] = typeSection.length - 2; + var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])); + var module2 = new WebAssembly.Module(bytes); + var instance = new WebAssembly.Instance(module2, { "e": { "f": func2 } }); + var wrappedFunc = instance.exports["f"]; + return wrappedFunc; + } + var freeTableIndexes = []; + var functionsInTableMap; + function getEmptyTableSlot() { + if (freeTableIndexes.length) { + return freeTableIndexes.pop(); + } + try { + wasmTable.grow(1); + } catch (err2) { + if (!(err2 instanceof RangeError)) { + throw err2; + } + throw "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH."; + } + return wasmTable.length - 1; + } + function updateTableMap(offset, count22) { + for (var i = offset; i < offset + count22; i++) { + var item = getWasmTableEntry(i); + if (item) { + functionsInTableMap.set(item, i); + } + } + } + var tempRet0 = 0; + var setTempRet0 = (value) => { + tempRet0 = value; + }; + var wasmBinary; + if (Module["wasmBinary"]) + wasmBinary = Module["wasmBinary"]; + var noExitRuntime = Module["noExitRuntime"] || true; + if (typeof WebAssembly !== "object") { + abort("no native wasm support detected"); + } + var wasmMemory; + var ABORT = false; + var EXITSTATUS; + function assert3(condition, text) { + if (!condition) { + abort(text); + } + } + function getCFunc(ident) { + var func2 = Module["_" + ident]; + return func2; + } + function ccall(ident, returnType, argTypes, args, opts) { + var toC = { "string": function(str) { + var ret2 = 0; + if (str !== null && str !== void 0 && str !== 0) { + var len = (str.length << 2) + 1; + ret2 = stackAlloc(len); + stringToUTF8(str, ret2, len); + } + return ret2; + }, "array": function(arr) { + var ret2 = stackAlloc(arr.length); + writeArrayToMemory(arr, ret2); + return ret2; + } }; + function convertReturnValue(ret2) { + if (returnType === "string") + return UTF8ToString(ret2); + if (returnType === "boolean") + return Boolean(ret2); + return ret2; + } + var func2 = getCFunc(ident); + var cArgs = []; + var stack2 = 0; + if (args) { + for (var i = 0; i < args.length; i++) { + var converter = toC[argTypes[i]]; + if (converter) { + if (stack2 === 0) + stack2 = stackSave(); + cArgs[i] = converter(args[i]); + } else { + cArgs[i] = args[i]; + } + } + } + var ret = func2.apply(null, cArgs); + function onDone(ret2) { + if (stack2 !== 0) + stackRestore(stack2); + return convertReturnValue(ret2); + } + ret = onDone(ret); + return ret; + } + function cwrap(ident, returnType, argTypes, opts) { + argTypes = argTypes || []; + var numericArgs = argTypes.every(function(type) { + return type === "number"; + }); + var numericRet = returnType !== "string"; + if (numericRet && numericArgs && !opts) { + return getCFunc(ident); + } + return function() { + return ccall(ident, returnType, argTypes, arguments, opts); + }; + } + var ALLOC_STACK = 1; + var UTF8Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf8") : void 0; + function UTF8ArrayToString(heap, idx, maxBytesToRead) { + var endIdx = idx + maxBytesToRead; + var endPtr = idx; + while (heap[endPtr] && !(endPtr >= endIdx)) + ++endPtr; + if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) { + return UTF8Decoder.decode(heap.subarray(idx, endPtr)); + } else { + var str = ""; + while (idx < endPtr) { + var u0 = heap[idx++]; + if (!(u0 & 128)) { + str += String.fromCharCode(u0); + continue; + } + var u1 = heap[idx++] & 63; + if ((u0 & 224) == 192) { + str += String.fromCharCode((u0 & 31) << 6 | u1); + continue; + } + var u2 = heap[idx++] & 63; + if ((u0 & 240) == 224) { + u0 = (u0 & 15) << 12 | u1 << 6 | u2; + } else { + u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63; + } + if (u0 < 65536) { + str += String.fromCharCode(u0); + } else { + var ch = u0 - 65536; + str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023); + } + } + } + return str; + } + function UTF8ToString(ptr, maxBytesToRead) { + return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : ""; + } + function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) { + if (!(maxBytesToWrite > 0)) + return 0; + var startIdx = outIdx; + var endIdx = outIdx + maxBytesToWrite - 1; + for (var i = 0; i < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 55296 && u <= 57343) { + var u1 = str.charCodeAt(++i); + u = 65536 + ((u & 1023) << 10) | u1 & 1023; + } + if (u <= 127) { + if (outIdx >= endIdx) + break; + heap[outIdx++] = u; + } else if (u <= 2047) { + if (outIdx + 1 >= endIdx) + break; + heap[outIdx++] = 192 | u >> 6; + heap[outIdx++] = 128 | u & 63; + } else if (u <= 65535) { + if (outIdx + 2 >= endIdx) + break; + heap[outIdx++] = 224 | u >> 12; + heap[outIdx++] = 128 | u >> 6 & 63; + heap[outIdx++] = 128 | u & 63; + } else { + if (outIdx + 3 >= endIdx) + break; + heap[outIdx++] = 240 | u >> 18; + heap[outIdx++] = 128 | u >> 12 & 63; + heap[outIdx++] = 128 | u >> 6 & 63; + heap[outIdx++] = 128 | u & 63; + } + } + heap[outIdx] = 0; + return outIdx - startIdx; + } + function stringToUTF8(str, outPtr, maxBytesToWrite) { + return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite); + } + function lengthBytesUTF8(str) { + var len = 0; + for (var i = 0; i < str.length; ++i) { + var u = str.charCodeAt(i); + if (u >= 55296 && u <= 57343) + u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023; + if (u <= 127) + ++len; + else if (u <= 2047) + len += 2; + else if (u <= 65535) + len += 3; + else + len += 4; + } + return len; + } + var UTF16Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf-16le") : void 0; + function writeArrayToMemory(array2, buffer3) { + HEAP8.set(array2, buffer3); + } + function writeAsciiToMemory(str, buffer3, dontAddNull) { + for (var i = 0; i < str.length; ++i) { + HEAP8[buffer3++ >> 0] = str.charCodeAt(i); + } + if (!dontAddNull) + HEAP8[buffer3 >> 0] = 0; + } + function alignUp(x, multiple) { + if (x % multiple > 0) { + x += multiple - x % multiple; + } + return x; + } + var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64; + function updateGlobalBufferAndViews(buf) { + buffer2 = buf; + Module["HEAP8"] = HEAP8 = new Int8Array(buf); + Module["HEAP16"] = HEAP16 = new Int16Array(buf); + Module["HEAP32"] = HEAP32 = new Int32Array(buf); + Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf); + Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf); + Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf); + Module["HEAPF32"] = HEAPF32 = new Float32Array(buf); + Module["HEAPF64"] = HEAPF64 = new Float64Array(buf); + } + var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216; + var wasmTable; + var __ATPRERUN__ = []; + var __ATINIT__ = []; + var __ATPOSTRUN__ = []; + var runtimeInitialized = false; + var runtimeExited = false; + var runtimeKeepaliveCounter = 0; + function keepRuntimeAlive() { + return noExitRuntime || runtimeKeepaliveCounter > 0; + } + function preRun() { + if (Module["preRun"]) { + if (typeof Module["preRun"] == "function") + Module["preRun"] = [Module["preRun"]]; + while (Module["preRun"].length) { + addOnPreRun(Module["preRun"].shift()); + } + } + callRuntimeCallbacks(__ATPRERUN__); + } + function initRuntime() { + runtimeInitialized = true; + callRuntimeCallbacks(__ATINIT__); + } + function exitRuntime() { + runtimeExited = true; + } + function postRun() { + if (Module["postRun"]) { + if (typeof Module["postRun"] == "function") + Module["postRun"] = [Module["postRun"]]; + while (Module["postRun"].length) { + addOnPostRun(Module["postRun"].shift()); + } + } + callRuntimeCallbacks(__ATPOSTRUN__); + } + function addOnPreRun(cb) { + __ATPRERUN__.unshift(cb); + } + function addOnInit(cb) { + __ATINIT__.unshift(cb); + } + function addOnPostRun(cb) { + __ATPOSTRUN__.unshift(cb); + } + var runDependencies = 0; + var runDependencyWatcher = null; + var dependenciesFulfilled = null; + function addRunDependency(id) { + runDependencies++; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + } + function removeRunDependency(id) { + runDependencies--; + if (Module["monitorRunDependencies"]) { + Module["monitorRunDependencies"](runDependencies); + } + if (runDependencies == 0) { + if (runDependencyWatcher !== null) { + clearInterval(runDependencyWatcher); + runDependencyWatcher = null; + } + if (dependenciesFulfilled) { + var callback = dependenciesFulfilled; + dependenciesFulfilled = null; + callback(); + } + } + } + Module["preloadedImages"] = {}; + Module["preloadedAudios"] = {}; + function abort(what) { + { + if (Module["onAbort"]) { + Module["onAbort"](what); + } + } + what = "Aborted(" + what + ")"; + err(what); + ABORT = true; + EXITSTATUS = 1; + what += ". Build with -s ASSERTIONS=1 for more info."; + var e = new WebAssembly.RuntimeError(what); + readyPromiseReject(e); + throw e; + } + var dataURIPrefix = "data:application/octet-stream;base64,"; + function isDataURI(filename) { + return filename.startsWith(dataURIPrefix); + } + function isFileURI(filename) { + return filename.startsWith("file://"); + } + var wasmBinaryFile; + wasmBinaryFile = "tfjs-backend-wasm.wasm"; + if (!isDataURI(wasmBinaryFile)) { + wasmBinaryFile = locateFile(wasmBinaryFile); + } + function getBinary(file) { + try { + if (file == wasmBinaryFile && wasmBinary) { + return new Uint8Array(wasmBinary); + } + if (readBinary) { + return readBinary(file); + } else { + throw "both async and sync fetching of the wasm failed"; + } + } catch (err2) { + abort(err2); + } + } + function getBinaryPromise() { + if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) { + if (typeof fetch === "function" && !isFileURI(wasmBinaryFile)) { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + if (!response["ok"]) { + throw "failed to load wasm binary file at '" + wasmBinaryFile + "'"; + } + return response["arrayBuffer"](); + }).catch(function() { + return getBinary(wasmBinaryFile); + }); + } else { + if (readAsync) { + return new Promise(function(resolve, reject) { + readAsync(wasmBinaryFile, function(response) { + resolve(new Uint8Array(response)); + }, reject); + }); + } + } + } + return Promise.resolve().then(function() { + return getBinary(wasmBinaryFile); + }); + } + function createWasm() { + var info = { "env": asmLibraryArg, "wasi_snapshot_preview1": asmLibraryArg }; + function receiveInstance(instance, module2) { + var exports3 = instance.exports; + Module["asm"] = exports3; + wasmMemory = Module["asm"]["memory"]; + updateGlobalBufferAndViews(wasmMemory.buffer); + wasmTable = Module["asm"]["__indirect_function_table"]; + addOnInit(Module["asm"]["__wasm_call_ctors"]); + removeRunDependency("wasm-instantiate"); + } + addRunDependency("wasm-instantiate"); + function receiveInstantiationResult(result) { + receiveInstance(result["instance"]); + } + function instantiateArrayBuffer(receiver) { + return getBinaryPromise().then(function(binary) { + return WebAssembly.instantiate(binary, info); + }).then(function(instance) { + return instance; + }).then(receiver, function(reason) { + err("failed to asynchronously prepare wasm: " + reason); + abort(reason); + }); + } + function instantiateAsync() { + if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") { + return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) { + var result = WebAssembly.instantiateStreaming(response, info); + return result.then(receiveInstantiationResult, function(reason) { + err("wasm streaming compile failed: " + reason); + err("falling back to ArrayBuffer instantiation"); + return instantiateArrayBuffer(receiveInstantiationResult); + }); + }); + } else { + return instantiateArrayBuffer(receiveInstantiationResult); + } + } + if (Module["instantiateWasm"]) { + try { + var exports2 = Module["instantiateWasm"](info, receiveInstance); + return exports2; + } catch (e) { + err("Module.instantiateWasm callback failed with error: " + e); + return false; + } + } + instantiateAsync().catch(readyPromiseReject); + return {}; + } + var tempDouble; + var tempI64; + function callRuntimeCallbacks(callbacks2) { + while (callbacks2.length > 0) { + var callback = callbacks2.shift(); + if (typeof callback == "function") { + callback(Module); + continue; + } + var func2 = callback.func; + if (typeof func2 === "number") { + if (callback.arg === void 0) { + getWasmTableEntry(func2)(); + } else { + getWasmTableEntry(func2)(callback.arg); + } + } else { + func2(callback.arg === void 0 ? null : callback.arg); + } + } + } + function demangle(func2) { + return func2; + } + function demangleAll(text) { + var regex = /\b_Z[\w\d_]+/g; + return text.replace(regex, function(x) { + var y = demangle(x); + return x === y ? x : y + " [" + x + "]"; + }); + } + var wasmTableMirror = []; + function getWasmTableEntry(funcPtr) { + var func2 = wasmTableMirror[funcPtr]; + if (!func2) { + if (funcPtr >= wasmTableMirror.length) + wasmTableMirror.length = funcPtr + 1; + wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr); + } + return func2; + } + function jsStackTrace() { + var error = new Error(); + if (!error.stack) { + try { + throw new Error(); + } catch (e) { + error = e; + } + if (!error.stack) { + return "(no stack trace available)"; + } + } + return error.stack.toString(); + } + function setWasmTableEntry(idx, func2) { + wasmTable.set(idx, func2); + wasmTableMirror[idx] = func2; + } + function _abort() { + abort(""); + } + function _emscripten_memcpy_big(dest, src, num) { + HEAPU8.copyWithin(dest, src, src + num); + } + function _emscripten_get_heap_max() { + return 2147483648; + } + function emscripten_realloc_buffer(size2) { + try { + wasmMemory.grow(size2 - buffer2.byteLength + 65535 >>> 16); + updateGlobalBufferAndViews(wasmMemory.buffer); + return 1; + } catch (e) { + } + } + function _emscripten_resize_heap(requestedSize) { + var oldSize = HEAPU8.length; + requestedSize = requestedSize >>> 0; + var maxHeapSize = _emscripten_get_heap_max(); + if (requestedSize > maxHeapSize) { + return false; + } + for (var cutDown = 1; cutDown <= 4; cutDown *= 2) { + var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown); + overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296); + var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536)); + var replacement = emscripten_realloc_buffer(newSize); + if (replacement) { + return true; + } + } + return false; + } + var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) { + var buffer3 = SYSCALLS.buffers[stream]; + if (curr === 0 || curr === 10) { + (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0)); + buffer3.length = 0; + } else { + buffer3.push(curr); + } + }, varargs: void 0, get: function() { + SYSCALLS.varargs += 4; + var ret = HEAP32[SYSCALLS.varargs - 4 >> 2]; + return ret; + }, getStr: function(ptr) { + var ret = UTF8ToString(ptr); + return ret; + }, get64: function(low, high) { + return low; + } }; + function _fd_close(fd) { + return 0; + } + function _fd_seek(fd, offset_low, offset_high, whence, newOffset) { + } + function _fd_write(fd, iov, iovcnt, pnum) { + var num = 0; + for (var i = 0; i < iovcnt; i++) { + var ptr = HEAP32[iov >> 2]; + var len = HEAP32[iov + 4 >> 2]; + iov += 8; + for (var j = 0; j < len; j++) { + SYSCALLS.printChar(fd, HEAPU8[ptr + j]); + } + num += len; + } + HEAP32[pnum >> 2] = num; + return 0; + } + function _setTempRet0(val) { + setTempRet0(val); + } + var ASSERTIONS = false; + var asmLibraryArg = { "abort": _abort, "emscripten_memcpy_big": _emscripten_memcpy_big, "emscripten_resize_heap": _emscripten_resize_heap, "fd_close": _fd_close, "fd_seek": _fd_seek, "fd_write": _fd_write, "setTempRet0": _setTempRet0 }; + var asm = createWasm(); + var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() { + return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["__wasm_call_ctors"]).apply(null, arguments); + }; + var _init = Module["_init"] = function() { + return (_init = Module["_init"] = Module["asm"]["init"]).apply(null, arguments); + }; + var _init_with_threads_count = Module["_init_with_threads_count"] = function() { + return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["init_with_threads_count"]).apply(null, arguments); + }; + var _get_threads_count = Module["_get_threads_count"] = function() { + return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["get_threads_count"]).apply(null, arguments); + }; + var _register_tensor = Module["_register_tensor"] = function() { + return (_register_tensor = Module["_register_tensor"] = Module["asm"]["register_tensor"]).apply(null, arguments); + }; + var _dispose_data = Module["_dispose_data"] = function() { + return (_dispose_data = Module["_dispose_data"] = Module["asm"]["dispose_data"]).apply(null, arguments); + }; + var _dispose = Module["_dispose"] = function() { + return (_dispose = Module["_dispose"] = Module["asm"]["dispose"]).apply(null, arguments); + }; + var _Abs = Module["_Abs"] = function() { + return (_Abs = Module["_Abs"] = Module["asm"]["Abs"]).apply(null, arguments); + }; + var _Add = Module["_Add"] = function() { + return (_Add = Module["_Add"] = Module["asm"]["Add"]).apply(null, arguments); + }; + var _AddN = Module["_AddN"] = function() { + return (_AddN = Module["_AddN"] = Module["asm"]["AddN"]).apply(null, arguments); + }; + var _All = Module["_All"] = function() { + return (_All = Module["_All"] = Module["asm"]["All"]).apply(null, arguments); + }; + var _Any = Module["_Any"] = function() { + return (_Any = Module["_Any"] = Module["asm"]["Any"]).apply(null, arguments); + }; + var _ArgMax = Module["_ArgMax"] = function() { + return (_ArgMax = Module["_ArgMax"] = Module["asm"]["ArgMax"]).apply(null, arguments); + }; + var _AvgPool = Module["_AvgPool"] = function() { + return (_AvgPool = Module["_AvgPool"] = Module["asm"]["AvgPool"]).apply(null, arguments); + }; + var _BatchMatMul = Module["_BatchMatMul"] = function() { + return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["BatchMatMul"]).apply(null, arguments); + }; + var _Ceil = Module["_Ceil"] = function() { + return (_Ceil = Module["_Ceil"] = Module["asm"]["Ceil"]).apply(null, arguments); + }; + var _ClipByValue = Module["_ClipByValue"] = function() { + return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["ClipByValue"]).apply(null, arguments); + }; + var _Conv2D2 = Module["_Conv2D"] = function() { + return (_Conv2D2 = Module["_Conv2D"] = Module["asm"]["Conv2D"]).apply(null, arguments); + }; + var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() { + return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["Conv2DBackpropInput"]).apply(null, arguments); + }; + var _Cos = Module["_Cos"] = function() { + return (_Cos = Module["_Cos"] = Module["asm"]["Cos"]).apply(null, arguments); + }; + var _Cosh = Module["_Cosh"] = function() { + return (_Cosh = Module["_Cosh"] = Module["asm"]["Cosh"]).apply(null, arguments); + }; + var _CropAndResize = Module["_CropAndResize"] = function() { + return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["CropAndResize"]).apply(null, arguments); + }; + var _Cumprod = Module["_Cumprod"] = function() { + return (_Cumprod = Module["_Cumprod"] = Module["asm"]["Cumprod"]).apply(null, arguments); + }; + var _Cumsum = Module["_Cumsum"] = function() { + return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Cumsum"]).apply(null, arguments); + }; + var _DepthToSpace = Module["_DepthToSpace"] = function() { + return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["DepthToSpace"]).apply(null, arguments); + }; + var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() { + return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["DepthwiseConv2dNative"]).apply(null, arguments); + }; + var _Elu = Module["_Elu"] = function() { + return (_Elu = Module["_Elu"] = Module["asm"]["Elu"]).apply(null, arguments); + }; + var _Equal = Module["_Equal"] = function() { + return (_Equal = Module["_Equal"] = Module["asm"]["Equal"]).apply(null, arguments); + }; + var _Exp = Module["_Exp"] = function() { + return (_Exp = Module["_Exp"] = Module["asm"]["Exp"]).apply(null, arguments); + }; + var _FlipLeftRight = Module["_FlipLeftRight"] = function() { + return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["FlipLeftRight"]).apply(null, arguments); + }; + var _Floor = Module["_Floor"] = function() { + return (_Floor = Module["_Floor"] = Module["asm"]["Floor"]).apply(null, arguments); + }; + var _FloorDiv = Module["_FloorDiv"] = function() { + return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["FloorDiv"]).apply(null, arguments); + }; + var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() { + return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["FusedBatchNorm"]).apply(null, arguments); + }; + var _FusedConv2D = Module["_FusedConv2D"] = function() { + return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["FusedConv2D"]).apply(null, arguments); + }; + var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() { + return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["FusedDepthwiseConv2D"]).apply(null, arguments); + }; + var _Gather = Module["_Gather"] = function() { + return (_Gather = Module["_Gather"] = Module["asm"]["Gather"]).apply(null, arguments); + }; + var _GatherNd = Module["_GatherNd"] = function() { + return (_GatherNd = Module["_GatherNd"] = Module["asm"]["GatherNd"]).apply(null, arguments); + }; + var _Greater = Module["_Greater"] = function() { + return (_Greater = Module["_Greater"] = Module["asm"]["Greater"]).apply(null, arguments); + }; + var _GreaterEqual = Module["_GreaterEqual"] = function() { + return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["GreaterEqual"]).apply(null, arguments); + }; + var _LeakyRelu = Module["_LeakyRelu"] = function() { + return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["LeakyRelu"]).apply(null, arguments); + }; + var _Less = Module["_Less"] = function() { + return (_Less = Module["_Less"] = Module["asm"]["Less"]).apply(null, arguments); + }; + var _LessEqual = Module["_LessEqual"] = function() { + return (_LessEqual = Module["_LessEqual"] = Module["asm"]["LessEqual"]).apply(null, arguments); + }; + var _Log = Module["_Log"] = function() { + return (_Log = Module["_Log"] = Module["asm"]["Log"]).apply(null, arguments); + }; + var _LogicalAnd = Module["_LogicalAnd"] = function() { + return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["LogicalAnd"]).apply(null, arguments); + }; + var _Max = Module["_Max"] = function() { + return (_Max = Module["_Max"] = Module["asm"]["Max"]).apply(null, arguments); + }; + var _MaxPool = Module["_MaxPool"] = function() { + return (_MaxPool = Module["_MaxPool"] = Module["asm"]["MaxPool"]).apply(null, arguments); + }; + var _Maximum = Module["_Maximum"] = function() { + return (_Maximum = Module["_Maximum"] = Module["asm"]["Maximum"]).apply(null, arguments); + }; + var _Mean = Module["_Mean"] = function() { + return (_Mean = Module["_Mean"] = Module["asm"]["Mean"]).apply(null, arguments); + }; + var _Min = Module["_Min"] = function() { + return (_Min = Module["_Min"] = Module["asm"]["Min"]).apply(null, arguments); + }; + var _Minimum = Module["_Minimum"] = function() { + return (_Minimum = Module["_Minimum"] = Module["asm"]["Minimum"]).apply(null, arguments); + }; + var _MirrorPad = Module["_MirrorPad"] = function() { + return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["MirrorPad"]).apply(null, arguments); + }; + var _Multiply = Module["_Multiply"] = function() { + return (_Multiply = Module["_Multiply"] = Module["asm"]["Multiply"]).apply(null, arguments); + }; + var _Neg = Module["_Neg"] = function() { + return (_Neg = Module["_Neg"] = Module["asm"]["Neg"]).apply(null, arguments); + }; + var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() { + return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["NonMaxSuppressionV3"]).apply(null, arguments); + }; + var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() { + return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["NonMaxSuppressionV4"]).apply(null, arguments); + }; + var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() { + return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["NonMaxSuppressionV5"]).apply(null, arguments); + }; + var _NotEqual = Module["_NotEqual"] = function() { + return (_NotEqual = Module["_NotEqual"] = Module["asm"]["NotEqual"]).apply(null, arguments); + }; + var _OneHot = Module["_OneHot"] = function() { + return (_OneHot = Module["_OneHot"] = Module["asm"]["OneHot"]).apply(null, arguments); + }; + var _PadV2 = Module["_PadV2"] = function() { + return (_PadV2 = Module["_PadV2"] = Module["asm"]["PadV2"]).apply(null, arguments); + }; + var _Pow = Module["_Pow"] = function() { + return (_Pow = Module["_Pow"] = Module["asm"]["Pow"]).apply(null, arguments); + }; + var _Prelu = Module["_Prelu"] = function() { + return (_Prelu = Module["_Prelu"] = Module["asm"]["Prelu"]).apply(null, arguments); + }; + var _Prod = Module["_Prod"] = function() { + return (_Prod = Module["_Prod"] = Module["asm"]["Prod"]).apply(null, arguments); + }; + var _RealDiv = Module["_RealDiv"] = function() { + return (_RealDiv = Module["_RealDiv"] = Module["asm"]["RealDiv"]).apply(null, arguments); + }; + var _Relu = Module["_Relu"] = function() { + return (_Relu = Module["_Relu"] = Module["asm"]["Relu"]).apply(null, arguments); + }; + var _Relu6 = Module["_Relu6"] = function() { + return (_Relu6 = Module["_Relu6"] = Module["asm"]["Relu6"]).apply(null, arguments); + }; + var _ResizeBilinear = Module["_ResizeBilinear"] = function() { + return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ResizeBilinear"]).apply(null, arguments); + }; + var _Reverse = Module["_Reverse"] = function() { + return (_Reverse = Module["_Reverse"] = Module["asm"]["Reverse"]).apply(null, arguments); + }; + var _RotateWithOffset = Module["_RotateWithOffset"] = function() { + return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["RotateWithOffset"]).apply(null, arguments); + }; + var _Round = Module["_Round"] = function() { + return (_Round = Module["_Round"] = Module["asm"]["Round"]).apply(null, arguments); + }; + var _Rsqrt = Module["_Rsqrt"] = function() { + return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Rsqrt"]).apply(null, arguments); + }; + var _ScatterNd = Module["_ScatterNd"] = function() { + return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["ScatterNd"]).apply(null, arguments); + }; + var _SelectV2 = Module["_SelectV2"] = function() { + return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["SelectV2"]).apply(null, arguments); + }; + var _Sigmoid = Module["_Sigmoid"] = function() { + return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sigmoid"]).apply(null, arguments); + }; + var _Sin = Module["_Sin"] = function() { + return (_Sin = Module["_Sin"] = Module["asm"]["Sin"]).apply(null, arguments); + }; + var _Softmax = Module["_Softmax"] = function() { + return (_Softmax = Module["_Softmax"] = Module["asm"]["Softmax"]).apply(null, arguments); + }; + var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() { + return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["SparseFillEmptyRows"]).apply(null, arguments); + }; + var _SparseReshape = Module["_SparseReshape"] = function() { + return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["SparseReshape"]).apply(null, arguments); + }; + var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() { + return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["SparseSegmentReduction"]).apply(null, arguments); + }; + var _Sqrt = Module["_Sqrt"] = function() { + return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sqrt"]).apply(null, arguments); + }; + var _Square = Module["_Square"] = function() { + return (_Square = Module["_Square"] = Module["asm"]["Square"]).apply(null, arguments); + }; + var _SquaredDifference = Module["_SquaredDifference"] = function() { + return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["SquaredDifference"]).apply(null, arguments); + }; + var _Step = Module["_Step"] = function() { + return (_Step = Module["_Step"] = Module["asm"]["Step"]).apply(null, arguments); + }; + var _StridedSlice = Module["_StridedSlice"] = function() { + return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["StridedSlice"]).apply(null, arguments); + }; + var _Sub = Module["_Sub"] = function() { + return (_Sub = Module["_Sub"] = Module["asm"]["Sub"]).apply(null, arguments); + }; + var _Sum = Module["_Sum"] = function() { + return (_Sum = Module["_Sum"] = Module["asm"]["Sum"]).apply(null, arguments); + }; + var _Tan = Module["_Tan"] = function() { + return (_Tan = Module["_Tan"] = Module["asm"]["Tan"]).apply(null, arguments); + }; + var _Tanh = Module["_Tanh"] = function() { + return (_Tanh = Module["_Tanh"] = Module["asm"]["Tanh"]).apply(null, arguments); + }; + var _Tile = Module["_Tile"] = function() { + return (_Tile = Module["_Tile"] = Module["asm"]["Tile"]).apply(null, arguments); + }; + var _TopK = Module["_TopK"] = function() { + return (_TopK = Module["_TopK"] = Module["asm"]["TopK"]).apply(null, arguments); + }; + var _Transform = Module["_Transform"] = function() { + return (_Transform = Module["_Transform"] = Module["asm"]["Transform"]).apply(null, arguments); + }; + var _Transpose = Module["_Transpose"] = function() { + return (_Transpose = Module["_Transpose"] = Module["asm"]["Transpose"]).apply(null, arguments); + }; + var __FusedMatMul = Module["__FusedMatMul"] = function() { + return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["_FusedMatMul"]).apply(null, arguments); + }; + var _malloc = Module["_malloc"] = function() { + return (_malloc = Module["_malloc"] = Module["asm"]["malloc"]).apply(null, arguments); + }; + var _free = Module["_free"] = function() { + return (_free = Module["_free"] = Module["asm"]["free"]).apply(null, arguments); + }; + var ___errno_location = Module["___errno_location"] = function() { + return (___errno_location = Module["___errno_location"] = Module["asm"]["__errno_location"]).apply(null, arguments); + }; + var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() { + return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["emscripten_main_thread_process_queued_calls"]).apply(null, arguments); + }; + var stackSave = Module["stackSave"] = function() { + return (stackSave = Module["stackSave"] = Module["asm"]["stackSave"]).apply(null, arguments); + }; + var stackRestore = Module["stackRestore"] = function() { + return (stackRestore = Module["stackRestore"] = Module["asm"]["stackRestore"]).apply(null, arguments); + }; + var stackAlloc = Module["stackAlloc"] = function() { + return (stackAlloc = Module["stackAlloc"] = Module["asm"]["stackAlloc"]).apply(null, arguments); + }; + var dynCall_iijjiiii = Module["dynCall_iijjiiii"] = function() { + return (dynCall_iijjiiii = Module["dynCall_iijjiiii"] = Module["asm"]["dynCall_iijjiiii"]).apply(null, arguments); + }; + var dynCall_jiji = Module["dynCall_jiji"] = function() { + return (dynCall_jiji = Module["dynCall_jiji"] = Module["asm"]["dynCall_jiji"]).apply(null, arguments); + }; + Module["cwrap"] = cwrap; + var calledRun; + function ExitStatus(status) { + this.name = "ExitStatus"; + this.message = "Program terminated with exit(" + status + ")"; + this.status = status; + } + dependenciesFulfilled = function runCaller() { + if (!calledRun) + run(); + if (!calledRun) + dependenciesFulfilled = runCaller; + }; + function run(args) { + args = args || arguments_; + if (runDependencies > 0) { + return; + } + preRun(); + if (runDependencies > 0) { + return; + } + function doRun() { + if (calledRun) + return; + calledRun = true; + Module["calledRun"] = true; + if (ABORT) + return; + initRuntime(); + readyPromiseResolve(Module); + if (Module["onRuntimeInitialized"]) + Module["onRuntimeInitialized"](); + postRun(); + } + if (Module["setStatus"]) { + Module["setStatus"]("Running..."); + setTimeout(function() { + setTimeout(function() { + Module["setStatus"](""); + }, 1); + doRun(); + }, 1); + } else { + doRun(); + } + } + Module["run"] = run; + function procExit(code) { + EXITSTATUS = code; + if (!keepRuntimeAlive()) { + if (Module["onExit"]) + Module["onExit"](code); + ABORT = true; + } + quit_(code, new ExitStatus(code)); + } + if (Module["preInit"]) { + if (typeof Module["preInit"] == "function") + Module["preInit"] = [Module["preInit"]]; + while (Module["preInit"].length > 0) { + Module["preInit"].pop()(); + } + } + run(); + var listenersAdded; + if (beforeListeners) { + listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) { + return !beforeListeners.uncaughtException.indexOf(listener) > -1; + }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) { + return !beforeListeners.unhandledRejection.indexOf(listener) > -1; + }) }; + } + var actualModule; + if (typeof WasmBackendModule3 !== "undefined") { + actualModule = WasmBackendModule3; + } else if (typeof WasmBackendModuleThreadedSimd !== "undefined") { + actualModule = WasmBackendModuleThreadedSimd; + } else { + throw new Error("Could not find wasm module in post.js"); + } + if (listenersAdded) { + var tmpDispose = actualModule["_dispose"]; + actualModule["_dispose"] = function() { + tmpDispose(); + listenersAdded.uncaughtException.forEach(function(listener) { + process.removeListener("uncaughtException", listener); + }); + listenersAdded.unhandledRejection.forEach(function(listener) { + process.removeListener("unhandledRejection", listener); + }); + }; + } + return WasmBackendModule3.ready; + }; + })(); + if (typeof exports === "object" && typeof module === "object") + module.exports = WasmBackendModule2; + else if (typeof define === "function" && define["amd"]) + define([], function() { + return WasmBackendModule2; + }); + else if (typeof exports === "object") + exports["WasmBackendModule"] = WasmBackendModule2; } - set(e, t) { - this.dataIdsCount++, this.data.set(e, t); +}); +var EPSILON_FLOAT32 = 1e-7; +var EPSILON_FLOAT16 = 1e-4; +var DataStorage = class { + constructor(backend2, dataMover) { + this.backend = backend2; + this.dataMover = dataMover; + this.data = /* @__PURE__ */ new WeakMap(); + this.dataIdsCount = 0; } - has(e) { - return this.data.has(e); + get(dataId) { + if (!this.data.has(dataId)) { + this.dataMover.moveData(this.backend, dataId); + } + return this.data.get(dataId); } - delete(e) { - return this.dataIdsCount--, this.data.delete(e); + set(dataId, value) { + this.dataIdsCount++; + this.data.set(dataId, value); + } + has(dataId) { + return this.data.has(dataId); + } + delete(dataId) { + this.dataIdsCount--; + return this.data.delete(dataId); } numDataIds() { return this.dataIdsCount; } }; -var sl = class { - refCount(e) { - return On("refCount"); +var KernelBackend = class { + refCount(dataId) { + return notYetImplemented("refCount"); } - incRef(e) { - return On("incRef"); + incRef(dataId) { + return notYetImplemented("incRef"); } timerAvailable() { return true; } - time(e) { - return On("time"); + time(f) { + return notYetImplemented("time"); } - read(e) { - return On("read"); + read(dataId) { + return notYetImplemented("read"); } - readSync(e) { - return On("readSync"); + readSync(dataId) { + return notYetImplemented("readSync"); } - readToGPU(e, t) { - return On("readToGPU"); + readToGPU(dataId, options4) { + return notYetImplemented("readToGPU"); } numDataIds() { - return On("numDataIds"); + return notYetImplemented("numDataIds"); } - disposeData(e, t) { - return On("disposeData"); + disposeData(dataId, force) { + return notYetImplemented("disposeData"); } - write(e, t, n) { - return On("write"); + write(values, shape, dtype) { + return notYetImplemented("write"); } - move(e, t, n, s, r) { - return On("move"); + move(dataId, values, shape, dtype, refCount) { + return notYetImplemented("move"); } memory() { - return On("memory"); + return notYetImplemented("memory"); } floatPrecision() { - return On("floatPrecision"); + return notYetImplemented("floatPrecision"); } epsilon() { - return this.floatPrecision() === 32 ? h$ : f$; + return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16; } dispose() { - return On("dispose"); + return notYetImplemented("dispose"); } }; -function On(e) { - throw new Error(`'${e}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`); +function notYetImplemented(kernelName) { + throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`); } -function jw(e) { - let t = e.length, n = 0; - for (; t > 0; ) - n = Math.random() * t | 0, t--, pd(e, t, n); -} -function m$(e, t) { - if (e.length !== t.length) - throw new Error(`Array sizes must match to be shuffled together First array length was ${e.length}Second array length was ${t.length}`); - let n = e.length, s = 0; - for (; n > 0; ) - s = Math.random() * n | 0, n--, pd(e, n, s), pd(t, n, s); -} -function Wu(e, t, n) { - return Math.max(e, Math.min(t, n)); -} -function g$(e) { - return e % 2 === 0 ? e : e + 1; -} -function pd(e, t, n) { - let s = e[t]; - e[t] = e[n], e[n] = s; -} -function b$(e) { - let t = 0; - for (let n = 0; n < e.length; n++) - t += e[n]; - return t; -} -function y$(e, t) { - let n = Math.random(); - return t * n + (1 - n) * e; -} -function v$(e, t) { - let n = 0; - for (let s = 0; s < e.length; s++) { - let r = Number(e[s]) - Number(t[s]); - n += r * r; +function shuffle(array2) { + let counter = array2.length; + let index2 = 0; + while (counter > 0) { + index2 = Math.random() * counter | 0; + counter--; + swap(array2, counter, index2); } - return n; } -function O(e, t) { - if (!e) - throw new Error(typeof t == "string" ? t : t()); +function shuffleCombo(array2, array22) { + if (array2.length !== array22.length) { + throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`); + } + let counter = array2.length; + let index2 = 0; + while (counter > 0) { + index2 = Math.random() * counter | 0; + counter--; + swap(array2, counter, index2); + swap(array22, counter, index2); + } } -function pn(e, t, n = "") { - O(Ir(e, t), () => n + ` Shapes ${e} and ${t} must match`); +function clamp(min7, x, max7) { + return Math.max(min7, Math.min(x, max7)); } -function ka(e) { - O(e != null, () => "The input to the tensor constructor must be a non-null value."); +function nearestLargerEven(val) { + return val % 2 === 0 ? val : val + 1; } -function ra(e, t = [], n = false) { - if (t == null && (t = []), Array.isArray(e) || Qt(e) && !n) - for (let s = 0; s < e.length; ++s) - ra(e[s], t, n); - else - t.push(e); - return t; +function swap(object2, left, right) { + const temp = object2[left]; + object2[left] = object2[right]; + object2[right] = temp; } -function pt(e) { - if (e.length === 0) +function sum(arr) { + let sum7 = 0; + for (let i = 0; i < arr.length; i++) { + sum7 += arr[i]; + } + return sum7; +} +function randUniform(a, b) { + const r = Math.random(); + return b * r + (1 - r) * a; +} +function distSquared(a, b) { + let result = 0; + for (let i = 0; i < a.length; i++) { + const diff = Number(a[i]) - Number(b[i]); + result += diff * diff; + } + return result; +} +function assert(expr, msg) { + if (!expr) { + throw new Error(typeof msg === "string" ? msg : msg()); + } +} +function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") { + assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`); +} +function assertNonNull(a) { + assert(a != null, () => `The input to the tensor constructor must be a non-null value.`); +} +function flatten(arr, result = [], skipTypedArray = false) { + if (result == null) { + result = []; + } + if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) { + for (let i = 0; i < arr.length; ++i) { + flatten(arr[i], result, skipTypedArray); + } + } else { + result.push(arr); + } + return result; +} +function sizeFromShape(shape) { + if (shape.length === 0) { return 1; - let t = e[0]; - for (let n = 1; n < e.length; n++) - t *= e[n]; - return t; + } + let size2 = shape[0]; + for (let i = 1; i < shape.length; i++) { + size2 *= shape[i]; + } + return size2; } -function x$(e) { - return e.length === 0; +function isScalarShape(shape) { + return shape.length === 0; } -function Ir(e, t) { - if (e === t) +function arraysEqual(n1, n2) { + if (n1 === n2) { return true; - if (e == null || t == null || e.length !== t.length) + } + if (n1 == null || n2 == null) { return false; - for (let n = 0; n < e.length; n++) - if (e[n] !== t[n]) + } + if (n1.length !== n2.length) { + return false; + } + for (let i = 0; i < n1.length; i++) { + if (n1[i] !== n2[i]) { return false; + } + } return true; } -function Zi(e) { - return e % 1 === 0; +function isInt(a) { + return a % 1 === 0; } -function w$(e) { - if (Math.tanh != null) - return Math.tanh(e); - if (e === 1 / 0) +function tanh(x) { + if (Math.tanh != null) { + return Math.tanh(x); + } + if (x === Infinity) { return 1; - if (e === -1 / 0) + } else if (x === -Infinity) { return -1; - { - let t = Math.exp(2 * e); - return (t - 1) / (t + 1); - } -} -function k$(e) { - let t = Math.ceil(Math.sqrt(e)); - return [t, Math.ceil(e / t)]; -} -function I$(e) { - let t = new Uint32Array(e); - for (let n = 0; n < e; ++n) - t[n] = n; - return jw(t), t; -} -function Mu(e, t) { - return t <= e.length ? e : e + " ".repeat(t - e.length); -} -function S$(e, t = (s) => 0, n) { - return new Promise((s, r) => { - let a = 0, i = () => { - if (e()) { - s(); - return; - } - a++; - let o = t(a); - if (n != null && a >= n) { - r(); - return; - } - setTimeout(i, o); - }; - i(); - }); -} -function C$(e, t) { - let n = 1, s = -1; - for (let a = 0; a < e.length; ++a) - if (e[a] >= 0) - n *= e[a]; - else if (e[a] === -1) { - if (s !== -1) - throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${s} and dim ${a}`); - s = a; - } else if (e[a] < 0) - throw Error(`Shapes can not be < 0. Found ${e[a]} at dim ${a}`); - if (s === -1) { - if (t > 0 && t !== n) - throw Error(`Size(${t}) must match the product of shape ${e}`); - return e; - } - if (n === 0) - throw Error(`Cannot infer the missing size in [${e}] when there are 0 elements`); - if (t % n !== 0) - throw Error(`The implicit shape can't be a fractional number. Got ${t} / ${n}`); - let r = e.slice(); - return r[s] = t / n, r; -} -function ts(e, t) { - let n = t.length; - return e = e == null ? t.map((s, r) => r) : [].concat(e), O(e.every((s) => s >= -n && s < n), () => `All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`), O(e.every((s) => Zi(s)), () => `All values in axis param must be integers but got axis ${e}`), e.map((s) => s < 0 ? n + s : s); -} -function Kw(e, t) { - let n = [], s = [], r = t != null && Array.isArray(t) && t.length === 0, a = t == null || r ? null : ts(t, e).sort(), i = 0; - for (let o = 0; o < e.length; ++o) { - if (a != null) { - if (a[i] === o && e[o] !== 1) - throw new Error(`Can't squeeze axis ${o} since its dim '${e[o]}' is not 1`); - (a[i] == null || a[i] > o) && e[o] === 1 && (n.push(e[o]), s.push(o)), a[i] <= o && i++; - } - e[o] !== 1 && (n.push(e[o]), s.push(o)); - } - return { newShape: n, keptDims: s }; -} -function Xw(e, t) { - let n = null; - if (e == null || e === "float32") - n = new Float32Array(t); - else if (e === "int32") - n = new Int32Array(t); - else if (e === "bool") - n = new Uint8Array(t); - else - throw new Error(`Unknown data type ${e}`); - return n; -} -function Yw(e, t) { - let n = null; - if (e == null || e === "float32") - n = new Float32Array(t); - else if (e === "int32") - n = new Int32Array(t); - else if (e === "bool") - n = new Uint8Array(t); - else if (e === "string") - n = new Array(t); - else - throw new Error(`Unknown data type ${e}`); - return n; -} -function Qw(e, t) { - for (let n = 0; n < e.length; n++) { - let s = e[n]; - if (isNaN(s) || !isFinite(s)) - throw Error(`A tensor of type ${t} being uploaded contains ${s}.`); - } -} -function Zw(e) { - return e === "bool" || e === "complex64" || e === "float32" || e === "int32" || e === "string"; -} -function N$(e, t) { - return !(t === "complex64" || t === "float32" && e !== "complex64" || t === "int32" && e !== "float32" && e !== "complex64" || t === "bool" && e === "bool"); -} -function Qt(e) { - return e instanceof Float32Array || e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray; -} -function Yf(e) { - if (e === "float32" || e === "int32") - return 4; - if (e === "complex64") - return 8; - if (e === "bool") - return 1; - throw new Error(`Unknown dtype ${e}`); -} -function Jw(e) { - if (e == null) - return 0; - let t = 0; - return e.forEach((n) => t += n.length), t; -} -function or(e) { - return typeof e == "string" || e instanceof String; -} -function ek(e) { - return typeof e == "boolean"; -} -function tk(e) { - return typeof e == "number"; -} -function qd(e) { - return Array.isArray(e) ? qd(e[0]) : e instanceof Float32Array ? "float32" : e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray ? "int32" : tk(e) ? "float32" : or(e) ? "string" : ek(e) ? "bool" : "float32"; -} -function fr(e) { - return !!(e && e.constructor && e.call && e.apply); -} -function hd(e, t) { - for (let n = t; n < e; ++n) - if (e % n === 0) - return n; - return e; -} -function oo(e) { - let t = e.length; - if (t < 2) - return []; - let n = new Array(t - 1); - n[t - 2] = e[t - 1]; - for (let s = t - 3; s >= 0; --s) - n[s] = n[s + 1] * e[s + 1]; - return n; -} -function nk(e, t, n, s = false) { - let r = new Array(); - if (t.length === 1) { - let a = t[0] * (s ? 2 : 1); - for (let i = 0; i < a; i++) - r[i] = n[e + i]; } else { - let a = t[0], i = t.slice(1), o = i.reduce((u, l) => u * l) * (s ? 2 : 1); - for (let u = 0; u < a; u++) - r[u] = nk(e + u * o, i, n, s); + const e2x = Math.exp(2 * x); + return (e2x - 1) / (e2x + 1); } - return r; } -function ji(e, t, n = false) { - if (e.length === 0) - return t[0]; - let s = e.reduce((r, a) => r * a) * (n ? 2 : 1); - if (s === 0) - return []; - if (s !== t.length) - throw new Error(`[${e}] does not match the input size ${t.length}${n ? " for a complex tensor" : ""}.`); - return nk(0, e, t, n); +function sizeToSquarishShape(size2) { + const width = Math.ceil(Math.sqrt(size2)); + return [width, Math.ceil(size2 / width)]; } -function rg(e, t) { - let n = jd(e, t); - for (let s = 0; s < n.length; s++) - n[s] = 1; - return n; +function createShuffledIndices(n) { + const shuffledIndices = new Uint32Array(n); + for (let i = 0; i < n; ++i) { + shuffledIndices[i] = i; + } + shuffle(shuffledIndices); + return shuffledIndices; } -function jd(e, t) { - if (t == null || t === "float32" || t === "complex64") - return new Float32Array(e); - if (t === "int32") - return new Int32Array(e); - if (t === "bool") - return new Uint8Array(e); - throw new Error(`Unknown data type ${t}`); +function rightPad(a, size2) { + if (size2 <= a.length) { + return a; + } + return a + " ".repeat(size2 - a.length); } -function T$(e, t) { - let n = e.reduce((s, r) => s * r, 1); - if (t == null || t === "float32") - return ji(e, new Float32Array(n)); - if (t === "int32") - return ji(e, new Int32Array(n)); - if (t === "bool") - return ji(e, new Uint8Array(n)); - throw new Error(`Unknown data type ${t}`); -} -function ag(e) { - e.forEach((t) => { - O(Number.isInteger(t) && t >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${e}].`); +function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) { + return new Promise((resolve, reject) => { + let tryCount = 0; + const tryFn = () => { + if (checkFn()) { + resolve(); + return; + } + tryCount++; + const nextBackoff = delayFn(tryCount); + if (maxCounter != null && tryCount >= maxCounter) { + reject(); + return; + } + setTimeout(tryFn, nextBackoff); + }; + tryFn(); }); } -function $$(e, t, n) { - if (t === 0) - return 0; - if (t === 1) - return e[0]; - let s = e[e.length - 1]; - for (let r = 0; r < e.length - 1; ++r) - s += n[r] * e[r]; - return s; -} -function _$(e, t, n) { - if (t === 0) - return []; - if (t === 1) - return [e]; - let s = new Array(t); - for (let r = 0; r < s.length - 1; ++r) - s[r] = Math.floor(e / n[r]), e -= s[r] * n[r]; - return s[s.length - 1] = e, s; -} -function ig(e) { - return e && e.then && typeof e.then == "function"; -} -var sx = "tfjsflags"; -var A$ = class { - constructor(e) { - this.global = e, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = E$, this.populateURLFlags(); - } - setPlatform(e, t) { - this.platform != null && (X().getBool("IS_TEST") || X().getBool("PROD") || console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${e}.`)), this.platformName = e, this.platform = t; - } - registerFlag(e, t, n) { - if (this.flagRegistry[e] = { evaluationFn: t, setHook: n }, this.urlFlags[e] != null) { - let s = this.urlFlags[e]; - X().getBool("IS_TEST") || X().getBool("PROD") || console.warn(`Setting feature override from URL ${e}: ${s}.`), this.set(e, s); +function inferFromImplicitShape(shape, size2) { + let shapeProd = 1; + let implicitIdx = -1; + for (let i = 0; i < shape.length; ++i) { + if (shape[i] >= 0) { + shapeProd *= shape[i]; + } else if (shape[i] === -1) { + if (implicitIdx !== -1) { + throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`); + } + implicitIdx = i; + } else if (shape[i] < 0) { + throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`); } } - async getAsync(e) { - return e in this.flags ? this.flags[e] : (this.flags[e] = await this.evaluateFlag(e), this.flags[e]); + if (implicitIdx === -1) { + if (size2 > 0 && size2 !== shapeProd) { + throw Error(`Size(${size2}) must match the product of shape ${shape}`); + } + return shape; } - get(e) { - if (e in this.flags) - return this.flags[e]; - let t = this.evaluateFlag(e); - if (ig(t)) - throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`); - return this.flags[e] = t, this.flags[e]; + if (shapeProd === 0) { + throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`); } - getNumber(e) { - return this.get(e); + if (size2 % shapeProd !== 0) { + throw Error(`The implicit shape can't be a fractional number. Got ${size2} / ${shapeProd}`); } - getBool(e) { - return this.get(e); + const newShape = shape.slice(); + newShape[implicitIdx] = size2 / shapeProd; + return newShape; +} +function parseAxisParam(axis, shape) { + const rank = shape.length; + axis = axis == null ? shape.map((s, i) => i) : [].concat(axis); + assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`); + assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`); + return axis.map((a) => a < 0 ? rank + a : a); +} +function squeezeShape(shape, axis) { + const newShape = []; + const keptDims = []; + const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0; + const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort(); + let j = 0; + for (let i = 0; i < shape.length; ++i) { + if (axes != null) { + if (axes[j] === i && shape[i] !== 1) { + throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`); + } + if ((axes[j] == null || axes[j] > i) && shape[i] === 1) { + newShape.push(shape[i]); + keptDims.push(i); + } + if (axes[j] <= i) { + j++; + } + } + if (shape[i] !== 1) { + newShape.push(shape[i]); + keptDims.push(i); + } + } + return { newShape, keptDims }; +} +function getTypedArrayFromDType(dtype, size2) { + let values = null; + if (dtype == null || dtype === "float32") { + values = new Float32Array(size2); + } else if (dtype === "int32") { + values = new Int32Array(size2); + } else if (dtype === "bool") { + values = new Uint8Array(size2); + } else { + throw new Error(`Unknown data type ${dtype}`); + } + return values; +} +function getArrayFromDType(dtype, size2) { + let values = null; + if (dtype == null || dtype === "float32") { + values = new Float32Array(size2); + } else if (dtype === "int32") { + values = new Int32Array(size2); + } else if (dtype === "bool") { + values = new Uint8Array(size2); + } else if (dtype === "string") { + values = new Array(size2); + } else { + throw new Error(`Unknown data type ${dtype}`); + } + return values; +} +function checkConversionForErrors(vals, dtype) { + for (let i = 0; i < vals.length; i++) { + const num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`); + } + } +} +function isValidDtype(dtype) { + return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string"; +} +function hasEncodingLoss(oldType, newType) { + if (newType === "complex64") { + return false; + } + if (newType === "float32" && oldType !== "complex64") { + return false; + } + if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") { + return false; + } + if (newType === "bool" && oldType === "bool") { + return false; + } + return true; +} +function isTypedArray(a) { + return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array || a instanceof Uint8ClampedArray; +} +function bytesPerElement(dtype) { + if (dtype === "float32" || dtype === "int32") { + return 4; + } else if (dtype === "complex64") { + return 8; + } else if (dtype === "bool") { + return 1; + } else { + throw new Error(`Unknown dtype ${dtype}`); + } +} +function bytesFromStringArray(arr) { + if (arr == null) { + return 0; + } + let bytes = 0; + arr.forEach((x) => bytes += x.length); + return bytes; +} +function isString(value) { + return typeof value === "string" || value instanceof String; +} +function isBoolean(value) { + return typeof value === "boolean"; +} +function isNumber(value) { + return typeof value === "number"; +} +function inferDtype(values) { + if (Array.isArray(values)) { + return inferDtype(values[0]); + } + if (values instanceof Float32Array) { + return "float32"; + } else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) { + return "int32"; + } else if (isNumber(values)) { + return "float32"; + } else if (isString(values)) { + return "string"; + } else if (isBoolean(values)) { + return "bool"; + } + return "float32"; +} +function isFunction(f) { + return !!(f && f.constructor && f.call && f.apply); +} +function nearestDivisor(size2, start) { + for (let i = start; i < size2; ++i) { + if (size2 % i === 0) { + return i; + } + } + return size2; +} +function computeStrides(shape) { + const rank = shape.length; + if (rank < 2) { + return []; + } + const strides2 = new Array(rank - 1); + strides2[rank - 2] = shape[rank - 1]; + for (let i = rank - 3; i >= 0; --i) { + strides2[i] = strides2[i + 1] * shape[i + 1]; + } + return strides2; +} +function createNestedArray(offset, shape, a, isComplex = false) { + const ret = new Array(); + if (shape.length === 1) { + const d = shape[0] * (isComplex ? 2 : 1); + for (let i = 0; i < d; i++) { + ret[i] = a[offset + i]; + } + } else { + const d = shape[0]; + const rest = shape.slice(1); + const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1); + for (let i = 0; i < d; i++) { + ret[i] = createNestedArray(offset + i * len, rest, a, isComplex); + } + } + return ret; +} +function toNestedArray(shape, a, isComplex = false) { + if (shape.length === 0) { + return a[0]; + } + const size2 = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1); + if (size2 === 0) { + return []; + } + if (size2 !== a.length) { + throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? " for a complex tensor" : ""}.`); + } + return createNestedArray(0, shape, a, isComplex); +} +function makeOnesTypedArray(size2, dtype) { + const array2 = makeZerosTypedArray(size2, dtype); + for (let i = 0; i < array2.length; i++) { + array2[i] = 1; + } + return array2; +} +function makeZerosTypedArray(size2, dtype) { + if (dtype == null || dtype === "float32" || dtype === "complex64") { + return new Float32Array(size2); + } else if (dtype === "int32") { + return new Int32Array(size2); + } else if (dtype === "bool") { + return new Uint8Array(size2); + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function makeZerosNestedTypedArray(shape, dtype) { + const size2 = shape.reduce((prev, curr) => prev * curr, 1); + if (dtype == null || dtype === "float32") { + return toNestedArray(shape, new Float32Array(size2)); + } else if (dtype === "int32") { + return toNestedArray(shape, new Int32Array(size2)); + } else if (dtype === "bool") { + return toNestedArray(shape, new Uint8Array(size2)); + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function assertNonNegativeIntegerDimensions(shape) { + shape.forEach((dimSize) => { + assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`); + }); +} +function locToIndex(locs, rank, strides2) { + if (rank === 0) { + return 0; + } else if (rank === 1) { + return locs[0]; + } + let index2 = locs[locs.length - 1]; + for (let i = 0; i < locs.length - 1; ++i) { + index2 += strides2[i] * locs[i]; + } + return index2; +} +function indexToLoc(index2, rank, strides2) { + if (rank === 0) { + return []; + } else if (rank === 1) { + return [index2]; + } + const locs = new Array(rank); + for (let i = 0; i < locs.length - 1; ++i) { + locs[i] = Math.floor(index2 / strides2[i]); + index2 -= locs[i] * strides2[i]; + } + locs[locs.length - 1] = index2; + return locs; +} +function isPromise(object2) { + return object2 && object2.then && typeof object2.then === "function"; +} +var TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags"; +var Environment = class { + constructor(global2) { + this.global = global2; + this.flags = {}; + this.flagRegistry = {}; + this.urlFlags = {}; + this.getQueryParams = getQueryParams; + this.populateURLFlags(); + } + setPlatform(platformName, platform) { + if (this.platform != null) { + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platformName}.`); + } + } + this.platformName = platformName; + this.platform = platform; + } + registerFlag(flagName, evaluationFn, setHook) { + this.flagRegistry[flagName] = { evaluationFn, setHook }; + if (this.urlFlags[flagName] != null) { + const flagValue = this.urlFlags[flagName]; + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`); + } + this.set(flagName, flagValue); + } + } + async getAsync(flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + this.flags[flagName] = await this.evaluateFlag(flagName); + return this.flags[flagName]; + } + get(flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + const flagValue = this.evaluateFlag(flagName); + if (isPromise(flagValue)) { + throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`); + } + this.flags[flagName] = flagValue; + return this.flags[flagName]; + } + getNumber(flagName) { + return this.get(flagName); + } + getBool(flagName) { + return this.get(flagName); } getFlags() { return this.flags; @@ -3720,5445 +6008,9306 @@ var A$ = class { get features() { return this.flags; } - set(e, t) { - if (this.flagRegistry[e] == null) - throw new Error(`Cannot set flag ${e} as it has not been registered.`); - this.flags[e] = t, this.flagRegistry[e].setHook != null && this.flagRegistry[e].setHook(t); + set(flagName, value) { + if (this.flagRegistry[flagName] == null) { + throw new Error(`Cannot set flag ${flagName} as it has not been registered.`); + } + this.flags[flagName] = value; + if (this.flagRegistry[flagName].setHook != null) { + this.flagRegistry[flagName].setHook(value); + } } - evaluateFlag(e) { - if (this.flagRegistry[e] == null) - throw new Error(`Cannot evaluate flag '${e}': no evaluation function found.`); - return this.flagRegistry[e].evaluationFn(); + evaluateFlag(flagName) { + if (this.flagRegistry[flagName] == null) { + throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`); + } + return this.flagRegistry[flagName].evaluationFn(); } - setFlags(e) { - this.flags = Object.assign({}, e); + setFlags(flags) { + this.flags = Object.assign({}, flags); } reset() { - this.flags = {}, this.urlFlags = {}, this.populateURLFlags(); + this.flags = {}; + this.urlFlags = {}; + this.populateURLFlags(); } populateURLFlags() { - if (typeof this.global == "undefined" || typeof this.global.location == "undefined" || typeof this.global.location.search == "undefined") + if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") { return; - let e = this.getQueryParams(this.global.location.search); - sx in e && e[sx].split(",").forEach((n) => { - let [s, r] = n.split(":"); - this.urlFlags[s] = D$(s, r); - }); + } + const urlParams = this.getQueryParams(this.global.location.search); + if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) { + const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(","); + keyValues.forEach((keyValue) => { + const [key, value] = keyValue.split(":"); + this.urlFlags[key] = parseValue(key, value); + }); + } } }; -function E$(e) { - let t = {}; - return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (n, ...s) => (R$(t, s[0], s[1]), s.join("="))), t; +function getQueryParams(queryString) { + const params = {}; + queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => { + decodeParam(params, t[0], t[1]); + return t.join("="); + }); + return params; } -function R$(e, t, n) { - e[decodeURIComponent(t)] = decodeURIComponent(n || ""); +function decodeParam(params, name, value) { + params[decodeURIComponent(name)] = decodeURIComponent(value || ""); } -function D$(e, t) { - if (t = t.toLowerCase(), t === "true" || t === "false") - return t === "true"; - if (`${+t}` === t) - return +t; - throw new Error(`Could not parse value flag value ${t} for flag ${e}.`); +function parseValue(flagName, value) { + value = value.toLowerCase(); + if (value === "true" || value === "false") { + return value === "true"; + } else if (`${+value}` === value) { + return +value; + } + throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`); } -function X() { - return sk; +function env() { + return ENV; } -var sk = null; -function F$(e) { - sk = e; +var ENV = null; +function setEnvironmentGlobal(environment) { + ENV = environment; } -var Of; -function rk() { - if (Of == null) { - let e; - if (typeof window != "undefined") - e = window; - else if (typeof global != "undefined") - e = global; - else if (typeof process != "undefined") - e = process; - else if (typeof self != "undefined") - e = self; - else +var globalNameSpace; +function getGlobalNamespace() { + if (globalNameSpace == null) { + let ns; + if (typeof window !== "undefined") { + ns = window; + } else if (typeof global !== "undefined") { + ns = global; + } else if (typeof process !== "undefined") { + ns = process; + } else if (typeof self !== "undefined") { + ns = self; + } else { throw new Error("Could not find a global object"); - Of = e; + } + globalNameSpace = ns; } - return Of; + return globalNameSpace; } -function O$() { - let e = rk(); - return e._tfGlobals == null && (e._tfGlobals = /* @__PURE__ */ new Map()), e._tfGlobals; +function getGlobalMap() { + const ns = getGlobalNamespace(); + if (ns._tfGlobals == null) { + ns._tfGlobals = /* @__PURE__ */ new Map(); + } + return ns._tfGlobals; } -function og(e, t) { - let n = O$(); - if (n.has(e)) - return n.get(e); - { - let s = t(); - return n.set(e, s), n.get(e); +function getGlobal(key, init22) { + const globalMap = getGlobalMap(); + if (globalMap.has(key)) { + return globalMap.get(key); + } else { + const singleton = init22(); + globalMap.set(key, singleton); + return globalMap.get(key); } } -var uo = "Abs"; -var rl = "Acos"; -var al = "Acosh"; -var Sr = "Add"; -var Ia = "AddN"; -var il = "All"; -var ol = "Any"; -var Sa = "ArgMax"; -var ul = "ArgMin"; -var ll = "Asin"; -var cl = "Asinh"; -var dl = "Atan"; -var pl = "Atanh"; -var hl = "Atan2"; -var Ca = "AvgPool"; -var ug = "AvgPoolGrad"; -var Kd = "AvgPool3D"; -var lg = "AvgPool3DGrad"; -var Na = "BatchMatMul"; -var lo = "BatchToSpaceND"; -var cg = "Bincount"; -var P$ = "BroadcastTo"; -var dg = "BroadcastArgs"; -var Ta = "Cast"; -var $a = "Ceil"; -var Cr = "ClipByValue"; -var Xd = "Complex"; -var Yd = "ComplexAbs"; -var co = "Concat"; -var _a = "Conv2D"; -var pg = "Conv2DBackpropFilter"; -var Aa = "Conv2DBackpropInput"; -var Qd = "Conv3D"; -var hg = "Conv3DBackpropFilterV2"; -var fg = "Conv3DBackpropInputV2"; -var Ea = "Cos"; -var Ra = "Cosh"; -var po = "Cumprod"; -var Da = "Cumsum"; -var ho = "CropAndResize"; -var mg = "DenseBincount"; -var fo = "DepthToSpace"; -var Fa = "DepthwiseConv2dNative"; -var gg = "DepthwiseConv2dNativeBackpropFilter"; -var bg = "DepthwiseConv2dNativeBackpropInput"; -var yg = "Diag"; -var Zd = "Dilation2D"; -var Qf = "Dilation2DBackpropInput"; -var Zf = "Dilation2DBackpropFilter"; -var Oa = "RealDiv"; -var Jd = "Einsum"; -var Pa = "Elu"; -var vg = "EluGrad"; -var fl = "Erf"; -var mo = "Equal"; -var za = "Exp"; -var go = "ExpandDims"; -var bo = "Expm1"; -var xg = "FFT"; -var ml = "Fill"; -var yo = "FlipLeftRight"; -var Ma = "Floor"; -var La = "FloorDiv"; -var Ba = "FusedBatchNorm"; -var vo = "GatherV2"; -var xo = "GatherNd"; -var wo = "Greater"; -var Va = "GreaterEqual"; -var Wa = "Identity"; -var wg = "IFFT"; -var ep = "Imag"; -var gl = "IsFinite"; -var bl = "IsInf"; -var yl = "IsNan"; -var Ua = "LeakyRelu"; -var ko = "Less"; -var Io = "LessEqual"; -var kg = "LinSpace"; -var Ga = "Log"; -var vl = "Log1p"; -var So = "LogicalAnd"; -var xl = "LogicalNot"; -var tp = "LogicalOr"; -var z$ = "LogSoftmax"; -var np = "LRN"; -var Ig = "LRNGrad"; -var Ha = "Max"; -var qa = "Maximum"; -var ja = "MaxPool"; -var Sg = "MaxPoolGrad"; -var sp = "MaxPool3D"; -var Cg = "MaxPool3DGrad"; -var Ng = "MaxPoolWithArgmax"; -var Ka = "Mean"; -var Xa = "Min"; -var Ya = "Minimum"; -var Qa = "MirrorPad"; -var wl = "Mod"; -var Tg = "Multinomial"; -var Za = "Multiply"; -var Co = "Neg"; -var No = "NotEqual"; -var To = "NonMaxSuppressionV3"; -var kl = "NonMaxSuppressionV4"; -var $o = "NonMaxSuppressionV5"; -var _o = "OnesLike"; -var Ao = "OneHot"; -var Eo = "Pack"; -var Ja = "PadV2"; -var Wde = "Pool"; -var ei = "Pow"; -var ti = "Prelu"; -var ni = "Prod"; -var Il = "Range"; -var rp = "Real"; -var Sl = "Reciprocal"; -var si = "Relu"; -var Ro = "Reshape"; -var Cl = "ResizeNearestNeighbor"; -var $g = "ResizeNearestNeighborGrad"; -var ri = "ResizeBilinear"; -var _g = "ResizeBilinearGrad"; -var ai = "Relu6"; -var Do = "Reverse"; -var Fo = "Round"; -var ii = "Rsqrt"; -var Oo = "ScatterNd"; -var Po = "Select"; -var Nl = "Selu"; -var zo = "Slice"; -var oi = "Sin"; -var Mo = "Sinh"; -var Tl = "Sign"; -var ui = "Sigmoid"; -var $l = "Softplus"; -var li = "Sqrt"; -var ci = "Sum"; -var Lo = "SpaceToBatchND"; -var Bo = "SplitV"; -var di = "Softmax"; -var ap = "SparseFillEmptyRows"; -var _l = "SparseReshape"; -var ip = "SparseSegmentMean"; -var op = "SparseSegmentSum"; -var up = "SparseToDense"; -var pi = "SquaredDifference"; -var Al = "Square"; -var Vo = "StridedSlice"; -var lp = "StringNGrams"; -var Ag = "StringSplit"; -var Eg = "StringToHashBucketFast"; -var hi = "Sub"; -var Wo = "Tan"; -var fi = "Tanh"; -var Nr = "Tile"; -var Uo = "TopK"; -var Go = "Transform"; -var mi = "Transpose"; -var Rg = "Unique"; -var Ho = "Unpack"; -var cp = "UnsortedSegmentSum"; -var qo = "ZerosLike"; -var gi = "Step"; -var fd = "FromPixels"; -var jo = "RotateWithOffset"; -var aa = "_FusedMatMul"; -var ia = "FusedConv2D"; -var oa = "FusedDepthwiseConv2D"; -function ir(...e) { - X().getBool("IS_TEST") || X().getBool("PROD") || console.warn(...e); +var Abs = "Abs"; +var Acos = "Acos"; +var Acosh = "Acosh"; +var Add = "Add"; +var AddN = "AddN"; +var All = "All"; +var Any = "Any"; +var ArgMax = "ArgMax"; +var ArgMin = "ArgMin"; +var Asin = "Asin"; +var Asinh = "Asinh"; +var Atan = "Atan"; +var Atanh = "Atanh"; +var Atan2 = "Atan2"; +var AvgPool = "AvgPool"; +var AvgPoolGrad = "AvgPoolGrad"; +var AvgPool3D = "AvgPool3D"; +var AvgPool3DGrad = "AvgPool3DGrad"; +var BatchMatMul = "BatchMatMul"; +var BatchToSpaceND = "BatchToSpaceND"; +var Bincount = "Bincount"; +var BroadcastTo = "BroadcastTo"; +var BroadcastArgs = "BroadcastArgs"; +var Cast = "Cast"; +var Ceil = "Ceil"; +var ClipByValue = "ClipByValue"; +var Complex = "Complex"; +var ComplexAbs = "ComplexAbs"; +var Concat = "Concat"; +var Conv2D = "Conv2D"; +var Conv2DBackpropFilter = "Conv2DBackpropFilter"; +var Conv2DBackpropInput = "Conv2DBackpropInput"; +var Conv3D = "Conv3D"; +var Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2"; +var Conv3DBackpropInputV2 = "Conv3DBackpropInputV2"; +var Cos = "Cos"; +var Cosh = "Cosh"; +var Cumprod = "Cumprod"; +var Cumsum = "Cumsum"; +var CropAndResize = "CropAndResize"; +var DenseBincount = "DenseBincount"; +var DepthToSpace = "DepthToSpace"; +var DepthwiseConv2dNative = "DepthwiseConv2dNative"; +var DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter"; +var DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput"; +var Diag = "Diag"; +var Dilation2D = "Dilation2D"; +var Dilation2DBackpropInput = "Dilation2DBackpropInput"; +var Dilation2DBackpropFilter = "Dilation2DBackpropFilter"; +var RealDiv = "RealDiv"; +var Einsum = "Einsum"; +var Elu = "Elu"; +var EluGrad = "EluGrad"; +var Erf = "Erf"; +var Equal = "Equal"; +var Exp = "Exp"; +var ExpandDims = "ExpandDims"; +var Expm1 = "Expm1"; +var FFT = "FFT"; +var Fill = "Fill"; +var FlipLeftRight = "FlipLeftRight"; +var Floor = "Floor"; +var FloorDiv = "FloorDiv"; +var FusedBatchNorm = "FusedBatchNorm"; +var GatherV2 = "GatherV2"; +var GatherNd = "GatherNd"; +var Greater = "Greater"; +var GreaterEqual = "GreaterEqual"; +var Identity = "Identity"; +var IFFT = "IFFT"; +var Imag = "Imag"; +var IsFinite = "IsFinite"; +var IsInf = "IsInf"; +var IsNan = "IsNan"; +var LeakyRelu = "LeakyRelu"; +var Less = "Less"; +var LessEqual = "LessEqual"; +var LinSpace = "LinSpace"; +var Log = "Log"; +var Log1p = "Log1p"; +var LogicalAnd = "LogicalAnd"; +var LogicalNot = "LogicalNot"; +var LogicalOr = "LogicalOr"; +var LogSoftmax = "LogSoftmax"; +var LRN = "LRN"; +var LRNGrad = "LRNGrad"; +var Max = "Max"; +var Maximum = "Maximum"; +var MaxPool = "MaxPool"; +var MaxPoolGrad = "MaxPoolGrad"; +var MaxPool3D = "MaxPool3D"; +var MaxPool3DGrad = "MaxPool3DGrad"; +var MaxPoolWithArgmax = "MaxPoolWithArgmax"; +var Mean = "Mean"; +var Min = "Min"; +var Minimum = "Minimum"; +var MirrorPad = "MirrorPad"; +var Mod = "Mod"; +var Multinomial = "Multinomial"; +var Multiply = "Multiply"; +var Neg = "Neg"; +var NotEqual = "NotEqual"; +var NonMaxSuppressionV3 = "NonMaxSuppressionV3"; +var NonMaxSuppressionV4 = "NonMaxSuppressionV4"; +var NonMaxSuppressionV5 = "NonMaxSuppressionV5"; +var OnesLike = "OnesLike"; +var OneHot = "OneHot"; +var Pack = "Pack"; +var PadV2 = "PadV2"; +var Pool = "Pool"; +var Pow = "Pow"; +var Prelu = "Prelu"; +var Prod = "Prod"; +var Range = "Range"; +var Real = "Real"; +var Reciprocal = "Reciprocal"; +var Relu = "Relu"; +var Reshape = "Reshape"; +var ResizeNearestNeighbor = "ResizeNearestNeighbor"; +var ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad"; +var ResizeBilinear = "ResizeBilinear"; +var ResizeBilinearGrad = "ResizeBilinearGrad"; +var Relu6 = "Relu6"; +var Reverse = "Reverse"; +var Round = "Round"; +var Rsqrt = "Rsqrt"; +var ScatterNd = "ScatterNd"; +var Select = "Select"; +var Selu = "Selu"; +var Slice = "Slice"; +var Sin = "Sin"; +var Sinh = "Sinh"; +var Sign = "Sign"; +var Sigmoid = "Sigmoid"; +var Softplus = "Softplus"; +var Sqrt = "Sqrt"; +var Sum = "Sum"; +var SpaceToBatchND = "SpaceToBatchND"; +var SplitV = "SplitV"; +var Softmax = "Softmax"; +var SparseFillEmptyRows = "SparseFillEmptyRows"; +var SparseReshape = "SparseReshape"; +var SparseSegmentMean = "SparseSegmentMean"; +var SparseSegmentSum = "SparseSegmentSum"; +var SparseToDense = "SparseToDense"; +var SquaredDifference = "SquaredDifference"; +var Square = "Square"; +var StridedSlice = "StridedSlice"; +var StringNGrams = "StringNGrams"; +var StringSplit = "StringSplit"; +var StringToHashBucketFast = "StringToHashBucketFast"; +var Sub = "Sub"; +var Tan = "Tan"; +var Tanh = "Tanh"; +var Tile = "Tile"; +var TopK = "TopK"; +var Transform = "Transform"; +var Transpose = "Transpose"; +var Unique = "Unique"; +var Unpack = "Unpack"; +var UnsortedSegmentSum = "UnsortedSegmentSum"; +var ZerosLike = "ZerosLike"; +var Step = "Step"; +var FromPixels = "FromPixels"; +var RotateWithOffset = "RotateWithOffset"; +var _FusedMatMul = "_FusedMatMul"; +var FusedConv2D = "FusedConv2D"; +var FusedDepthwiseConv2D = "FusedDepthwiseConv2D"; +function warn(...msg) { + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.warn(...msg); + } } -function M$(...e) { - X().getBool("IS_TEST") || X().getBool("PROD") || console.log(...e); +function log2(...msg) { + if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) { + console.log(...msg); + } } -var Ji = og("kernelRegistry", () => /* @__PURE__ */ new Map()); -var Uu = og("gradRegistry", () => /* @__PURE__ */ new Map()); -function Jf(e, t) { - let n = Dg(e, t); - return Ji.get(n); +var kernelRegistry = getGlobal("kernelRegistry", () => /* @__PURE__ */ new Map()); +var gradRegistry = getGlobal("gradRegistry", () => /* @__PURE__ */ new Map()); +function getKernel(kernelName, backendName) { + const key = makeKey(kernelName, backendName); + return kernelRegistry.get(key); } -function rx(e) { - return Uu.get(e); +function getGradient(kernelName) { + return gradRegistry.get(kernelName); } -function em(e) { - let t = Ji.entries(), n = []; - for (; ; ) { - let { done: s, value: r } = t.next(); - if (s) +function getKernelsForBackend(backendName) { + const it = kernelRegistry.entries(); + const result = []; + while (true) { + const { done, value } = it.next(); + if (done) { break; - let [a, i] = r, [o] = a.split("_"); - o === e && n.push(i); + } + const [key, config3] = value; + const [backend2] = key.split("_"); + if (backend2 === backendName) { + result.push(config3); + } } - return n; + return result; } -function El(e) { - let { kernelName: t, backendName: n } = e, s = Dg(t, n); - Ji.has(s) && ir(`The kernel '${t}' for backend '${n}' is already registered`), Ji.set(s, e); +function registerKernel(config3) { + const { kernelName, backendName } = config3; + const key = makeKey(kernelName, backendName); + if (kernelRegistry.has(key)) { + warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`); + } + kernelRegistry.set(key, config3); } -function L$(e) { - let { kernelName: t } = e; - Uu.has(t) && X().getBool("DEBUG") && ir(`Overriding the gradient for '${t}'`), Uu.set(t, e); +function registerGradient(config3) { + const { kernelName } = config3; + if (gradRegistry.has(kernelName)) { + if (env().getBool("DEBUG")) { + warn(`Overriding the gradient for '${kernelName}'`); + } + } + gradRegistry.set(kernelName, config3); } -function Ude(e, t) { - let n = Dg(e, t); - if (!Ji.has(n)) - throw new Error(`The kernel '${e}' for backend '${t}' is not registered`); - Ji.delete(n); +function unregisterKernel(kernelName, backendName) { + const key = makeKey(kernelName, backendName); + if (!kernelRegistry.has(key)) { + throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`); + } + kernelRegistry.delete(key); } -function Gde(e) { - if (!Uu.has(e)) - throw new Error(`The gradient '${e}' for backend is not registered`); - Uu.delete(e); +function unregisterGradient(kernelName) { + if (!gradRegistry.has(kernelName)) { + throw new Error(`The gradient '${kernelName}' for backend is not registered`); + } + gradRegistry.delete(kernelName); } -function Hde(e, t) { - em(e).forEach((s) => { - let r = Object.assign({}, s, { backendName: t }); - El(r); +function copyRegisteredKernels(registeredBackendName, newBackendName) { + const kernels = getKernelsForBackend(registeredBackendName); + kernels.forEach((kernelConfig) => { + const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName }); + registerKernel(newKernelConfig); }); } -function Dg(e, t) { - return `${t}_${e}`; +function makeKey(kernelName, backendName) { + return `${backendName}_${kernelName}`; } -var w = {}; -Ae(w, { arraysEqual: () => Ir, assert: () => O, assertNonNegativeIntegerDimensions: () => ag, assertNonNull: () => ka, assertShapesMatch: () => pn, bytesFromStringArray: () => Jw, bytesPerElement: () => Yf, checkConversionForErrors: () => Qw, clamp: () => Wu, computeStrides: () => oo, createScalarValue: () => H$, createShuffledIndices: () => I$, decodeString: () => md, distSquared: () => v$, encodeString: () => Dl, fetch: () => j$, fingerPrint64: () => G$, flatten: () => ra, getArrayFromDType: () => Yw, getTypedArrayFromDType: () => Xw, hasEncodingLoss: () => N$, hexToLong: () => Rl, indexToLoc: () => _$, inferDtype: () => qd, inferFromImplicitShape: () => C$, isBoolean: () => ek, isFunction: () => fr, isInt: () => Zi, isNumber: () => tk, isPromise: () => ig, isScalarShape: () => x$, isString: () => or, isTypedArray: () => Qt, isValidDtype: () => Zw, locToIndex: () => $$, makeOnesTypedArray: () => rg, makeZerosNestedTypedArray: () => T$, makeZerosTypedArray: () => jd, nearestDivisor: () => hd, nearestLargerEven: () => g$, now: () => Gu, parseAxisParam: () => ts, randUniform: () => y$, repeatedTry: () => S$, rightPad: () => Mu, shuffle: () => jw, shuffleCombo: () => m$, sizeFromShape: () => pt, sizeToSquarishShape: () => k$, squeezeShape: () => Kw, sum: () => b$, swap: () => pd, tanh: () => w$, toNestedArray: () => ji, toTypedArray: () => dp }); -var ax = wa(QT()); -var jr = ax.default || ax; -function Rl(e) { - return jr.fromString(e, true, 16); +var util_exports = {}; +__export2(util_exports, { + arraysEqual: () => arraysEqual, + assert: () => assert, + assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions, + assertNonNull: () => assertNonNull, + assertShapesMatch: () => assertShapesMatch, + bytesFromStringArray: () => bytesFromStringArray, + bytesPerElement: () => bytesPerElement, + checkConversionForErrors: () => checkConversionForErrors, + clamp: () => clamp, + computeStrides: () => computeStrides, + createScalarValue: () => createScalarValue, + createShuffledIndices: () => createShuffledIndices, + decodeString: () => decodeString, + distSquared: () => distSquared, + encodeString: () => encodeString, + fetch: () => fetch3, + fingerPrint64: () => fingerPrint64, + flatten: () => flatten, + getArrayFromDType: () => getArrayFromDType, + getTypedArrayFromDType: () => getTypedArrayFromDType, + hasEncodingLoss: () => hasEncodingLoss, + hexToLong: () => hexToLong, + indexToLoc: () => indexToLoc, + inferDtype: () => inferDtype, + inferFromImplicitShape: () => inferFromImplicitShape, + isBoolean: () => isBoolean, + isFunction: () => isFunction, + isInt: () => isInt, + isNumber: () => isNumber, + isPromise: () => isPromise, + isScalarShape: () => isScalarShape, + isString: () => isString, + isTypedArray: () => isTypedArray, + isValidDtype: () => isValidDtype, + locToIndex: () => locToIndex, + makeOnesTypedArray: () => makeOnesTypedArray, + makeZerosNestedTypedArray: () => makeZerosNestedTypedArray, + makeZerosTypedArray: () => makeZerosTypedArray, + nearestDivisor: () => nearestDivisor, + nearestLargerEven: () => nearestLargerEven, + now: () => now2, + parseAxisParam: () => parseAxisParam, + randUniform: () => randUniform, + repeatedTry: () => repeatedTry, + rightPad: () => rightPad, + shuffle: () => shuffle, + shuffleCombo: () => shuffleCombo, + sizeFromShape: () => sizeFromShape, + sizeToSquarishShape: () => sizeToSquarishShape, + squeezeShape: () => squeezeShape, + sum: () => sum, + swap: () => swap, + tanh: () => tanh, + toNestedArray: () => toNestedArray, + toTypedArray: () => toTypedArray +}); +var LongExports = __toESM(require_long()); +var Long = LongExports.default || LongExports; +function hexToLong(hex) { + return Long.fromString(hex, true, 16); } -var ak = Rl("c3a5c85c97cb3127"); -var Hr = Rl("b492b66fbe98f273"); -var on = Rl("9ae16a3b2f90404f"); -function tm(e) { - return e.xor(e.shru(47)); +var k0 = hexToLong("c3a5c85c97cb3127"); +var k1 = hexToLong("b492b66fbe98f273"); +var k2 = hexToLong("9ae16a3b2f90404f"); +function shiftMix(val) { + return val.xor(val.shru(47)); } -function ik(e, t, n) { - let s = e.slice(t, t + n); - return jr.fromBytes(Array.from(s), true, true); +function fetch2(s, offset, numBytes) { + const bytes = s.slice(offset, offset + numBytes); + return Long.fromBytes(Array.from(bytes), true, true); } -function lt(e, t) { - return ik(e, t, 8); +function fetch64(s, offset) { + return fetch2(s, offset, 8); } -function ix(e, t) { - return ik(e, t, 4); +function fetch32(s, offset) { + return fetch2(s, offset, 4); } -function Lt(e, t) { - return t === 0 ? e : e.shru(t).or(e.shl(64 - t)); +function rotate64(val, shift) { + return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift)); } -function lr(e, t, n = Rl("9ddfea08eb382d69")) { - let s = e.xor(t).mul(n); - s = s.xor(s.shru(47)); - let r = t.xor(s).mul(n); - return r = r.xor(r.shru(47)), r = r.mul(n), r; +function hashLen16(u, v, mul2 = hexToLong("9ddfea08eb382d69")) { + let a = u.xor(v).mul(mul2); + a = a.xor(a.shru(47)); + let b = v.xor(a).mul(mul2); + b = b.xor(b.shru(47)); + b = b.mul(mul2); + return b; } -function B$(e, t, n, s, r, a) { - r = r.add(e), a = Lt(a.add(r).add(s), 21); - let i = r; - return r = r.add(t), r = r.add(n), a = a.add(Lt(r, 44)), [r.add(s), a.add(i)]; +function weakHashLen32WithSeeds(w, x, y, z, a, b) { + a = a.add(w); + b = rotate64(b.add(a).add(z), 21); + const c = a; + a = a.add(x); + a = a.add(y); + b = b.add(rotate64(a, 44)); + return [a.add(z), b.add(c)]; } -function Vc(e, t, n, s) { - return B$(lt(e, t), lt(e, t + 8), lt(e, t + 16), lt(e, t + 24), n, s); +function weakHashLen32WithSeedsStr(s, offset, a, b) { + return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b); } -function V$(e, t = e.length) { - if (t >= 8) { - let n = on.add(t * 2), s = lt(e, 0).add(on), r = lt(e, t - 8), a = Lt(r, 37).mul(n).add(s), i = Lt(s, 25).add(r).mul(n); - return lr(a, i, n); +function hashLen0to16(s, len = s.length) { + if (len >= 8) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).add(k2); + const b = fetch64(s, len - 8); + const c = rotate64(b, 37).mul(mul2).add(a); + const d = rotate64(a, 25).add(b).mul(mul2); + return hashLen16(c, d, mul2); } - if (t >= 4) { - let n = on.add(t * 2), s = ix(e, 0); - return lr(s.shl(3).add(t), ix(e, t - 4), n); + if (len >= 4) { + const mul2 = k2.add(len * 2); + const a = fetch32(s, 0); + return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul2); } - if (t > 0) { - let n = e[0], s = e[t >> 1], r = e[t - 1], a = n + (s << 8), i = t + (r << 2); - return tm(on.mul(a).xor(ak.mul(i))).mul(on); + if (len > 0) { + const a = s[0]; + const b = s[len >> 1]; + const c = s[len - 1]; + const y = a + (b << 8); + const z = len + (c << 2); + return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2); } - return on; + return k2; } -function W$(e, t = e.length) { - let n = on.add(t * 2), s = lt(e, 0).mul(Hr), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(on); - return lr(Lt(s.add(r), 43).add(Lt(a, 30)).add(i), s.add(Lt(r.add(on), 18)).add(a), n); +function hashLen17to32(s, len = s.length) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).mul(k1); + const b = fetch64(s, 8); + const c = fetch64(s, len - 8).mul(mul2); + const d = fetch64(s, len - 16).mul(k2); + return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul2); } -function U$(e, t = e.length) { - let n = on.add(t * 2), s = lt(e, 0).mul(on), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(on), o = Lt(s.add(r), 43).add(Lt(a, 30)).add(i), u = lr(o, s.add(Lt(r.add(on), 18)).add(a), n), l = lt(e, 16).mul(n), c = lt(e, 24), p = o.add(lt(e, t - 32)).mul(n), d = u.add(lt(e, t - 24)).mul(n); - return lr(Lt(l.add(c), 43).add(Lt(p, 30)).add(d), l.add(Lt(c.add(s), 18)).add(p), n); +function hashLen33to64(s, len = s.length) { + const mul2 = k2.add(len * 2); + const a = fetch64(s, 0).mul(k2); + const b = fetch64(s, 8); + const c = fetch64(s, len - 8).mul(mul2); + const d = fetch64(s, len - 16).mul(k2); + const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d); + const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul2); + const e = fetch64(s, 16).mul(mul2); + const f = fetch64(s, 24); + const g = y.add(fetch64(s, len - 32)).mul(mul2); + const h = z.add(fetch64(s, len - 24)).mul(mul2); + return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul2); } -function G$(e, t = e.length) { - let n = jr.fromNumber(81, true); - if (t <= 32) - return t <= 16 ? V$(e, t) : W$(e, t); - if (t <= 64) - return U$(e, t); - let s = n, r = n.mul(Hr).add(113), a = tm(r.mul(on).add(113)).mul(on), i = [jr.UZERO, jr.UZERO], o = [jr.UZERO, jr.UZERO]; - s = s.mul(on).add(lt(e, 0)); - let u = 0, l = (t - 1 >> 6) * 64, c = l + (t - 1 & 63) - 63; - do - s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(Hr), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(Hr), s = s.xor(o[1]), r = r.add(i[0]).add(lt(e, u + 40)), a = Lt(a.add(o[0]), 33).mul(Hr), i = Vc(e, u, i[1].mul(Hr), s.add(o[0])), o = Vc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], u += 64; - while (u !== l); - let p = Hr.add(a.and(255).shl(1)); - return u = c, o[0] = o[0].add(t - 1 & 63), i[0] = i[0].add(o[0]), o[0] = o[0].add(i[0]), s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(p), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(p), s = s.xor(o[1].mul(9)), r = r.add(i[0].mul(9).add(lt(e, u + 40))), a = Lt(a.add(o[0]), 33).mul(p), i = Vc(e, u, i[1].mul(p), s.add(o[0])), o = Vc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], lr(lr(i[0], o[0], p).add(tm(r).mul(ak)).add(a), lr(i[1], o[1], p).add(s), p); -} -function H$(e, t) { - return t === "string" ? Dl(e) : dp([e], t); -} -function q$(e, t) { - return e instanceof Float32Array && t === "float32" || e instanceof Int32Array && t === "int32" || e instanceof Uint8Array && t === "bool"; -} -function dp(e, t) { - if (t === "string") - throw new Error("Cannot convert a string[] to a TypedArray"); - if (Array.isArray(e) && (e = ra(e)), X().getBool("DEBUG") && Qw(e, t), q$(e, t)) - return e; - if (t == null || t === "float32" || t === "complex64") - return new Float32Array(e); - if (t === "int32") - return new Int32Array(e); - if (t === "bool") { - let n = new Uint8Array(e.length); - for (let s = 0; s < n.length; ++s) - Math.round(e[s]) !== 0 && (n[s] = 1); - return n; - } else - throw new Error(`Unknown data type ${t}`); -} -function Gu() { - return X().platform.now(); -} -function j$(e, t) { - return X().platform.fetch(e, t); -} -function Dl(e, t = "utf-8") { - return t = t || "utf-8", X().platform.encode(e, t); -} -function md(e, t = "utf-8") { - return t = t || "utf-8", X().platform.decode(e, t); -} -var K$ = class { - constructor(e, t) { - this.backendTimer = e, this.logger = t, t == null && (this.logger = new Y$()); - } - profileKernel(e, t, n) { - let s, r = () => { - s = n(); - }, a, i = Gu(); - if (this.backendTimer.timerAvailable()) - a = this.backendTimer.time(r); - else { - r(); - for (let u of s) - u.dataSync(); - a = Promise.resolve({ kernelMs: Gu() - i }); +function fingerPrint64(s, len = s.length) { + const seed = Long.fromNumber(81, true); + if (len <= 32) { + if (len <= 16) { + return hashLen0to16(s, len); + } else { + return hashLen17to32(s, len); } - if (X().getBool("CHECK_COMPUTATION_FOR_ERRORS")) - for (let u = 0; u < s.length; u++) { - let l = s[u]; - l.data().then((c) => { - X$(c, l.dtype, e); + } else if (len <= 64) { + return hashLen33to64(s, len); + } + let x = seed; + let y = seed.mul(k1).add(113); + let z = shiftMix(y.mul(k2).add(113)).mul(k2); + let v = [Long.UZERO, Long.UZERO]; + let w = [Long.UZERO, Long.UZERO]; + x = x.mul(k2).add(fetch64(s, 0)); + let offset = 0; + const end = (len - 1 >> 6) * 64; + const last64 = end + (len - 1 & 63) - 63; + do { + x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1); + y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1); + x = x.xor(w[1]); + y = y.add(v[0]).add(fetch64(s, offset + 40)); + z = rotate64(z.add(w[0]), 33).mul(k1); + v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0])); + w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16))); + [z, x] = [x, z]; + offset += 64; + } while (offset !== end); + const mul2 = k1.add(z.and(255).shl(1)); + offset = last64; + w[0] = w[0].add(len - 1 & 63); + v[0] = v[0].add(w[0]); + w[0] = w[0].add(v[0]); + x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul2); + y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul2); + x = x.xor(w[1].mul(9)); + y = y.add(v[0].mul(9).add(fetch64(s, offset + 40))); + z = rotate64(z.add(w[0]), 33).mul(mul2); + v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul2), x.add(w[0])); + w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16))); + [z, x] = [x, z]; + return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2); +} +function createScalarValue(value, dtype) { + if (dtype === "string") { + return encodeString(value); + } + return toTypedArray([value], dtype); +} +function noConversionNeeded(a, dtype) { + return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool"; +} +function toTypedArray(a, dtype) { + if (dtype === "string") { + throw new Error("Cannot convert a string[] to a TypedArray"); + } + if (Array.isArray(a)) { + a = flatten(a); + } + if (env().getBool("DEBUG")) { + checkConversionForErrors(a, dtype); + } + if (noConversionNeeded(a, dtype)) { + return a; + } + if (dtype == null || dtype === "float32" || dtype === "complex64") { + return new Float32Array(a); + } else if (dtype === "int32") { + return new Int32Array(a); + } else if (dtype === "bool") { + const bool = new Uint8Array(a.length); + for (let i = 0; i < bool.length; ++i) { + if (Math.round(a[i]) !== 0) { + bool[i] = 1; + } + } + return bool; + } else { + throw new Error(`Unknown data type ${dtype}`); + } +} +function now2() { + return env().platform.now(); +} +function fetch3(path, requestInits) { + return env().platform.fetch(path, requestInits); +} +function encodeString(s, encoding = "utf-8") { + encoding = encoding || "utf-8"; + return env().platform.encode(s, encoding); +} +function decodeString(bytes, encoding = "utf-8") { + encoding = encoding || "utf-8"; + return env().platform.decode(bytes, encoding); +} +var Profiler = class { + constructor(backendTimer, logger) { + this.backendTimer = backendTimer; + this.logger = logger; + if (logger == null) { + this.logger = new Logger(); + } + } + profileKernel(kernelName, inputs, f) { + let outputs; + const holdResultWrapperFn = () => { + outputs = f(); + }; + let timer; + const start = now2(); + if (this.backendTimer.timerAvailable()) { + timer = this.backendTimer.time(holdResultWrapperFn); + } else { + holdResultWrapperFn(); + for (const output of outputs) { + output.dataSync(); + } + timer = Promise.resolve({ kernelMs: now2() - start }); + } + if (env().getBool("CHECK_COMPUTATION_FOR_ERRORS")) { + for (let i = 0; i < outputs.length; i++) { + const output = outputs[i]; + output.data().then((tensorVals) => { + checkComputationForErrors(tensorVals, output.dtype, kernelName); }); } - return { kernelName: e, outputs: s, inputs: t, timeMs: a.then((u) => u.kernelMs), extraInfo: a.then((u) => u.getExtraProfileInfo != null ? u.getExtraProfileInfo() : "") }; + } + const kernelProfile = { + kernelName, + outputs, + inputs, + timeMs: timer.then((timing) => timing.kernelMs), + extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "") + }; + return kernelProfile; } - logKernelProfile(e) { - let { kernelName: t, outputs: n, timeMs: s, inputs: r, extraInfo: a } = e; - n.forEach((i) => { - Promise.all([i.data(), s, a]).then((o) => { - this.logger.logKernelProfile(t, i, o[0], o[1], r, o[2]); + logKernelProfile(kernelProfile) { + const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile; + outputs.forEach((result) => { + Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => { + this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]); }); }); } }; -function X$(e, t, n) { - if (t !== "float32") +function checkComputationForErrors(vals, dtype, kernelName) { + if (dtype !== "float32") { return false; - for (let s = 0; s < e.length; s++) { - let r = e[s]; - if (isNaN(r) || !isFinite(r)) - return console.warn(`Found ${r} in the result of '${n}'`), true; + } + for (let i = 0; i < vals.length; i++) { + const num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + console.warn(`Found ${num} in the result of '${kernelName}'`); + return true; + } } return false; } -var Y$ = class { - logKernelProfile(e, t, n, s, r, a) { - let i = typeof s == "number" ? Mu(`${s}ms`, 9) : s.error, o = Mu(e, 25), u = t.rank, l = t.size, c = Mu(t.shape.toString(), 14), p = ""; - for (let d in r) { - let h = r[d]; - if (h != null) { - let f = h.shape || t.shape, m = f.length; - p += `${d}: ${m}D ${m > 0 ? f : ""} `; +var Logger = class { + logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) { + const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"]; + const paddedName = rightPad(name, 25); + const rank = result.rank; + const size2 = result.size; + const shape = rightPad(result.shape.toString(), 14); + let inputShapesDescription = ""; + for (const name2 in inputs) { + const input2 = inputs[name2]; + if (input2 != null) { + const inputShape = input2.shape || result.shape; + const inputRank = inputShape.length; + inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `; } } - console.log(`%c${o} %c${i} %c${u}D ${c} %c${l} %c${p} %c${a}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue"); + console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size2} %c${inputShapesDescription} %c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue"); } }; -function Q$(e, t, n) { - let s = {}, r = {}; - for (let u = 0; u < t.length; u++) - s[t[u].id] = true; - for (let u = 0; u < e.length; u++) { - let l = e[u], c = l.inputs; - for (let p in c) { - let d = c[p], h = false; - for (let f = 0; f < t.length; f++) - if (s[d.id]) { - l.outputs.forEach((m) => s[m.id] = true), h = true, r[l.id] = true; +function getFilteredNodesXToY(tape, xs, y) { + const tensorsFromX = {}; + const nodesFromX = {}; + for (let i = 0; i < xs.length; i++) { + tensorsFromX[xs[i].id] = true; + } + for (let i = 0; i < tape.length; i++) { + const node2 = tape[i]; + const nodeInputs = node2.inputs; + for (const inputName in nodeInputs) { + const input2 = nodeInputs[inputName]; + let anyInputFromX = false; + for (let j = 0; j < xs.length; j++) { + if (tensorsFromX[input2.id]) { + node2.outputs.forEach((output) => tensorsFromX[output.id] = true); + anyInputFromX = true; + nodesFromX[node2.id] = true; break; } - if (h) - break; - } - } - let a = {}; - a[n.id] = true; - let i = {}; - for (let u = e.length - 1; u >= 0; u--) { - let l = e[u], c = l.inputs; - for (let p = 0; p < l.outputs.length; p++) - if (a[l.outputs[p].id]) { - for (let d in c) - a[c[d].id] = true, i[l.id] = true; + } + if (anyInputFromX) { break; } - } - let o = []; - for (let u = 0; u < e.length; u++) { - let l = e[u]; - if (r[l.id] && i[l.id]) { - let c = {}; - for (let d in l.inputs) { - let h = l.inputs[d]; - s[h.id] && (c[d] = h); - } - let p = Object.assign({}, l); - p.inputs = c, p.outputs = l.outputs, o.push(p); } } - return o; + const tensorsLeadToY = {}; + tensorsLeadToY[y.id] = true; + const nodesToY = {}; + for (let i = tape.length - 1; i >= 0; i--) { + const node2 = tape[i]; + const nodeInputs = node2.inputs; + for (let j = 0; j < node2.outputs.length; j++) { + if (tensorsLeadToY[node2.outputs[j].id]) { + for (const inputName in nodeInputs) { + tensorsLeadToY[nodeInputs[inputName].id] = true; + nodesToY[node2.id] = true; + } + break; + } + } + } + const filteredTape = []; + for (let i = 0; i < tape.length; i++) { + const node2 = tape[i]; + if (nodesFromX[node2.id] && nodesToY[node2.id]) { + const prunedInputs = {}; + for (const inputName in node2.inputs) { + const nodeInput = node2.inputs[inputName]; + if (tensorsFromX[nodeInput.id]) { + prunedInputs[inputName] = nodeInput; + } + } + const prunedNode = Object.assign({}, node2); + prunedNode.inputs = prunedInputs; + prunedNode.outputs = node2.outputs; + filteredTape.push(prunedNode); + } + } + return filteredTape; } -function Z$(e, t, n, s) { - for (let r = t.length - 1; r >= 0; r--) { - let a = t[r], i = []; - if (a.outputs.forEach((u) => { - let l = e[u.id]; - l != null ? i.push(l) : i.push(null); - }), a.gradient == null) - throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`); - let o = a.gradient(i); - for (let u in a.inputs) { - if (!(u in o)) - throw new Error(`Cannot backprop through input ${u}. Available gradients found: ${Object.keys(o)}.`); - let l = n(() => o[u]()); - if (l.dtype !== "float32") - throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${u} must have 'float32' dtype, but has '${l.dtype}'`); - let c = a.inputs[u]; - if (!Ir(l.shape, c.shape)) - throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${u}' has shape '${l.shape}', which does not match the shape of the input '${c.shape}'`); - if (e[c.id] == null) - e[c.id] = l; - else { - let p = e[c.id]; - e[c.id] = s(p, l), p.dispose(); +function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) { + for (let i = filteredTape.length - 1; i >= 0; i--) { + const node2 = filteredTape[i]; + const dys = []; + node2.outputs.forEach((o) => { + const gradTensor = tensorAccumulatedGradientMap[o.id]; + if (gradTensor != null) { + dys.push(gradTensor); + } else { + dys.push(null); + } + }); + if (node2.gradient == null) { + throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`); + } + const inputGradients = node2.gradient(dys); + for (const inputName in node2.inputs) { + if (!(inputName in inputGradients)) { + throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`); + } + const dx = tidy2(() => inputGradients[inputName]()); + if (dx.dtype !== "float32") { + throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`); + } + const x = node2.inputs[inputName]; + if (!arraysEqual(dx.shape, x.shape)) { + throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`); + } + if (tensorAccumulatedGradientMap[x.id] == null) { + tensorAccumulatedGradientMap[x.id] = dx; + } else { + const curGradient = tensorAccumulatedGradientMap[x.id]; + tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx); + curGradient.dispose(); } } } } -var ox = 20; -var Cu = 3; -var Pf = 7; -function J$(e, t, n, s) { - let r = oo(t), a = e_(e, t, n, r), i = t.length, o = ed(e, t, n, r, a), u = ["Tensor"]; - return s && (u.push(` dtype: ${n}`), u.push(` rank: ${i}`), u.push(` shape: [${t}]`), u.push(" values:")), u.push(o.map((l) => " " + l).join(` -`)), u.join(` -`); -} -function e_(e, t, n, s) { - let r = pt(t), a = s[s.length - 1], i = new Array(a).fill(0), o = t.length, u = n === "complex64" ? Au(e) : e; - if (o > 1) - for (let l = 0; l < r / a; l++) { - let c = l * a; - for (let p = 0; p < a; p++) - i[p] = Math.max(i[p], _u(u[c + p], 0, n).length); - } - return i; -} -function _u(e, t, n) { - let s; - return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(Pf))} + ${parseFloat(e[1].toFixed(Pf))}j` : or(e) ? s = `'${e}'` : n === "bool" ? s = ok(e) : s = parseFloat(e.toFixed(Pf)).toString(), Mu(s, t); -} -function ok(e) { - return e === 0 ? "false" : "true"; -} -function ed(e, t, n, s, r, a = true) { - let i = n === "complex64" ? 2 : 1, o = t[0], u = t.length; - if (u === 0) { - if (n === "complex64") { - let m = Au(e); - return [_u(m[0], 0, n)]; - } - return n === "bool" ? [ok(e[0])] : [e[0].toString()]; +var FORMAT_LIMIT_NUM_VALS = 20; +var FORMAT_NUM_FIRST_LAST_VALS = 3; +var FORMAT_NUM_SIG_DIGITS = 7; +function tensorToString(vals, shape, dtype, verbose) { + const strides2 = computeStrides(shape); + const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides2); + const rank = shape.length; + const valsLines = subTensorToString(vals, shape, dtype, strides2, padPerCol); + const lines2 = ["Tensor"]; + if (verbose) { + lines2.push(` dtype: ${dtype}`); + lines2.push(` rank: ${rank}`); + lines2.push(` shape: [${shape}]`); + lines2.push(` values:`); } - if (u === 1) { - if (o > ox) { - let g = Cu * i, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((o - Cu) * i, o * i)); - return n === "complex64" && (b = Au(b), y = Au(y)), ["[" + b.map((v, x) => _u(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => _u(v, r[o - Cu + x], n)).join(", ") + "]"]; - } - let m = n === "complex64" ? Au(e) : Array.from(e); - return ["[" + m.map((g, b) => _u(g, r[b], n)).join(", ") + "]"]; - } - let l = t.slice(1), c = s.slice(1), p = s[0] * i, d = []; - if (o > ox) { - for (let m = 0; m < Cu; m++) { - let g = m * p, b = g + p; - d.push(...ed(e.slice(g, b), l, n, c, r, false)); - } - d.push("..."); - for (let m = o - Cu; m < o; m++) { - let g = m * p, b = g + p; - d.push(...ed(e.slice(g, b), l, n, c, r, m === o - 1)); - } - } else - for (let m = 0; m < o; m++) { - let g = m * p, b = g + p; - d.push(...ed(e.slice(g, b), l, n, c, r, m === o - 1)); - } - let h = u === 2 ? "," : ""; - d[0] = "[" + d[0] + h; - for (let m = 1; m < d.length - 1; m++) - d[m] = " " + d[m] + h; - let f = `, -`; - for (let m = 2; m < u; m++) - f += ` -`; - return d[d.length - 1] = " " + d[d.length - 1] + "]" + (a ? "" : f), d; + lines2.push(valsLines.map((l) => " " + l).join("\n")); + return lines2.join("\n"); } -function Au(e) { - let t = []; - for (let n = 0; n < e.length; n += 2) - t.push([e[n], e[n + 1]]); - return t; -} -var Vt = class { - constructor(e, t, n) { - if (this.dtype = t, this.shape = e.slice(), this.size = pt(e), n != null) { - let s = n.length; - O(s === this.size, () => `Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`); - } - if (t === "complex64") - throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag)."); - this.values = n || Yw(t, this.size), this.strides = oo(e); - } - set(e, ...t) { - t.length === 0 && (t = [0]), O(t.length === this.rank, () => `The number of provided coordinates (${t.length}) must match the rank (${this.rank})`); - let n = this.locToIndex(t); - this.values[n] = e; - } - get(...e) { - e.length === 0 && (e = [0]); - let t = 0; - for (let s of e) { - if (s < 0 || s >= this.shape[t]) { - let r = `Requested out of range element at ${e}. Buffer shape=${this.shape}`; - throw new Error(r); +function computeMaxSizePerColumn(vals, shape, dtype, strides2) { + const n = sizeFromShape(shape); + const numCols = strides2[strides2.length - 1]; + const padPerCol = new Array(numCols).fill(0); + const rank = shape.length; + const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals; + if (rank > 1) { + for (let row = 0; row < n / numCols; row++) { + const offset = row * numCols; + for (let j = 0; j < numCols; j++) { + padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length); } - t++; } - let n = e[e.length - 1]; - for (let s = 0; s < e.length - 1; ++s) - n += this.strides[s] * e[s]; - return this.values[n]; } - locToIndex(e) { - if (this.rank === 0) + return padPerCol; +} +function valToString(val, pad3, dtype) { + let valStr; + if (Array.isArray(val)) { + valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`; + } else if (isString(val)) { + valStr = `'${val}'`; + } else if (dtype === "bool") { + valStr = boolNumToString(val); + } else { + valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(); + } + return rightPad(valStr, pad3); +} +function boolNumToString(v) { + return v === 0 ? "false" : "true"; +} +function subTensorToString(vals, shape, dtype, strides2, padPerCol, isLast = true) { + const storagePerElement = dtype === "complex64" ? 2 : 1; + const size2 = shape[0]; + const rank = shape.length; + if (rank === 0) { + if (dtype === "complex64") { + const complexTuple = createComplexTuples(vals); + return [valToString(complexTuple[0], 0, dtype)]; + } + if (dtype === "bool") { + return [boolNumToString(vals[0])]; + } + return [vals[0].toString()]; + } + if (rank === 1) { + if (size2 > FORMAT_LIMIT_NUM_VALS) { + const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement; + let firstVals = Array.from(vals.slice(0, firstValsSize)); + let lastVals = Array.from(vals.slice((size2 - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size2 * storagePerElement)); + if (dtype === "complex64") { + firstVals = createComplexTuples(firstVals); + lastVals = createComplexTuples(lastVals); + } + return [ + "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size2 - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]" + ]; + } + const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals); + return [ + "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]" + ]; + } + const subshape = shape.slice(1); + const substrides = strides2.slice(1); + const stride = strides2[0] * storagePerElement; + const lines2 = []; + if (size2 > FORMAT_LIMIT_NUM_VALS) { + for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) { + const start = i * stride; + const end = start + stride; + lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false)); + } + lines2.push("..."); + for (let i = size2 - FORMAT_NUM_FIRST_LAST_VALS; i < size2; i++) { + const start = i * stride; + const end = start + stride; + lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size2 - 1)); + } + } else { + for (let i = 0; i < size2; i++) { + const start = i * stride; + const end = start + stride; + lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size2 - 1)); + } + } + const sep = rank === 2 ? "," : ""; + lines2[0] = "[" + lines2[0] + sep; + for (let i = 1; i < lines2.length - 1; i++) { + lines2[i] = " " + lines2[i] + sep; + } + let newLineSep = ",\n"; + for (let i = 2; i < rank; i++) { + newLineSep += "\n"; + } + lines2[lines2.length - 1] = " " + lines2[lines2.length - 1] + "]" + (isLast ? "" : newLineSep); + return lines2; +} +function createComplexTuples(vals) { + const complexTuples = []; + for (let i = 0; i < vals.length; i += 2) { + complexTuples.push([vals[i], vals[i + 1]]); + } + return complexTuples; +} +var TensorBuffer = class { + constructor(shape, dtype, values) { + this.dtype = dtype; + this.shape = shape.slice(); + this.size = sizeFromShape(shape); + if (values != null) { + const n = values.length; + assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`); + } + if (dtype === "complex64") { + throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`); + } + this.values = values || getArrayFromDType(dtype, this.size); + this.strides = computeStrides(shape); + } + set(value, ...locs) { + if (locs.length === 0) { + locs = [0]; + } + assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`); + const index2 = this.locToIndex(locs); + this.values[index2] = value; + } + get(...locs) { + if (locs.length === 0) { + locs = [0]; + } + let i = 0; + for (const loc of locs) { + if (loc < 0 || loc >= this.shape[i]) { + const msg = `Requested out of range element at ${locs}. Buffer shape=${this.shape}`; + throw new Error(msg); + } + i++; + } + let index2 = locs[locs.length - 1]; + for (let i2 = 0; i2 < locs.length - 1; ++i2) { + index2 += this.strides[i2] * locs[i2]; + } + return this.values[index2]; + } + locToIndex(locs) { + if (this.rank === 0) { return 0; - if (this.rank === 1) - return e[0]; - let t = e[e.length - 1]; - for (let n = 0; n < e.length - 1; ++n) - t += this.strides[n] * e[n]; - return t; + } else if (this.rank === 1) { + return locs[0]; + } + let index2 = locs[locs.length - 1]; + for (let i = 0; i < locs.length - 1; ++i) { + index2 += this.strides[i] * locs[i]; + } + return index2; } - indexToLoc(e) { - if (this.rank === 0) + indexToLoc(index2) { + if (this.rank === 0) { return []; - if (this.rank === 1) - return [e]; - let t = new Array(this.shape.length); - for (let n = 0; n < t.length - 1; ++n) - t[n] = Math.floor(e / this.strides[n]), e -= t[n] * this.strides[n]; - return t[t.length - 1] = e, t; + } else if (this.rank === 1) { + return [index2]; + } + const locs = new Array(this.shape.length); + for (let i = 0; i < locs.length - 1; ++i) { + locs[i] = Math.floor(index2 / this.strides[i]); + index2 -= locs[i] * this.strides[i]; + } + locs[locs.length - 1] = index2; + return locs; } get rank() { return this.shape.length; } toTensor() { - return cs().makeTensor(this.values, this.shape, this.dtype); + return trackerFn().makeTensor(this.values, this.shape, this.dtype); } }; -var cs = null; -var Gi = null; -var t_ = null; -function n_(e) { - cs = e; +var trackerFn = null; +var opHandler = null; +var deprecationWarningFn = null; +function setTensorTracker(fn) { + trackerFn = fn; } -function s_(e) { - Gi = e; +function setOpHandler(handler) { + opHandler = handler; } -function r_(e) { - t_ = e; +function setDeprecationWarningFn(fn) { + deprecationWarningFn = fn; } -var et = class { - constructor(e, t, n, s) { - this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t || "float32", this.size = pt(e), this.strides = oo(e), this.dataId = n, this.id = s, this.rankType = this.rank < 5 ? this.rank.toString() : "higher"; +var Tensor = class { + constructor(shape, dtype, dataId, id) { + this.kept = false; + this.isDisposedInternal = false; + this.shape = shape.slice(); + this.dtype = dtype || "float32"; + this.size = sizeFromShape(shape); + this.strides = computeStrides(shape); + this.dataId = dataId; + this.id = id; + this.rankType = this.rank < 5 ? this.rank.toString() : "higher"; } get rank() { return this.shape.length; } async buffer() { - let e = await this.data(); - return Gi.buffer(this.shape, this.dtype, e); + const vals = await this.data(); + return opHandler.buffer(this.shape, this.dtype, vals); } bufferSync() { - return Gi.buffer(this.shape, this.dtype, this.dataSync()); + return opHandler.buffer(this.shape, this.dtype, this.dataSync()); } async array() { - let e = await this.data(); - return ji(this.shape, e, this.dtype === "complex64"); + const vals = await this.data(); + return toNestedArray(this.shape, vals, this.dtype === "complex64"); } arraySync() { - return ji(this.shape, this.dataSync(), this.dtype === "complex64"); + return toNestedArray(this.shape, this.dataSync(), this.dtype === "complex64"); } async data() { this.throwIfDisposed(); - let e = cs().read(this.dataId); + const data = trackerFn().read(this.dataId); if (this.dtype === "string") { - let t = await e; + const bytes = await data; try { - return t.map((n) => md(n)); - } catch (n) { + return bytes.map((b) => decodeString(b)); + } catch (e) { throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); } } - return e; + return data; } - dataToGPU(e) { - return this.throwIfDisposed(), cs().readToGPU(this.dataId, e); + dataToGPU(options4) { + this.throwIfDisposed(); + return trackerFn().readToGPU(this.dataId, options4); } dataSync() { this.throwIfDisposed(); - let e = cs().readSync(this.dataId); - if (this.dtype === "string") + const data = trackerFn().readSync(this.dataId); + if (this.dtype === "string") { try { - return e.map((t) => md(t)); - } catch (t) { + return data.map((b) => decodeString(b)); + } catch (e) { throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes()."); } - return e; + } + return data; } async bytes() { this.throwIfDisposed(); - let e = await cs().read(this.dataId); - return this.dtype === "string" ? e : new Uint8Array(e.buffer); + const data = await trackerFn().read(this.dataId); + if (this.dtype === "string") { + return data; + } else { + return new Uint8Array(data.buffer); + } } dispose() { - this.isDisposed || (cs().disposeTensor(this), this.isDisposedInternal = true); + if (this.isDisposed) { + return; + } + trackerFn().disposeTensor(this); + this.isDisposedInternal = true; } get isDisposed() { return this.isDisposedInternal; } throwIfDisposed() { - if (this.isDisposed) - throw new Error("Tensor is disposed."); + if (this.isDisposed) { + throw new Error(`Tensor is disposed.`); + } } - print(e = false) { - return Gi.print(this, e); + print(verbose = false) { + return opHandler.print(this, verbose); } clone() { - return this.throwIfDisposed(), Gi.clone(this); + this.throwIfDisposed(); + return opHandler.clone(this); } - toString(e = false) { - let t = this.dataSync(); - return J$(t, this.shape, this.dtype, e); + toString(verbose = false) { + const vals = this.dataSync(); + return tensorToString(vals, this.shape, this.dtype, verbose); } - cast(e) { - return this.throwIfDisposed(), Gi.cast(this, e); + cast(dtype) { + this.throwIfDisposed(); + return opHandler.cast(this, dtype); } - variable(e = true, t, n) { - return this.throwIfDisposed(), cs().makeVariable(this, e, t, n); + variable(trainable = true, name, dtype) { + this.throwIfDisposed(); + return trackerFn().makeVariable(this, trainable, name, dtype); } }; -Object.defineProperty(et, Symbol.hasInstance, { value: (e) => !!e && e.data != null && e.dataSync != null && e.throwIfDisposed != null }); -function a_() { - return og("Tensor", () => et); -} -a_(); -var gd = class extends et { - constructor(e, t, n, s) { - super(e.shape, e.dtype, e.dataId, s), this.trainable = t, this.name = n; +Object.defineProperty(Tensor, Symbol.hasInstance, { + value: (instance) => { + return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null; } - assign(e) { - if (e.dtype !== this.dtype) - throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`); - if (!Ir(e.shape, this.shape)) - throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`); - cs().disposeTensor(this), this.dataId = e.dataId, cs().incRef(this, null); +}); +function getGlobalTensorClass() { + return getGlobal("Tensor", () => { + return Tensor; + }); +} +getGlobalTensorClass(); +var Variable = class extends Tensor { + constructor(initialValue, trainable, name, tensorId) { + super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId); + this.trainable = trainable; + this.name = name; + } + assign(newValue) { + if (newValue.dtype !== this.dtype) { + throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`); + } + if (!arraysEqual(newValue.shape, this.shape)) { + throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`); + } + trackerFn().disposeTensor(this); + this.dataId = newValue.dataId; + trackerFn().incRef(this, null); } dispose() { - cs().disposeVariable(this), this.isDisposedInternal = true; + trackerFn().disposeVariable(this); + this.isDisposedInternal = true; } }; -Object.defineProperty(gd, Symbol.hasInstance, { value: (e) => e instanceof et && e.assign != null && e.assign instanceof Function }); -var _s = {}; -Ae(_s, { assertTypesMatch: () => pk, getTensorsInContainer: () => Fg, isTensorInList: () => u_, makeTypesMatch: () => vt }); -var i_ = ((e) => (e.R0 = "R0", e.R1 = "R1", e.R2 = "R2", e.R3 = "R3", e.R4 = "R4", e.R5 = "R5", e.R6 = "R6", e))(i_ || {}); -var uk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "int32", e.complex64 = "complex64", e))(uk || {}); -var lk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "bool", e.complex64 = "complex64", e))(lk || {}); -var ck = ((e) => (e.float32 = "float32", e.int32 = "float32", e.bool = "float32", e.complex64 = "complex64", e))(ck || {}); -var dk = ((e) => (e.float32 = "complex64", e.int32 = "complex64", e.bool = "complex64", e.complex64 = "complex64", e))(dk || {}); -var o_ = { float32: ck, int32: uk, bool: lk, complex64: dk }; -function cn(e, t) { - if (e === "string" || t === "string") { - if (e === "string" && t === "string") +Object.defineProperty(Variable, Symbol.hasInstance, { + value: (instance) => { + return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function; + } +}); +var tensor_util_exports = {}; +__export2(tensor_util_exports, { + assertTypesMatch: () => assertTypesMatch, + getTensorsInContainer: () => getTensorsInContainer, + isTensorInList: () => isTensorInList, + makeTypesMatch: () => makeTypesMatch +}); +var Rank = ((Rank18) => { + Rank18["R0"] = "R0"; + Rank18["R1"] = "R1"; + Rank18["R2"] = "R2"; + Rank18["R3"] = "R3"; + Rank18["R4"] = "R4"; + Rank18["R5"] = "R5"; + Rank18["R6"] = "R6"; + return Rank18; +})(Rank || {}); +var UpcastInt32AndMap = ((UpcastInt32AndMap2) => { + UpcastInt32AndMap2["float32"] = "float32"; + UpcastInt32AndMap2["int32"] = "int32"; + UpcastInt32AndMap2["bool"] = "int32"; + UpcastInt32AndMap2["complex64"] = "complex64"; + return UpcastInt32AndMap2; +})(UpcastInt32AndMap || {}); +var UpcastBoolAndMap = ((UpcastBoolAndMap2) => { + UpcastBoolAndMap2["float32"] = "float32"; + UpcastBoolAndMap2["int32"] = "int32"; + UpcastBoolAndMap2["bool"] = "bool"; + UpcastBoolAndMap2["complex64"] = "complex64"; + return UpcastBoolAndMap2; +})(UpcastBoolAndMap || {}); +var UpcastFloat32AndMap = ((UpcastFloat32AndMap2) => { + UpcastFloat32AndMap2["float32"] = "float32"; + UpcastFloat32AndMap2["int32"] = "float32"; + UpcastFloat32AndMap2["bool"] = "float32"; + UpcastFloat32AndMap2["complex64"] = "complex64"; + return UpcastFloat32AndMap2; +})(UpcastFloat32AndMap || {}); +var UpcastComplex64AndMap = ((UpcastComplex64AndMap2) => { + UpcastComplex64AndMap2["float32"] = "complex64"; + UpcastComplex64AndMap2["int32"] = "complex64"; + UpcastComplex64AndMap2["bool"] = "complex64"; + UpcastComplex64AndMap2["complex64"] = "complex64"; + return UpcastComplex64AndMap2; +})(UpcastComplex64AndMap || {}); +var upcastTypeMap = { + "float32": UpcastFloat32AndMap, + "int32": UpcastInt32AndMap, + "bool": UpcastBoolAndMap, + "complex64": UpcastComplex64AndMap +}; +function upcastType(typeA, typeB) { + if (typeA === "string" || typeB === "string") { + if (typeA === "string" && typeB === "string") { return "string"; - throw new Error(`Can not upcast ${e} with ${t}`); + } + throw new Error(`Can not upcast ${typeA} with ${typeB}`); } - return o_[e][t]; + return upcastTypeMap[typeA][typeB]; } -function pp(e) { - return cn(e, "int32"); +function sumOutType(type) { + return upcastType(type, "int32"); } -function vt(e, t) { - if (e.dtype === t.dtype) - return [e, t]; - let n = cn(e.dtype, t.dtype); - return [e.cast(n), t.cast(n)]; +function makeTypesMatch(a, b) { + if (a.dtype === b.dtype) { + return [a, b]; + } + const dtype = upcastType(a.dtype, b.dtype); + return [a.cast(dtype), b.cast(dtype)]; } -function pk(e, t) { - O(e.dtype === t.dtype, () => `The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`); +function assertTypesMatch(a, b) { + assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`); } -function u_(e, t) { - return t.some((n) => n.id === e.id); +function isTensorInList(tensor2, tensorList) { + return tensorList.some((x) => x.id === tensor2.id); } -function Fg(e) { - let t = []; - return hk(e, t, /* @__PURE__ */ new Set()), t; +function getTensorsInContainer(result) { + const list = []; + const seen = /* @__PURE__ */ new Set(); + walkTensorContainer(result, list, seen); + return list; } -function hk(e, t, n) { - if (e == null) - return; - if (e instanceof et) { - t.push(e); +function walkTensorContainer(container, list, seen) { + if (container == null) { return; } - if (!l_(e)) + if (container instanceof Tensor) { + list.push(container); return; - let s = e; - for (let r in s) { - let a = s[r]; - n.has(a) || (n.add(a), hk(a, t, n)); + } + if (!isIterable(container)) { + return; + } + const iterable = container; + for (const k in iterable) { + const val = iterable[k]; + if (!seen.has(val)) { + seen.add(val); + walkTensorContainer(val, list, seen); + } } } -function l_(e) { - return Array.isArray(e) || typeof e == "object"; +function isIterable(obj) { + return Array.isArray(obj) || typeof obj === "object"; } -function zf(e) { - return e.kernelName != null; +function isRegisteredKernelInvocation(kernelInvocation) { + return kernelInvocation.kernelName != null; } -var ux = class { +var EngineState = class { constructor() { - this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() { - return Array.from(new Set(this.kernels.map((e) => e.name))); - } }; + this.registeredVariables = {}; + this.nextTapeNodeId = 0; + this.numBytes = 0; + this.numTensors = 0; + this.numStringTensors = 0; + this.numDataBuffers = 0; + this.gradientDepth = 0; + this.kernelDepth = 0; + this.scopeStack = []; + this.numDataMovesStack = []; + this.nextScopeId = 0; + this.tensorInfo = /* @__PURE__ */ new WeakMap(); + this.profiling = false; + this.activeProfile = { + newBytes: 0, + newTensors: 0, + peakBytes: 0, + kernels: [], + result: null, + get kernelNames() { + return Array.from(new Set(this.kernels.map((k) => k.name))); + } + }; } dispose() { - for (let e in this.registeredVariables) - this.registeredVariables[e].dispose(); + for (const variableName in this.registeredVariables) { + this.registeredVariables[variableName].dispose(); + } } }; -var nm = class { - constructor(e) { - this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new ux(); +var _Engine = class { + constructor(ENV8) { + this.ENV = ENV8; + this.registry = {}; + this.registryFactory = {}; + this.pendingBackendInitId = 0; + this.state = new EngineState(); } async ready() { - if (this.pendingBackendInit != null) + if (this.pendingBackendInit != null) { return this.pendingBackendInit.then(() => { }); - if (this.backendInstance != null) + } + if (this.backendInstance != null) { return; - let e = this.getSortedBackends(); - for (let t = 0; t < e.length; t++) { - let n = e[t]; - if (await this.initializeBackend(n).success) { - await this.setBackend(n); + } + const sortedBackends = this.getSortedBackends(); + for (let i = 0; i < sortedBackends.length; i++) { + const backendName = sortedBackends[i]; + const success = await this.initializeBackend(backendName).success; + if (success) { + await this.setBackend(backendName); return; } } - throw new Error("Could not initialize any backends, all backend initializations failed."); + throw new Error(`Could not initialize any backends, all backend initializations failed.`); } get backend() { - if (this.pendingBackendInit != null) + if (this.pendingBackendInit != null) { throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); + } if (this.backendInstance == null) { - let { name: e, asyncInit: t } = this.initializeBackendsAndReturnBest(); - if (t) - throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); - this.setBackend(e); + const { name, asyncInit } = this.initializeBackendsAndReturnBest(); + if (asyncInit) { + throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`); + } + this.setBackend(name); } return this.backendInstance; } backendNames() { return Object.keys(this.registryFactory); } - findBackend(e) { - if (!(e in this.registry)) - if (e in this.registryFactory) { - let { asyncInit: t } = this.initializeBackend(e); - if (t) + findBackend(backendName) { + if (!(backendName in this.registry)) { + if (backendName in this.registryFactory) { + const { asyncInit } = this.initializeBackend(backendName); + if (asyncInit) { return null; - } else + } + } else { return null; - return this.registry[e]; - } - findBackendFactory(e) { - return e in this.registryFactory ? this.registryFactory[e].factory : null; - } - registerBackend(e, t, n = 1) { - return e in this.registryFactory ? (ir(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t, priority: n }, true); - } - async setBackend(e) { - if (this.registryFactory[e] == null) - throw new Error(`Backend name '${e}' not found in registry`); - if (this.backendName = e, this.registry[e] == null) { - this.backendInstance = null; - let { success: t, asyncInit: n } = this.initializeBackend(e); - if (!(n ? await t : t)) - return false; + } } - return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new K$(this.backendInstance), true; + return this.registry[backendName]; + } + findBackendFactory(backendName) { + if (!(backendName in this.registryFactory)) { + return null; + } + return this.registryFactory[backendName].factory; + } + registerBackend(backendName, factory, priority = 1) { + if (backendName in this.registryFactory) { + warn(`${backendName} backend was already registered. Reusing existing backend factory.`); + return false; + } + this.registryFactory[backendName] = { factory, priority }; + return true; + } + async setBackend(backendName) { + if (this.registryFactory[backendName] == null) { + throw new Error(`Backend name '${backendName}' not found in registry`); + } + this.backendName = backendName; + if (this.registry[backendName] == null) { + this.backendInstance = null; + const { success, asyncInit } = this.initializeBackend(backendName); + const result = asyncInit ? await success : success; + if (!result) { + return false; + } + } + this.backendInstance = this.registry[backendName]; + this.setupRegisteredKernels(); + this.profiler = new Profiler(this.backendInstance); + return true; } setupRegisteredKernels() { - em(this.backendName).forEach((t) => { - t.setupFunc != null && t.setupFunc(this.backendInstance); + const kernels = getKernelsForBackend(this.backendName); + kernels.forEach((kernel) => { + if (kernel.setupFunc != null) { + kernel.setupFunc(this.backendInstance); + } }); } - disposeRegisteredKernels(e) { - em(e).forEach((n) => { - n.disposeFunc != null && n.disposeFunc(this.registry[e]); + disposeRegisteredKernels(backendName) { + const kernels = getKernelsForBackend(backendName); + kernels.forEach((kernel) => { + if (kernel.disposeFunc != null) { + kernel.disposeFunc(this.registry[backendName]); + } }); } - initializeBackend(e) { - let t = this.registryFactory[e]; - if (t == null) - throw new Error(`Cannot initialize backend ${e}, no registration found.`); + initializeBackend(backendName) { + const registryFactoryEntry = this.registryFactory[backendName]; + if (registryFactoryEntry == null) { + throw new Error(`Cannot initialize backend ${backendName}, no registration found.`); + } try { - let n = t.factory(); - if (n && !(n instanceof sl) && typeof n.then == "function") { - let s = ++this.pendingBackendInitId, r = n.then((a) => s < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (s < this.pendingBackendInitId || (this.pendingBackendInit = null, ir(`Initialization of backend ${e} failed`), ir(a.stack || a.message)), false)); - return this.pendingBackendInit = r, { success: r, asyncInit: true }; - } else - return this.registry[e] = n, { success: true, asyncInit: false }; - } catch (n) { - return ir(`Initialization of backend ${e} failed`), ir(n.stack || n.message), { success: false, asyncInit: false }; + const backend2 = registryFactoryEntry.factory(); + if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === "function") { + const promiseId = ++this.pendingBackendInitId; + const success = backend2.then((backendInstance) => { + if (promiseId < this.pendingBackendInitId) { + return false; + } + this.registry[backendName] = backendInstance; + this.pendingBackendInit = null; + return true; + }).catch((err) => { + if (promiseId < this.pendingBackendInitId) { + return false; + } + this.pendingBackendInit = null; + warn(`Initialization of backend ${backendName} failed`); + warn(err.stack || err.message); + return false; + }); + this.pendingBackendInit = success; + return { success, asyncInit: true }; + } else { + this.registry[backendName] = backend2; + return { success: true, asyncInit: false }; + } + } catch (err) { + warn(`Initialization of backend ${backendName} failed`); + warn(err.stack || err.message); + return { success: false, asyncInit: false }; } } - removeBackend(e) { - if (!(e in this.registryFactory)) - throw new Error(`${e} backend not found in registry`); - this.backendName === e && this.pendingBackendInit != null && this.pendingBackendInitId++, e in this.registry && (this.disposeRegisteredKernels(e), this.registry[e].dispose(), delete this.registry[e]), delete this.registryFactory[e], this.backendName === e && (this.pendingBackendInit = null, this.backendName = null, this.backendInstance = null); + removeBackend(backendName) { + if (!(backendName in this.registryFactory)) { + throw new Error(`${backendName} backend not found in registry`); + } + if (this.backendName === backendName && this.pendingBackendInit != null) { + this.pendingBackendInitId++; + } + if (backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + delete this.registryFactory[backendName]; + if (this.backendName === backendName) { + this.pendingBackendInit = null; + this.backendName = null; + this.backendInstance = null; + } } getSortedBackends() { - if (Object.keys(this.registryFactory).length === 0) + if (Object.keys(this.registryFactory).length === 0) { throw new Error("No backend found in registry."); - return Object.keys(this.registryFactory).sort((e, t) => this.registryFactory[t].priority - this.registryFactory[e].priority); + } + return Object.keys(this.registryFactory).sort((a, b) => { + return this.registryFactory[b].priority - this.registryFactory[a].priority; + }); } initializeBackendsAndReturnBest() { - let e = this.getSortedBackends(); - for (let t = 0; t < e.length; t++) { - let n = e[t], { success: s, asyncInit: r } = this.initializeBackend(n); - if (r || s) - return { name: n, asyncInit: r }; + const sortedBackends = this.getSortedBackends(); + for (let i = 0; i < sortedBackends.length; i++) { + const backendName = sortedBackends[i]; + const { success, asyncInit } = this.initializeBackend(backendName); + if (asyncInit || success) { + return { name: backendName, asyncInit }; + } } - throw new Error("Could not initialize any backends, all backend initializations failed."); + throw new Error(`Could not initialize any backends, all backend initializations failed.`); } - moveData(e, t) { - let n = this.state.tensorInfo.get(t), s = n.backend, r = this.readSync(t), a = s.refCount(t); - s.disposeData(t, true), n.backend = e, e.move(t, r, n.shape, n.dtype, a), this.shouldCheckForMemLeaks() && this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; + moveData(backend2, dataId) { + const info = this.state.tensorInfo.get(dataId); + const srcBackend = info.backend; + const values = this.readSync(dataId); + const refCount = srcBackend.refCount(dataId); + srcBackend.disposeData(dataId, true); + info.backend = backend2; + backend2.move(dataId, values, info.shape, info.dtype, refCount); + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; + } } - tidy(e, t) { - let n = null; - if (t == null) { - if (typeof e != "function") + tidy(nameOrFn, fn) { + let name = null; + if (fn == null) { + if (typeof nameOrFn !== "function") { throw new Error("Please provide a function to tidy()"); - t = e; + } + fn = nameOrFn; } else { - if (typeof e != "string" && !(e instanceof String)) + if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) { throw new Error("When calling with two arguments, the first argument to tidy() must be a string"); - if (typeof t != "function") + } + if (typeof fn !== "function") { throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function"); - n = e; + } + name = nameOrFn; } - let s; - return this.scopedRun(() => this.startScope(n), () => this.endScope(s), () => (s = t(), s instanceof Promise && console.error("Cannot return a Promise inside of tidy."), s)); + let result; + return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => { + result = fn(); + if (result instanceof Promise) { + console.error("Cannot return a Promise inside of tidy."); + } + return result; + }); } - scopedRun(e, t, n) { - e(); + scopedRun(start, end, f) { + start(); try { - let s = n(); - return t(), s; - } catch (s) { - throw t(), s; + const res = f(); + end(); + return res; + } catch (ex) { + end(); + throw ex; } } nextTensorId() { - return nm.nextTensorId++; + return _Engine.nextTensorId++; } nextVariableId() { - return nm.nextVariableId++; + return _Engine.nextVariableId++; } - clone(e) { - let t = M.runKernel(Wa, { x: e }), n = { x: e }, s = (a) => ({ x: () => { - let i = "float32", o = { x: a }, u = { dtype: i }; - return M.runKernel(Ta, o, u); - } }), r = []; - return this.addTapeNode(this.state.activeScope.name, n, [t], s, r, {}), t; + clone(x) { + const y = ENGINE.runKernel(Identity, { x }); + const inputs = { x }; + const grad2 = (dy) => ({ + x: () => { + const dtype = "float32"; + const gradInputs = { x: dy }; + const attrs = { dtype }; + return ENGINE.runKernel(Cast, gradInputs, attrs); + } + }); + const saved = []; + this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {}); + return y; } - runKernel(e, t, n) { - if (this.backendName == null && this.backend, !(Jf(e, this.backendName) != null)) - throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`); - return this.runKernelFunc({ kernelName: e, inputs: t, attrs: n }); + runKernel(kernelName, inputs, attrs) { + if (this.backendName == null) { + this.backend; + } + const hasKernel = getKernel(kernelName, this.backendName) != null; + if (!hasKernel) { + throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`); + } + return this.runKernelFunc({ kernelName, inputs, attrs }); } shouldCheckForMemLeaks() { return this.ENV.getBool("IS_TEST"); } - checkKernelForMemLeak(e, t, n) { - let s = this.backend.numDataIds(), r = 0; - n.forEach((o) => { - r += o.dtype === "complex64" ? 3 : 1; + checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) { + const numDataIdsAfter = this.backend.numDataIds(); + let numOutputDataIds = 0; + outInfos.forEach((info) => { + numOutputDataIds += info.dtype === "complex64" ? 3 : 1; }); - let a = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1], i = s - t - r - a; - if (i > 0) - throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`); + const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]; + const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves; + if (dataIdsLeaked > 0) { + throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`); + } } - runKernelFunc(e) { - let t, n = [], s = this.isTapeOn(), r = this.state.numBytes, a = this.state.numTensors; - this.shouldCheckForMemLeaks() && this.state.numDataMovesStack.push(0); - let i; - this.backendName == null && this.backend; - let o, u = zf(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : ""; - if (zf(e)) { - let { kernelName: h, inputs: f, attrs: m } = e; - this.backendName == null && this.backend; - let g = Jf(h, this.backendName); - O(g != null, () => `Cannot find registered kernel '${h}' for backend '${this.backendName}'`), i = () => { - let b = this.backend.numDataIds(); - o = g.kernelFunc({ inputs: f, attrs: m, backend: this.backend }); - let y = Array.isArray(o) ? o : [o]; - this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(h, b, y); - let v = y.map((x) => { - if (x.rank != null) - return x; - let { dataId: k, shape: T, dtype: N } = x; - return this.makeTensorFromDataId(k, T, N); - }); - if (s) { - let x = this.getTensorsForGradient(h, f, v); - n = this.saveTensorsForBackwardMode(x); + runKernelFunc(kernelParams) { + let outputs; + let saved = []; + const isTapeOn = this.isTapeOn(); + const startingBytecount = this.state.numBytes; + const startingNumTensors = this.state.numTensors; + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack.push(0); + } + let kernelFunc3; + if (this.backendName == null) { + this.backend; + } + let out; + const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : ""; + if (isRegisteredKernelInvocation(kernelParams)) { + const { kernelName, inputs: inputs2, attrs: attrs2 } = kernelParams; + if (this.backendName == null) { + this.backend; + } + const kernel = getKernel(kernelName, this.backendName); + assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`); + kernelFunc3 = () => { + const numDataIdsBefore = this.backend.numDataIds(); + out = kernel.kernelFunc({ inputs: inputs2, attrs: attrs2, backend: this.backend }); + const outInfos = Array.isArray(out) ? out : [out]; + if (this.shouldCheckForMemLeaks()) { + this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos); } - return v; + const outTensors = outInfos.map((outInfo) => { + if (outInfo.rank != null) { + return outInfo; + } + const { dataId, shape, dtype } = outInfo; + return this.makeTensorFromDataId(dataId, shape, dtype); + }); + if (isTapeOn) { + const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors); + saved = this.saveTensorsForBackwardMode(tensorsToSave); + } + return outTensors; }; } else { - let { forwardFunc: h } = e, f = (m) => { - !s || (n = m.map((g) => this.keep(this.clone(g)))); + const { forwardFunc } = kernelParams; + const saveFunc = (tensors) => { + if (!isTapeOn) { + return; + } + saved = tensors.map((tensor2) => this.keep(this.clone(tensor2))); }; - i = () => { - let m = this.backend.numDataIds(); - o = this.tidy(() => h(this.backend, f)); - let g = Array.isArray(o) ? o : [o]; - return this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(u, m, g), g; + kernelFunc3 = () => { + const numDataIdsBefore = this.backend.numDataIds(); + out = this.tidy(() => forwardFunc(this.backend, saveFunc)); + const outs = Array.isArray(out) ? out : [out]; + if (this.shouldCheckForMemLeaks()) { + this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs); + } + return outs; }; } - let { inputs: l, attrs: c } = e, p = zf(e) ? null : e.backwardsFunc, d; - return this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => { - !this.ENV.getBool("DEBUG") && !this.state.profiling ? t = i() : (d = this.profiler.profileKernel(u, l, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t = d.outputs); - }), s && this.addTapeNode(u, l, t, p, n, c), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - r, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(l).map((h) => l[h] != null ? l[h].shape : null), outputShapes: t.map((h) => h.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(o) ? t : t[0]; + const { inputs, attrs } = kernelParams; + const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc; + let kernelProfile; + this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => { + if (!this.ENV.getBool("DEBUG") && !this.state.profiling) { + outputs = kernelFunc3(); + } else { + kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3()); + if (this.ENV.getBool("DEBUG")) { + this.profiler.logKernelProfile(kernelProfile); + } + outputs = kernelProfile.outputs; + } + }); + if (isTapeOn) { + this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs); + } + if (this.state.profiling) { + this.state.activeProfile.kernels.push({ + name: kernelOrScopeName, + bytesAdded: this.state.numBytes - startingBytecount, + totalBytesSnapshot: this.state.numBytes, + tensorsAdded: this.state.numTensors - startingNumTensors, + totalTensorsSnapshot: this.state.numTensors, + inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null), + outputShapes: outputs.map((item) => item.shape), + kernelTimeMs: kernelProfile.timeMs, + extraInfo: kernelProfile.extraInfo + }); + } + return Array.isArray(out) ? outputs : outputs[0]; } - saveTensorsForBackwardMode(e) { - return e.map((n) => this.keep(this.clone(n))); + saveTensorsForBackwardMode(tensors) { + const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2))); + return saved; } - getTensorsForGradient(e, t, n) { - let s = rx(e); - if (s != null) { - let r = s.inputsToSave || [], a = s.outputsToSave || [], i; - s.saveAllInputs ? (O(Array.isArray(t), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t).map((u) => t[u])) : i = r.map((u) => t[u]); - let o = n.filter((u, l) => a[l]); - return i.concat(o); + getTensorsForGradient(kernelName, inputs, outputs) { + const gradConfig = getGradient(kernelName); + if (gradConfig != null) { + const inputsToSave = gradConfig.inputsToSave || []; + const outputsToSave = gradConfig.outputsToSave || []; + let inputTensorsToSave; + if (gradConfig.saveAllInputs) { + assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array."); + inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]); + } else { + inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]); + } + const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]); + return inputTensorsToSave.concat(outputTensorsToSave); } return []; } - makeTensor(e, t, n, s) { - if (e == null) + makeTensor(values, shape, dtype, backend2) { + if (values == null) { throw new Error("Values passed to engine.makeTensor() are null"); - n = n || "float32", s = s || this.backend; - let r = e; - n === "string" && or(e[0]) && (r = e.map((o) => Dl(o))); - let a = s.write(r, t, n), i = new et(t, n, a, this.nextTensorId()); - if (this.trackTensor(i, s), n === "string") { - let o = this.state.tensorInfo.get(a), u = Jw(r); - this.state.numBytes += u - o.bytes, o.bytes = u; } - return i; + dtype = dtype || "float32"; + backend2 = backend2 || this.backend; + let backendVals = values; + if (dtype === "string" && isString(values[0])) { + backendVals = values.map((d) => encodeString(d)); + } + const dataId = backend2.write(backendVals, shape, dtype); + const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend2); + if (dtype === "string") { + const info = this.state.tensorInfo.get(dataId); + const newBytes = bytesFromStringArray(backendVals); + this.state.numBytes += newBytes - info.bytes; + info.bytes = newBytes; + } + return t; } - makeTensorFromDataId(e, t, n, s) { - n = n || "float32"; - let r = new et(t, n, e, this.nextTensorId()); - return this.trackTensor(r, s), r; + makeTensorFromDataId(dataId, shape, dtype, backend2) { + dtype = dtype || "float32"; + const t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend2); + return t; } - makeVariable(e, t = true, n, s) { - n = n || this.nextVariableId().toString(), s != null && s !== e.dtype && (e = e.cast(s)); - let r = new gd(e, t, n, this.nextTensorId()); - if (this.state.registeredVariables[r.name] != null) - throw new Error(`Variable with name ${r.name} was already registered`); - return this.state.registeredVariables[r.name] = r, this.incRef(r, this.backend), r; + makeVariable(initialValue, trainable = true, name, dtype) { + name = name || this.nextVariableId().toString(); + if (dtype != null && dtype !== initialValue.dtype) { + initialValue = initialValue.cast(dtype); + } + const v = new Variable(initialValue, trainable, name, this.nextTensorId()); + if (this.state.registeredVariables[v.name] != null) { + throw new Error(`Variable with name ${v.name} was already registered`); + } + this.state.registeredVariables[v.name] = v; + this.incRef(v, this.backend); + return v; } - trackTensor(e, t) { - this.state.numTensors++, e.dtype === "string" && this.state.numStringTensors++; - let n = 0; - e.dtype !== "complex64" && e.dtype !== "string" && (n = e.size * Yf(e.dtype)), this.state.numBytes += n, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t || this.backend, dtype: e.dtype, shape: e.shape, bytes: n })), e instanceof gd || this.track(e); + trackTensor(a, backend2) { + this.state.numTensors++; + if (a.dtype === "string") { + this.state.numStringTensors++; + } + let bytes = 0; + if (a.dtype !== "complex64" && a.dtype !== "string") { + bytes = a.size * bytesPerElement(a.dtype); + } + this.state.numBytes += bytes; + if (!this.state.tensorInfo.has(a.dataId)) { + this.state.numDataBuffers++; + this.state.tensorInfo.set(a.dataId, { + backend: backend2 || this.backend, + dtype: a.dtype, + shape: a.shape, + bytes + }); + } + if (!(a instanceof Variable)) { + this.track(a); + } } - incRef(e, t) { - this.trackTensor(e, t), this.backend.incRef(e.dataId); + incRef(a, backend2) { + this.trackTensor(a, backend2); + this.backend.incRef(a.dataId); } - removeDataId(e, t) { - this.state.tensorInfo.has(e) && this.state.tensorInfo.get(e).backend === t && (this.state.tensorInfo.delete(e), this.state.numDataBuffers--); + removeDataId(dataId, backend2) { + if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) { + this.state.tensorInfo.delete(dataId); + this.state.numDataBuffers--; + } } - disposeTensor(e) { - if (!this.state.tensorInfo.has(e.dataId)) + disposeTensor(a) { + if (!this.state.tensorInfo.has(a.dataId)) { return; - let t = this.state.tensorInfo.get(e.dataId); - if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t.bytes), e.dtype !== "complex64" && e.dtype !== "string") { - let n = e.size * Yf(e.dtype); - this.state.numBytes -= n; } - t.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t.backend); + const info = this.state.tensorInfo.get(a.dataId); + this.state.numTensors--; + if (a.dtype === "string") { + this.state.numStringTensors--; + this.state.numBytes -= info.bytes; + } + if (a.dtype !== "complex64" && a.dtype !== "string") { + const bytes = a.size * bytesPerElement(a.dtype); + this.state.numBytes -= bytes; + } + if (info.backend.disposeData(a.dataId)) { + this.removeDataId(a.dataId, info.backend); + } } disposeVariables() { - for (let e in this.state.registeredVariables) { - let t = this.state.registeredVariables[e]; - this.disposeVariable(t); + for (const varName in this.state.registeredVariables) { + const v = this.state.registeredVariables[varName]; + this.disposeVariable(v); } } - disposeVariable(e) { - this.disposeTensor(e), this.state.registeredVariables[e.name] != null && delete this.state.registeredVariables[e.name]; + disposeVariable(v) { + this.disposeTensor(v); + if (this.state.registeredVariables[v.name] != null) { + delete this.state.registeredVariables[v.name]; + } } memory() { - let e = this.backend.memory(); - return e.numTensors = this.state.numTensors, e.numDataBuffers = this.state.numDataBuffers, e.numBytes = this.state.numBytes, this.state.numStringTensors > 0 && (e.unreliable = true, e.reasons == null && (e.reasons = []), e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")), e; + const info = this.backend.memory(); + info.numTensors = this.state.numTensors; + info.numDataBuffers = this.state.numDataBuffers; + info.numBytes = this.state.numBytes; + if (this.state.numStringTensors > 0) { + info.unreliable = true; + if (info.reasons == null) { + info.reasons = []; + } + info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)"); + } + return info; } - async profile(e) { + async profile(query) { this.state.profiling = true; - let t = this.state.numBytes, n = this.state.numTensors; - this.state.activeProfile.kernels = [], this.state.activeProfile.result = await e(), this.state.profiling = false, this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((s) => s.totalBytesSnapshot)), this.state.activeProfile.newBytes = this.state.numBytes - t, this.state.activeProfile.newTensors = this.state.numTensors - n; - for (let s of this.state.activeProfile.kernels) - s.kernelTimeMs = await s.kernelTimeMs, s.extraInfo = await s.extraInfo; + const startBytes = this.state.numBytes; + const startNumTensors = this.state.numTensors; + this.state.activeProfile.kernels = []; + this.state.activeProfile.result = await query(); + this.state.profiling = false; + this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot)); + this.state.activeProfile.newBytes = this.state.numBytes - startBytes; + this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors; + for (const kernel of this.state.activeProfile.kernels) { + kernel.kernelTimeMs = await kernel.kernelTimeMs; + kernel.extraInfo = await kernel.extraInfo; + } return this.state.activeProfile; } isTapeOn() { return this.state.gradientDepth > 0 && this.state.kernelDepth === 0; } - addTapeNode(e, t, n, s, r, a) { - let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t, outputs: n, saved: r }, o = rx(e); - o != null && (s = o.gradFunc), s != null && (i.gradient = (u) => (u = u.map((l, c) => { - if (l == null) { - let p = n[c], d = jd(p.size, p.dtype); - return this.makeTensor(d, p.shape, p.dtype); - } - return l; - }), s(u.length > 1 ? u : u[0], r, a))), this.state.activeTape.push(i); + addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) { + const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved }; + const gradConfig = getGradient(kernelName); + if (gradConfig != null) { + gradientsFunc = gradConfig.gradFunc; + } + if (gradientsFunc != null) { + tapeNode.gradient = (dys) => { + dys = dys.map((dy, i) => { + if (dy == null) { + const output = outputs[i]; + const vals = makeZerosTypedArray(output.size, output.dtype); + return this.makeTensor(vals, output.shape, output.dtype); + } + return dy; + }); + return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs); + }; + } + this.state.activeTape.push(tapeNode); } - keep(e) { - return e.kept = true, e; + keep(result) { + result.kept = true; + return result; } startTape() { - this.state.gradientDepth === 0 && (this.state.activeTape = []), this.state.gradientDepth++; + if (this.state.gradientDepth === 0) { + this.state.activeTape = []; + } + this.state.gradientDepth++; } endTape() { this.state.gradientDepth--; } - startScope(e) { - let t = { track: [], name: "unnamed scope", id: this.state.nextScopeId++ }; - e && (t.name = e), this.state.scopeStack.push(t), this.state.activeScope = t; - } - endScope(e) { - let t = Fg(e), n = new Set(t.map((r) => r.id)); - for (let r = 0; r < this.state.activeScope.track.length; r++) { - let a = this.state.activeScope.track[r]; - !a.kept && !n.has(a.id) && a.dispose(); + startScope(name) { + const scopeInfo = { + track: [], + name: "unnamed scope", + id: this.state.nextScopeId++ + }; + if (name) { + scopeInfo.name = name; } - let s = this.state.scopeStack.pop(); - this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1], t.forEach((r) => { - !r.kept && r.scopeId === s.id && this.track(r); + this.state.scopeStack.push(scopeInfo); + this.state.activeScope = scopeInfo; + } + endScope(result) { + const tensorsToTrackInParent = getTensorsInContainer(result); + const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id)); + for (let i = 0; i < this.state.activeScope.track.length; i++) { + const tensor2 = this.state.activeScope.track[i]; + if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) { + tensor2.dispose(); + } + } + const oldScope = this.state.scopeStack.pop(); + this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1]; + tensorsToTrackInParent.forEach((tensor2) => { + if (!tensor2.kept && tensor2.scopeId === oldScope.id) { + this.track(tensor2); + } }); } - gradients(e, t, n, s = false) { - if (O(t.length > 0, () => "gradients() received an empty list of xs."), n != null && n.dtype !== "float32") - throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`); - let r = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", e)); - O(r instanceof et, () => "The result y returned by f() must be a tensor."); - let a = Q$(this.state.activeTape, t, r); - if (!s && a.length === 0 && t.length > 0) + gradients(f, xs, dy, allowNoGradients = false) { + assert(xs.length > 0, () => "gradients() received an empty list of xs."); + if (dy != null && dy.dtype !== "float32") { + throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`); + } + const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f)); + assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor."); + const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y); + if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) { throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y."); + } return this.tidy("backward", () => { - let i = {}; - i[r.id] = n == null ? c_(r.shape) : n, Z$(i, a, (u) => this.tidy(u), d_); - let o = t.map((u) => i[u.id]); - return this.state.gradientDepth === 0 && (this.state.activeTape.forEach((u) => { - for (let l of u.saved) - l.dispose(); - }), this.state.activeTape = null), { value: r, grads: o }; + const accumulatedGradientMap = {}; + accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy; + backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add); + const grads2 = xs.map((x) => accumulatedGradientMap[x.id]); + if (this.state.gradientDepth === 0) { + this.state.activeTape.forEach((node2) => { + for (const tensor2 of node2.saved) { + tensor2.dispose(); + } + }); + this.state.activeTape = null; + } + return { value: y, grads: grads2 }; }); } - customGrad(e) { - return O(fr(e), () => "The f passed in customGrad(f) must be a function."), (...t) => { - O(t.every((i) => i instanceof et), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); - let n, s = {}; - t.forEach((i, o) => { - s[o] = i; + customGrad(f) { + assert(isFunction(f), () => "The f passed in customGrad(f) must be a function."); + return (...inputs) => { + assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors"); + let res; + const inputMap = {}; + inputs.forEach((input2, i) => { + inputMap[i] = input2; }); - let r = (i, o) => (n = e(...t, o), O(n.value instanceof et, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), O(fr(n.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), n.value), a = (i, o) => { - let u = n.gradFunc(i, o), l = Array.isArray(u) ? u : [u]; - O(l.length === t.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."), O(l.every((p) => p instanceof et), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); - let c = {}; - return l.forEach((p, d) => { - c[d] = () => p; - }), c; + const forwardFunc = (_, save) => { + res = f(...[...inputs, save]); + assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"); + assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."); + return res.value; }; - return this.runKernelFunc({ forwardFunc: r, backwardsFunc: a, inputs: s }); + const backwardsFunc = (dy, saved) => { + const gradRes = res.gradFunc(dy, saved); + const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes]; + assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."); + assert(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."); + const gradMap = {}; + grads2.forEach((grad2, i) => { + gradMap[i] = () => grad2; + }); + return gradMap; + }; + return this.runKernelFunc({ + forwardFunc, + backwardsFunc, + inputs: inputMap + }); }; } - readSync(e) { - return this.state.tensorInfo.get(e).backend.readSync(e); + readSync(dataId) { + const info = this.state.tensorInfo.get(dataId); + return info.backend.readSync(dataId); } - read(e) { - return this.state.tensorInfo.get(e).backend.read(e); + read(dataId) { + const info = this.state.tensorInfo.get(dataId); + return info.backend.read(dataId); } - readToGPU(e, t) { - return this.state.tensorInfo.get(e).backend.readToGPU(e, t); + readToGPU(dataId, options4) { + const info = this.state.tensorInfo.get(dataId); + return info.backend.readToGPU(dataId, options4); } - async time(e) { - let t = Gu(), n = await this.backend.time(e); - return n.wallMs = Gu() - t, n; + async time(query) { + const start = now2(); + const timingInfo = await this.backend.time(query); + timingInfo.wallMs = now2() - start; + return timingInfo; } - track(e) { - return this.state.activeScope != null && (e.scopeId = this.state.activeScope.id, this.state.activeScope.track.push(e)), e; + track(result) { + if (this.state.activeScope != null) { + result.scopeId = this.state.activeScope.id; + this.state.activeScope.track.push(result); + } + return result; } get registeredVariables() { return this.state.registeredVariables; } reset() { - this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new ux(); - for (let e in this.registry) - this.disposeRegisteredKernels(e), this.registry[e].dispose(), delete this.registry[e]; - this.backendName = null, this.backendInstance = null, this.pendingBackendInit = null; + this.pendingBackendInitId++; + this.state.dispose(); + this.ENV.reset(); + this.state = new EngineState(); + for (const backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + this.backendName = null; + this.backendInstance = null; + this.pendingBackendInit = null; } }; -var Og = nm; -Og.nextTensorId = 0; -Og.nextVariableId = 0; -function c_(e) { - let t = rg(pt(e), "float32"); - return M.makeTensor(t, e, "float32"); +var Engine = _Engine; +Engine.nextTensorId = 0; +Engine.nextVariableId = 0; +function ones(shape) { + const values = makeOnesTypedArray(sizeFromShape(shape), "float32"); + return ENGINE.makeTensor(values, shape, "float32"); } -function fk() { - let e = rk(); - if (e._tfengine == null) { - let t = new A$(e); - e._tfengine = new Og(t); +function getOrMakeEngine() { + const ns = getGlobalNamespace(); + if (ns._tfengine == null) { + const environment = new Environment(ns); + ns._tfengine = new Engine(environment); } - return F$(e._tfengine.ENV), n_(() => e._tfengine), e._tfengine; + setEnvironmentGlobal(ns._tfengine.ENV); + setTensorTracker(() => ns._tfengine); + return ns._tfengine; } -var M = fk(); -function d_(e, t) { - let n = { a: e, b: t }; - return M.runKernel(Sr, n); +var ENGINE = getOrMakeEngine(); +function add(a, b) { + const inputs = { a, b }; + return ENGINE.runKernel(Add, inputs); } -var hp = {}; -Ae(hp, { isBrowser: () => mk, isMobile: () => f_, mockIsMobile: () => h_ }); -function p_() { - return typeof navigator != "undefined" && navigator != null; +var device_util_exports = {}; +__export2(device_util_exports, { + isBrowser: () => isBrowser, + isMobile: () => isMobile, + mockIsMobile: () => mockIsMobile +}); +function _isNavigatorDefined() { + return typeof navigator !== "undefined" && navigator != null; } -var sm; -function h_(e) { - sm = e; +var isMobileMockValue; +function mockIsMobile(value) { + isMobileMockValue = value; } -function f_(e) { - if (sm !== void 0) - return sm; - if (e || p_()) { - if (e || (e = navigator), e.product === "ReactNative") - return true; - let t = e.userAgent || e.vendor || (typeof window != "undefined" ? window.opera : ""); - if (!t) { - let n = e; - return n.userAgentData && n.userAgentData.mobile; +function isMobile(nav) { + if (isMobileMockValue !== void 0) { + return isMobileMockValue; + } + if (nav || _isNavigatorDefined()) { + if (!nav) { + nav = navigator; } - return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(t) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0, 4)); + if (nav.product === "ReactNative") { + return true; + } + const a = nav.userAgent || nav.vendor || (typeof window !== "undefined" ? window.opera : ""); + if (!a) { + const navAny = nav; + return navAny.userAgentData && navAny.userAgentData.mobile; + } + return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0, 4)); } return false; } -function mk() { - return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined"; +function isBrowser() { + return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined"; } -var Kn = X(); -Kn.registerFlag("DEBUG", () => false, (e) => { - e && console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance."); +var ENV2 = env(); +ENV2.registerFlag("DEBUG", () => false, (debugValue) => { + if (debugValue) { + console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance."); + } }); -Kn.registerFlag("IS_BROWSER", () => mk()); -Kn.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined"); -Kn.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)); -Kn.registerFlag("PROD", () => false); -Kn.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => Kn.getBool("DEBUG")); -Kn.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true); -Kn.registerFlag("IS_TEST", () => false); -Kn.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true); -Kn.registerFlag("WRAP_TO_IMAGEBITMAP", () => false); -Kn.registerFlag("ENGINE_COMPILE_ONLY", () => false); -function Rs(e, t) { - let n = e; - if (Qt(e)) - return t === "string" ? [] : [e.length]; - if (!Array.isArray(e)) +ENV2.registerFlag("IS_BROWSER", () => isBrowser()); +ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"); +ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor)); +ENV2.registerFlag("PROD", () => false); +ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG")); +ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true); +ENV2.registerFlag("IS_TEST", () => false); +ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true); +ENV2.registerFlag("WRAP_TO_IMAGEBITMAP", () => false); +ENV2.registerFlag("ENGINE_COMPILE_ONLY", () => false); +function inferShape(val, dtype) { + let firstElem = val; + if (isTypedArray(val)) { + return dtype === "string" ? [] : [val.length]; + } + if (!Array.isArray(val)) { return []; - let s = []; - for (; Array.isArray(n) || Qt(n) && t !== "string"; ) - s.push(n.length), n = n[0]; - return Array.isArray(e) && X().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && gk(e, s, []), s; + } + const shape = []; + while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") { + shape.push(firstElem.length); + firstElem = firstElem[0]; + } + if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) { + deepAssertShapeConsistency(val, shape, []); + } + return shape; } -function gk(e, t, n) { - if (n = n || [], !Array.isArray(e) && !Qt(e)) { - O(t.length === 0, () => `Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`); +function deepAssertShapeConsistency(val, shape, indices) { + indices = indices || []; + if (!Array.isArray(val) && !isTypedArray(val)) { + assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`); return; } - O(t.length > 0, () => `Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`), O(e.length === t[0], () => `Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`); - let s = t.slice(1); - for (let r = 0; r < e.length; ++r) - gk(e[r], s, n.concat(r)); -} -function lx(e, t, n, s) { - if (e !== "string_or_numeric") { - if (e == null) - throw new Error("Expected dtype cannot be null."); - if (e !== "numeric" && e !== t || e === "numeric" && t === "string") - throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`); + assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`); + assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`); + const subShape = shape.slice(1); + for (let i = 0; i < val.length; ++i) { + deepAssertShapeConsistency(val[i], subShape, indices.concat(i)); } } -function _(e, t, n, s = "numeric") { - if (e instanceof et) - return lx(s, e.dtype, t, n), e; - let r = qd(e); - if (r !== "string" && ["bool", "int32", "float32"].indexOf(s) >= 0 && (r = s), lx(s, r, t, n), e == null || !Qt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string") { - let u = e == null ? "null" : e.constructor.name; - throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${u}'`); +function assertDtype(expectedDtype, actualDType, argName, functionName) { + if (expectedDtype === "string_or_numeric") { + return; + } + if (expectedDtype == null) { + throw new Error(`Expected dtype cannot be null.`); + } + if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") { + throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`); } - let a = Rs(e, r); - !Qt(e) && !Array.isArray(e) && (e = [e]); - let o = r !== "string" ? dp(e, r) : ra(e, [], true); - return M.makeTensor(o, a, r); } -function Hu(e, t, n, s = "numeric") { - if (!Array.isArray(e)) - throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``); - return e.map((a, i) => _(a, `${t}[${i}]`, n, s)); +function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") { + if (x instanceof Tensor) { + assertDtype(parseAsDtype, x.dtype, argName, functionName); + return x; + } + let inferredDtype = inferDtype(x); + if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) { + inferredDtype = parseAsDtype; + } + assertDtype(parseAsDtype, inferredDtype, argName, functionName); + if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") { + const type = x == null ? "null" : x.constructor.name; + throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`); + } + const inferredShape = inferShape(x, inferredDtype); + if (!isTypedArray(x) && !Array.isArray(x)) { + x = [x]; + } + const skipTypedArray = true; + const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray); + return ENGINE.makeTensor(values, inferredShape, inferredDtype); } -var m_ = "__op"; -function L(e) { - let t = Object.keys(e); - if (t.length !== 1) - throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`); - let n = t[0], s = e[n]; - n.endsWith("_") && (n = n.substring(0, n.length - 1)), n = n + m_; - let r = (...a) => { - M.startScope(n); +function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") { + if (!Array.isArray(arg)) { + throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``); + } + const tensors = arg; + return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype)); +} +var OP_SCOPE_SUFFIX = "__op"; +function op(f) { + const keys = Object.keys(f); + if (keys.length !== 1) { + throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`); + } + let opName = keys[0]; + const fn = f[opName]; + if (opName.endsWith("_")) { + opName = opName.substring(0, opName.length - 1); + } + opName = opName + OP_SCOPE_SUFFIX; + const f2 = (...args) => { + ENGINE.startScope(opName); try { - let i = s(...a); - return ig(i) && console.error("Cannot return a Promise inside of tidy."), M.endScope(i), i; - } catch (i) { - throw M.endScope(null), i; + const result = fn(...args); + if (isPromise(result)) { + console.error("Cannot return a Promise inside of tidy."); + } + ENGINE.endScope(result); + return result; + } catch (ex) { + ENGINE.endScope(null); + throw ex; } }; - return Object.defineProperty(r, "name", { value: n, configurable: true }), r; + Object.defineProperty(f2, "name", { value: opName, configurable: true }); + return f2; } -function g_(e, t) { - let n = _(e, "real", "complex"), s = _(t, "imag", "complex"); - pn(n.shape, s.shape, `real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`); - let r = { real: n, imag: s }; - return M.runKernel(Xd, r); +function complex_(real5, imag5) { + const $real = convertToTensor(real5, "real", "complex"); + const $imag = convertToTensor(imag5, "imag", "complex"); + assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`); + const inputs = { real: $real, imag: $imag }; + return ENGINE.runKernel(Complex, inputs); } -var ua = L({ complex_: g_ }); -function Tr(e, t, n, s) { - if (s == null && (s = qd(e)), s === "complex64") - throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag)."); - if (!Qt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string") +var complex = op({ complex_ }); +function makeTensor(values, shape, inferredShape, dtype) { + if (dtype == null) { + dtype = inferDtype(values); + } + if (dtype === "complex64") { + throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`); + } + if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") { throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray"); - if (t != null) { - ag(t); - let r = pt(t), a = pt(n); - O(r === a, () => `Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`); - for (let i = 0; i < n.length; ++i) { - let o = n[i], u = i === n.length - 1 ? o !== pt(t.slice(i)) : true; - O(n[i] === t[i] || !u, () => `Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `); + } + if (shape != null) { + assertNonNegativeIntegerDimensions(shape); + const providedSize = sizeFromShape(shape); + const inferredSize = sizeFromShape(inferredShape); + assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`); + for (let i = 0; i < inferredShape.length; ++i) { + const inferred = inferredShape[i]; + const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true; + assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `); } } - return !Qt(e) && !Array.isArray(e) && (e = [e]), t = t || n, e = s !== "string" ? dp(e, s) : ra(e, [], true), M.makeTensor(e, t, s); -} -function fs(e, t, n) { - let s = Rs(e, n); - return Tr(e, t, s, n); -} -var rm = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 }; -var bd = 4; -async function b_(e, t) { - let n = [], s = [], r = Array.isArray(e) ? e.map((i) => i.name) : Object.keys(e); - for (let i = 0; i < r.length; ++i) { - let o = r[i], u = Array.isArray(e) ? e[i].tensor : e[o]; - if (u.dtype !== "float32" && u.dtype !== "int32" && u.dtype !== "bool" && u.dtype !== "string" && u.dtype !== "complex64") - throw new Error(`Unsupported dtype in weight '${o}': ${u.dtype}`); - let l = { name: o, shape: u.shape, dtype: u.dtype }; - if (u.dtype === "string") { - let c = new Promise(async (p) => { - let d = await u.bytes(), h = d.reduce((g, b) => g + b.length, 0) + bd * d.length, f = new Uint8Array(h), m = 0; - for (let g = 0; g < d.length; g++) { - let b = d[g], y = new Uint8Array(new Uint32Array([b.length]).buffer); - f.set(y, m), m += bd, f.set(b, m), m += b.length; - } - p(f); - }); - s.push(c); - } else - s.push(u.data()); - t != null && (l.group = t), n.push(l); + if (!isTypedArray(values) && !Array.isArray(values)) { + values = [values]; } - let a = await Promise.all(s); - return { data: y_(a), specs: n }; + shape = shape || inferredShape; + values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true); + return ENGINE.makeTensor(values, shape, dtype); } -function bk(e, t) { - let n = {}, s, r = 0; - for (let a of t) { - let i = a.name, o = a.dtype, u = a.shape, l = pt(u), c; - if ("quantization" in a) { - let p = a.quantization; - if (p.dtype === "uint8" || p.dtype === "uint16") { - if (!("min" in p && "scale" in p)) - throw new Error(`Weight ${a.name} with quantization ${p.dtype} doesn't have corresponding metadata min and scale.`); - } else if (p.dtype === "float16") { - if (o !== "float32") - throw new Error(`Weight ${a.name} is quantized with ${p.dtype} which only supports weights of type float32 not ${o}.`); - } else - throw new Error(`Weight ${a.name} has unknown quantization dtype ${p.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`); - let d = rm[p.dtype], h = e.slice(r, r + l * d), f = p.dtype === "uint8" ? new Uint8Array(h) : new Uint16Array(h); - if (o === "float32") - if (p.dtype === "uint8" || p.dtype === "uint16") { - c = new Float32Array(f.length); - for (let m = 0; m < f.length; m++) { - let g = f[m]; - c[m] = g * p.scale + p.min; - } - } else if (p.dtype === "float16") - s === void 0 && (s = S_()), c = s(f); - else - throw new Error(`Unsupported quantization type ${p.dtype} for weight type float32.`); - else if (o === "int32") { - if (p.dtype !== "uint8" && p.dtype !== "uint16") - throw new Error(`Unsupported quantization type ${p.dtype} for weight type int32.`); - c = new Int32Array(f.length); - for (let m = 0; m < f.length; m++) { - let g = f[m]; - c[m] = Math.round(g * p.scale + p.min); +function tensor(values, shape, dtype) { + const inferredShape = inferShape(values, dtype); + return makeTensor(values, shape, inferredShape, dtype); +} +var DTYPE_VALUE_SIZE_MAP = { + "float32": 4, + "float16": 2, + "int32": 4, + "uint16": 2, + "uint8": 1, + "bool": 1, + "complex64": 8 +}; +var NUM_BYTES_STRING_LENGTH = 4; +async function encodeWeights(tensors, group) { + const specs = []; + const dataPromises = []; + const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors); + for (let i = 0; i < names.length; ++i) { + const name = names[i]; + const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name]; + if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") { + throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`); + } + const spec = { name, shape: t.shape, dtype: t.dtype }; + if (t.dtype === "string") { + const utf8bytes = new Promise(async (resolve) => { + const vals = await t.bytes(); + const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length; + const bytes = new Uint8Array(totalNumBytes); + let offset = 0; + for (let i2 = 0; i2 < vals.length; i2++) { + const val = vals[i2]; + const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer); + bytes.set(bytesOfLength, offset); + offset += NUM_BYTES_STRING_LENGTH; + bytes.set(val, offset); + offset += val.length; } - } else - throw new Error(`Unsupported dtype in weight '${i}': ${o}`); - r += l * d; - } else if (o === "string") { - let p = pt(a.shape); - c = []; - for (let d = 0; d < p; d++) { - let h = new Uint32Array(e.slice(r, r + bd))[0]; - r += bd; - let f = new Uint8Array(e.slice(r, r + h)); - c.push(f), r += h; + resolve(bytes); + }); + dataPromises.push(utf8bytes); + } else { + dataPromises.push(t.data()); + } + if (group != null) { + spec.group = group; + } + specs.push(spec); + } + const tensorValues = await Promise.all(dataPromises); + return { data: concatenateTypedArrays(tensorValues), specs }; +} +function decodeWeights(buffer2, specs) { + const out = {}; + let float16Decode; + let offset = 0; + for (const spec of specs) { + const name = spec.name; + const dtype = spec.dtype; + const shape = spec.shape; + const size2 = sizeFromShape(shape); + let values; + if ("quantization" in spec) { + const quantization = spec.quantization; + if (quantization.dtype === "uint8" || quantization.dtype === "uint16") { + if (!("min" in quantization && "scale" in quantization)) { + throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`); + } + } else if (quantization.dtype === "float16") { + if (dtype !== "float32") { + throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`); + } + } else { + throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`); + } + const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; + const byteBuffer = buffer2.slice(offset, offset + size2 * quantizationSizeFactor); + const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer); + if (dtype === "float32") { + if (quantization.dtype === "uint8" || quantization.dtype === "uint16") { + values = new Float32Array(quantizedArray.length); + for (let i = 0; i < quantizedArray.length; i++) { + const v = quantizedArray[i]; + values[i] = v * quantization.scale + quantization.min; + } + } else if (quantization.dtype === "float16") { + if (float16Decode === void 0) { + float16Decode = getFloat16Decoder(); + } + values = float16Decode(quantizedArray); + } else { + throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`); + } + } else if (dtype === "int32") { + if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") { + throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`); + } + values = new Int32Array(quantizedArray.length); + for (let i = 0; i < quantizedArray.length; i++) { + const v = quantizedArray[i]; + values[i] = Math.round(v * quantization.scale + quantization.min); + } + } else { + throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); + } + offset += size2 * quantizationSizeFactor; + } else if (dtype === "string") { + const size22 = sizeFromShape(spec.shape); + values = []; + for (let i = 0; i < size22; i++) { + const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0]; + offset += NUM_BYTES_STRING_LENGTH; + const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength)); + values.push(bytes); + offset += byteLength; } } else { - let p = rm[o], d = e.slice(r, r + l * p); - if (o === "float32") - c = new Float32Array(d); - else if (o === "int32") - c = new Int32Array(d); - else if (o === "bool") - c = new Uint8Array(d); - else if (o === "complex64") { - c = new Float32Array(d); - let h = new Float32Array(c.length / 2), f = new Float32Array(c.length / 2); - for (let b = 0; b < h.length; b++) - h[b] = c[b * 2], f[b] = c[b * 2 + 1]; - let m = fs(h, u, "float32"), g = fs(f, u, "float32"); - n[i] = ua(m, g), m.dispose(), g.dispose(); - } else - throw new Error(`Unsupported dtype in weight '${i}': ${o}`); - r += l * p; + const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype]; + const byteBuffer = buffer2.slice(offset, offset + size2 * dtypeFactor); + if (dtype === "float32") { + values = new Float32Array(byteBuffer); + } else if (dtype === "int32") { + values = new Int32Array(byteBuffer); + } else if (dtype === "bool") { + values = new Uint8Array(byteBuffer); + } else if (dtype === "complex64") { + values = new Float32Array(byteBuffer); + const real5 = new Float32Array(values.length / 2); + const image2 = new Float32Array(values.length / 2); + for (let i = 0; i < real5.length; i++) { + real5[i] = values[i * 2]; + image2[i] = values[i * 2 + 1]; + } + const realTensor = tensor(real5, shape, "float32"); + const imageTensor = tensor(image2, shape, "float32"); + out[name] = complex(realTensor, imageTensor); + realTensor.dispose(); + imageTensor.dispose(); + } else { + throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`); + } + offset += size2 * dtypeFactor; + } + if (dtype !== "complex64") { + out[name] = tensor(values, shape, dtype); } - o !== "complex64" && (n[i] = fs(c, u, o)); } - return n; + return out; } -function y_(e) { - if (e === null) - throw new Error(`Invalid input value: ${JSON.stringify(e)}`); - let t = 0, n = []; - e.forEach((a) => { - if (t += a.byteLength, n.push(a.byteLength === a.buffer.byteLength ? a : new a.constructor(a)), !(a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array)) - throw new Error(`Unsupported TypedArray subtype: ${a.constructor.name}`); +function concatenateTypedArrays(xs) { + if (xs === null) { + throw new Error(`Invalid input value: ${JSON.stringify(xs)}`); + } + let totalByteLength = 0; + const normalizedXs = []; + xs.forEach((x) => { + totalByteLength += x.byteLength; + normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x)); + if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) { + throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`); + } }); - let s = new Uint8Array(t), r = 0; - return n.forEach((a) => { - s.set(new Uint8Array(a.buffer), r), r += a.byteLength; - }), s.buffer; -} -var Pg = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined"); -function cx(e) { - return Pg ? Buffer.byteLength(e) : new Blob([e]).size; -} -function v_(e) { - if (Pg) - return Buffer.from(e).toString("base64"); - let t = new Uint8Array(e), n = ""; - for (let s = 0, r = t.length; s < r; s++) - n += String.fromCharCode(t[s]); - return btoa(n); -} -function x_(e) { - if (Pg) { - let s = Buffer.from(e, "base64"); - return s.buffer.slice(s.byteOffset, s.byteOffset + s.byteLength); - } - let t = atob(e), n = new Uint8Array(t.length); - for (let s = 0; s < t.length; ++s) - n.set([t.charCodeAt(s)], s); - return n.buffer; -} -function zg(e) { - if (e.length === 1) - return e[0]; - let t = 0; - e.forEach((r) => { - t += r.byteLength; + const y = new Uint8Array(totalByteLength); + let offset = 0; + normalizedXs.forEach((x) => { + y.set(new Uint8Array(x.buffer), offset); + offset += x.byteLength; }); - let n = new Uint8Array(t), s = 0; - return e.forEach((r) => { - n.set(new Uint8Array(r), s), s += r.byteLength; - }), n.buffer; + return y.buffer; } -function dx(e) { - let t = "/"; - for (e = e.trim(); e.endsWith(t); ) - e = e.slice(0, e.length - 1); - let n = e.split(t); - return n[n.length - 1]; -} -function yk(e, t) { - let n = { modelTopology: e.modelTopology, format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, weightsManifest: t }; - return e.signature != null && (n.signature = e.signature), e.userDefinedMetadata != null && (n.userDefinedMetadata = e.userDefinedMetadata), e.modelInitializer != null && (n.modelInitializer = e.modelInitializer), e.trainingConfig != null && (n.trainingConfig = e.trainingConfig), n; -} -async function Mg(e, t) { - let n = { modelTopology: e.modelTopology, format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy }; - if (e.trainingConfig != null && (n.trainingConfig = e.trainingConfig), e.weightsManifest != null) { - let [s, r] = await t(e.weightsManifest); - n.weightSpecs = s, n.weightData = r; +var useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined"); +function stringByteLength(str) { + if (useNodeBuffer) { + return Buffer.byteLength(str); } - return e.signature != null && (n.signature = e.signature), e.userDefinedMetadata != null && (n.userDefinedMetadata = e.userDefinedMetadata), e.modelInitializer != null && (n.modelInitializer = e.modelInitializer), n; + return new Blob([str]).size; } -function Fl(e) { - if (e.modelTopology instanceof ArrayBuffer) +function arrayBufferToBase64String(buffer2) { + if (useNodeBuffer) { + return Buffer.from(buffer2).toString("base64"); + } + const buf = new Uint8Array(buffer2); + let s = ""; + for (let i = 0, l = buf.length; i < l; i++) { + s += String.fromCharCode(buf[i]); + } + return btoa(s); +} +function base64StringToArrayBuffer(str) { + if (useNodeBuffer) { + const buf = Buffer.from(str, "base64"); + return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength); + } + const s = atob(str); + const buffer2 = new Uint8Array(s.length); + for (let i = 0; i < s.length; ++i) { + buffer2.set([s.charCodeAt(i)], i); + } + return buffer2.buffer; +} +function concatenateArrayBuffers(buffers) { + if (buffers.length === 1) { + return buffers[0]; + } + let totalByteLength = 0; + buffers.forEach((buffer2) => { + totalByteLength += buffer2.byteLength; + }); + const temp = new Uint8Array(totalByteLength); + let offset = 0; + buffers.forEach((buffer2) => { + temp.set(new Uint8Array(buffer2), offset); + offset += buffer2.byteLength; + }); + return temp.buffer; +} +function basename(path) { + const SEPARATOR = "/"; + path = path.trim(); + while (path.endsWith(SEPARATOR)) { + path = path.slice(0, path.length - 1); + } + const items = path.split(SEPARATOR); + return items[items.length - 1]; +} +function getModelJSONForModelArtifacts(artifacts, manifest) { + const result = { + modelTopology: artifacts.modelTopology, + format: artifacts.format, + generatedBy: artifacts.generatedBy, + convertedBy: artifacts.convertedBy, + weightsManifest: manifest + }; + if (artifacts.signature != null) { + result.signature = artifacts.signature; + } + if (artifacts.userDefinedMetadata != null) { + result.userDefinedMetadata = artifacts.userDefinedMetadata; + } + if (artifacts.modelInitializer != null) { + result.modelInitializer = artifacts.modelInitializer; + } + if (artifacts.trainingConfig != null) { + result.trainingConfig = artifacts.trainingConfig; + } + return result; +} +async function getModelArtifactsForJSON(modelJSON, loadWeights2) { + const modelArtifacts = { + modelTopology: modelJSON.modelTopology, + format: modelJSON.format, + generatedBy: modelJSON.generatedBy, + convertedBy: modelJSON.convertedBy + }; + if (modelJSON.trainingConfig != null) { + modelArtifacts.trainingConfig = modelJSON.trainingConfig; + } + if (modelJSON.weightsManifest != null) { + const [weightSpecs, weightData] = await loadWeights2(modelJSON.weightsManifest); + modelArtifacts.weightSpecs = weightSpecs; + modelArtifacts.weightData = weightData; + } + if (modelJSON.signature != null) { + modelArtifacts.signature = modelJSON.signature; + } + if (modelJSON.userDefinedMetadata != null) { + modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata; + } + if (modelJSON.modelInitializer != null) { + modelArtifacts.modelInitializer = modelJSON.modelInitializer; + } + return modelArtifacts; +} +function getModelArtifactsInfoForJSON(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { throw new Error("Expected JSON model topology, received ArrayBuffer."); - return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: e.modelTopology == null ? 0 : cx(JSON.stringify(e.modelTopology)), weightSpecsBytes: e.weightSpecs == null ? 0 : cx(JSON.stringify(e.weightSpecs)), weightDataBytes: e.weightData == null ? 0 : e.weightData.byteLength }; -} -function w_() { - let e = (n) => { - let s = n << 13, r = 0; - for (; (s & 8388608) === 0; ) - r -= 8388608, s <<= 1; - return s &= -8388609, r += 947912704, s | r; - }, t = new Uint32Array(2048); - t[0] = 0; - for (let n = 1; n < 1024; n++) - t[n] = e(n); - for (let n = 1024; n < 2048; n++) - t[n] = 939524096 + (n - 1024 << 13); - return t; -} -function k_() { - let e = new Uint32Array(64); - e[0] = 0, e[31] = 1199570944, e[32] = 2147483648, e[63] = 3347054592; - for (let t = 1; t < 31; t++) - e[t] = t << 23; - for (let t = 33; t < 63; t++) - e[t] = 2147483648 + (t - 32 << 23); - return e; -} -function I_() { - let e = new Uint32Array(64); - for (let t = 0; t < 64; t++) - e[t] = 1024; - return e[0] = e[32] = 0, e; -} -function S_() { - let e = w_(), t = k_(), n = I_(); - return (s) => { - let r = new ArrayBuffer(4 * s.length), a = new Uint32Array(r); - for (let i = 0; i < s.length; i++) { - let o = s[i], u = e[n[o >> 10] + (o & 1023)] + t[o >> 10]; - a[i] = u; - } - return new Float32Array(r); + } + return { + dateSaved: new Date(), + modelTopologyType: "JSON", + modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)), + weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)), + weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength }; } -var xt = class { +function computeFloat16MantisaTable() { + const convertMantissa = (i) => { + let m = i << 13; + let e = 0; + while ((m & 8388608) === 0) { + e -= 8388608; + m <<= 1; + } + m &= ~8388608; + e += 947912704; + return m | e; + }; + const mantisaTable = new Uint32Array(2048); + mantisaTable[0] = 0; + for (let i = 1; i < 1024; i++) { + mantisaTable[i] = convertMantissa(i); + } + for (let i = 1024; i < 2048; i++) { + mantisaTable[i] = 939524096 + (i - 1024 << 13); + } + return mantisaTable; +} +function computeFloat16ExponentTable() { + const exponentTable = new Uint32Array(64); + exponentTable[0] = 0; + exponentTable[31] = 1199570944; + exponentTable[32] = 2147483648; + exponentTable[63] = 3347054592; + for (let i = 1; i < 31; i++) { + exponentTable[i] = i << 23; + } + for (let i = 33; i < 63; i++) { + exponentTable[i] = 2147483648 + (i - 32 << 23); + } + return exponentTable; +} +function computeFloat16OffsetTable() { + const offsetTable = new Uint32Array(64); + for (let i = 0; i < 64; i++) { + offsetTable[i] = 1024; + } + offsetTable[0] = offsetTable[32] = 0; + return offsetTable; +} +function getFloat16Decoder() { + const mantisaTable = computeFloat16MantisaTable(); + const exponentTable = computeFloat16ExponentTable(); + const offsetTable = computeFloat16OffsetTable(); + return (quantizedArray) => { + const buffer2 = new ArrayBuffer(4 * quantizedArray.length); + const bufferUint32View = new Uint32Array(buffer2); + for (let index2 = 0; index2 < quantizedArray.length; index2++) { + const float16Bits = quantizedArray[index2]; + const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10]; + bufferUint32View[index2] = float32Bits; + } + return new Float32Array(buffer2); + }; +} +var IORouterRegistry = class { constructor() { - this.saveRouters = [], this.loadRouters = []; + this.saveRouters = []; + this.loadRouters = []; } static getInstance() { - return xt.instance == null && (xt.instance = new xt()), xt.instance; + if (IORouterRegistry.instance == null) { + IORouterRegistry.instance = new IORouterRegistry(); + } + return IORouterRegistry.instance; } - static registerSaveRouter(e) { - xt.getInstance().saveRouters.push(e); + static registerSaveRouter(saveRouter) { + IORouterRegistry.getInstance().saveRouters.push(saveRouter); } - static registerLoadRouter(e) { - xt.getInstance().loadRouters.push(e); + static registerLoadRouter(loadRouter) { + IORouterRegistry.getInstance().loadRouters.push(loadRouter); } - static getSaveHandlers(e) { - return xt.getHandlers(e, "save"); + static getSaveHandlers(url) { + return IORouterRegistry.getHandlers(url, "save"); } - static getLoadHandlers(e, t) { - return xt.getHandlers(e, "load", t); + static getLoadHandlers(url, loadOptions) { + return IORouterRegistry.getHandlers(url, "load", loadOptions); } - static getHandlers(e, t, n) { - let s = []; - return (t === "load" ? xt.getInstance().loadRouters : xt.getInstance().saveRouters).forEach((a) => { - let i = a(e, n); - i !== null && s.push(i); - }), s; + static getHandlers(url, handlerType, loadOptions) { + const validHandlers = []; + const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters; + routers.forEach((router) => { + const handler = router(url, loadOptions); + if (handler !== null) { + validHandlers.push(handler); + } + }); + return validHandlers; } }; -var C_ = (e) => xt.registerSaveRouter(e); -var N_ = (e) => xt.registerLoadRouter(e); -var T_ = (e) => xt.getSaveHandlers(e); -var $_ = (e, t) => xt.getLoadHandlers(e, t); -var am = "tensorflowjs"; -var im = 1; -var Qr = "models_store"; -var ur = "model_info_store"; -function vk() { - if (!X().getBool("IS_BROWSER")) +var registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter); +var registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter); +var getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url); +var getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions); +var DATABASE_NAME = "tensorflowjs"; +var DATABASE_VERSION = 1; +var MODEL_STORE_NAME = "models_store"; +var INFO_STORE_NAME = "model_info_store"; +function getIndexedDBFactory() { + if (!env().getBool("IS_BROWSER")) { throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser."); - let e = typeof window == "undefined" ? self : window, t = e.indexedDB || e.mozIndexedDB || e.webkitIndexedDB || e.msIndexedDB || e.shimIndexedDB; - if (t == null) - throw new Error("The current browser does not appear to support IndexedDB."); - return t; -} -function om(e) { - let t = e.result; - t.createObjectStore(Qr, { keyPath: "modelPath" }), t.createObjectStore(ur, { keyPath: "modelPath" }); -} -var la = class { - constructor(e) { - if (this.indexedDB = vk(), e == null || !e) - throw new Error("For IndexedDB, modelPath must not be null, undefined or empty."); - this.modelPath = e; } - async save(e) { - if (e.modelTopology instanceof ArrayBuffer) + const theWindow = typeof window === "undefined" ? self : window; + const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB; + if (factory == null) { + throw new Error("The current browser does not appear to support IndexedDB."); + } + return factory; +} +function setUpDatabase(openRequest) { + const db = openRequest.result; + db.createObjectStore(MODEL_STORE_NAME, { keyPath: "modelPath" }); + db.createObjectStore(INFO_STORE_NAME, { keyPath: "modelPath" }); +} +var BrowserIndexedDB = class { + constructor(modelPath) { + this.indexedDB = getIndexedDBFactory(); + if (modelPath == null || !modelPath) { + throw new Error("For IndexedDB, modelPath must not be null, undefined or empty."); + } + this.modelPath = modelPath; + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); - return this.databaseAction(this.modelPath, e); + } + return this.databaseAction(this.modelPath, modelArtifacts); } async load() { return this.databaseAction(this.modelPath); } - databaseAction(e, t) { - return new Promise((n, s) => { - let r = this.indexedDB.open(am, im); - r.onupgradeneeded = () => om(r), r.onsuccess = () => { - let a = r.result; - if (t == null) { - let i = a.transaction(Qr, "readonly"), u = i.objectStore(Qr).get(this.modelPath); - u.onsuccess = () => { - if (u.result == null) - return a.close(), s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`)); - n(u.result.modelArtifacts); - }, u.onerror = (l) => (a.close(), s(u.error)), i.oncomplete = () => a.close(); + databaseAction(modelPath, modelArtifacts) { + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + if (modelArtifacts == null) { + const modelTx = db.transaction(MODEL_STORE_NAME, "readonly"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const getRequest = modelStore.get(this.modelPath); + getRequest.onsuccess = () => { + if (getRequest.result == null) { + db.close(); + return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`)); + } else { + resolve(getRequest.result.modelArtifacts); + } + }; + getRequest.onerror = (error) => { + db.close(); + return reject(getRequest.error); + }; + modelTx.oncomplete = () => db.close(); } else { - let i = Fl(t), o = a.transaction(ur, "readwrite"), u = o.objectStore(ur), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), c; - l.onsuccess = () => { - c = a.transaction(Qr, "readwrite"); - let d = c.objectStore(Qr).put({ modelPath: this.modelPath, modelArtifacts: t, modelArtifactsInfo: i }); - d.onsuccess = () => n({ modelArtifactsInfo: i }), d.onerror = (h) => { - u = o.objectStore(ur); - let f = u.delete(this.modelPath); - f.onsuccess = () => (a.close(), s(d.error)), f.onerror = (m) => (a.close(), s(d.error)); + const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts); + const infoTx = db.transaction(INFO_STORE_NAME, "readwrite"); + let infoStore = infoTx.objectStore(INFO_STORE_NAME); + const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo }); + let modelTx; + putInfoRequest.onsuccess = () => { + modelTx = db.transaction(MODEL_STORE_NAME, "readwrite"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const putModelRequest = modelStore.put({ + modelPath: this.modelPath, + modelArtifacts, + modelArtifactsInfo + }); + putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo }); + putModelRequest.onerror = (error) => { + infoStore = infoTx.objectStore(INFO_STORE_NAME); + const deleteInfoRequest = infoStore.delete(this.modelPath); + deleteInfoRequest.onsuccess = () => { + db.close(); + return reject(putModelRequest.error); + }; + deleteInfoRequest.onerror = (error2) => { + db.close(); + return reject(putModelRequest.error); + }; }; - }, l.onerror = (p) => (a.close(), s(l.error)), o.oncomplete = () => { - c == null ? a.close() : c.oncomplete = () => a.close(); + }; + putInfoRequest.onerror = (error) => { + db.close(); + return reject(putInfoRequest.error); + }; + infoTx.oncomplete = () => { + if (modelTx == null) { + db.close(); + } else { + modelTx.oncomplete = () => db.close(); + } }; } - }, r.onerror = (a) => s(r.error); + }; + openRequest.onerror = (error) => reject(openRequest.error); }); } }; -la.URL_SCHEME = "indexeddb://"; -var xk = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(la.URL_SCHEME) ? __(e.slice(la.URL_SCHEME.length)) : null; -xt.registerSaveRouter(xk); -xt.registerLoadRouter(xk); -function __(e) { - return new la(e); +BrowserIndexedDB.URL_SCHEME = "indexeddb://"; +var indexedDBRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) { + return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(indexedDBRouter); +IORouterRegistry.registerLoadRouter(indexedDBRouter); +function browserIndexedDB(modelPath) { + return new BrowserIndexedDB(modelPath); } -function A_(e) { - return e.startsWith(la.URL_SCHEME) ? e.slice(la.URL_SCHEME.length) : e; +function maybeStripScheme(key) { + return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key; } -var E_ = class { +var BrowserIndexedDBManager = class { constructor() { - this.indexedDB = vk(); + this.indexedDB = getIndexedDBFactory(); } async listModels() { - return new Promise((e, t) => { - let n = this.indexedDB.open(am, im); - n.onupgradeneeded = () => om(n), n.onsuccess = () => { - let s = n.result, r = s.transaction(ur, "readonly"), i = r.objectStore(ur).getAll(); - i.onsuccess = () => { - let o = {}; - for (let u of i.result) - o[u.modelPath] = u.modelArtifactsInfo; - e(o); - }, i.onerror = (o) => (s.close(), t(i.error)), r.oncomplete = () => s.close(); - }, n.onerror = (s) => t(n.error); + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + const tx = db.transaction(INFO_STORE_NAME, "readonly"); + const store = tx.objectStore(INFO_STORE_NAME); + const getAllInfoRequest = store.getAll(); + getAllInfoRequest.onsuccess = () => { + const out = {}; + for (const item of getAllInfoRequest.result) { + out[item.modelPath] = item.modelArtifactsInfo; + } + resolve(out); + }; + getAllInfoRequest.onerror = (error) => { + db.close(); + return reject(getAllInfoRequest.error); + }; + tx.oncomplete = () => db.close(); + }; + openRequest.onerror = (error) => reject(openRequest.error); }); } - async removeModel(e) { - return e = A_(e), new Promise((t, n) => { - let s = this.indexedDB.open(am, im); - s.onupgradeneeded = () => om(s), s.onsuccess = () => { - let r = s.result, a = r.transaction(ur, "readwrite"), i = a.objectStore(ur), o = i.get(e), u; - o.onsuccess = () => { - if (o.result == null) - return r.close(), n(new Error(`Cannot find model with path '${e}' in IndexedDB.`)); - { - let l = i.delete(e), c = () => { - u = r.transaction(Qr, "readwrite"); - let d = u.objectStore(Qr).delete(e); - d.onsuccess = () => t(o.result.modelArtifactsInfo), d.onerror = (h) => n(o.error); + async removeModel(path) { + path = maybeStripScheme(path); + return new Promise((resolve, reject) => { + const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = () => setUpDatabase(openRequest); + openRequest.onsuccess = () => { + const db = openRequest.result; + const infoTx = db.transaction(INFO_STORE_NAME, "readwrite"); + const infoStore = infoTx.objectStore(INFO_STORE_NAME); + const getInfoRequest = infoStore.get(path); + let modelTx; + getInfoRequest.onsuccess = () => { + if (getInfoRequest.result == null) { + db.close(); + return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`)); + } else { + const deleteInfoRequest = infoStore.delete(path); + const deleteModelData = () => { + modelTx = db.transaction(MODEL_STORE_NAME, "readwrite"); + const modelStore = modelTx.objectStore(MODEL_STORE_NAME); + const deleteModelRequest = modelStore.delete(path); + deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo); + deleteModelRequest.onerror = (error) => reject(getInfoRequest.error); + }; + deleteInfoRequest.onsuccess = deleteModelData; + deleteInfoRequest.onerror = (error) => { + deleteModelData(); + db.close(); + return reject(getInfoRequest.error); }; - l.onsuccess = c, l.onerror = (p) => (c(), r.close(), n(o.error)); } - }, o.onerror = (l) => (r.close(), n(o.error)), a.oncomplete = () => { - u == null ? r.close() : u.oncomplete = () => r.close(); }; - }, s.onerror = (r) => n(s.error); + getInfoRequest.onerror = (error) => { + db.close(); + return reject(getInfoRequest.error); + }; + infoTx.oncomplete = () => { + if (modelTx == null) { + db.close(); + } else { + modelTx.oncomplete = () => db.close(); + } + }; + }; + openRequest.onerror = (error) => reject(openRequest.error); }); } }; -var Gs = "/"; -var Hi = "tensorflowjs_models"; -var wk = "info"; -var R_ = "model_topology"; -var D_ = "weight_specs"; -var F_ = "weight_data"; -var O_ = "model_metadata"; -function kk(e) { - return { info: [Hi, e, wk].join(Gs), topology: [Hi, e, R_].join(Gs), weightSpecs: [Hi, e, D_].join(Gs), weightData: [Hi, e, F_].join(Gs), modelMetadata: [Hi, e, O_].join(Gs) }; +var PATH_SEPARATOR = "/"; +var PATH_PREFIX = "tensorflowjs_models"; +var INFO_SUFFIX = "info"; +var MODEL_TOPOLOGY_SUFFIX = "model_topology"; +var WEIGHT_SPECS_SUFFIX = "weight_specs"; +var WEIGHT_DATA_SUFFIX = "weight_data"; +var MODEL_METADATA_SUFFIX = "model_metadata"; +function getModelKeys(path) { + return { + info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR), + topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR), + weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR), + weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR), + modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR) + }; } -function Ik(e) { - for (let t of Object.values(e)) - window.localStorage.removeItem(t); -} -function P_(e) { - let t = e.split(Gs); - if (t.length < 3) - throw new Error(`Invalid key format: ${e}`); - return t.slice(1, t.length - 1).join(Gs); -} -function z_(e) { - return e.startsWith(ca.URL_SCHEME) ? e.slice(ca.URL_SCHEME.length) : e; -} -var ca = class { - constructor(e) { - if (!X().getBool("IS_BROWSER") || typeof window == "undefined" || typeof window.localStorage == "undefined") - throw new Error("The current environment does not support local storage."); - if (this.LS = window.localStorage, e == null || !e) - throw new Error("For local storage, modelPath must not be null, undefined or empty."); - this.modelPath = e, this.keys = kk(this.modelPath); +function removeItems(keys) { + for (const key of Object.values(keys)) { + window.localStorage.removeItem(key); } - async save(e) { - if (e.modelTopology instanceof ArrayBuffer) +} +function getModelPathFromKey(key) { + const items = key.split(PATH_SEPARATOR); + if (items.length < 3) { + throw new Error(`Invalid key format: ${key}`); + } + return items.slice(1, items.length - 1).join(PATH_SEPARATOR); +} +function maybeStripScheme2(key) { + return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key; +} +var BrowserLocalStorage = class { + constructor(modelPath) { + if (!env().getBool("IS_BROWSER") || typeof window === "undefined" || typeof window.localStorage === "undefined") { + throw new Error("The current environment does not support local storage."); + } + this.LS = window.localStorage; + if (modelPath == null || !modelPath) { + throw new Error("For local storage, modelPath must not be null, undefined or empty."); + } + this.modelPath = modelPath; + this.keys = getModelKeys(this.modelPath); + } + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet."); - { - let t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), s = Fl(e); + } else { + const topology = JSON.stringify(modelArtifacts.modelTopology); + const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs); + const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts); try { - this.LS.setItem(this.keys.info, JSON.stringify(s)), this.LS.setItem(this.keys.topology, t), this.LS.setItem(this.keys.weightSpecs, n), this.LS.setItem(this.keys.weightData, v_(e.weightData)); - let r = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 }; - return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(r)), { modelArtifactsInfo: s }; - } catch (r) { - throw Ik(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`); + this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo)); + this.LS.setItem(this.keys.topology, topology); + this.LS.setItem(this.keys.weightSpecs, weightSpecs); + this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData)); + const metadata = { + format: modelArtifacts.format, + generatedBy: modelArtifacts.generatedBy, + convertedBy: modelArtifacts.convertedBy, + signature: modelArtifacts.signature != null ? modelArtifacts.signature : void 0, + userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : void 0, + modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : void 0, + trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : void 0 + }; + this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata)); + return { modelArtifactsInfo }; + } catch (err) { + removeItems(this.keys); + throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`); } } } async load() { - let e = JSON.parse(this.LS.getItem(this.keys.info)); - if (e == null) + const info = JSON.parse(this.LS.getItem(this.keys.info)); + if (info == null) { throw new Error(`In local storage, there is no model with name '${this.modelPath}'`); - if (e.modelTopologyType !== "JSON") - throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet."); - let t = {}, n = JSON.parse(this.LS.getItem(this.keys.topology)); - if (n == null) - throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`); - t.modelTopology = n; - let s = JSON.parse(this.LS.getItem(this.keys.weightSpecs)); - if (s == null) - throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`); - t.weightSpecs = s; - let r = this.LS.getItem(this.keys.modelMetadata); - if (r != null) { - let i = JSON.parse(r); - t.format = i.format, t.generatedBy = i.generatedBy, t.convertedBy = i.convertedBy, i.signature != null && (t.signature = i.signature), i.userDefinedMetadata != null && (t.userDefinedMetadata = i.userDefinedMetadata), i.modelInitializer != null && (t.modelInitializer = i.modelInitializer), i.trainingConfig != null && (t.trainingConfig = i.trainingConfig); } - let a = this.LS.getItem(this.keys.weightData); - if (a == null) - throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`); - return t.weightData = x_(a), t; - } -}; -ca.URL_SCHEME = "localstorage://"; -var Sk = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(ca.URL_SCHEME) ? M_(e.slice(ca.URL_SCHEME.length)) : null; -xt.registerSaveRouter(Sk); -xt.registerLoadRouter(Sk); -function M_(e) { - return new ca(e); -} -var L_ = class { - constructor() { - O(X().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), O(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage; - } - async listModels() { - let e = {}, t = Hi + Gs, n = Gs + wk; - for (let s = 0; s < this.LS.length; ++s) { - let r = this.LS.key(s); - if (r.startsWith(t) && r.endsWith(n)) { - let a = P_(r); - e[a] = JSON.parse(this.LS.getItem(r)); + if (info.modelTopologyType !== "JSON") { + throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet."); + } + const out = {}; + const topology = JSON.parse(this.LS.getItem(this.keys.topology)); + if (topology == null) { + throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`); + } + out.modelTopology = topology; + const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs)); + if (weightSpecs == null) { + throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`); + } + out.weightSpecs = weightSpecs; + const metadataString = this.LS.getItem(this.keys.modelMetadata); + if (metadataString != null) { + const metadata = JSON.parse(metadataString); + out.format = metadata.format; + out.generatedBy = metadata.generatedBy; + out.convertedBy = metadata.convertedBy; + if (metadata.signature != null) { + out.signature = metadata.signature; + } + if (metadata.userDefinedMetadata != null) { + out.userDefinedMetadata = metadata.userDefinedMetadata; + } + if (metadata.modelInitializer != null) { + out.modelInitializer = metadata.modelInitializer; + } + if (metadata.trainingConfig != null) { + out.trainingConfig = metadata.trainingConfig; } } - return e; - } - async removeModel(e) { - e = z_(e); - let t = kk(e); - if (this.LS.getItem(t.info) == null) - throw new Error(`Cannot find model at path '${e}'`); - let n = JSON.parse(this.LS.getItem(t.info)); - return Ik(t), n; + const weightDataBase64 = this.LS.getItem(this.keys.weightData); + if (weightDataBase64 == null) { + throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`); + } + out.weightData = base64StringToArrayBuffer(weightDataBase64); + return out; } }; -var Ki = "://"; -var zn = class { +BrowserLocalStorage.URL_SCHEME = "localstorage://"; +var localStorageRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) { + return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)); + } else { + return null; + } + } +}; +IORouterRegistry.registerSaveRouter(localStorageRouter); +IORouterRegistry.registerLoadRouter(localStorageRouter); +function browserLocalStorage(modelPath) { + return new BrowserLocalStorage(modelPath); +} +var BrowserLocalStorageManager = class { + constructor() { + assert(env().getBool("IS_BROWSER"), () => "Current environment is not a web browser"); + assert(typeof window === "undefined" || typeof window.localStorage !== "undefined", () => "Current browser does not appear to support localStorage"); + this.LS = window.localStorage; + } + async listModels() { + const out = {}; + const prefix = PATH_PREFIX + PATH_SEPARATOR; + const suffix = PATH_SEPARATOR + INFO_SUFFIX; + for (let i = 0; i < this.LS.length; ++i) { + const key = this.LS.key(i); + if (key.startsWith(prefix) && key.endsWith(suffix)) { + const modelPath = getModelPathFromKey(key); + out[modelPath] = JSON.parse(this.LS.getItem(key)); + } + } + return out; + } + async removeModel(path) { + path = maybeStripScheme2(path); + const keys = getModelKeys(path); + if (this.LS.getItem(keys.info) == null) { + throw new Error(`Cannot find model at path '${path}'`); + } + const info = JSON.parse(this.LS.getItem(keys.info)); + removeItems(keys); + return info; + } +}; +var URL_SCHEME_SUFFIX = "://"; +var ModelStoreManagerRegistry = class { constructor() { this.managers = {}; } static getInstance() { - return zn.instance == null && (zn.instance = new zn()), zn.instance; + if (ModelStoreManagerRegistry.instance == null) { + ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry(); + } + return ModelStoreManagerRegistry.instance; } - static registerManager(e, t) { - O(e != null, () => "scheme must not be undefined or null."), e.endsWith(Ki) && (e = e.slice(0, e.indexOf(Ki))), O(e.length > 0, () => "scheme must not be an empty string."); - let n = zn.getInstance(); - O(n.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), n.managers[e] = t; + static registerManager(scheme, manager) { + assert(scheme != null, () => "scheme must not be undefined or null."); + if (scheme.endsWith(URL_SCHEME_SUFFIX)) { + scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX)); + } + assert(scheme.length > 0, () => "scheme must not be an empty string."); + const registry = ModelStoreManagerRegistry.getInstance(); + assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`); + registry.managers[scheme] = manager; } - static getManager(e) { - let t = this.getInstance().managers[e]; - if (t == null) - throw new Error(`Cannot find model manager for scheme '${e}'`); - return t; + static getManager(scheme) { + const manager = this.getInstance().managers[scheme]; + if (manager == null) { + throw new Error(`Cannot find model manager for scheme '${scheme}'`); + } + return manager; } static getSchemes() { return Object.keys(this.getInstance().managers); } }; -function td(e) { - if (e.indexOf(Ki) === -1) - throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${zn.getSchemes().join(",")}`); - return { scheme: e.split(Ki)[0], path: e.split(Ki)[1] }; +function parseURL(url) { + if (url.indexOf(URL_SCHEME_SUFFIX) === -1) { + throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`); + } + return { + scheme: url.split(URL_SCHEME_SUFFIX)[0], + path: url.split(URL_SCHEME_SUFFIX)[1] + }; } -async function Ck(e, t, n = false) { - O(e !== t, () => `Old path and new path are the same: '${e}'`); - let s = xt.getLoadHandlers(e); - O(s.length > 0, () => `Copying failed because no load handler is found for source URL ${e}.`), O(s.length < 2, () => `Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`); - let r = s[0], a = xt.getSaveHandlers(t); - O(a.length > 0, () => `Copying failed because no save handler is found for destination URL ${t}.`), O(a.length < 2, () => `Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`); - let i = a[0], o = td(e).scheme, u = td(e).path, l = o === td(e).scheme, c = await r.load(); - n && l && await zn.getManager(o).removeModel(u); - let p = await i.save(c); - return n && !l && await zn.getManager(o).removeModel(u), p.modelArtifactsInfo; +async function cloneModelInternal(sourceURL, destURL, deleteSource = false) { + assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`); + const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL); + assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`); + assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`); + const loadHandler = loadHandlers[0]; + const saveHandlers = IORouterRegistry.getSaveHandlers(destURL); + assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`); + assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`); + const saveHandler = saveHandlers[0]; + const sourceScheme = parseURL(sourceURL).scheme; + const sourcePath = parseURL(sourceURL).path; + const sameMedium = sourceScheme === parseURL(sourceURL).scheme; + const modelArtifacts = await loadHandler.load(); + if (deleteSource && sameMedium) { + await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath); + } + const saveResult = await saveHandler.save(modelArtifacts); + if (deleteSource && !sameMedium) { + await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath); + } + return saveResult.modelArtifactsInfo; } -async function B_() { - let e = zn.getSchemes(), t = {}; - for (let n of e) { - let s = await zn.getManager(n).listModels(); - for (let r in s) { - let a = n + Ki + r; - t[a] = s[r]; +async function listModels() { + const schemes = ModelStoreManagerRegistry.getSchemes(); + const out = {}; + for (const scheme of schemes) { + const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels(); + for (const path in schemeOut) { + const url = scheme + URL_SCHEME_SUFFIX + path; + out[url] = schemeOut[path]; } } - return t; + return out; } -async function V_(e) { - let t = td(e); - return zn.getManager(t.scheme).removeModel(t.path); +async function removeModel(url) { + const schemeAndPath = parseURL(url); + const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme); + return manager.removeModel(schemeAndPath.path); } -async function W_(e, t) { - return Ck(e, t, false); +async function copyModel(sourceURL, destURL) { + const deleteSource = false; + return cloneModelInternal(sourceURL, destURL, deleteSource); } -async function U_(e, t) { - return Ck(e, t, true); +async function moveModel(sourceURL, destURL) { + const deleteSource = true; + return cloneModelInternal(sourceURL, destURL, deleteSource); } -var G_ = class { - fetch(e, t) { - return fetch(e, t); +var PlatformBrowser = class { + fetch(path, init22) { + return fetch(path, init22); } now() { return performance.now(); } - encode(e, t) { - if (t !== "utf-8" && t !== "utf8") - throw new Error(`Browser's encoder only supports utf-8, but got ${t}`); - return this.textEncoder == null && (this.textEncoder = new TextEncoder()), this.textEncoder.encode(e); + encode(text, encoding) { + if (encoding !== "utf-8" && encoding !== "utf8") { + throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`); + } + if (this.textEncoder == null) { + this.textEncoder = new TextEncoder(); + } + return this.textEncoder.encode(text); } - decode(e, t) { - return new TextDecoder(t).decode(e); + decode(bytes, encoding) { + return new TextDecoder(encoding).decode(bytes); } }; -if (X().get("IS_BROWSER")) { - X().setPlatform("browser", new G_()); +if (env().get("IS_BROWSER")) { + env().setPlatform("browser", new PlatformBrowser()); try { - zn.registerManager(ca.URL_SCHEME, new L_()); - } catch (e) { + ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager()); + } catch (err) { } try { - zn.registerManager(la.URL_SCHEME, new E_()); - } catch (e) { + ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager()); + } catch (err) { } } -var H_ = { importFetch: () => ZT() }; -var Mf; -var q_ = class { +var getNodeFetch = { + importFetch: () => require_browser() +}; +var systemFetch; +var PlatformNode = class { constructor() { - this.util = JT(), this.textEncoder = new this.util.TextEncoder(); + this.util = require_util(); + this.textEncoder = new this.util.TextEncoder(); } - fetch(e, t) { - return X().global.fetch != null ? X().global.fetch(e, t) : (Mf == null && (Mf = H_.importFetch()), Mf(e, t)); + fetch(path, requestInits) { + if (env().global.fetch != null) { + return env().global.fetch(path, requestInits); + } + if (systemFetch == null) { + systemFetch = getNodeFetch.importFetch(); + } + return systemFetch(path, requestInits); } now() { - let e = process.hrtime(); - return e[0] * 1e3 + e[1] / 1e6; + const time2 = process.hrtime(); + return time2[0] * 1e3 + time2[1] / 1e6; } - encode(e, t) { - if (t !== "utf-8" && t !== "utf8") - throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`); - return this.textEncoder.encode(e); + encode(text, encoding) { + if (encoding !== "utf-8" && encoding !== "utf8") { + throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`); + } + return this.textEncoder.encode(text); } - decode(e, t) { - return e.length === 0 ? "" : new this.util.TextDecoder(t).decode(e); + decode(bytes, encoding) { + if (bytes.length === 0) { + return ""; + } + return new this.util.TextDecoder(encoding).decode(bytes); } }; -X().get("IS_NODE") && !X().get("IS_BROWSER") && X().setPlatform("node", new q_()); -function De(e, t = "float32", n) { - return t = t || "float32", ag(e), new Vt(e, t, n); +if (env().get("IS_NODE") && !env().get("IS_BROWSER")) { + env().setPlatform("node", new PlatformNode()); } -function j_(e, t) { - let n = _(e, "x", "cast"); - if (!Zw(t)) - throw new Error(`Failed to cast to unknown dtype ${t}`); - if (t === "string" && n.dtype !== "string" || t !== "string" && n.dtype === "string") - throw new Error("Only strings can be casted to strings"); - let s = { x: n }, r = { dtype: t }; - return M.runKernel(Ta, s, r); +function buffer(shape, dtype = "float32", values) { + dtype = dtype || "float32"; + assertNonNegativeIntegerDimensions(shape); + return new TensorBuffer(shape, dtype, values); } -var ce = L({ cast_: j_ }); -function K_(e) { - let n = { x: _(e, "x", "clone", "string_or_numeric") }; - return M.runKernel(Wa, n); -} -var cr = L({ clone_: K_ }); -function X_(e, t = false) { - console.log(e.toString(t)); -} -fk(); -var Y_ = { buffer: De, cast: ce, clone: cr, print: X_ }; -s_(Y_); -var En = {}; -Ae(En, { browserFiles: () => sA, browserHTTPRequest: () => uA, concatenateArrayBuffers: () => zg, copyModel: () => W_, decodeWeights: () => bk, encodeWeights: () => b_, fromMemory: () => cA, getLoadHandlers: () => $_, getModelArtifactsForJSON: () => Mg, getModelArtifactsInfoForJSON: () => Fl, getSaveHandlers: () => T_, http: () => Bg, isHTTPScheme: () => lm, listModels: () => B_, loadWeights: () => rA, moveModel: () => U_, registerLoadRouter: () => N_, registerSaveRouter: () => C_, removeModel: () => V_, weightsLoaderFactory: () => Tk, withSaveHandler: () => dA }); -var Q_ = "model"; -var Z_ = ".json"; -var J_ = ".weights.bin"; -function px(e) { - return new Promise((t) => setTimeout(t)).then(e); -} -var um = class { - constructor(e) { - if (!X().getBool("IS_BROWSER")) - throw new Error("browserDownloads() cannot proceed because the current environment is not a browser."); - e.startsWith(um.URL_SCHEME) && (e = e.slice(um.URL_SCHEME.length)), (e == null || e.length === 0) && (e = Q_), this.modelJsonFileName = e + Z_, this.weightDataFileName = e + J_; +function cast_(x, dtype) { + const $x = convertToTensor(x, "x", "cast"); + if (!isValidDtype(dtype)) { + throw new Error(`Failed to cast to unknown dtype ${dtype}`); } - async save(e) { - if (typeof document == "undefined") + if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") { + throw new Error("Only strings can be casted to strings"); + } + const inputs = { x: $x }; + const attrs = { dtype }; + return ENGINE.runKernel(Cast, inputs, attrs); +} +var cast = op({ cast_ }); +function clone_(x) { + const $x = convertToTensor(x, "x", "clone", "string_or_numeric"); + const inputs = { x: $x }; + return ENGINE.runKernel(Identity, inputs); +} +var clone = op({ clone_ }); +function print(x, verbose = false) { + console.log(x.toString(verbose)); +} +getOrMakeEngine(); +var opHandler2 = { + buffer, + cast, + clone, + print +}; +setOpHandler(opHandler2); +var io_exports = {}; +__export2(io_exports, { + browserFiles: () => browserFiles, + browserHTTPRequest: () => browserHTTPRequest, + concatenateArrayBuffers: () => concatenateArrayBuffers, + copyModel: () => copyModel, + decodeWeights: () => decodeWeights, + encodeWeights: () => encodeWeights, + fromMemory: () => fromMemory, + getLoadHandlers: () => getLoadHandlers, + getModelArtifactsForJSON: () => getModelArtifactsForJSON, + getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON, + getSaveHandlers: () => getSaveHandlers, + http: () => http, + isHTTPScheme: () => isHTTPScheme, + listModels: () => listModels, + loadWeights: () => loadWeights, + moveModel: () => moveModel, + registerLoadRouter: () => registerLoadRouter, + registerSaveRouter: () => registerSaveRouter, + removeModel: () => removeModel, + weightsLoaderFactory: () => weightsLoaderFactory, + withSaveHandler: () => withSaveHandler +}); +var DEFAULT_FILE_NAME_PREFIX = "model"; +var DEFAULT_JSON_EXTENSION_NAME = ".json"; +var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin"; +function defer(f) { + return new Promise((resolve) => setTimeout(resolve)).then(f); +} +var _BrowserDownloads = class { + constructor(fileNamePrefix) { + if (!env().getBool("IS_BROWSER")) { + throw new Error("browserDownloads() cannot proceed because the current environment is not a browser."); + } + if (fileNamePrefix.startsWith(_BrowserDownloads.URL_SCHEME)) { + fileNamePrefix = fileNamePrefix.slice(_BrowserDownloads.URL_SCHEME.length); + } + if (fileNamePrefix == null || fileNamePrefix.length === 0) { + fileNamePrefix = DEFAULT_FILE_NAME_PREFIX; + } + this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME; + this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME; + } + async save(modelArtifacts) { + if (typeof document === "undefined") { throw new Error("Browser downloads are not supported in this environment since `document` is not present"); - let t = window.URL.createObjectURL(new Blob([e.weightData], { type: "application/octet-stream" })); - if (e.modelTopology instanceof ArrayBuffer) + } + const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: "application/octet-stream" })); + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet."); - { - let n = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], s = yk(e, n), r = window.URL.createObjectURL(new Blob([JSON.stringify(s)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; - if (a.download = this.modelJsonFileName, a.href = r, await px(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) { - let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; - i.download = this.weightDataFileName, i.href = t, await px(() => i.dispatchEvent(new MouseEvent("click"))); + } else { + const weightsManifest = [{ + paths: ["./" + this.weightDataFileName], + weights: modelArtifacts.weightSpecs + }]; + const modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest); + const modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: "application/json" })); + const jsonAnchor = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor; + jsonAnchor.download = this.modelJsonFileName; + jsonAnchor.href = modelJsonURL; + await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click"))); + if (modelArtifacts.weightData != null) { + const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor; + weightDataAnchor.download = this.weightDataFileName; + weightDataAnchor.href = weightsURL; + await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click"))); } - return { modelArtifactsInfo: Fl(e) }; + return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) }; } } }; -var yd = um; -yd.URL_SCHEME = "downloads://"; -var eA = class { - constructor(e) { - if (e == null || e.length < 1) - throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`); - this.jsonFile = e[0], this.weightsFiles = e.slice(1); +var BrowserDownloads = _BrowserDownloads; +BrowserDownloads.URL_SCHEME = "downloads://"; +var BrowserFiles = class { + constructor(files) { + if (files == null || files.length < 1) { + throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`); + } + this.jsonFile = files[0]; + this.weightsFiles = files.slice(1); } async load() { - return new Promise((e, t) => { - let n = new FileReader(); - n.onload = (s) => { - let r = JSON.parse(s.target.result), a = r.modelTopology; - if (a == null) { - t(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`)); + return new Promise((resolve, reject) => { + const jsonReader = new FileReader(); + jsonReader.onload = (event) => { + const modelJSON = JSON.parse(event.target.result); + const modelTopology = modelJSON.modelTopology; + if (modelTopology == null) { + reject(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`)); return; } - if (r.weightsManifest == null) { - t(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`)); + const weightsManifest = modelJSON.weightsManifest; + if (weightsManifest == null) { + reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`)); return; } if (this.weightsFiles.length === 0) { - e({ modelTopology: a }); + resolve({ modelTopology }); return; } - let o = Mg(r, (u) => this.loadWeights(u)); - e(o); - }, n.onerror = (s) => t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), n.readAsText(this.jsonFile); + const modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2)); + resolve(modelArtifactsPromise); + }; + jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`); + jsonReader.readAsText(this.jsonFile); }); } - loadWeights(e) { - let t = [], n = []; - for (let a of e) - t.push(...a.weights), n.push(...a.paths); - let s = this.checkManifestAndWeightFiles(e), r = n.map((a) => this.loadWeightsFile(a, s[a])); - return Promise.all(r).then((a) => [t, zg(a)]); + loadWeights(weightsManifest) { + const weightSpecs = []; + const paths = []; + for (const entry of weightsManifest) { + weightSpecs.push(...entry.weights); + paths.push(...entry.paths); + } + const pathToFile = this.checkManifestAndWeightFiles(weightsManifest); + const promises = paths.map((path) => this.loadWeightsFile(path, pathToFile[path])); + return Promise.all(promises).then((buffers) => [weightSpecs, concatenateArrayBuffers(buffers)]); } - loadWeightsFile(e, t) { - return new Promise((n, s) => { - let r = new FileReader(); - r.onload = (a) => { - let i = a.target.result; - n(i); - }, r.onerror = (a) => s(`Failed to weights data from file of path '${e}'.`), r.readAsArrayBuffer(t); + loadWeightsFile(path, file) { + return new Promise((resolve, reject) => { + const weightFileReader = new FileReader(); + weightFileReader.onload = (event) => { + const weightData = event.target.result; + resolve(weightData); + }; + weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`); + weightFileReader.readAsArrayBuffer(file); }); } - checkManifestAndWeightFiles(e) { - let t = [], n = this.weightsFiles.map((r) => dx(r.name)), s = {}; - for (let r of e) - r.paths.forEach((a) => { - let i = dx(a); - if (t.indexOf(i) !== -1) - throw new Error(`Duplicate file basename found in weights manifest: '${i}'`); - if (t.push(i), n.indexOf(i) === -1) - throw new Error(`Weight file with basename '${i}' is not provided.`); - s[a] = this.weightsFiles[n.indexOf(i)]; + checkManifestAndWeightFiles(manifest) { + const basenames = []; + const fileNames = this.weightsFiles.map((file) => basename(file.name)); + const pathToFile = {}; + for (const group of manifest) { + group.paths.forEach((path) => { + const pathBasename = basename(path); + if (basenames.indexOf(pathBasename) !== -1) { + throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`); + } + basenames.push(pathBasename); + if (fileNames.indexOf(pathBasename) === -1) { + throw new Error(`Weight file with basename '${pathBasename}' is not provided.`); + } else { + pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)]; + } }); - if (t.length !== this.weightsFiles.length) - throw new Error(`Mismatch in the number of files in weights manifest (${t.length}) and the number of weight files provided (${this.weightsFiles.length}).`); - return s; + } + if (basenames.length !== this.weightsFiles.length) { + throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${this.weightsFiles.length}).`); + } + return pathToFile; } }; -var tA = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(yd.URL_SCHEME) ? nA(e.slice(yd.URL_SCHEME.length)) : null; -xt.registerSaveRouter(tA); -function nA(e = "model") { - return new yd(e); -} -function sA(e) { - return new eA(e); -} -function hx(e, t, n, s) { - i(e), n = n == null ? 0 : n, s = s == null ? 1 : s, o(n, s); - let r = 0, a = (u) => (u.then((l) => { - let c = n + ++r / e.length * (s - n); - return t(c), l; - }), u); - function i(u) { - O(u != null && Array.isArray(u) && u.length > 0, () => "promises must be a none empty array"); - } - function o(u, l) { - O(u >= 0 && u <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${u}`), O(l >= 0 && l <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${l}`), O(l >= u, () => `startFraction must be no more than endFraction, but got startFraction ${u} and endFraction ${l}`); - } - return Promise.all(e.map(a)); -} -async function Nk(e, t) { - t == null && (t = {}); - let n = t.fetchFunc == null ? X().platform.fetch : t.fetchFunc, s = e.map((p) => n(p, t.requestInit, { isBinary: true })), r = 0, a = 0.5, o = (t.onProgress == null ? await Promise.all(s) : await hx(s, t.onProgress, r, a)).map((p) => p.arrayBuffer()), u = 0.5, l = 1; - return t.onProgress == null ? await Promise.all(o) : await hx(o, t.onProgress, u, l); -} -async function rA(e, t = "", n, s) { - return Tk((i) => Nk(i, { requestInit: s }))(e, t, n); -} -function Tk(e) { - return async (t, n = "", s) => { - let r = t.map(() => false), a = {}, i = s != null ? s.map(() => false) : [], o = []; - if (t.forEach((h, f) => { - let m = 0; - h.weights.forEach((g) => { - let b = "quantization" in g ? g.quantization.dtype : g.dtype, y = rm[b] * pt(g.shape), v = () => { - r[f] = true, a[f] == null && (a[f] = []), a[f].push({ manifestEntry: g, groupOffset: m, sizeBytes: y }); - }; - s != null ? s.forEach((x, k) => { - x === g.name && (v(), i[k] = true); - }) : v(), o.push(g.name), m += y; - }); - }), !i.every((h) => h)) { - let h = s.filter((f, m) => !i[m]); - throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}. -Manifest JSON has weights with names: ${o.join(", ")}.`); +var browserDownloadsRouter = (url) => { + if (!env().getBool("IS_BROWSER")) { + return null; + } else { + if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) { + return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)); + } else { + return null; } - let u = r.reduce((h, f, m) => (f && h.push(m), h), []), l = []; - u.forEach((h) => { - t[h].paths.forEach((f) => { - let m = n + (n.endsWith("/") ? "" : "/") + f; - l.push(m); + } +}; +IORouterRegistry.registerSaveRouter(browserDownloadsRouter); +function browserDownloads(fileNamePrefix = "model") { + return new BrowserDownloads(fileNamePrefix); +} +function browserFiles(files) { + return new BrowserFiles(files); +} +function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) { + checkPromises(promises); + startFraction = startFraction == null ? 0 : startFraction; + endFraction = endFraction == null ? 1 : endFraction; + checkFraction(startFraction, endFraction); + let resolvedPromise = 0; + const registerMonitor = (promise) => { + promise.then((value) => { + const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction); + onProgress(fraction); + return value; + }); + return promise; + }; + function checkPromises(promises2) { + assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array"); + } + function checkFraction(startFraction2, endFraction2) { + assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`); + assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`); + assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`); + } + return Promise.all(promises.map(registerMonitor)); +} +async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) { + if (loadOptions == null) { + loadOptions = {}; + } + const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc; + const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true })); + const fetchStartFraction = 0; + const fetchEndFraction = 0.5; + const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction); + const bufferPromises = responses.map((response) => response.arrayBuffer()); + const bufferStartFraction = 0.5; + const bufferEndFraction = 1; + const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction); + return buffers; +} +async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) { + const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit }); + const loadWeights2 = weightsLoaderFactory(fetchWeights); + return loadWeights2(manifest, filePathPrefix, weightNames); +} +function weightsLoaderFactory(fetchWeightsFunction) { + return async (manifest, filePathPrefix = "", weightNames) => { + const groupIndicesToFetchMap = manifest.map(() => false); + const groupWeightsToFetch = {}; + const weightsFound = weightNames != null ? weightNames.map(() => false) : []; + const allManifestWeightNames = []; + manifest.forEach((manifestGroupConfig, groupIndex) => { + let groupOffset = 0; + manifestGroupConfig.weights.forEach((weightsEntry) => { + const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype; + const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape); + const enqueueWeightsForFetchingFn = () => { + groupIndicesToFetchMap[groupIndex] = true; + if (groupWeightsToFetch[groupIndex] == null) { + groupWeightsToFetch[groupIndex] = []; + } + groupWeightsToFetch[groupIndex].push({ + manifestEntry: weightsEntry, + groupOffset, + sizeBytes: weightsBytes + }); + }; + if (weightNames != null) { + weightNames.forEach((weightName, weightIndex) => { + if (weightName === weightsEntry.name) { + enqueueWeightsForFetchingFn(); + weightsFound[weightIndex] = true; + } + }); + } else { + enqueueWeightsForFetchingFn(); + } + allManifestWeightNames.push(weightsEntry.name); + groupOffset += weightsBytes; }); }); - let c = await e(l), p = {}, d = 0; - return u.forEach((h) => { - let f = t[h].paths.length, m = 0; - for (let x = 0; x < f; x++) - m += c[d + x].byteLength; - let g = new ArrayBuffer(m), b = new Uint8Array(g), y = 0; - for (let x = 0; x < f; x++) { - let k = new Uint8Array(c[d + x]); - b.set(k, y), y += k.byteLength; + if (!weightsFound.every((found) => found)) { + const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]); + throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. +Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`); + } + const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => { + if (shouldFetch) { + accumulator.push(i); } - a[h].forEach((x) => { - let k = g.slice(x.groupOffset, x.groupOffset + x.sizeBytes), T = bk(k, [x.manifestEntry]); - for (let N in T) - p[N] = T[N]; - }), d += f; - }), p; + return accumulator; + }, []); + const fetchUrls = []; + groupIndicesToFetch.forEach((i) => { + manifest[i].paths.forEach((filepath) => { + const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath; + fetchUrls.push(fetchUrl); + }); + }); + const buffers = await fetchWeightsFunction(fetchUrls); + const weightsTensorMap = {}; + let bufferIndexOffset = 0; + groupIndicesToFetch.forEach((i) => { + const numBuffers = manifest[i].paths.length; + let groupBytes = 0; + for (let i2 = 0; i2 < numBuffers; i2++) { + groupBytes += buffers[bufferIndexOffset + i2].byteLength; + } + const groupBuffer = new ArrayBuffer(groupBytes); + const groupByteBuffer = new Uint8Array(groupBuffer); + let groupBufferOffset = 0; + for (let i2 = 0; i2 < numBuffers; i2++) { + const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i2]); + groupByteBuffer.set(buffer2, groupBufferOffset); + groupBufferOffset += buffer2.byteLength; + } + const weightsEntries = groupWeightsToFetch[i]; + weightsEntries.forEach((weightsEntry) => { + const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes); + const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]); + for (const name in nameToTensorMap) { + weightsTensorMap[name] = nameToTensorMap[name]; + } + }); + bufferIndexOffset += numBuffers; + }); + return weightsTensorMap; }; } -var aA = "application/octet-stream"; -var iA = "application/json"; -var Lg = class { - constructor(e, t) { - if (this.DEFAULT_METHOD = "POST", t == null && (t = {}), this.weightPathPrefix = t.weightPathPrefix, this.onProgress = t.onProgress, this.weightUrlConverter = t.weightUrlConverter, t.fetchFunc != null ? (O(typeof t.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t.fetchFunc) : this.fetch = X().platform.fetch, O(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && O(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t.requestInit != null && t.requestInit.body != null) +var OCTET_STREAM_MIME_TYPE = "application/octet-stream"; +var JSON_TYPE = "application/json"; +var HTTPRequest = class { + constructor(path, loadOptions) { + this.DEFAULT_METHOD = "POST"; + if (loadOptions == null) { + loadOptions = {}; + } + this.weightPathPrefix = loadOptions.weightPathPrefix; + this.onProgress = loadOptions.onProgress; + this.weightUrlConverter = loadOptions.weightUrlConverter; + if (loadOptions.fetchFunc != null) { + assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"); + this.fetch = loadOptions.fetchFunc; + } else { + this.fetch = env().platform.fetch; + } + assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty."); + if (Array.isArray(path)) { + assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`); + } + this.path = path; + if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) { throw new Error("requestInit is expected to have no pre-existing body, but has one."); - this.requestInit = t.requestInit || {}; + } + this.requestInit = loadOptions.requestInit || {}; } - async save(e) { - if (e.modelTopology instanceof ArrayBuffer) + async save(modelArtifacts) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet."); - let t = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit); - t.body = new FormData(); - let n = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], s = yk(e, n); - t.body.append("model.json", new Blob([JSON.stringify(s)], { type: iA }), "model.json"), e.weightData != null && t.body.append("model.weights.bin", new Blob([e.weightData], { type: aA }), "model.weights.bin"); - let r = await this.fetch(this.path, t); - if (r.ok) - return { modelArtifactsInfo: Fl(e), responses: [r] }; - throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${r.status}.`); + } + const init22 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit); + init22.body = new FormData(); + const weightsManifest = [{ + paths: ["./model.weights.bin"], + weights: modelArtifacts.weightSpecs + }]; + const modelTopologyAndWeightManifest = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest); + init22.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), "model.json"); + if (modelArtifacts.weightData != null) { + init22.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), "model.weights.bin"); + } + const response = await this.fetch(this.path, init22); + if (response.ok) { + return { + modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts), + responses: [response] + }; + } else { + throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`); + } } async load() { - let e = await this.fetch(this.path, this.requestInit); - if (!e.ok) - throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`); - let t; - try { - t = await e.json(); - } catch (r) { - let a = `Failed to parse model JSON of response from ${this.path}.`; - throw this.path.endsWith(".pb") ? a += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository." : a += " Please make sure the server is serving valid JSON for this request.", new Error(a); + const modelConfigRequest = await this.fetch(this.path, this.requestInit); + if (!modelConfigRequest.ok) { + throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`); } - let n = t.modelTopology, s = t.weightsManifest; - if (n == null && s == null) + let modelJSON; + try { + modelJSON = await modelConfigRequest.json(); + } catch (e) { + let message = `Failed to parse model JSON of response from ${this.path}.`; + if (this.path.endsWith(".pb")) { + message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository."; + } else { + message += " Please make sure the server is serving valid JSON for this request."; + } + throw new Error(message); + } + const modelTopology = modelJSON.modelTopology; + const weightsManifest = modelJSON.weightsManifest; + if (modelTopology == null && weightsManifest == null) { throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`); - return Mg(t, (r) => this.loadWeights(r)); + } + return getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2)); } - async loadWeights(e) { - let t = Array.isArray(this.path) ? this.path[1] : this.path, [n, s] = oA(t), r = this.weightPathPrefix || n, a = []; - for (let l of e) - a.push(...l.weights); - let i = [], o = []; - for (let l of e) - for (let c of l.paths) - this.weightUrlConverter != null ? o.push(this.weightUrlConverter(c)) : i.push(r + c + s); - this.weightUrlConverter && i.push(...await Promise.all(o)); - let u = await Nk(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress }); - return [a, zg(u)]; + async loadWeights(weightsManifest) { + const weightPath = Array.isArray(this.path) ? this.path[1] : this.path; + const [prefix, suffix] = parseUrl(weightPath); + const pathPrefix = this.weightPathPrefix || prefix; + const weightSpecs = []; + for (const entry of weightsManifest) { + weightSpecs.push(...entry.weights); + } + const fetchURLs = []; + const urlPromises = []; + for (const weightsGroup of weightsManifest) { + for (const path of weightsGroup.paths) { + if (this.weightUrlConverter != null) { + urlPromises.push(this.weightUrlConverter(path)); + } else { + fetchURLs.push(pathPrefix + path + suffix); + } + } + } + if (this.weightUrlConverter) { + fetchURLs.push(...await Promise.all(urlPromises)); + } + const buffers = await loadWeightsAsArrayBuffer(fetchURLs, { + requestInit: this.requestInit, + fetchFunc: this.fetch, + onProgress: this.onProgress + }); + return [weightSpecs, concatenateArrayBuffers(buffers)]; } }; -Lg.URL_SCHEME_REGEX = /^https?:\/\//; -function oA(e) { - let t = e.lastIndexOf("/"), n = e.lastIndexOf("?"), s = e.substring(0, t), r = n > t ? e.substring(n) : ""; - return [s + "/", r]; +HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//; +function parseUrl(url) { + const lastSlash = url.lastIndexOf("/"); + const lastSearchParam = url.lastIndexOf("?"); + const prefix = url.substring(0, lastSlash); + const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : ""; + return [prefix + "/", suffix]; } -function lm(e) { - return e.match(Lg.URL_SCHEME_REGEX) != null; +function isHTTPScheme(url) { + return url.match(HTTPRequest.URL_SCHEME_REGEX) != null; } -var $k = (e, t) => { - if (typeof fetch == "undefined" && (t == null || t.fetchFunc == null)) +var httpRouter = (url, loadOptions) => { + if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) { return null; - { - let n = true; - if (Array.isArray(e) ? n = e.every((s) => lm(s)) : n = lm(e), n) - return Bg(e, t); + } else { + let isHTTP = true; + if (Array.isArray(url)) { + isHTTP = url.every((urlItem) => isHTTPScheme(urlItem)); + } else { + isHTTP = isHTTPScheme(url); + } + if (isHTTP) { + return http(url, loadOptions); + } } return null; }; -xt.registerSaveRouter($k); -xt.registerLoadRouter($k); -function Bg(e, t) { - return new Lg(e, t); +IORouterRegistry.registerSaveRouter(httpRouter); +IORouterRegistry.registerLoadRouter(httpRouter); +function http(path, loadOptions) { + return new HTTPRequest(path, loadOptions); } -function uA(e, t) { - return Bg(e, t); +function browserHTTPRequest(path, loadOptions) { + return http(path, loadOptions); } -var Lf = class { - constructor(e) { - this.modelArtifacts = e; +var PassthroughLoader = class { + constructor(modelArtifacts) { + this.modelArtifacts = modelArtifacts; } async load() { return this.modelArtifacts; } }; -var lA = class { - constructor(e) { - this.saveHandler = e; +var PassthroughSaver = class { + constructor(saveHandler) { + this.saveHandler = saveHandler; } - async save(e) { - return this.saveHandler(e); + async save(modelArtifacts) { + return this.saveHandler(modelArtifacts); } }; -function cA(e, t, n, s) { - return arguments.length === 1 ? e.modelTopology != null || e.weightSpecs != null ? new Lf(e) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Lf({ modelTopology: e })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Lf({ modelTopology: e, weightSpecs: t, weightData: n, trainingConfig: s })); -} -function dA(e) { - return new lA(e); -} -var pA = {}; -Ae(pA, { confusionMatrix: () => bA }); -function hA(e, t, n = false, s = false) { - let r = _(e, "a", "matMul"), a = _(t, "b", "matMul"); - [r, a] = vt(r, a); - let i = { a: r, b: a }, o = { transposeA: n, transposeB: s }; - return M.runKernel(Na, i, o); -} -var We = L({ matMul_: hA }); -function fA(e, t, n = 1, s = 0) { - if (t < 2) - throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`); - let a = { indices: _(e, "indices", "oneHot", "int32") }, i = { depth: t, onValue: n, offValue: s }; - return M.runKernel(Ao, a, i); -} -var vd = L({ oneHot_: fA }); -function mA(e, t) { - let n = _(e, "x", "transpose"); - if (t == null && (t = n.shape.map((a, i) => i).reverse()), O(n.rank === t.length, () => `Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`), t.forEach((a) => { - O(a >= 0 && a < n.rank, () => `All entries in 'perm' must be between 0 and ${n.rank - 1} but got ${t}`); - }), n.rank <= 1) - return n.clone(); - let s = { x: n }, r = { perm: t }; - return M.runKernel(mi, s, r); -} -var Ge = L({ transpose_: mA }); -function gA(e, t, n) { - let s = _(e, "labels", "confusionMatrix"), r = _(t, "predictions", "confusionMatrix"); - O(n == null || n > 0 && Number.isInteger(n), () => `If provided, numClasses must be a positive integer, but got ${n}`), O(s.rank === 1, () => `Expected the rank of labels to be 1, but got ${s.rank}`), O(r.rank === 1, () => `Expected the rank of predictions to be 1, but got ${r.rank}`), O(s.shape[0] === r.shape[0], () => `Mismatch in the number of examples: ${s.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`), O(n > 0 && Number.isInteger(n), () => `numClasses is required to be a positive integer, but got ${n}`); - let a = vd(ce(s, "int32"), n), i = vd(ce(r, "int32"), n), o = Ge(a), u = We(o, i); - return ce(u, "int32"); -} -var bA = L({ confusionMatrix_: gA }); -var Ko = {}; -Ae(Ko, { assertAndGetBroadcastShape: () => ot, getBroadcastDims: () => _k, getReductionAxes: () => _t }); -function _k(e, t) { - let n = e.length, s = []; - for (let r = 0; r < n; r++) { - let a = n - 1 - r, i = e[a] || 1; - (t[t.length - 1 - r] || 1) > 1 && i === 1 && s.unshift(a); +function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) { + if (arguments.length === 1) { + const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null; + if (isModelArtifacts) { + return new PassthroughLoader(modelArtifacts); + } else { + console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."); + return new PassthroughLoader({ modelTopology: modelArtifacts }); + } + } else { + console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."); + return new PassthroughLoader({ + modelTopology: modelArtifacts, + weightSpecs, + weightData, + trainingConfig + }); } - return s; } -function _t(e, t) { - let n = []; - for (let s = 0; s < t.length; s++) { - let r = e[e.length - s - 1], a = t.length - s - 1, i = t[a]; - (r == null || r === 1 && i > 1) && n.unshift(a); +function withSaveHandler(saveHandler) { + return new PassthroughSaver(saveHandler); +} +var math_exports = {}; +__export2(math_exports, { + confusionMatrix: () => confusionMatrix +}); +function matMul_(a, b, transposeA = false, transposeB = false) { + let $a = convertToTensor(a, "a", "matMul"); + let $b = convertToTensor(b, "b", "matMul"); + [$a, $b] = makeTypesMatch($a, $b); + const inputs = { a: $a, b: $b }; + const attrs = { transposeA, transposeB }; + return ENGINE.runKernel(BatchMatMul, inputs, attrs); +} +var matMul = op({ matMul_ }); +function oneHot_(indices, depth, onValue = 1, offValue = 0) { + if (depth < 2) { + throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`); } - return n; + const $indices = convertToTensor(indices, "indices", "oneHot", "int32"); + const inputs = { indices: $indices }; + const attrs = { depth, onValue, offValue }; + return ENGINE.runKernel(OneHot, inputs, attrs); } -function ot(e, t) { - let n = [], s = Math.max(e.length, t.length); - for (let r = 0; r < s; r++) { - let a = e[e.length - r - 1]; - a == null && (a = 1); - let i = t[t.length - r - 1]; - if (i == null && (i = 1), a === 1) - n.unshift(i); - else if (i === 1) - n.unshift(a); - else if (a !== i) { - let o = `Operands could not be broadcast together with shapes ${e} and ${t}.`; - throw Error(o); - } else - n.unshift(a); +var oneHot = op({ oneHot_ }); +function transpose_(x, perm) { + const $x = convertToTensor(x, "x", "transpose"); + if (perm == null) { + perm = $x.shape.map((s, i) => i).reverse(); } - return n; + assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`); + perm.forEach((axis) => { + assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`); + }); + if ($x.rank <= 1) { + return $x.clone(); + } + const inputs = { x: $x }; + const attrs = { perm }; + return ENGINE.runKernel(Transpose, inputs, attrs); } -var Ak = {}; -Ae(Ak, { fromPixels: () => CA, fromPixelsAsync: () => IA, toPixels: () => SA }); -function yA(e, t, n) { - if (ka(e), t != null && t.length !== 3) +var transpose = op({ transpose_ }); +function confusionMatrix_(labels2, predictions, numClasses) { + const $labels = convertToTensor(labels2, "labels", "confusionMatrix"); + const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix"); + assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`); + assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`); + assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`); + assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`); + assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`); + const oneHotLabels = oneHot(cast($labels, "int32"), numClasses); + const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses); + const oneHotLabelsT = transpose(oneHotLabels); + const product = matMul(oneHotLabelsT, oneHotPredictions); + return cast(product, "int32"); +} +var confusionMatrix = op({ confusionMatrix_ }); +var broadcast_util_exports = {}; +__export2(broadcast_util_exports, { + assertAndGetBroadcastShape: () => assertAndGetBroadcastShape, + getBroadcastDims: () => getBroadcastDims, + getReductionAxes: () => getReductionAxes +}); +function getBroadcastDims(inShape, outShape) { + const inRank = inShape.length; + const dims = []; + for (let i = 0; i < inRank; i++) { + const dim = inRank - 1 - i; + const a = inShape[dim] || 1; + const b = outShape[outShape.length - 1 - i] || 1; + if (b > 1 && a === 1) { + dims.unshift(dim); + } + } + return dims; +} +function getReductionAxes(inShape, outShape) { + const result = []; + for (let i = 0; i < outShape.length; i++) { + const inDim = inShape[inShape.length - i - 1]; + const outAxis = outShape.length - i - 1; + const outDim = outShape[outAxis]; + if (inDim == null || inDim === 1 && outDim > 1) { + result.unshift(outAxis); + } + } + return result; +} +function assertAndGetBroadcastShape(shapeA, shapeB) { + const result = []; + const l = Math.max(shapeA.length, shapeB.length); + for (let i = 0; i < l; i++) { + let a = shapeA[shapeA.length - i - 1]; + if (a == null) { + a = 1; + } + let b = shapeB[shapeB.length - i - 1]; + if (b == null) { + b = 1; + } + if (a === 1) { + result.unshift(b); + } else if (b === 1) { + result.unshift(a); + } else if (a !== b) { + const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`; + throw Error(errMsg); + } else { + result.unshift(a); + } + } + return result; +} +var browser_exports = {}; +__export2(browser_exports, { + fromPixels: () => fromPixels, + fromPixelsAsync: () => fromPixelsAsync, + toPixels: () => toPixels +}); +function tensor3d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 3) { throw new Error("tensor3d() requires shape to have three numbers"); - let s = Rs(e, n); - if (s.length !== 3 && s.length !== 1) + } + const inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 3 && inferredShape.length !== 1) { throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray"); - if (s.length === 1 && t == null) + } + if (inferredShape.length === 1 && shape == null) { throw new Error("tensor3d() requires shape to be provided when `values` are a flat array"); - return Tr(e, t, s, n); + } + return makeTensor(values, shape, inferredShape, dtype); } -var Wr; -function Ek(e, t = 3) { - if (t > 4) +var fromPixels2DContext; +function fromPixels_(pixels, numChannels = 3) { + if (numChannels > 4) { throw new Error("Cannot construct Tensor with more than 4 channels from pixels."); - if (e == null) + } + if (pixels == null) { throw new Error("pixels passed to tf.browser.fromPixels() can not be null"); - let n = false, s = false, r = false, a = false, i = false, o = false; - if (e.data instanceof Uint8Array) - n = true; - else if (typeof ImageData != "undefined" && e instanceof ImageData) - s = true; - else if (typeof HTMLVideoElement != "undefined" && e instanceof HTMLVideoElement) - r = true; - else if (typeof HTMLImageElement != "undefined" && e instanceof HTMLImageElement) - a = true; - else if (e.getContext != null) - i = true; - else if (typeof ImageBitmap != "undefined" && e instanceof ImageBitmap) - o = true; - else - throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`); - if (r && r && e.readyState < 2) - throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the