diff --git a/TODO.md b/TODO.md
index aa1b58e5..da7726bc 100644
--- a/TODO.md
+++ b/TODO.md
@@ -20,25 +20,33 @@ N/A
-## Known Issues
+## Known Issues & Limitations
### Face with Attention
`FaceMesh-Attention` is not supported when using `WASM` backend due to missing kernel op in **TFJS**
+No issues with default model `FaceMesh`
### Object Detection
`NanoDet` model is not supported when using `WASM` backend due to missing kernel op in **TFJS**
+No issues with default model `MB3-CenterNet`
### WebGPU
Experimental support only until support is officially added in Chromium
-Enable via
+Enable via
### Firefox
Running in **web workers** requires `OffscreenCanvas` which is still disabled by default in **Firefox**
-Enable via `about:config` -> `gfx.offscreencanvas.enabled`
+Enable via `about:config` -> `gfx.offscreencanvas.enabled`
+[Details](https://developer.mozilla.org/en-US/docs/Web/API/OffscreenCanvas#browser_compatibility)
+
+### Safari
+
+No support for running in **web workers** as Safari still does not support `OffscreenCanvas`
+[Details](https://developer.mozilla.org/en-US/docs/Web/API/OffscreenCanvas#browser_compatibility)
@@ -49,7 +57,13 @@ Enable via `about:config` -> `gfx.offscreencanvas.enabled`
- New method [`human.video()`](https://vladmandic.github.io/human/typedoc/classes/Human.html#video)
Runs continous detection of an input **video**
instead of processing each frame manually using `human.detect()`
-- New simple demo [*Live*](https://vladmandic.github.io/human/demo/video/index.html) | [*Code*](https://github.com/vladmandic/human/blob/main/demo/video/index.html)
+- New demo for **webcam** and **video** methods [*Live*](https://vladmandic.github.io/human/demo/video/index.html) | [*Code*](https://github.com/vladmandic/human/blob/main/demo/video/index.html)
+ *Full HTML and JavaScript code in less than a screen*
+- Redesigned [`human.segmentation`](https://vladmandic.github.io/human/typedoc/classes/Human.html#segmentation)
+ *Breaking changes*
+- New model `rvm` for high-quality body segmentation in real-time
+ *Not part of default deployment, download from [human-models](https://github.com/vladmandic/human-models/tree/main/models)*
+- New demo for **segmentation** methods [*Live*](https://vladmandic.github.io/human/demo/segmentation/index.html) | [*Code*](https://github.com/vladmandic/human/blob/main/demo/segmentation/index.html)
*Full HTML and JavaScript code in less than a screen*
- New advanced demo using **BabylonJS and VRM** [*Live*](https://vladmandic.github.io/human-bjs-vrm) | [*Code*](https://github.com/vladmandic/human-bjs-vrm)
- Update **TypeDoc** generation [*Link*](https://vladmandic.github.io/human/typedoc)
diff --git a/demo/faceid/index.js b/demo/faceid/index.js
index 2cd259dd..63fee5da 100644
--- a/demo/faceid/index.js
+++ b/demo/faceid/index.js
@@ -4,6 +4,353 @@
author: '
*/
-import*as S from"../../dist/human.esm.js";var l,L="human",f="person",v=(...a)=>console.log("indexdb",...a);async function h(){return l?!0:new Promise(a=>{let n=indexedDB.open(L,1);n.onerror=o=>v("error:",o),n.onupgradeneeded=o=>{v("create:",o.target),l=o.target.result,l.createObjectStore(f,{keyPath:"id",autoIncrement:!0})},n.onsuccess=o=>{l=o.target.result,v("open:",l),a(!0)}})}async function C(){let a=[];return l||await h(),new Promise(n=>{let o=l.transaction([f],"readwrite").objectStore(f).openCursor(null,"next");o.onerror=i=>v("load error:",i),o.onsuccess=i=>{i.target.result?(a.push(i.target.result.value),i.target.result.continue()):n(a)}})}async function b(){return l||await h(),new Promise(a=>{let n=l.transaction([f],"readwrite").objectStore(f).count();n.onerror=o=>v("count error:",o),n.onsuccess=()=>a(n.result)})}async function x(a){l||await h();let n={name:a.name,descriptor:a.descriptor,image:a.image};l.transaction([f],"readwrite").objectStore(f).put(n),v("save:",n)}async function D(a){l||await h(),l.transaction([f],"readwrite").objectStore(f).delete(a.id),v("delete:",a)}var g={cacheSensitivity:0,modelBasePath:"../../models",filter:{equalization:!0},face:{enabled:!0,detector:{rotation:!0,return:!0,cropFactor:1.6,mask:!1},description:{enabled:!0},iris:{enabled:!0},emotion:{enabled:!1},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},gesture:{enabled:!0}},B={order:2,multiplier:25,min:.2,max:.8},d={minConfidence:.6,minSize:224,maxTime:3e4,blinkMin:10,blinkMax:800,threshold:.5,mask:g.face.detector.mask,rotation:g.face.detector.rotation,cropFactor:g.face.detector.cropFactor,...B},e={faceCount:{status:!1,val:0},faceConfidence:{status:!1,val:0},facingCenter:{status:!1,val:0},lookingCenter:{status:!1,val:0},blinkDetected:{status:!1,val:0},faceSize:{status:!1,val:0},antispoofCheck:{status:!1,val:0},livenessCheck:{status:!1,val:0},age:{status:!1,val:0},gender:{status:!1,val:0},timeout:{status:!0,val:0},descriptor:{status:!1,val:0},elapsedMs:{status:void 0,val:0},detectFPS:{status:void 0,val:0},drawFPS:{status:void 0,val:0}},E=()=>e.faceCount.status&&e.faceSize.status&&e.blinkDetected.status&&e.facingCenter.status&&e.lookingCenter.status&&e.faceConfidence.status&&e.antispoofCheck.status&&e.livenessCheck.status&&e.descriptor.status&&e.age.status&&e.gender.status,c={face:null,record:null},u={start:0,end:0,time:0},s=new S.Human(g);s.env.perfadd=!1;s.draw.options.font='small-caps 18px "Lato"';s.draw.options.lineHeight=20;var t={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")},y={detect:0,draw:0},I=0,r=(...a)=>{t.log.innerText+=a.join(" ")+`
-`,console.log(...a)};async function H(){let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth}}},n=await navigator.mediaDevices.getUserMedia(a),o=new Promise(i=>{t.video.onloadeddata=()=>i(!0)});t.video.srcObject=n,t.video.play(),await o,t.canvas.width=t.video.videoWidth,t.canvas.height=t.video.videoHeight,t.canvas.style.width="50%",t.canvas.style.height="50%",s.env.initial&&r("video:",t.video.videoWidth,t.video.videoHeight,"|",n.getVideoTracks()[0].label),t.canvas.onclick=()=>{t.video.paused?t.video.play():t.video.pause()}}async function T(){var a;if(!t.video.paused){(a=c.face)!=null&&a.tensor&&s.tf.dispose(c.face.tensor),await s.detect(t.video);let n=s.now();e.detectFPS.val=Math.round(1e4/(n-y.detect))/10,y.detect=n,requestAnimationFrame(T)}}function P(){let a=32;for(let[n,o]of Object.entries(e)){let i=document.getElementById(`ok-${n}`);i||(i=document.createElement("div"),i.id=`ok-${n}`,i.innerText=n,i.className="ok",i.style.top=`${a}px`,t.ok.appendChild(i)),typeof o.status=="boolean"&&(i.style.backgroundColor=o.status?"lightgreen":"lightcoral");let m=o.status?"ok":"fail";i.innerText=`${n}: ${o.val===0?m:o.val}`,a+=28}}async function R(){var o;let a=s.next(s.result);s.draw.canvas(t.video,t.canvas),await s.draw.all(t.canvas,a);let n=s.now();if(e.drawFPS.val=Math.round(1e4/(n-y.draw))/10,y.draw=n,e.faceCount.val=s.result.face.length,e.faceCount.status=e.faceCount.val===1,e.faceCount.status){let i=Object.values(s.result.gesture).map(m=>m.gesture);(i.includes("blink left eye")||i.includes("blink right eye"))&&(u.start=s.now()),u.start>0&&!i.includes("blink left eye")&&!i.includes("blink right eye")&&(u.end=s.now()),e.blinkDetected.status=e.blinkDetected.status||Math.abs(u.end-u.start)>d.blinkMin&&Math.abs(u.end-u.start)=d.minConfidence,e.antispoofCheck.val=s.result.face[0].real||0,e.antispoofCheck.status=e.antispoofCheck.val>=d.minConfidence,e.livenessCheck.val=s.result.face[0].live||0,e.livenessCheck.status=e.livenessCheck.val>=d.minConfidence,e.faceSize.val=Math.min(s.result.face[0].box[2],s.result.face[0].box[3]),e.faceSize.status=e.faceSize.val>=d.minSize,e.descriptor.val=((o=s.result.face[0].embedding)==null?void 0:o.length)||0,e.descriptor.status=e.descriptor.val>0,e.age.val=s.result.face[0].age||0,e.age.status=e.age.val>0,e.gender.val=s.result.face[0].genderScore||0,e.gender.status=e.gender.val>=d.minConfidence}return e.timeout.status=e.elapsedMs.val<=d.maxTime,P(),E()||!e.timeout.status?(t.video.pause(),s.result.face[0]):(e.elapsedMs.val=Math.trunc(s.now()-I),new Promise(i=>{setTimeout(async()=>{await R(),i(s.result.face[0])},30)}))}async function z(){var a,n,o,i;if(t.name.value.length>0){let m=(a=t.canvas.getContext("2d"))==null?void 0:a.getImageData(0,0,t.canvas.width,t.canvas.height),p={id:0,name:t.name.value,descriptor:(n=c.face)==null?void 0:n.embedding,image:m};await x(p),r("saved face record:",p.name,"descriptor length:",(i=(o=c.face)==null?void 0:o.embedding)==null?void 0:i.length),r("known face records:",await b())}else r("invalid name")}async function j(){c.record&&c.record.id>0&&await D(c.record)}async function $(){var i,m,p,k;if(t.canvas.style.height="",(i=t.canvas.getContext("2d"))==null||i.clearRect(0,0,d.minSize,d.minSize),!((m=c==null?void 0:c.face)!=null&&m.tensor)||!((p=c==null?void 0:c.face)!=null&&p.embedding))return!1;if(console.log("face record:",c.face),r(`detected face: ${c.face.gender} ${c.face.age||0}y distance ${c.face.iris||0}cm/${Math.round(100*(c.face.iris||0)/2.54)/100}in`),s.tf.browser.toPixels(c.face.tensor,t.canvas),await b()===0)return r("face database is empty: nothing to compare face with"),document.body.style.background="black",t.delete.style.display="none",!1;let a=await C(),n=a.map(w=>w.descriptor).filter(w=>w.length>0),o=s.match(c.face.embedding,n,B);return c.record=a[o.index]||null,c.record&&(r(`best match: ${c.record.name} | id: ${c.record.id} | similarity: ${Math.round(1e3*o.similarity)/10}%`),t.name.value=c.record.name,t.source.style.display="",(k=t.source.getContext("2d"))==null||k.putImageData(c.record.image,0,0)),document.body.style.background=o.similarity>d.threshold?"darkgreen":"maroon",o.similarity>d.threshold}async function M(){var a,n;return e.faceCount.status=!1,e.faceConfidence.status=!1,e.facingCenter.status=!1,e.blinkDetected.status=!1,e.faceSize.status=!1,e.antispoofCheck.status=!1,e.livenessCheck.status=!1,e.age.status=!1,e.gender.status=!1,e.elapsedMs.val=0,t.match.style.display="none",t.retry.style.display="none",t.source.style.display="none",t.canvas.style.height="50%",document.body.style.background="black",await H(),await T(),I=s.now(),c.face=await R(),t.canvas.width=((a=c.face.tensor)==null?void 0:a.shape[1])||d.minSize,t.canvas.height=((n=c.face.tensor)==null?void 0:n.shape[0])||d.minSize,t.source.width=t.canvas.width,t.source.height=t.canvas.height,t.canvas.style.width="",t.match.style.display="flex",t.save.style.display="flex",t.delete.style.display="flex",t.retry.style.display="block",E()?$():(r("did not find valid face"),!1)}async function q(){var a,n;r("human version:",s.version,"| tfjs version:",s.tf.version["tfjs-core"]),r("options:",JSON.stringify(d).replace(/{|}|"|\[|\]/g,"").replace(/,/g," ")),r("initializing webcam..."),await H(),r("loading human models..."),await s.load(),r("initializing human..."),r("face embedding model:",g.face.description.enabled?"faceres":"",(a=g.face.mobilefacenet)!=null&&a.enabled?"mobilefacenet":"",(n=g.face.insightface)!=null&&n.enabled?"insightface":""),r("loading face database..."),r("known face records:",await b()),t.retry.addEventListener("click",M),t.save.addEventListener("click",z),t.delete.addEventListener("click",j),await s.warmup(),await M()}window.onload=q;
+
+// demo/faceid/index.ts
+import * as H 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 = {
+ cacheSensitivity: 0,
+ modelBasePath: "../../models",
+ filter: { equalization: true },
+ face: {
+ enabled: true,
+ detector: { rotation: true, return: true, cropFactor: 1.6, mask: false },
+ description: { enabled: true },
+ 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: 3e4,
+ 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: { status: false, val: 0 },
+ faceConfidence: { status: false, val: 0 },
+ facingCenter: { status: false, val: 0 },
+ lookingCenter: { status: false, val: 0 },
+ blinkDetected: { status: false, val: 0 },
+ faceSize: { status: false, val: 0 },
+ antispoofCheck: { status: false, val: 0 },
+ livenessCheck: { status: false, val: 0 },
+ age: { status: false, val: 0 },
+ gender: { status: false, val: 0 },
+ timeout: { status: true, val: 0 },
+ descriptor: { status: false, val: 0 },
+ elapsedMs: { status: void 0, val: 0 },
+ detectFPS: { status: void 0, val: 0 },
+ drawFPS: { status: void 0, val: 0 }
+};
+var allOk = () => ok.faceCount.status && ok.faceSize.status && ok.blinkDetected.status && ok.facingCenter.status && ok.lookingCenter.status && ok.faceConfidence.status && ok.antispoofCheck.status && ok.livenessCheck.status && ok.descriptor.status && ok.age.status && ok.gender.status;
+var current = { face: null, record: null };
+var blink = {
+ start: 0,
+ end: 0,
+ time: 0
+};
+var human = new H.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 startTime = 0;
+var log2 = (...msg) => {
+ dom.log.innerText += msg.join(" ") + "\n";
+ console.log(...msg);
+};
+async function 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;
+ void dom.video.play();
+ await ready;
+ dom.canvas.width = dom.video.videoWidth;
+ dom.canvas.height = dom.video.videoHeight;
+ dom.canvas.style.width = "50%";
+ dom.canvas.style.height = "50%";
+ if (human.env.initial)
+ log2("video:", dom.video.videoWidth, dom.video.videoHeight, "|", stream.getVideoTracks()[0].label);
+ dom.canvas.onclick = () => {
+ if (dom.video.paused)
+ void dom.video.play();
+ else
+ dom.video.pause();
+ };
+}
+async function detectionLoop() {
+ var _a;
+ if (!dom.video.paused) {
+ if ((_a = current.face) == null ? void 0 : _a.tensor)
+ human.tf.dispose(current.face.tensor);
+ await human.detect(dom.video);
+ const now = human.now();
+ ok.detectFPS.val = Math.round(1e4 / (now - timestamp.detect)) / 10;
+ timestamp.detect = now;
+ requestAnimationFrame(detectionLoop);
+ }
+}
+function drawValidationTests() {
+ let y = 32;
+ for (const [key, val] of Object.entries(ok)) {
+ let el = document.getElementById(`ok-${key}`);
+ if (!el) {
+ el = document.createElement("div");
+ el.id = `ok-${key}`;
+ el.innerText = key;
+ el.className = "ok";
+ el.style.top = `${y}px`;
+ dom.ok.appendChild(el);
+ }
+ if (typeof val.status === "boolean")
+ el.style.backgroundColor = val.status ? "lightgreen" : "lightcoral";
+ const status = val.status ? "ok" : "fail";
+ el.innerText = `${key}: ${val.val === 0 ? status : val.val}`;
+ y += 28;
+ }
+}
+async function validationLoop() {
+ var _a;
+ const interpolated = human.next(human.result);
+ human.draw.canvas(dom.video, dom.canvas);
+ await human.draw.all(dom.canvas, interpolated);
+ const now = human.now();
+ ok.drawFPS.val = Math.round(1e4 / (now - timestamp.draw)) / 10;
+ timestamp.draw = now;
+ ok.faceCount.val = human.result.face.length;
+ ok.faceCount.status = ok.faceCount.val === 1;
+ if (ok.faceCount.status) {
+ 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.status = ok.blinkDetected.status || Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax;
+ if (ok.blinkDetected.status && blink.time === 0)
+ blink.time = Math.trunc(blink.end - blink.start);
+ ok.facingCenter.status = gestures.includes("facing center");
+ ok.lookingCenter.status = gestures.includes("looking center");
+ ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0;
+ ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence;
+ ok.antispoofCheck.val = human.result.face[0].real || 0;
+ ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence;
+ ok.livenessCheck.val = human.result.face[0].live || 0;
+ ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence;
+ ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]);
+ ok.faceSize.status = ok.faceSize.val >= options.minSize;
+ ok.descriptor.val = ((_a = human.result.face[0].embedding) == null ? void 0 : _a.length) || 0;
+ ok.descriptor.status = ok.descriptor.val > 0;
+ ok.age.val = human.result.face[0].age || 0;
+ ok.age.status = ok.age.val > 0;
+ ok.gender.val = human.result.face[0].genderScore || 0;
+ ok.gender.status = ok.gender.val >= options.minConfidence;
+ }
+ ok.timeout.status = ok.elapsedMs.val <= options.maxTime;
+ drawValidationTests();
+ if (allOk() || !ok.timeout.status) {
+ dom.video.pause();
+ return human.result.face[0];
+ }
+ ok.elapsedMs.val = Math.trunc(human.now() - startTime);
+ return new Promise((resolve) => {
+ setTimeout(async () => {
+ await validationLoop();
+ resolve(human.result.face[0]);
+ }, 30);
+ });
+}
+async function saveRecords() {
+ var _a, _b, _c, _d;
+ 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, "descriptor length:", (_d = (_c = current.face) == null ? void 0 : _c.embedding) == null ? void 0 : _d.length);
+ log2("known face records:", await count());
+ } else {
+ log2("invalid name");
+ }
+}
+async function deleteRecord() {
+ if (current.record && current.record.id > 0) {
+ await remove(current.record);
+ }
+}
+async function detectFace() {
+ var _a, _b, _c, _d;
+ dom.canvas.style.height = "";
+ (_a = dom.canvas.getContext("2d")) == null ? void 0 : _a.clearRect(0, 0, options.minSize, options.minSize);
+ if (!((_b = current == null ? void 0 : current.face) == null ? void 0 : _b.tensor) || !((_c = current == null ? void 0 : current.face) == null ? void 0 : _c.embedding))
+ return false;
+ console.log("face record:", current.face);
+ log2(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${current.face.iris || 0}cm/${Math.round(100 * (current.face.iris || 0) / 2.54) / 100}in`);
+ human.tf.browser.toPixels(current.face.tensor, dom.canvas);
+ if (await count() === 0) {
+ log2("face database is empty: nothing to compare face with");
+ document.body.style.background = "black";
+ dom.delete.style.display = "none";
+ return false;
+ }
+ const db2 = await load();
+ const descriptors = db2.map((rec) => rec.descriptor).filter((desc) => desc.length > 0);
+ const res = 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 = "";
+ (_d = dom.source.getContext("2d")) == null ? void 0 : _d.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;
+ ok.faceCount.status = false;
+ ok.faceConfidence.status = false;
+ ok.facingCenter.status = false;
+ ok.blinkDetected.status = false;
+ ok.faceSize.status = false;
+ ok.antispoofCheck.status = false;
+ ok.livenessCheck.status = false;
+ ok.age.status = false;
+ ok.gender.status = false;
+ ok.elapsedMs.val = 0;
+ dom.match.style.display = "none";
+ dom.retry.style.display = "none";
+ dom.source.style.display = "none";
+ dom.canvas.style.height = "50%";
+ document.body.style.background = "black";
+ await webCam();
+ await detectionLoop();
+ startTime = human.now();
+ current.face = await validationLoop();
+ dom.canvas.width = ((_a = current.face.tensor) == null ? void 0 : _a.shape[1]) || options.minSize;
+ dom.canvas.height = ((_b = current.face.tensor) == null ? void 0 : _b.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;
+ }
+ return detectFace();
+}
+async function init() {
+ var _a, _b;
+ log2("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]);
+ log2("options:", JSON.stringify(options).replace(/{|}|"|\[|\]/g, "").replace(/,/g, " "));
+ log2("initializing webcam...");
+ await webCam();
+ log2("loading human models...");
+ await human.load();
+ log2("initializing human...");
+ log2("face embedding model:", humanConfig.face.description.enabled ? "faceres" : "", ((_a = humanConfig.face["mobilefacenet"]) == null ? void 0 : _a.enabled) ? "mobilefacenet" : "", ((_b = humanConfig.face["insightface"]) == null ? void 0 : _b.enabled) ? "insightface" : "");
+ log2("loading face database...");
+ log2("known face records:", await count());
+ dom.retry.addEventListener("click", main);
+ dom.save.addEventListener("click", saveRecords);
+ dom.delete.addEventListener("click", deleteRecord);
+ await human.warmup();
+ await main();
+}
+window.onload = init;
//# sourceMappingURL=index.js.map
diff --git a/demo/faceid/index.js.map b/demo/faceid/index.js.map
index ba7ac80d..a2e25b78 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 * as H 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 cacheSensitivity: 0,\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: true, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model\n // insightface: { enabled: true, modelPath: 'https://vladmandic.github.io/insightface/models/insightface-mobilenet-swish.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: 30000, // 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: Record = { // must meet all rules\n faceCount: { status: false, val: 0 },\n faceConfidence: { status: false, val: 0 },\n facingCenter: { status: false, val: 0 },\n lookingCenter: { status: false, val: 0 },\n blinkDetected: { status: false, val: 0 },\n faceSize: { status: false, val: 0 },\n antispoofCheck: { status: false, val: 0 },\n livenessCheck: { status: false, val: 0 },\n age: { status: false, val: 0 },\n gender: { status: false, val: 0 },\n timeout: { status: true, val: 0 },\n descriptor: { status: false, val: 0 },\n elapsedMs: { status: undefined, val: 0 }, // total time while waiting for valid face\n detectFPS: { status: undefined, val: 0 }, // mark detection fps performance\n drawFPS: { status: undefined, val: 0 }, // mark redraw fps performance\n};\n\nconst allOk = () => ok.faceCount.status\n && ok.faceSize.status\n && ok.blinkDetected.status\n && ok.facingCenter.status\n && ok.lookingCenter.status\n && ok.faceConfidence.status\n && ok.antispoofCheck.status\n && ok.livenessCheck.status\n && ok.descriptor.status\n && ok.age.status\n && ok.gender.status;\n\nconst current: { face: H.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 H.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\nlet startTime = 0;\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\n\nasync function webCam() { // initialize 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 void dom.video.play();\n await ready;\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n dom.canvas.style.width = '50%';\n dom.canvas.style.height = '50%';\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) void 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?.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 ok.detectFPS.val = Math.round(10000 / (now - timestamp.detect)) / 10;\n timestamp.detect = now;\n requestAnimationFrame(detectionLoop); // start new frame immediately\n }\n}\n\nfunction drawValidationTests() {\n let y = 32;\n for (const [key, val] of Object.entries(ok)) {\n let el = document.getElementById(`ok-${key}`);\n if (!el) {\n el = document.createElement('div');\n el.id = `ok-${key}`;\n el.innerText = key;\n el.className = 'ok';\n el.style.top = `${y}px`;\n dom.ok.appendChild(el);\n }\n if (typeof val.status === 'boolean') el.style.backgroundColor = val.status ? 'lightgreen' : 'lightcoral';\n const status = val.status ? 'ok' : 'fail';\n el.innerText = `${key}: ${val.val === 0 ? status : val.val}`;\n y += 28;\n }\n}\n\nasync function validationLoop(): Promise { // main screen refresh loop\n const interpolated = human.next(human.result); // smoothen result using last-known results\n 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 ok.drawFPS.val = Math.round(10000 / (now - timestamp.draw)) / 10;\n timestamp.draw = now;\n ok.faceCount.val = human.result.face.length;\n ok.faceCount.status = ok.faceCount.val === 1; // must be exactly detected face\n if (ok.faceCount.status) { // skip the rest if no face\n const gestures: string[] = Object.values(human.result.gesture).map((gesture: H.GestureResult) => 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.status = ok.blinkDetected.status || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);\n if (ok.blinkDetected.status && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);\n ok.facingCenter.status = gestures.includes('facing center');\n ok.lookingCenter.status = gestures.includes('looking center'); // must face camera and look at camera\n ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0;\n ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence;\n ok.antispoofCheck.val = human.result.face[0].real || 0;\n ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence;\n ok.livenessCheck.val = human.result.face[0].live || 0;\n ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence;\n ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]);\n ok.faceSize.status = ok.faceSize.val >= options.minSize;\n ok.descriptor.val = human.result.face[0].embedding?.length || 0;\n ok.descriptor.status = ok.descriptor.val > 0;\n ok.age.val = human.result.face[0].age || 0;\n ok.age.status = ok.age.val > 0;\n ok.gender.val = human.result.face[0].genderScore || 0;\n ok.gender.status = ok.gender.val >= options.minConfidence;\n }\n // run again\n ok.timeout.status = ok.elapsedMs.val <= options.maxTime;\n drawValidationTests();\n if (allOk() || !ok.timeout.status) { // all criteria met\n dom.video.pause();\n return human.result.face[0];\n }\n ok.elapsedMs.val = Math.trunc(human.now() - startTime);\n return new Promise((resolve) => {\n setTimeout(async () => {\n await validationLoop(); // run validation loop until conditions are met\n 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\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, 'descriptor length:', current.face?.embedding?.length);\n log('known face records:', await indexDb.count());\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.style.height = '';\n dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);\n if (!current?.face?.tensor || !current?.face?.embedding) return false;\n console.log('face record:', current.face); // eslint-disable-line no-console\n log(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${current.face.iris || 0}cm/${Math.round(100 * (current.face.iris || 0) / 2.54) / 100}in`);\n human.tf.browser.toPixels(current.face.tensor as unknown as H.TensorLike, dom.canvas);\n if (await indexDb.count() === 0) {\n log('face database is empty: nothing to compare face with');\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).filter((desc) => desc.length > 0);\n const res = 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.status = false;\n ok.faceConfidence.status = false;\n ok.facingCenter.status = false;\n ok.blinkDetected.status = false;\n ok.faceSize.status = false;\n ok.antispoofCheck.status = false;\n ok.livenessCheck.status = false;\n ok.age.status = false;\n ok.gender.status = false;\n ok.elapsedMs.val = 0;\n dom.match.style.display = 'none';\n dom.retry.style.display = 'none';\n dom.source.style.display = 'none';\n dom.canvas.style.height = '50%';\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 }\n return detectFace();\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 log('initializing webcam...');\n await webCam(); // start webcam\n log('loading human models...');\n await human.load(); // preload all models\n log('initializing human...');\n log('face embedding model:', humanConfig.face.description.enabled ? 'faceres' : '', humanConfig.face['mobilefacenet']?.enabled ? 'mobilefacenet' : '', humanConfig.face['insightface']?.enabled ? 'insightface' : '');\n log('loading face database...');\n log('known face records:', await indexDb.count());\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 interface FaceRecord { id: number, name: string, descriptor: number[], image: ImageData }\n\nconst log = (...msg) => console.log('indexdb', ...msg); // eslint-disable-line no-console\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;\n log('open:', db);\n resolve(true);\n };\n });\n}\n\nexport async function load(): Promise {\n const faceDB: FaceRecord[] = [];\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": ["log", "db"]
}
diff --git a/demo/index.html b/demo/index.html
index f1af65a9..afabf9c6 100644
--- a/demo/index.html
+++ b/demo/index.html
@@ -110,10 +110,6 @@
-
-
-
-
diff --git a/demo/index.js b/demo/index.js
index 616cf881..6d5142df 100644
--- a/demo/index.js
+++ b/demo/index.js
@@ -111,7 +111,6 @@ const ui = {
results: false, // show results tree
lastFrame: 0, // time of last frame processing
viewportSet: false, // internal, has custom viewport been set
- background: null, // holds instance of segmentation background image
transferCanvas: null, // canvas used to transfer data to and from worker
// webrtc
@@ -263,21 +262,7 @@ async function drawResults(input) {
// draw fps chart
await menu.process.updateChart('FPS', ui.detectFPS);
- document.getElementById('segmentation-container').style.display = userConfig.segmentation.enabled ? 'block' : 'none';
- if (userConfig.segmentation.enabled && ui.buffered) { // refresh segmentation if using buffered output
- const seg = await human.segmentation(input, ui.background);
- if (seg.alpha) {
- const canvasSegMask = document.getElementById('segmentation-mask');
- const ctxSegMask = canvasSegMask.getContext('2d');
- ctxSegMask.clearRect(0, 0, canvasSegMask.width, canvasSegMask.height); // need to clear as seg.alpha is alpha based canvas so it adds
- ctxSegMask.drawImage(seg.alpha, 0, 0, seg.alpha.width, seg.alpha.height, 0, 0, canvasSegMask.width, canvasSegMask.height);
- const canvasSegCanvas = document.getElementById('segmentation-canvas');
- const ctxSegCanvas = canvasSegCanvas.getContext('2d');
- ctxSegCanvas.clearRect(0, 0, canvasSegCanvas.width, canvasSegCanvas.height); // need to clear as seg.alpha is alpha based canvas so it adds
- ctxSegCanvas.drawImage(seg.canvas, 0, 0, seg.alpha.width, seg.alpha.height, 0, 0, canvasSegCanvas.width, canvasSegCanvas.height);
- }
- // result.canvas = seg.alpha;
- } else if (!result.canvas || ui.buffered) { // refresh with input if using buffered output or if missing canvas
+ if (!result.canvas || ui.buffered) { // refresh with input if using buffered output or if missing canvas
const image = await human.image(input, false);
result.canvas = image.canvas;
human.tf.dispose(image.tensor);
@@ -743,7 +728,6 @@ function setupMenu() {
menu.image.addBool('technicolor', userConfig.filter, 'technicolor', (val) => userConfig.filter.technicolor = val);
menu.image.addBool('polaroid', userConfig.filter, 'polaroid', (val) => userConfig.filter.polaroid = val);
menu.image.addHTML(' input');
- menu.image.addHTML(' background');
menu.process = new Menu(document.body, '', { top, left: x[2] });
menu.process.addList('backend', ['cpu', 'webgl', 'wasm', 'humangl'], userConfig.backend, (val) => userConfig.backend = val);
@@ -791,8 +775,6 @@ function setupMenu() {
menu.models.addHTML('
');
menu.models.addBool('gestures', userConfig.gesture, 'enabled', (val) => userConfig.gesture.enabled = val);
menu.models.addHTML('
');
- menu.models.addBool('body segmentation', userConfig.segmentation, 'enabled', (val) => userConfig.segmentation.enabled = val);
- menu.models.addHTML('
');
menu.models.addBool('object detection', userConfig.object, 'enabled', (val) => userConfig.object.enabled = val);
menu.models.addHTML('
');
menu.models.addBool('face compare', compare, 'enabled', (val) => {
@@ -860,42 +842,12 @@ async function processDataURL(f, action) {
if (e.target.result.startsWith('data:video')) await processVideo(e.target.result, f.name);
document.getElementById('canvas').style.display = 'none';
}
- if (action === 'background') {
- const image = new Image();
- image.onerror = async () => status('image loading error');
- image.onload = async () => {
- ui.background = image;
- if (document.getElementById('canvas').style.display === 'block') { // replace canvas used for video
- const canvas = document.getElementById('canvas');
- const ctx = canvas.getContext('2d');
- const seg = await human.segmentation(canvas, ui.background, userConfig);
- if (seg.canvas) ctx.drawImage(seg.canvas, 0, 0);
- } else {
- const canvases = document.getElementById('samples-container').children; // replace loaded images
- for (const canvas of canvases) {
- const ctx = canvas.getContext('2d');
- const seg = await human.segmentation(canvas, ui.background, userConfig);
- if (seg.canvas) ctx.drawImage(seg.canvas, 0, 0);
- }
- }
- };
- image.src = e.target.result;
- }
resolve(true);
};
reader.readAsDataURL(f);
});
}
-async function runSegmentation() {
- document.getElementById('file-background').onchange = async (evt) => {
- userConfig.segmentation.enabled = true;
- evt.preventDefault();
- if (evt.target.files.length < 2) ui.columns = 1;
- for (const f of evt.target.files) await processDataURL(f, 'background');
- };
-}
-
async function dragAndDrop() {
document.body.addEventListener('dragenter', (evt) => evt.preventDefault());
document.body.addEventListener('dragleave', (evt) => evt.preventDefault());
@@ -1071,9 +1023,6 @@ async function main() {
// init drag & drop
await dragAndDrop();
- // init segmentation
- await runSegmentation();
-
if (params.has('image')) {
try {
const image = JSON.parse(params.get('image'));
diff --git a/demo/nodejs/node-canvas.js b/demo/nodejs/node-canvas.js
index 71bcb26c..ca250c0b 100644
--- a/demo/nodejs/node-canvas.js
+++ b/demo/nodejs/node-canvas.js
@@ -54,9 +54,6 @@ async function main() {
// run detection
const result = await human.detect(imageData);
- // run segmentation
- // const seg = await human.segmentation(inputCanvas);
- // log.data('Segmentation:', { data: seg.data.length, alpha: typeof seg.alpha, canvas: typeof seg.canvas });
// print results summary
const persons = result.persons; // invoke persons getter, only used to print summary on console
diff --git a/demo/segmentation/index.html b/demo/segmentation/index.html
new file mode 100644
index 00000000..d0907d58
--- /dev/null
+++ b/demo/segmentation/index.html
@@ -0,0 +1,61 @@
+
+
+
+
+
+ Human Demo
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/demo/segmentation/index.js b/demo/segmentation/index.js
new file mode 100644
index 00000000..8c7854be
--- /dev/null
+++ b/demo/segmentation/index.js
@@ -0,0 +1,98 @@
+/**
+ * Human demo for browsers
+ * @default Human Library
+ * @summary
+ * @author
+ * @copyright
+ * @license MIT
+ */
+
+import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
+
+const humanConfig = { // user configuration for human, used to fine-tune behavior
+ // backend: 'wasm',
+ // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.20.0/dist/',
+ modelBasePath: 'https://vladmandic.github.io/human-models/models/',
+ filter: { enabled: true, equalization: false, flip: false },
+ face: { enabled: false },
+ body: { enabled: false },
+ hand: { enabled: false },
+ object: { enabled: false },
+ gesture: { enabled: false },
+ segmentation: {
+ enabled: true,
+ modelPath: 'rvm.json', // can use rvm, selfie or meet
+ ratio: 0.5,
+ mode: 'default',
+ },
+};
+
+const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
+
+const log = (...msg) => console.log(...msg); // eslint-disable-line no-console
+
+async function main() {
+ // gather dom elements
+ const dom = {
+ video: document.getElementById('video'),
+ webcam: document.getElementById('webcam'),
+ output: document.getElementById('output'),
+ merge: document.getElementById('merge'),
+ mode: document.getElementById('mode'),
+ composite: document.getElementById('composite'),
+ ratio: document.getElementById('ratio'),
+ fps: document.getElementById('fps'),
+ };
+ // set defaults
+ dom.fps.innerText = 'initializing';
+ dom.ratio.valueAsNumber = human.config.segmentation.ratio;
+ dom.video.src = '../assets/rijeka.mp4';
+ dom.composite.innerHTML = ['source-atop', 'color', 'color-burn', 'color-dodge', 'copy', 'darken', 'destination-atop', 'destination-in', 'destination-out', 'destination-over', 'difference', 'exclusion', 'hard-light', 'hue', 'lighten', 'lighter', 'luminosity', 'multiply', 'overlay', 'saturation', 'screen', 'soft-light', 'source-in', 'source-out', 'source-over', 'xor'].map((gco) => ``).join(''); // eslint-disable-line max-len
+ const ctxMerge = dom.merge.getContext('2d');
+
+ log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);
+ log('platform:', human.env.platform, '| agent:', human.env.agent);
+ await human.load(); // preload all models
+ log('backend:', human.tf.getBackend(), '| available:', human.env.backends);
+ log('models stats:', human.getModelStats());
+ log('models loaded:', Object.values(human.models).filter((model) => model !== null).length);
+ await human.warmup(); // warmup function to initialize backend for future faster detection
+ const numTensors = human.tf.engine().state.numTensors;
+
+ // initialize webcam
+ dom.webcam.onplay = () => { // start processing on video play
+ log('start processing');
+ dom.output.width = human.webcam.width;
+ dom.output.height = human.webcam.height;
+ dom.merge.width = human.webcam.width;
+ dom.merge.height = human.webcam.height;
+ loop(); // eslint-disable-line no-use-before-define
+ };
+ await human.webcam.start({ element: dom.webcam, crop: true, width: 960, height: 720 }); // use human webcam helper methods and associate webcam stream with a dom element
+ if (!human.webcam.track) dom.fps.innerText = 'webcam error';
+
+ // processing loop
+ async function loop() {
+ if (!human.webcam.element || human.webcam.paused) return; // check if webcam is valid and playing
+ human.config.segmentation.mode = dom.mode.value; // get segmentation mode from ui
+ human.config.segmentation.ratio = dom.ratio.valueAsNumber; // get segmentation downsample ratio from ui
+ const t0 = Date.now();
+ const rgba = await human.segmentation(human.webcam.element, human.config); // run model and process results
+ const t1 = Date.now();
+ if (!rgba) {
+ dom.fps.innerText = 'error';
+ return;
+ }
+ dom.fps.innerText = `fps: ${Math.round(10000 / (t1 - t0)) / 10}`; // mark performance
+ human.tf.browser.toPixels(rgba, dom.output); // draw raw output
+ human.tf.dispose(rgba); // dispose tensors
+ ctxMerge.globalCompositeOperation = 'source-over';
+ ctxMerge.drawImage(dom.video, 0, 0); // draw original video to first stacked canvas
+ ctxMerge.globalCompositeOperation = dom.composite.value;
+ ctxMerge.drawImage(dom.output, 0, 0); // draw processed output to second stacked canvas
+ if (numTensors !== human.tf.engine().state.numTensors) log({ leak: human.tf.engine().state.numTensors - numTensors }); // check for memory leaks
+ requestAnimationFrame(loop);
+ }
+}
+
+window.onload = main;
diff --git a/demo/typescript/index.js b/demo/typescript/index.js
index 50a2ccfe..c35e7f49 100644
--- a/demo/typescript/index.js
+++ b/demo/typescript/index.js
@@ -4,6 +4,96 @@
author: '
*/
-import*as i from"../../dist/human.esm.js";var m={modelBasePath:"../../models",filter:{enabled:!0,equalization:!1,flip:!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},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new i.Human(m);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},s={detectFPS:0,drawFPS:0,frames:0,averageMs:0},o=(...t)=>{a.log.innerText+=t.join(" ")+`
-`,console.log(...t)},d=t=>a.fps.innerText=t,f=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function l(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&o("allocated tensors:",t-n.tensors),n.tensors=t,s.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,s.frames++,s.averageMs=Math.round(1e3*(e.now()-n.start)/s.frames)/1e3,s.frames%100===0&&!a.video.paused&&o("performance",{...s,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(l)}async function c(){if(!a.video.paused){let r=e.next(e.result);e.config.filter.flip?e.draw.canvas(r.canvas,a.canvas):e.draw.canvas(a.video,a.canvas),await e.draw.all(a.canvas,r),f(r.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,d(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(c,30)}async function u(){await e.webcam.start({element:a.video,crop:!0}),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function w(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),d("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.getModelStats()),o("models loaded:",Object.values(e.models).filter(t=>t!==null).length),d("initializing..."),await e.warmup(),await u(),await l(),await c()}window.onload=w;
+
+// demo/typescript/index.ts
+import * as H from "../../dist/human.esm.js";
+var humanConfig = {
+ modelBasePath: "../../models",
+ filter: { enabled: true, equalization: false, flip: 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 },
+ segmentation: { enabled: false },
+ gesture: { enabled: true }
+};
+var human = new H.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, start: 0 };
+var fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 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.toString() + " | performance: " + JSON.stringify(msg).replace(/"|{|}/g, "").replace(/,/g, " | ");
+async function detectionLoop() {
+ if (!dom.video.paused) {
+ if (timestamp.start === 0)
+ timestamp.start = human.now();
+ 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;
+ fps.detectFPS = Math.round(1e3 * 1e3 / (human.now() - timestamp.detect)) / 1e3;
+ fps.frames++;
+ fps.averageMs = Math.round(1e3 * (human.now() - timestamp.start) / fps.frames) / 1e3;
+ if (fps.frames % 100 === 0 && !dom.video.paused)
+ log("performance", { ...fps, tensors: timestamp.tensors });
+ }
+ timestamp.detect = human.now();
+ requestAnimationFrame(detectionLoop);
+}
+async function drawLoop() {
+ if (!dom.video.paused) {
+ const interpolated = human.next(human.result);
+ if (human.config.filter.flip)
+ human.draw.canvas(interpolated.canvas, dom.canvas);
+ else
+ human.draw.canvas(dom.video, dom.canvas);
+ await human.draw.all(dom.canvas, interpolated);
+ perf(interpolated.performance);
+ }
+ const now = human.now();
+ fps.drawFPS = Math.round(1e3 * 1e3 / (now - timestamp.draw)) / 1e3;
+ timestamp.draw = now;
+ status(dom.video.paused ? "paused" : `fps: ${fps.detectFPS.toFixed(1).padStart(5, " ")} detect | ${fps.drawFPS.toFixed(1).padStart(5, " ")} draw`);
+ setTimeout(drawLoop, 30);
+}
+async function webCam() {
+ await human.webcam.start({ element: dom.video, crop: true });
+ dom.canvas.width = human.webcam.width;
+ dom.canvas.height = human.webcam.height;
+ dom.canvas.onclick = async () => {
+ if (human.webcam.paused)
+ await human.webcam.play();
+ else
+ human.webcam.pause();
+ };
+}
+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("models stats:", human.getModelStats());
+ log("models loaded:", Object.values(human.models).filter((model) => model !== null).length);
+ status("initializing...");
+ await human.warmup();
+ await webCam();
+ await detectionLoop();
+ await drawLoop();
+}
+window.onload = main;
//# sourceMappingURL=index.js.map
diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map
index 0f131def..605a670d 100644
--- a/demo/typescript/index.js.map
+++ b/demo/typescript/index.js.map
@@ -2,6 +2,6 @@
"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 * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n // backend: 'wasm',\n // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.20.0/dist/',\n // cacheSensitivity: 0,\n // async: false,\n modelBasePath: '../../models',\n filter: { enabled: true, equalization: false, flip: false },\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 segmentation: { enabled: false },\n gesture: { enabled: true },\n};\n\nconst human = new H.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// human.draw.options.fillPolygons = true;\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, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 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 console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + (human.tf.memory().numTensors as number).toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (timestamp.start === 0) timestamp.start = human.now();\n // 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 fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n fps.frames++;\n fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n }\n timestamp.detect = human.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 = human.next(human.result); // smoothen result using last-known results\n if (human.config.filter.flip) human.draw.canvas(interpolated.canvas as HTMLCanvasElement, dom.canvas); // draw processed image to screen canvas\n else human.draw.canvas(dom.video, dom.canvas); // draw original video to screen canvas // better than using procesed image as this loop happens faster than processing loop\n await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n perf(interpolated.performance); // write performance data\n }\n const now = human.now();\n fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n timestamp.draw = now;\n status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function webCam() {\n await human.webcam.start({ element: dom.video, crop: true }); // use human webcam helper methods and associate webcam stream with a dom element\n dom.canvas.width = human.webcam.width;\n dom.canvas.height = human.webcam.height;\n dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click\n if (human.webcam.paused) await human.webcam.play();\n else human.webcam.pause();\n };\n}\n\nasync function main() { // main entry point\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('platform:', human.env.platform, '| agent:', human.env.agent);\n status('loading...');\n await human.load(); // preload all models\n log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n log('models stats:', human.getModelStats());\n log('models loaded:', Object.values(human.models).filter((model) => model !== null).length);\n status('initializing...');\n await human.warmup(); // warmup function to initialize backend for future faster detection\n await webCam(); // start webcam\n await detectionLoop(); // start detection loop\n await drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n"],
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}
diff --git a/models/README.md b/models/README.md
index 84e90b89..4618f458 100644
--- a/models/README.md
+++ b/models/README.md
@@ -3,308 +3,3 @@
For details see Wiki:
- [**List of Models & Credits**](https://github.com/vladmandic/human/wiki/Models)
-
-## Model signatures:
-
-```js
-INFO: graph model: /home/vlado/dev/human/models/iris.json
-INFO: created on: 2020-10-12T18:46:47.060Z
-INFO: metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO: model inputs based on signature
- { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ -1, 64, 64, 3 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ -1, 1, 1, 228 ] }
-INFO: tensors: 191
-DATA: weights: {
- files: [ 'iris.bin' ],
- size: { disk: 2599092, memory: 2599092 },
- count: { total: 191, float32: 189, int32: 2 },
- quantized: { none: 191 },
- values: { total: 649773, float32: 649764, int32: 9 }
-}
-DATA: kernel ops: {
- graph: [ 'Const', 'Placeholder', 'Identity' ],
- convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
- arithmetic: [ 'AddV2' ],
- basic_math: [ 'Prelu' ],
- transformation: [ 'Pad' ],
- slice_join: [ 'ConcatV2' ]
-}
-INFO: graph model: /home/vlado/dev/human/models/facemesh.json
-INFO: created on: 2020-10-12T18:46:46.944Z
-INFO: metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO: model inputs based on signature
- { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ 1, 192, 192, 3 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 266 ] }
- { id: 1, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
- { id: 2, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 1404 ] }
-INFO: tensors: 118
-DATA: weights: {
- files: [ 'facemesh.bin' ],
- size: { disk: 2955780, memory: 2955780 },
- count: { total: 118, float32: 114, int32: 4 },
- quantized: { none: 118 },
- values: { total: 738945, float32: 738919, int32: 26 }
-}
-DATA: kernel ops: {
- graph: [ 'Placeholder', 'Const', 'NoOp', 'Identity' ],
- convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
- arithmetic: [ 'AddV2' ],
- basic_math: [ 'Prelu', 'Sigmoid' ],
- transformation: [ 'Pad', 'Reshape' ]
-}
-INFO: graph model: /home/vlado/dev/human/models/emotion.json
-INFO: created on: 2020-11-05T20:11:29.740Z
-INFO: metadata: { generatedBy: 'https://github.com/oarriaga/face_classification', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO: model inputs based on signature
- { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ -1, 64, 64, 1 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ -1, 7 ] }
-INFO: tensors: 23
-DATA: weights: {
- files: [ 'emotion.bin' ],
- size: { disk: 820516, memory: 820516 },
- count: { total: 23, float32: 22, int32: 1 },
- quantized: { none: 23 },
- values: { total: 205129, float32: 205127, int32: 2 }
-}
-DATA: kernel ops: {
- graph: [ 'Const', 'Placeholder', 'Identity' ],
- convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
- arithmetic: [ 'AddV2' ],
- basic_math: [ 'Relu' ],
- reduction: [ 'Mean' ],
- normalization: [ 'Softmax' ]
-}
-INFO: graph model: /home/vlado/dev/human/models/faceres.json
-INFO: created on: 2021-03-21T14:12:59.863Z
-INFO: metadata: { generatedBy: 'https://github.com/HSE-asavchenko/HSE_FaceRec_tf', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO: model inputs based on signature
- { name: 'input_1', dtype: 'DT_FLOAT', shape: [ -1, 224, 224, 3 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'gender_pred/Sigmoid:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
- { id: 1, name: 'global_pooling/Mean', dytpe: 'DT_FLOAT', shape: [ 1, 1024 ] }
- { id: 2, name: 'age_pred/Softmax:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
-INFO: tensors: 128
-DATA: weights: {
- files: [ 'faceres.bin' ],
- size: { disk: 6978814, memory: 13957620 },
- count: { total: 128, float32: 127, int32: 1 },
- quantized: { float16: 127, none: 1 },
- values: { total: 3489405, float32: 3489403, int32: 2 }
-}
-DATA: kernel ops: {
- graph: [ 'Const', 'Placeholder' ],
- convolution: [ 'Conv2D', 'DepthwiseConv2dNative' ],
- arithmetic: [ 'Add', 'Minimum', 'Maximum', 'Mul' ],
- basic_math: [ 'Relu', 'Sigmoid' ],
- reduction: [ 'Mean' ],
- matrices: [ '_FusedMatMul' ],
- normalization: [ 'Softmax' ]
-}
-INFO: graph model: /home/vlado/dev/human/models/blazeface.json
-INFO: created on: 2020-10-15T19:57:26.419Z
-INFO: metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO: model inputs based on signature
- { name: 'input:0', dtype: 'DT_FLOAT', shape: [ 1, 256, 256, 3 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 384, 16 ] }
- { id: 1, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 512, 1 ] }
- { id: 2, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 384, 1 ] }
- { id: 3, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 512, 16 ] }
-INFO: tensors: 112
-DATA: weights: {
- files: [ 'blazeface.bin' ],
- size: { disk: 538928, memory: 538928 },
- count: { total: 112, float32: 106, int32: 6 },
- quantized: { none: 112 },
- values: { total: 134732, float32: 134704, int32: 28 }
-}
-DATA: kernel ops: {
- graph: [ 'Const', 'Placeholder', 'Identity' ],
- convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
- arithmetic: [ 'AddV2' ],
- basic_math: [ 'Relu' ],
- transformation: [ 'Pad', 'Reshape' ]
-}
-INFO: graph model: /home/vlado/dev/human/models/mb3-centernet.json
-INFO: created on: 2021-05-19T11:50:13.013Z
-INFO: metadata: { generatedBy: 'https://github.com/610265158/mobilenetv3_centernet', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO: model inputs based on signature
- { name: 'tower_0/images', dtype: 'DT_FLOAT', shape: [ 1, 512, 512, 3 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'tower_0/wh', dytpe: 'DT_FLOAT', shape: [ 1, 128, 128, 4 ] }
- { id: 1, name: 'tower_0/keypoints', dytpe: 'DT_FLOAT', shape: [ 1, 128, 128, 80 ] }
- { id: 2, name: 'tower_0/detections', dytpe: 'DT_FLOAT', shape: [ 1, 100, 6 ] }
-INFO: tensors: 267
-DATA: weights: {
- files: [ 'mb3-centernet.bin' ],
- size: { disk: 4030290, memory: 8060260 },
- count: { total: 267, float32: 227, int32: 40 },
- quantized: { float16: 227, none: 40 },
- values: { total: 2015065, float32: 2014985, int32: 80 }
-}
-DATA: kernel ops: {
- graph: [ 'Const', 'Placeholder', 'Identity' ],
- convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative', 'Conv2D', 'MaxPool' ],
- arithmetic: [ 'Mul', 'Add', 'FloorDiv', 'FloorMod', 'Sub' ],
- basic_math: [ 'Relu6', 'Relu', 'Sigmoid' ],
- reduction: [ 'Mean' ],
- image: [ 'ResizeBilinear' ],
- slice_join: [ 'ConcatV2', 'GatherV2', 'StridedSlice' ],
- transformation: [ 'Reshape', 'Cast', 'ExpandDims' ],
- logical: [ 'Equal' ],
- evaluation: [ 'TopKV2' ]
-}
-INFO: graph model: /home/vlado/dev/human/models/movenet-lightning.json
-INFO: created on: 2021-05-29T12:26:32.994Z
-INFO: metadata: { generatedBy: 'https://tfhub.dev/google/movenet/singlepose/lightning/4', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO: model inputs based on signature
- { name: 'input:0', dtype: 'DT_INT32', shape: [ 1, 192, 192, 3 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 1, 17, 3 ] }
-INFO: tensors: 180
-DATA: weights: {
- files: [ 'movenet-lightning.bin' ],
- size: { disk: 4650216, memory: 9300008 },
- count: { total: 180, int32: 31, float32: 149 },
- quantized: { none: 31, float16: 149 },
- values: { total: 2325002, int32: 106, float32: 2324896 }
-}
-DATA: kernel ops: {
- graph: [ 'Const', 'Placeholder', 'Identity' ],
- transformation: [ 'Cast', 'ExpandDims', 'Squeeze', 'Reshape' ],
- slice_join: [ 'Unpack', 'Pack', 'GatherNd', 'ConcatV2' ],
- arithmetic: [ 'Sub', 'Mul', 'AddV2', 'FloorDiv', 'SquaredDifference', 'RealDiv' ],
- convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative' ],
- image: [ 'ResizeBilinear' ],
- basic_math: [ 'Sigmoid', 'Sqrt' ],
- reduction: [ 'ArgMax' ]
-}
-INFO: graph model: /home/vlado/dev/human/models/selfie.json
-INFO: created on: 2021-06-04T13:46:56.904Z
-INFO: metadata: { generatedBy: 'https://github.com/PINTO0309/PINTO_model_zoo/tree/main/109_Selfie_Segmentation', convertedBy: 'https://github.com/vladmandic', version: '561.undefined' }
-INFO: model inputs based on signature
- { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ 1, 256, 256, 3 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'activation_10:0', dytpe: 'DT_FLOAT', shape: [ 1, 256, 256, 1 ] }
-INFO: tensors: 136
-DATA: weights: {
- files: [ 'selfie.bin' ],
- size: { disk: 212886, memory: 425732 },
- count: { total: 136, int32: 4, float32: 132 },
- quantized: { none: 4, float16: 132 },
- values: { total: 106433, int32: 10, float32: 106423 }
-}
-DATA: kernel ops: {
- graph: [ 'Const', 'Placeholder' ],
- convolution: [ 'Conv2D', 'DepthwiseConv2dNative', 'AvgPool', 'Conv2DBackpropInput' ],
- arithmetic: [ 'Add', 'Mul', 'AddV2', 'AddN' ],
- basic_math: [ 'Relu6', 'Relu', 'Sigmoid' ],
- image: [ 'ResizeBilinear' ]
-}
-INFO: graph model: /home/vlado/dev/human/models/handtrack.json
-INFO: created on: 2021-09-21T12:09:47.583Z
-INFO: metadata: { generatedBy: 'https://github.com/victordibia/handtracking', convertedBy: 'https://github.com/vladmandic', version: '561.undefined' }
-INFO: model inputs based on signature
- { name: 'input_tensor:0', dtype: 'DT_UINT8', shape: [ 1, 320, 320, 3 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
- { id: 1, name: 'Identity_4:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
- { id: 2, name: 'Identity_6:0', dytpe: 'DT_FLOAT', shape: [ 1, 12804, 4 ] }
- { id: 3, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 100, 4 ] }
- { id: 4, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 100, 8 ] }
- { id: 5, name: 'Identity_5:0', dytpe: 'DT_FLOAT', shape: [ 1 ] }
- { id: 6, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
- { id: 7, name: 'Identity_7:0', dytpe: 'DT_FLOAT', shape: [ 1, 12804, 8 ] }
-INFO: tensors: 619
-DATA: weights: {
- files: [ 'handtrack.bin' ],
- size: { disk: 2964837, memory: 11846016 },
- count: { total: 619, int32: 347, float32: 272 },
- quantized: { none: 347, uint8: 272 },
- values: { total: 2961504, int32: 1111, float32: 2960393 }
-}
-DATA: kernel ops: {
- graph: [ 'Const', 'Placeholder', 'Identity', 'Shape', 'NoOp' ],
- control: [ 'TensorListReserve', 'Enter', 'TensorListFromTensor', 'Merge', 'LoopCond', 'Switch', 'Exit', 'TensorListStack', 'NextIteration', 'TensorListSetItem', 'TensorListGetItem' ],
- logical: [ 'Less', 'LogicalAnd', 'Select', 'Greater', 'GreaterEqual' ],
- convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative' ],
- arithmetic: [ 'AddV2', 'Mul', 'Sub', 'Minimum', 'Maximum' ],
- transformation: [ 'Cast', 'ExpandDims', 'Squeeze', 'Reshape', 'Pad' ],
- slice_join: [ 'Unpack', 'StridedSlice', 'Pack', 'ConcatV2', 'Slice', 'GatherV2', 'Split' ],
- image: [ 'ResizeBilinear' ],
- basic_math: [ 'Reciprocal', 'Sigmoid', 'Exp' ],
- matrices: [ 'Transpose' ],
- dynamic: [ 'NonMaxSuppressionV5', 'Where' ],
- creation: [ 'Fill', 'Range' ],
- evaluation: [ 'TopKV2' ],
- reduction: [ 'Sum' ]
-}
-INFO: graph model: /home/vlado/dev/human/models/antispoof.json
-INFO: created on: 2021-10-13T14:20:27.100Z
-INFO: metadata: { generatedBy: 'https://www.kaggle.com/anku420/fake-face-detection', convertedBy: 'https://github.com/vladmandic', version: '716.undefined' }
-INFO: model inputs based on signature
- { name: 'conv2d_input', dtype: 'DT_FLOAT', shape: [ -1, 128, 128, 3 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'activation_4', dytpe: 'DT_FLOAT', shape: [ -1, 1 ] }
-INFO: tensors: 11
-DATA: weights: {
- files: [ 'antispoof.bin' ],
- size: { disk: 853098, memory: 1706188 },
- count: { total: 11, float32: 10, int32: 1 },
- quantized: { float16: 10, none: 1 },
- values: { total: 426547, float32: 426545, int32: 2 }
-}
-DATA: kernel ops: { graph: [ 'Const', 'Placeholder', 'Identity' ], convolution: [ '_FusedConv2D', 'MaxPool' ], basic_math: [ 'Relu', 'Sigmoid' ], transformation: [ 'Reshape' ], matrices: [ '_FusedMatMul' ] }
-INFO: graph model: /home/vlado/dev/human/models/handlandmark-full.json
-INFO: created on: 2021-10-31T12:27:49.343Z
-INFO: metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: '808.undefined' }
-INFO: model inputs based on signature
- { name: 'input_1', dtype: 'DT_FLOAT', shape: [ 1, 224, 224, 3 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 63 ] }
- { id: 1, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 63 ] }
- { id: 2, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
- { id: 3, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
-INFO: tensors: 103
-DATA: weights: {
- files: [ 'handlandmark-full.bin' ],
- size: { disk: 5431368, memory: 10862728 },
- count: { total: 103, float32: 102, int32: 1 },
- quantized: { float16: 102, none: 1 },
- values: { total: 2715682, float32: 2715680, int32: 2 }
-}
-DATA: kernel ops: {
- graph: [ 'Const', 'Placeholder', 'Identity' ],
- convolution: [ 'Conv2D', 'DepthwiseConv2dNative' ],
- arithmetic: [ 'AddV2', 'AddN' ],
- basic_math: [ 'Relu6', 'Sigmoid' ],
- reduction: [ 'Mean' ],
- matrices: [ '_FusedMatMul' ]
-}
-INFO: graph model: /home/vlado/dev/human/models/liveness.json
-INFO: created on: 2021-11-09T12:39:11.760Z
-INFO: metadata: { generatedBy: 'https://github.com/leokwu/livenessnet', convertedBy: 'https://github.com/vladmandic', version: '808.undefined' }
-INFO: model inputs based on signature
- { name: 'conv2d_1_input', dtype: 'DT_FLOAT', shape: [ -1, 32, 32, 3 ] }
-INFO: model outputs based on signature
- { id: 0, name: 'activation_6', dytpe: 'DT_FLOAT', shape: [ -1, 2 ] }
-INFO: tensors: 23
-DATA: weights: {
- files: [ 'liveness.bin' ],
- size: { disk: 592976, memory: 592976 },
- count: { total: 23, float32: 22, int32: 1 },
- quantized: { none: 23 },
- values: { total: 148244, float32: 148242, int32: 2 }
-}
-DATA: kernel ops: {
- graph: [ 'Const', 'Placeholder', 'Identity' ],
- convolution: [ '_FusedConv2D', 'MaxPool' ],
- arithmetic: [ 'Mul', 'Add', 'AddV2' ],
- transformation: [ 'Reshape' ],
- matrices: [ '_FusedMatMul' ],
- normalization: [ 'Softmax' ]
-}
-```
diff --git a/src/config.ts b/src/config.ts
index b054b450..08ff32c8 100644
--- a/src/config.ts
+++ b/src/config.ts
@@ -1,5 +1,14 @@
/* eslint-disable no-multi-spaces */
+/** Possible TensorFlow backends */
+export type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';
+
+/** Possible values for `human.warmup` */
+export type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';
+
+/** Possible segmentation model behavior */
+export type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'
+
/** Generic config type inherited by all module types */
export interface GenericConfig {
/** is module enabled? */
@@ -144,8 +153,10 @@ export interface ObjectConfig extends GenericConfig {
* remove background or replace it with user-provided background
*/
export interface SegmentationConfig extends GenericConfig {
- /** blur segmentation output by pixels for more realistic image */
- blur: number,
+ /** downsample ratio, adjust to reflect approximately how much of input is taken by body */
+ ratio: number,
+ /** possible rvm segmentation mode */
+ mode: SegmentationEnum,
}
/** Run input through image filters before inference
@@ -208,12 +219,6 @@ export interface GestureConfig {
/** is gesture detection enabled? */
enabled: boolean,
}
-/** Possible TensorFlow backends */
-export type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';
-
-/** Possible values for `human.warmup` */
-export type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';
-
/**
* Configuration interface definition for **Human** library
* Contains all configurable parameters
@@ -450,8 +455,9 @@ const config: Config = {
},
segmentation: {
enabled: false,
- modelPath: 'selfie.json',
- blur: 8,
+ modelPath: 'rvm.json',
+ ratio: 0.5,
+ mode: 'default',
},
};
diff --git a/src/human.ts b/src/human.ts
index cf50c4ee..44293a25 100644
--- a/src/human.ts
+++ b/src/human.ts
@@ -16,9 +16,9 @@ import { setModelLoadOptions } from './tfjs/load';
import * as tf from '../dist/tfjs.esm.js';
import * as app from '../package.json';
import * as backend from './tfjs/backend';
+import * as draw from './draw/draw';
import * as blazepose from './body/blazepose';
import * as centernet from './object/centernet';
-import * as draw from './draw/draw';
import * as efficientpose from './body/efficientpose';
import * as face from './face/face';
import * as facemesh from './face/facemesh';
@@ -29,13 +29,15 @@ import * as handtrack from './hand/handtrack';
import * as humangl from './tfjs/humangl';
import * as image from './image/image';
import * as interpolate from './util/interpolate';
+import * as meet from './segmentation/meet';
import * as match from './face/match';
import * as models from './models';
import * as movenet from './body/movenet';
import * as nanodet from './object/nanodet';
import * as persons from './util/persons';
import * as posenet from './body/posenet';
-import * as segmentation from './segmentation/segmentation';
+import * as rvm from './segmentation/rvm';
+import * as selfie from './segmentation/selfie';
import * as warmups from './warmup';
// type definitions
@@ -251,18 +253,23 @@ export class Human {
return image.process(input, this.config, getTensor);
}
- /** Segmentation method takes any input and returns processed canvas with body segmentation
- * - Segmentation is not triggered as part of detect process
+ /** Segmentation method takes any input and returns RGBA tensor
+ * Note: Segmentation is not triggered as part of detect process
+ *
* @param input - {@link Input}
- * @param background - {@link Input}
- * - Optional parameter background is used to fill the background with specific input
- * Returns:
- * - `data` as raw data array with per-pixel segmentation values
- * - `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
- * - `alpha` as grayscale canvas that represents segmentation alpha values
+ * Returns tensor which contains image data in RGBA format
*/
- async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {
- return segmentation.process(input, background, this.config);
+ async segmentation(input: Input, userConfig?: Partial): Promise {
+ if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;
+ if (!this.config.segmentation.enabled) return null;
+ const processed = await image.process(input, this.config);
+ if (!processed.tensor) return null;
+ let tensor: Tensor | null = null;
+ if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);
+ if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);
+ if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);
+ tf.dispose(processed.tensor);
+ return tensor;
}
/** Enhance method performs additional enhacements to face image previously detected for futher processing
diff --git a/src/models.ts b/src/models.ts
index 44bf8b12..b9ae9d0a 100644
--- a/src/models.ts
+++ b/src/models.ts
@@ -4,27 +4,29 @@
import { env } from './util/env';
import { log } from './util/util';
-import * as gear from './gear/gear';
-import * as ssrnetAge from './gear/ssrnet-age';
-import * as ssrnetGender from './gear/ssrnet-gender';
import * as antispoof from './face/antispoof';
import * as blazeface from './face/blazeface';
import * as blazepose from './body/blazepose';
import * as centernet from './object/centernet';
import * as efficientpose from './body/efficientpose';
import * as emotion from './gear/emotion';
-import * as mobilefacenet from './face/mobilefacenet';
-import * as insightface from './face/insightface';
import * as facemesh from './face/facemesh';
import * as faceres from './face/faceres';
+import * as gear from './gear/gear';
import * as handpose from './hand/handpose';
import * as handtrack from './hand/handtrack';
+import * as insightface from './face/insightface';
import * as iris from './face/iris';
import * as liveness from './face/liveness';
+import * as meet from './segmentation/meet';
+import * as mobilefacenet from './face/mobilefacenet';
import * as movenet from './body/movenet';
import * as nanodet from './object/nanodet';
import * as posenet from './body/posenet';
-import * as segmentation from './segmentation/segmentation';
+import * as rvm from './segmentation/rvm';
+import * as selfie from './segmentation/selfie';
+import * as ssrnetAge from './gear/ssrnet-age';
+import * as ssrnetGender from './gear/ssrnet-gender';
import { modelStats, ModelInfo } from './tfjs/load';
import type { GraphModel } from './tfjs/types';
import type { Human } from './human';
@@ -54,17 +56,18 @@ export class Models {
handskeleton: null | GraphModel | Promise = null;
handtrack: null | GraphModel | Promise = null;
liveness: null | GraphModel | Promise = null;
+ meet: null | GraphModel | Promise = null;
movenet: null | GraphModel | Promise = null;
nanodet: null | GraphModel | Promise = null;
posenet: null | GraphModel | Promise = null;
- segmentation: null | GraphModel | Promise = null;
+ selfie: null | GraphModel | Promise = null;
+ rvm: null | GraphModel | Promise = null;
antispoof: null | GraphModel | Promise = null;
}
/** structure that holds global stats for currently loaded models */
export interface ModelStats {
numLoadedModels: number,
- numEnabledModels: undefined,
numDefinedModels: number,
percentageLoaded: number,
totalSizeFromManifest: number,
@@ -90,7 +93,6 @@ export const getModelStats = (currentInstance: Human): ModelStats => {
const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
return {
numLoadedModels: Object.values(modelStats).length,
- numEnabledModels: undefined,
numDefinedModels: Object.keys(instance.models).length,
percentageLoaded,
totalSizeFromManifest,
@@ -141,7 +143,9 @@ export async function load(currentInstance: Human): Promise {
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);
if (instance.config.object.enabled && !instance.models.centernet && instance.config.object.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);
if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);
- if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);
+ if (instance.config.segmentation.enabled && !instance.models.selfie && instance.config.segmentation.modelPath?.includes('selfie')) instance.models.selfie = selfie.load(instance.config);
+ if (instance.config.segmentation.enabled && !instance.models.meet && instance.config.segmentation.modelPath?.includes('meet')) instance.models.meet = meet.load(instance.config);
+ if (instance.config.segmentation.enabled && !instance.models.rvm && instance.config.segmentation.modelPath?.includes('rvm')) instance.models.rvm = rvm.load(instance.config);
// models are loaded in parallel asynchronously so lets wait until they are actually loaded
for await (const model of Object.keys(instance.models)) {
@@ -159,7 +163,7 @@ export function validateModel(currentInstance: Human | null, model: GraphModel |
if (!instance) log('instance not registred');
if (!instance?.config?.validateModels) return null;
const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];
- const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul'];
+ const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];
const ops: string[] = [];
const missing: string[] = [];
interface Op { name: string, category: string, op: string }
diff --git a/src/segmentation/meet.ts b/src/segmentation/meet.ts
new file mode 100644
index 00000000..1a2e95ee
--- /dev/null
+++ b/src/segmentation/meet.ts
@@ -0,0 +1,54 @@
+/**
+ * Image segmentation for body detection model
+ *
+ * Based on:
+ * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)
+ */
+
+import { log } from '../util/util';
+import * as tf from '../../dist/tfjs.esm.js';
+import { loadModel } from '../tfjs/load';
+import { constants } from '../tfjs/constants';
+import type { GraphModel, Tensor } from '../tfjs/types';
+import type { Config } from '../config';
+import { env } from '../util/env';
+
+let model: GraphModel;
+
+export async function load(config: Config): Promise {
+ if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);
+ else if (config.debug) log('cached model:', model['modelUrl']);
+ return model;
+}
+
+export async function predict(input: Tensor, config: Config): Promise {
+ if (!model) model = await load(config);
+ if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model
+ const t: Record = {};
+ t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);
+ t.norm = tf.div(t.resize, constants.tf255);
+ t.res = model.execute(t.norm) as Tensor;
+ t.squeeze = tf.squeeze(t.res, 0);
+ // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg
+ [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);
+ // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel
+ t.fg = tf.softmax(t.fgRaw);
+ t.mul = tf.mul(t.fg, constants.tf255);
+ t.expand = tf.expandDims(t.mul, 2);
+ t.output = tf.image.resizeBilinear(t.expand, [input.shape[1], input.shape[2]]);
+ let rgba: Tensor;
+ switch (config.segmentation.mode || 'default') {
+ case 'default':
+ t.input = tf.squeeze(input);
+ t.concat = tf.concat([t.input, t.output], -1);
+ rgba = tf.cast(t.concat, 'int32'); // combined original with alpha
+ break;
+ case 'alpha':
+ rgba = tf.cast(t.output, 'int32'); // just get alpha value from model
+ break;
+ default:
+ rgba = tf.tensor(0);
+ }
+ Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));
+ return rgba;
+}
diff --git a/src/segmentation/rvm.ts b/src/segmentation/rvm.ts
new file mode 100644
index 00000000..b75891cd
--- /dev/null
+++ b/src/segmentation/rvm.ts
@@ -0,0 +1,104 @@
+/**
+ * Image segmentation for body detection model
+ *
+ * Based on:
+ * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)
+ */
+
+import { log } from '../util/util';
+import * as tf from '../../dist/tfjs.esm.js';
+import { loadModel } from '../tfjs/load';
+import { constants } from '../tfjs/constants';
+import type { GraphModel, Tensor } from '../tfjs/types';
+import type { Config } from '../config';
+import { env } from '../util/env';
+
+let model: GraphModel;
+
+// internal state varaibles
+const outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];
+const t: Record = {}; // contains input tensor and recurrent states
+let ratio = 0;
+
+function init(config: Config) {
+ tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);
+ t.r1i = tf.tensor(0.0);
+ t.r2i = tf.tensor(0.0);
+ t.r3i = tf.tensor(0.0);
+ t.r4i = tf.tensor(0.0);
+ ratio = config.segmentation.ratio || 0.5;
+ t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio
+}
+
+export async function load(config: Config): Promise {
+ if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);
+ else if (config.debug) log('cached model:', model['modelUrl']);
+ init(config);
+ return model;
+}
+
+const normalize = (r: Tensor) => tf.tidy(() => {
+ const squeeze = tf.squeeze(r, ([0]));
+ const mul = tf.mul(squeeze, constants.tf255);
+ const cast = tf.cast(mul, 'int32');
+ return cast as Tensor;
+});
+
+function getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present
+ const rgb = fgr
+ ? normalize(fgr) // normalize and use value
+ : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion
+ const a = pha
+ ? normalize(pha) // normalize and use value
+ : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion
+ const rgba = tf.concat([rgb, a], -1);
+ tf.dispose([rgb, a]);
+ return rgba;
+}
+
+function getState(state: Tensor): Tensor { // gets internal recurrent states
+ return tf.tidy(() => {
+ const r: Record = {};
+ r.unstack = tf.unstack(state, -1);
+ r.concat = tf.concat(r.unstack, 1);
+ r.split = tf.split(r.concat, 4, 1);
+ r.stack = tf.concat(r.split, 2);
+ r.squeeze = tf.squeeze(r.stack, [0]);
+ r.expand = tf.expandDims(r.squeeze, -1);
+ r.add = tf.add(r.expand, 1);
+ r.mul = tf.mul(r.add, 127.5);
+ r.cast = tf.cast(r.mul, 'int32');
+ r.tile = tf.tile(r.cast, [1, 1, 3]) as Tensor;
+ r.alpha = tf.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, 'int32');
+ return tf.concat([r.tile, r.alpha], -1) as Tensor;
+ });
+}
+
+export async function predict(input: Tensor, config: Config): Promise {
+ if (!model) model = await load(config);
+ if (!model?.['executor']) return null;
+ // const expand = tf.expandDims(input, 0);
+ t.src = tf.div(input, 255);
+ if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed
+ const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model
+ let rgba: Tensor;
+ switch (config.segmentation.mode || 'default') {
+ case 'default':
+ rgba = getRGBA(fgr, pha);
+ break;
+ case 'alpha':
+ rgba = getRGBA(null, pha);
+ break;
+ case 'foreground':
+ rgba = getRGBA(fgr, null);
+ break;
+ case 'state':
+ rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o
+ break;
+ default:
+ rgba = tf.tensor(0);
+ }
+ tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);
+ [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states
+ return rgba;
+}
diff --git a/src/segmentation/selfie.ts b/src/segmentation/selfie.ts
new file mode 100644
index 00000000..addd7e0f
--- /dev/null
+++ b/src/segmentation/selfie.ts
@@ -0,0 +1,49 @@
+/**
+ * Image segmentation for body detection model
+ *
+ * Based on:
+ * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)
+ */
+
+import { log } from '../util/util';
+import * as tf from '../../dist/tfjs.esm.js';
+import { loadModel } from '../tfjs/load';
+import { constants } from '../tfjs/constants';
+import type { GraphModel, Tensor } from '../tfjs/types';
+import type { Config } from '../config';
+import { env } from '../util/env';
+
+let model: GraphModel;
+
+export async function load(config: Config): Promise {
+ if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);
+ else if (config.debug) log('cached model:', model['modelUrl']);
+ return model;
+}
+
+export async function predict(input: Tensor, config: Config): Promise {
+ if (!model) model = await load(config);
+ if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model
+ const t: Record = {};
+ t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);
+ t.norm = tf.div(t.resize, constants.tf255);
+ t.res = model.execute(t.norm) as Tensor;
+ t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]
+ t.alpha = tf.image.resizeBilinear(t.squeeze, [input.shape[1], input.shape[2]]); // model selfie has a single channel that we can use directly
+ t.mul = tf.mul(t.alpha, constants.tf255);
+ let rgba: Tensor;
+ switch (config.segmentation.mode || 'default') {
+ case 'default':
+ t.input = tf.squeeze(input);
+ t.concat = tf.concat([t.input, t.mul], -1);
+ rgba = tf.cast(t.concat, 'int32'); // combined original with alpha
+ break;
+ case 'alpha':
+ rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model
+ break;
+ default:
+ rgba = tf.tensor(0);
+ }
+ Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));
+ return rgba;
+}
diff --git a/src/tfjs/load.ts b/src/tfjs/load.ts
index 98421d80..db477cb0 100644
--- a/src/tfjs/load.ts
+++ b/src/tfjs/load.ts
@@ -3,7 +3,7 @@ import * as tf from '../../dist/tfjs.esm.js';
import type { GraphModel } from './types';
import type { Config } from '../config';
import * as modelsDefs from '../../models/models.json';
-import { validateModel } from '../models';
+// import { validateModel } from '../models';
const options = {
cacheModels: true,
@@ -86,6 +86,6 @@ export async function loadModel(modelPath: string | undefined): Promise tf.dispose(t));
else tf.dispose(res);
} catch {
- log('compile fail model:', modelName);
+ if (instance.config.debug) log('compile fail model:', modelName);
}
tf.dispose(tensor);
}
diff --git a/test/build.log b/test/build.log
index 6580d0fd..f7faa44e 100644
--- a/test/build.log
+++ b/test/build.log
@@ -38,3 +38,2690 @@
2022-09-30 10:10:53 [35mSTATE:[39m Models {"folder":"../nanodet/models","models":4}
2022-09-30 10:10:53 [35mSTATE:[39m Models: {"count":57,"totalSize":383017442}
2022-09-30 10:10:53 [36mINFO: [39m Human Build complete... {"logFile":"test/build.log"}
+2022-10-02 11:25:38 [36mINFO: [39m @vladmandic/human version 2.11.0
+2022-10-02 11:25:38 [36mINFO: [39m User: vlado Platform: linux Arch: x64 Node: v18.10.0
+2022-10-02 11:25:38 [36mINFO: [39m Application: {"name":"@vladmandic/human","version":"2.11.0"}
+2022-10-02 11:25:38 [36mINFO: [39m Environment: {"profile":"development","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true}
+2022-10-02 11:25:38 [36mINFO: [39m Toolchain: {"build":"0.7.14","esbuild":"0.15.10","typescript":"4.8.4","typedoc":"0.23.15","eslint":"8.24.0"}
+2022-10-02 11:25:38 [36mINFO: [39m Build: {"profile":"development","steps":["serve","watch","compile"]}
+2022-10-02 11:25:38 [35mSTATE:[39m WebServer: {"ssl":false,"port":10030,"root":"."}
+2022-10-02 11:25:38 [35mSTATE:[39m WebServer: {"ssl":true,"port":10031,"root":".","sslKey":"node_modules/@vladmandic/build/cert/https.key","sslCrt":"node_modules/@vladmandic/build/cert/https.crt"}
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+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":78,"inputBytes":669502,"outputBytes":501672}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":167,"outputBytes":1118}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":78,"inputBytes":669522,"outputBytes":501688}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":206,"outputBytes":1205}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":78,"inputBytes":669609,"outputBytes":501779}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1125,"outputBytes":1439}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2169,"outputBytes":856}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":78,"inputBytes":669260,"outputBytes":504265}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":11,"inputBytes":2346,"outputBytes":2818325}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":78,"inputBytes":3486729,"outputBytes":1694086}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":78,"inputBytes":3486729,"outputBytes":3115540}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5841,"outputBytes":3814}
+2022-10-02 15:03:07 [35mSTATE:[39m Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17155,"outputBytes":13538}
diff --git a/test/test-backend-node-gpu.js b/test/test-backend-node-gpu.js
index e817b972..0e2c07d9 100644
--- a/test/test-backend-node-gpu.js
+++ b/test/test-backend-node-gpu.js
@@ -21,7 +21,7 @@ const config = {
hand: { enabled: true },
body: { enabled: true },
object: { enabled: true },
- segmentation: { enabled: true },
+ segmentation: { enabled: false },
filter: { enabled: false },
};
diff --git a/test/test-backend-node-wasm.js b/test/test-backend-node-wasm.js
index cf259b28..da4cc623 100644
--- a/test/test-backend-node-wasm.js
+++ b/test/test-backend-node-wasm.js
@@ -10,7 +10,7 @@ H.env.Image = Image; // requires monkey-patch as wasm does not have tf.browser n
const config = {
cacheSensitivity: 0,
- modelBasePath: 'https://vladmandic.github.io/human/models/',
+ modelBasePath: 'https://vladmandic.github.io/human-models/models/',
backend: 'wasm',
// wasmPath: 'node_modules/@tensorflow/tfjs-backend-wasm/dist/',
wasmPath: `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`,
@@ -30,7 +30,7 @@ const config = {
hand: { enabled: true, rotation: false },
body: { enabled: true },
object: { enabled: true },
- segmentation: { enabled: true },
+ segmentation: { enabled: false },
filter: { enabled: false },
};
diff --git a/test/test-backend-node.js b/test/test-backend-node.js
index 76a9f5c9..0bb67fa3 100644
--- a/test/test-backend-node.js
+++ b/test/test-backend-node.js
@@ -22,7 +22,7 @@ const config = {
hand: { enabled: true },
body: { enabled: true },
object: { enabled: true },
- segmentation: { enabled: true },
+ segmentation: { enabled: false },
filter: { enabled: false },
};
diff --git a/test/test-node-main.js b/test/test-node-main.js
index b68285a1..23c228f5 100644
--- a/test/test-node-main.js
+++ b/test/test-node-main.js
@@ -294,7 +294,7 @@ async function test(Human, inputConfig) {
await human.load();
const models = Object.keys(human.models).map((model) => ({ name: model, loaded: (human.models[model] !== null), url: human.models[model] ? human.models[model]['modelUrl'] : null }));
const loaded = models.filter((model) => model.loaded);
- if (models.length === 23 && loaded.length === 12) log('state', 'passed: models loaded', models.length, loaded.length, models);
+ if (models.length === 25 && loaded.length === 11) log('state', 'passed: models loaded', models.length, loaded.length, models);
else log('error', 'failed: models loaded', models.length, loaded.length, models);
log('info', 'memory:', { memory: human.tf.memory() });
log('info', 'state:', { state: human.tf.engine().state });
@@ -539,14 +539,18 @@ async function test(Human, inputConfig) {
const ctx = inputCanvas.getContext('2d');
ctx.drawImage(inputImage, 0, 0); // draw input image onto canvas
res = await human.detect(inputCanvas);
- if (!res || res?.face?.length !== 1) log('error', 'failed: monkey patch');
+ if (res?.face?.length !== 1) log('error', 'failed: monkey patch');
else log('state', 'passed: monkey patch');
// test segmentation
- res = await human.segmentation(inputCanvas, inputCanvas);
- if (!res || !res.data || !res.canvas) log('error', 'failed: segmentation');
- else log('state', 'passed: segmentation', [res.data.length]);
- human.env.Canvas = undefined;
+ config.segmentation = { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/rvm.json' };
+ res = await human.segmentation(inputCanvas, config);
+ if (res?.shape?.length !== 3) log('error', 'failed: segmentation');
+ else log('state', 'passed: segmentation', [res.size]);
+ human.tf.dispose(res);
+ config.segmentation = { enabled: false };
+
+ human.env.Canvas = null; // disable canvas monkey-patch
// check if all instances reported same
const tensors1 = human.tf.engine().state.numTensors;
diff --git a/test/test.log b/test/test.log
index ba3d682f..b6f57b2e 100644
--- a/test/test.log
+++ b/test/test.log
@@ -1,1002 +1,999 @@
-2022-09-30 10:04:19 [36mINFO: [39m @vladmandic/human version 2.11.0
-2022-09-30 10:04:19 [36mINFO: [39m User: vlado Platform: linux Arch: x64 Node: v18.10.0
-2022-09-30 10:04:19 [36mINFO: [39m demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-fetch.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]}]
-2022-09-30 10:04:19 [36mINFO: [39m {"cmd":"../demo/nodejs/node.js","args":[]} start
-2022-09-30 10:04:20 [36mINFO: [39m {"cmd":"../demo/nodejs/node-simple.js","args":[]} start
-2022-09-30 10:04:21 [36mINFO: [39m {"cmd":"../demo/nodejs/node-fetch.js","args":[]} start
-2022-09-30 10:04:24 [36mINFO: [39m {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start
-2022-09-30 10:04:24 [36mINFO: [39m {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start
-2022-09-30 10:04:25 [36mINFO: [39m {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start
-2022-09-30 10:04:26 [36mINFO: [39m {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start
-2022-09-30 10:04:27 [36mINFO: [39m {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start
-2022-09-30 10:04:38 [36mINFO: [39m {"cmd":"../demo/facematch/node-match.js","args":[]} start
-2022-09-30 10:04:39 [36mINFO: [39m tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"]
-2022-09-30 10:04:39 [36mINFO: [39m
-2022-09-30 10:04:39 [36mINFO: [39m test-node-load.js start
-2022-09-30 10:04:40 [36mINFO: [39m test-node-load.js load start {"human":"2.11.0","tf":"3.20.0","progress":0}
-2022-09-30 10:04:40 [32mDATA: [39m test-node-load.js load interval {"elapsed":1,"progress":0}
-2022-09-30 10:04:40 [32mDATA: [39m test-node-load.js load interval {"elapsed":11,"progress":0}
-2022-09-30 10:04:40 [32mDATA: [39m test-node-load.js load interval {"elapsed":23,"progress":0.05339166087267679}
-2022-09-30 10:04:40 [32mDATA: [39m test-node-load.js load interval {"elapsed":33,"progress":0.2135162934143239}
-2022-09-30 10:04:40 [32mDATA: [39m test-node-load.js load interval {"elapsed":60,"progress":0.3299591712723044}
-2022-09-30 10:04:40 [32mDATA: [39m test-node-load.js load interval {"elapsed":81,"progress":0.5125946867158943}
-2022-09-30 10:04:40 [32mDATA: [39m test-node-load.js load interval {"elapsed":91,"progress":0.7259096583739463}
-2022-09-30 10:04:40 [35mSTATE:[39m test-node-load.js passed {"progress":1}
-2022-09-30 10:04:40 [36mINFO: [39m test-node-load.js load final {"progress":1}
-2022-09-30 10:04:40 [32mDATA: [39m test-node-load.js load interval {"elapsed":366,"progress":1}
-2022-09-30 10:04:40 [36mINFO: [39m
-2022-09-30 10:04:40 [36mINFO: [39m test-node-gear.js start
-2022-09-30 10:04:40 [32mDATA: [39m test-node-gear.js input: ["samples/in/ai-face.jpg"]
-2022-09-30 10:04:42 [35mSTATE:[39m test-node-gear.js passed: gear faceres samples/in/ai-face.jpg
-2022-09-30 10:04:42 [32mDATA: [39m test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92}
-2022-09-30 10:04:42 [35mSTATE:[39m test-node-gear.js passed: gear gear samples/in/ai-face.jpg
-2022-09-30 10:04:42 [32mDATA: [39m test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}]}
-2022-09-30 10:04:42 [35mSTATE:[39m test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg
-2022-09-30 10:04:42 [32mDATA: [39m test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99}
-2022-09-30 10:04:42 [36mINFO: [39m
-2022-09-30 10:04:42 [36mINFO: [39m test-backend-node.js start
-2022-09-30 10:04:42 [36mINFO: [39m test-backend-node.js test: configuration validation
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js passed: configuration default validation []
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}]
-2022-09-30 10:04:42 [36mINFO: [39m test-backend-node.js test: model load
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js passed: models loaded 23 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"insightface","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"file://models/selfie.json"},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}]
-2022-09-30 10:04:42 [36mINFO: [39m test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1921,"numDataBuffers":1921,"numBytes":63673064}}
-2022-09-30 10:04:42 [36mINFO: [39m test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":63673064,"numTensors":1921,"numStringTensors":0,"numDataBuffers":1921,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}}
-2022-09-30 10:04:42 [36mINFO: [39m test-backend-node.js test: warmup
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js passed: create human
-2022-09-30 10:04:42 [36mINFO: [39m test-backend-node.js human version: 2.11.0
-2022-09-30 10:04:42 [36mINFO: [39m test-backend-node.js platform: linux x64 agent: NodeJS v18.10.0
-2022-09-30 10:04:42 [36mINFO: [39m test-backend-node.js tfjs version: 3.20.0
-2022-09-30 10:04:42 [36mINFO: [39m test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v18.10.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"3.20.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.7.3-dev20220521","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169}
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js passed: set backend: tensorflow
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js tensors 1921
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js passed: load models
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js result: defined models: 23 loaded models: 12
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js passed: warmup: none default
-2022-09-30 10:04:42 [32mDATA: [39m test-backend-node.js result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {}
-2022-09-30 10:04:42 [32mDATA: [39m test-backend-node.js result: performance: load: null total: null
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js passed: warmup none result match
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js event: image
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js event: detect
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js event: warmup
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js passed: warmup: face default
-2022-09-30 10:04:42 [32mDATA: [39m test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4}
-2022-09-30 10:04:42 [32mDATA: [39m test-backend-node.js result: performance: load: null total: 361
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js passed: warmup face result match
-2022-09-30 10:04:42 [35mSTATE:[39m test-backend-node.js event: image
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js event: detect
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js event: warmup
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: warmup: body default
-2022-09-30 10:04:43 [32mDATA: [39m test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17}
-2022-09-30 10:04:43 [32mDATA: [39m test-backend-node.js result: performance: load: null total: 253
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: warmup body result match
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]}
-2022-09-30 10:04:43 [36mINFO: [39m test-backend-node.js test: details verification
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js start default
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864}
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js event: image
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js event: detect
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: detect: samples/in/ai-body.jpg default
-2022-09-30 10:04:43 [32mDATA: [39m test-backend-node.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17}
-2022-09-30 10:04:43 [32mDATA: [39m test-backend-node.js result: performance: load: null total: 246
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details face length 1
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details face score 1 0.93 1
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details face age/gender 23.7 female 0.97 85.47
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details face arrays 4 478 1024
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"}
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details face anti-spoofing 0.79
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details face liveness 0.83
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details body length 1
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details body 0.92 17 6
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details hand length 1
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details hand 0.51 0.73 point
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details hand arrays 21 5 7
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details gesture length 7
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"}
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details object length 1
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: details object 0.72 person
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928}
-2022-09-30 10:04:43 [35mSTATE:[39m test-backend-node.js event: image
-2022-09-30 10:04:44 [35mSTATE:[39m test-backend-node.js event: detect
-2022-09-30 10:04:44 [35mSTATE:[39m test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32
-2022-09-30 10:04:44 [35mSTATE:[39m test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928}
-2022-09-30 10:04:44 [35mSTATE:[39m test-backend-node.js event: image
-2022-09-30 10:04:44 [35mSTATE:[39m test-backend-node.js event: detect
-2022-09-30 10:04:44 [35mSTATE:[39m test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32
-2022-09-30 10:04:44 [35mSTATE:[39m test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864}
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-2022-09-30 10:05:03 [35mSTATE:[39m test-backend-node-gpu.js passed: details body 0.92 17 6
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-2022-09-30 10:05:03 [35mSTATE:[39m test-backend-node-gpu.js passed: details hand 0.51 0.73 point
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-2022-09-30 10:05:03 [35mSTATE:[39m test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"}
-2022-09-30 10:05:03 [35mSTATE:[39m test-backend-node-gpu.js passed: details object length 1
-2022-09-30 10:05:03 [35mSTATE:[39m test-backend-node-gpu.js passed: details object 0.72 person
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-2022-09-30 10:05:05 [32mDATA: [39m test-backend-node-gpu.js result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17}
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-2022-09-30 10:05:05 [35mSTATE:[39m test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync
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+2022-10-02 15:07:05 [35mSTATE:[39m all tests complete
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"../demo/nodejs/node.js","passed":1,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"../demo/nodejs/node-fetch.js","passed":1,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"test-node-load.js","passed":1,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"test-node-gear.js","passed":3,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"test-backend-node.js","passed":125,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"test-backend-node-gpu.js","passed":125,"failed":0}
+2022-10-02 15:07:05 [36mINFO: [39m status {"test":"test-backend-node-wasm.js","passed":124,"failed":2}
+2022-10-02 15:07:05 [36mINFO: [39m failures {"count":2}
+2022-10-02 15:07:05 [33mWARN: [39m failed {"test":"test-backend-node-wasm.js","message":["error",["failed: nanodet",[]]]}
+2022-10-02 15:07:05 [33mWARN: [39m failed {"test":"test-backend-node-wasm.js","message":["error",["failed: face attention",{"annotations":0}]]}
diff --git a/tsconfig.json b/tsconfig.json
index bbde1847..81afeb82 100644
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -52,7 +52,7 @@
"tabSize": 2
},
"exclude": ["node_modules/", "types/", "dist/**/*.js"],
- "include": ["src", "tfjs/*.ts", "types/human.d.ts", "test/**/*.ts", "demo/**/*.ts"],
+ "include": ["src", "tfjs/*.ts", "types/human.d.ts", "test/**/*.ts", "demo/**/*.ts", "demo/segmentation/index.js", "demo/index.js"],
"typedocOptions": {
"externalPattern": ["node_modules/", "tfjs/"]
}
diff --git a/wiki b/wiki
index 7ea124ad..b6432fc4 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 7ea124ad02f27fa74241e5bfc6f34ceab1062de5
+Subproject commit b6432fc419f6ee9aaf36e94b6be21930edb50bc0