refactor draw and models namespaces

CHANGELOG.md

@@ -9,7 +9,10 @@
   
 ## Changelog
   
-### **HEAD -> main** 2022/11/16 mandic00@live.com
+### **HEAD -> main** 2022/11/17 mandic00@live.com
+
+
+### **origin/main** 2022/11/16 mandic00@live.com
 
 - added webcam id specification
 - include external typedefs

README.md

@@ -68,7 +68,7 @@
 - **Full** [[*Live*]](https://vladmandic.github.io/human/demo/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo): Main browser demo app that showcases all Human capabilities
 - **Simple** [[*Live*]](https://vladmandic.github.io/human/demo/typescript/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/typescript): Simple demo in WebCam processing demo in TypeScript
 - **Embedded** [[*Live*]](https://vladmandic.github.io/human/demo/video/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/video/index.html): Even simpler demo with tiny code embedded in HTML file
-- **Face Match** [[*Live*]](https://vladmandic.github.io/human/demo/facematch/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facematch): Extract faces from images, calculates face descriptors and simmilarities and matches them to known database
+- **Face Match** [[*Live*]](https://vladmandic.github.io/human/demo/facematch/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facematch): Extract faces from images, calculates face descriptors and similarities and matches them to known database
 - **Face ID** [[*Live*]](https://vladmandic.github.io/human/demo/faceid/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/faceid): Runs multiple checks to validate webcam input before performing face match to faces in IndexDB
 - **Multi-thread** [[*Live*]](https://vladmandic.github.io/human/demo/multithread/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/multithread): Runs each Human module in a separate web worker for highest possible performance  
 - **NextJS** [[*Live*]](https://vladmandic.github.io/human-next/out/index.html) [[*Details*]](https://github.com/vladmandic/human-next): Use Human with TypeScript, NextJS and ReactJS
@@ -377,6 +377,16 @@ drawResults(); // start draw loop
 
 And for even better results, you can run detection in a separate web worker thread
 
+<br><hr><br>
+
+## Detailed Usage
+
+- [**Wiki Home**](https://github.com/vladmandic/human/wiki)
+- [**List of all available methods, properies and namespaces**](https://github.com/vladmandic/human/wiki/Usage)
+- [**TypeDoc API Specification - Main class**](https://vladmandic.github.io/human/typedoc/classes/Human.html)
+- [**TypeDoc API Specification - Full**](https://vladmandic.github.io/human/typedoc/)
+
+
 <br><hr><br>
 
 ## TypeDefs

TODO.md

@@ -80,6 +80,15 @@ Architecture:
 - Upgrade to **TFJS 4.0** with **strong typing**  
   see [notes](https://github.com/vladmandic/human#typedefs) on how to use  
 - `TypeDef` refactoring  
+- Re-architect `human.models` namespace for better dynamic model handling  
+  Added additional methods `load`, `list`, `loaded`, `reset`  
 - Add named export for improved bundler support when using non-default imports  
-- Support for `NodeJS` v19  
+- Support for **NodeJS v19**  
 - Upgrade to **TypeScript 4.9**  
+
+Breaking changes:
+- Replaced `result.face[n].iris` with `result.face[n].distance`  
+- Replaced `human.getModelStats()` with `human.models.stats()`  
+- Moved `human.similarity`, `human.distance` and `human.match` to namespace `human.match.*`  
+- Obsolete `human.enhance()`  
+- Obsolete `human.gl`  

demo/faceid/index.ts

@@ -250,7 +250,7 @@ async function detectFace() {
   }
   const db = await indexDb.load();
   const descriptors = db.map((rec) => rec.descriptor).filter((desc) => desc.length > 0);
-  const res = human.match(current.face.embedding, descriptors, matchOptions);
+  const res = human.match.find(current.face.embedding, descriptors, matchOptions);
   current.record = db[res.index] || null;
   if (current.record) {
     log(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1000 * res.similarity) / 10}%`);

demo/facematch/README.md

@@ -11,7 +11,7 @@
 ## Browser Face Recognition Demo
 
 - `demo/facematch`: Demo for Browsers that uses all face description and embedding features to  
-detect, extract and identify all faces plus calculate simmilarity between them
+detect, extract and identify all faces plus calculate similarity between them
 
 It highlights functionality such as:
 

demo/facematch/facematch.js

@@ -1,7 +1,7 @@
 /**
  * Human demo for browsers
  *
- * Demo for face descriptor analysis and face simmilarity analysis
+ * Demo for face descriptor analysis and face similarity analysis
  */
 
 /** @type {Human} */
@@ -70,6 +70,9 @@ async function SelectFaceCanvas(face) {
   document.getElementById('orig').style.filter = 'blur(16px)';
   if (face.tensor) {
     title('Sorting Faces by Similarity');
+    const c = document.getElementById('orig');
+    await human.tf.browser.toPixels(face.tensor, c);
+    /*
     const enhanced = human.enhance(face);
     if (enhanced) {
       const c = document.getElementById('orig');
@@ -81,8 +84,9 @@ async function SelectFaceCanvas(face) {
       ctx.font = 'small-caps 0.4rem "Lato"';
       ctx.fillStyle = 'rgba(255, 255, 255, 1)';
     }
+    */
     const arr = db.map((rec) => rec.embedding);
-    const res = await human.match(face.embedding, arr);
+    const res = await human.match.find(face.embedding, arr);
     log('Match:', db[res.index].name);
     const emotion = face.emotion[0] ? `${Math.round(100 * face.emotion[0].score)}% ${face.emotion[0].emotion}` : 'N/A';
     document.getElementById('desc').innerHTML = `
@@ -103,7 +107,7 @@ async function SelectFaceCanvas(face) {
   for (const canvas of canvases) {
     // calculate similarity from selected face to current one in the loop
     const current = all[canvas.tag.sample][canvas.tag.face];
-    const similarity = human.similarity(face.embedding, current.embedding);
+    const similarity = human.match.similarity(face.embedding, current.embedding);
     canvas.tag.similarity = similarity;
     // get best match
     // draw the canvas
@@ -120,7 +124,7 @@ async function SelectFaceCanvas(face) {
     ctx.font = 'small-caps 1rem "Lato"';
     const start = human.now();
     const arr = db.map((rec) => rec.embedding);
-    const res = await human.match(current.embedding, arr);
+    const res = await human.match.find(current.embedding, arr);
     time += (human.now() - start);
     if (res.similarity > minScore) ctx.fillText(`DB: ${(100 * res.similarity).toFixed(1)}% ${db[res.index].name}`, 4, canvas.height - 30);
   }
@@ -161,7 +165,7 @@ async function AddFaceCanvas(index, res, fileName) {
     ctx.fillStyle = 'rgba(255, 255, 255, 1)';
     ctx.fillText(`${res.face[i].age}y ${(100 * (res.face[i].genderScore || 0)).toFixed(1)}% ${res.face[i].gender}`, 4, canvas.height - 6);
     const arr = db.map((rec) => rec.embedding);
-    const result = human.match(res.face[i].embedding, arr);
+    const result = human.match.find(res.face[i].embedding, arr);
     ctx.font = 'small-caps 1rem "Lato"';
     if (result.similarity && res.similarity > minScore) ctx.fillText(`${(100 * result.similarity).toFixed(1)}% ${db[result.index].name}`, 4, canvas.height - 30);
     document.getElementById('faces').appendChild(canvas);
@@ -256,7 +260,7 @@ async function main() {
   title('');
   log('Ready');
   human.validate(userConfig);
-  human.similarity([], []);
+  human.match.similarity([], []);
 }
 
 window.onload = main;

demo/index.js

@@ -222,21 +222,13 @@ async function calcSimmilarity(result) {
     compare.original = result;
     log('setting face compare baseline:', result.face[0]);
     if (result.face[0].tensor) {
-      const enhanced = human.enhance(result.face[0]);
-      if (enhanced) {
       const c = document.getElementById('orig');
-        const squeeze = human.tf.squeeze(enhanced);
+      human.tf.browser.toPixels(result.face[0].tensor, c);
-        const norm = human.tf.div(squeeze, 255);
-        human.tf.browser.toPixels(norm, c);
-        human.tf.dispose(enhanced);
-        human.tf.dispose(squeeze);
-        human.tf.dispose(norm);
-      }
     } else {
       document.getElementById('compare-canvas').getContext('2d').drawImage(compare.original.canvas, 0, 0, 200, 200);
     }
   }
-  const similarity = human.similarity(compare.original.face[0].embedding, result.face[0].embedding);
+  const similarity = human.match.similarity(compare.original.face[0].embedding, result.face[0].embedding);
   document.getElementById('similarity').innerText = `similarity: ${Math.trunc(1000 * similarity) / 10}%`;
 }
 

demo/nodejs/README.md

@@ -82,7 +82,7 @@ node demo/nodejs/node.js
     detector: { modelPath: 'handdetect.json' },
     skeleton: { modelPath: 'handskeleton.json' }
   },
-  object: { enabled: true, modelPath: 'mb3-centernet.json', minConfidence: 0.2, iouThreshold: 0.4, maxDetected: 10, skipFrames: 19 }
+  object: { enabled: true, modelPath: 'centernet.json', minConfidence: 0.2, iouThreshold: 0.4, maxDetected: 10, skipFrames: 19 }
 }
 08:52:15.673 Human: version: 2.0.0
 08:52:15.674 Human: tfjs version: 3.6.0
@@ -96,7 +96,7 @@ node demo/nodejs/node.js
 08:52:15.847 Human: load model: file://models/handdetect.json
 08:52:15.847 Human: load model: file://models/handskeleton.json
 08:52:15.914 Human: load model: file://models/movenet-lightning.json
-08:52:15.957 Human: load model: file://models/mb3-centernet.json
+08:52:15.957 Human: load model: file://models/centernet.json
 08:52:16.015 Human: load model: file://models/faceres.json
 08:52:16.015 Human: tf engine state: 50796152 bytes 1318 tensors
 2021-06-01 08:52:16 INFO:  Loaded: [ 'face', 'movenet', 'handpose', 'emotion', 'centernet', 'faceres', [length]: 6 ]

demo/nodejs/node-similarity.js

@@ -57,7 +57,7 @@ async function main() {
   if (!res1 || !res1.face || res1.face.length === 0 || !res2 || !res2.face || res2.face.length === 0) {
     throw new Error('Could not detect face descriptors');
   }
-  const similarity = human.similarity(res1.face[0].embedding, res2.face[0].embedding, { order: 2 });
+  const similarity = human.match.similarity(res1.face[0].embedding, res2.face[0].embedding, { order: 2 });
   log.data('Similarity: ', similarity);
 }
 

demo/segmentation/index.js

@@ -52,7 +52,7 @@ async function main() {
   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 stats:', human.models.stats());
   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;

demo/typescript/index.js

@@ -4,100 +4,6 @@
   author: <https://github.com/vladmandic>'
 */
 
-
+import*as m from"../../dist/human.esm.js";var f=1920,b={modelBasePath:"../../models",filter:{enabled:!0,equalization:!1,flip:!1,width:f},face:{enabled:!0,detector:{rotation:!0},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!0},hand:{enabled:!1},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new m.Human(b);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(" ")+`
-// demo/typescript/index.ts
+`,console.log(...t)},r=t=>a.fps.innerText=t,g=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function u(){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(u)}async function p(){var d,i,c;if(!a.video.paused){let l=e.next(e.result),w=await e.image(a.video);e.draw.canvas(w.canvas,a.canvas);let v={bodyLabels:`person confidence [score] and ${(c=(i=(d=e.result)==null?void 0:d.body)==null?void 0:i[0])==null?void 0:c.keypoints.length} keypoints`};await e.draw.all(a.canvas,l,v),g(l.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,r(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(p,30)}async function h(){let d=(await e.webcam.enumerate())[0].deviceId;await e.webcam.start({element:a.video,crop:!0,width:f,id:d}),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 y(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),r("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.models.stats()),o("models loaded:",Object.values(e.models).filter(t=>t!==null).length),o("environment",e.env),r("initializing..."),await e.warmup(),await h(),await u(),await p()}window.onload=y;
-import * as H from "../../dist/human.esm.js";
-var width = 1920;
-var humanConfig = {
-  modelBasePath: "../../models",
-  filter: { enabled: true, equalization: false, flip: false, width },
-  face: { enabled: true, detector: { rotation: true }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },
-  body: { enabled: true },
-  hand: { enabled: false },
-  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() {
-  var _a, _b, _c;
-  if (!dom.video.paused) {
-    const interpolated = human.next(human.result);
-    const processed = await human.image(dom.video);
-    human.draw.canvas(processed.canvas, dom.canvas);
-    const opt = { bodyLabels: `person confidence [score] and ${(_c = (_b = (_a = human.result) == null ? void 0 : _a.body) == null ? void 0 : _b[0]) == null ? void 0 : _c.keypoints.length} keypoints` };
-    await human.draw.all(dom.canvas, interpolated, opt);
-    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() {
-  const devices = await human.webcam.enumerate();
-  const id = devices[0].deviceId;
-  await human.webcam.start({ element: dom.video, crop: true, width, id });
-  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);
-  log("environment", human.env);
-  status("initializing...");
-  await human.warmup();
-  await webCam();
-  await detectionLoop();
-  await drawLoop();
-}
-window.onload = main;
 //# sourceMappingURL=index.js.map

demo/typescript/index.ts

@@ -100,7 +100,7 @@ async function main() { // main entry point
   status('loading...');
   await human.load(); // preload all models
   log('backend:', human.tf.getBackend(), '| available:', human.env.backends);
-  log('models stats:', human.getModelStats());
+  log('models stats:', human.models.stats());
   log('models loaded:', Object.values(human.models).filter((model) => model !== null).length);
   log('environment', human.env);
   status('initializing...');

models/models.json

@@ -1,6 +1,7 @@
 {
   "antispoof": 853098,
   "blazeface": 538928,
+  "centernet": 4030290,
   "emotion": 820516,
   "facemesh": 1477958,
   "faceres": 6978814,
@@ -8,7 +9,6 @@
   "handtrack": 2964837,
   "iris": 2599092,
   "liveness": 592976,
-  "mb3-centernet": 4030290,
   "models": 0,
   "movenet-lightning": 4650216,
   "age": 161240,

src/config.ts

@@ -449,7 +449,7 @@ const config: Config = {
   },
   object: {
     enabled: false,
-    modelPath: 'mb3-centernet.json',
+    modelPath: 'centernet.json',
     minConfidence: 0.2,
     iouThreshold: 0.4,
     maxDetected: 10,

src/face/faceres.ts

@@ -2,7 +2,7 @@
  * FaceRes model implementation
  *
  * Returns Age, Gender, Descriptor
- * Implements Face simmilarity function
+ * Implements Face similarity function
  *
  * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)
  */

src/human.ts

@@ -22,11 +22,9 @@ import * as centernet from './object/centernet';
 import * as efficientpose from './body/efficientpose';
 import * as face from './face/face';
 import * as facemesh from './face/facemesh';
-import * as faceres from './face/faceres';
 import * as gesture from './gesture/gesture';
 import * as handpose from './hand/handpose';
 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';
@@ -41,7 +39,7 @@ import * as selfie from './segmentation/selfie';
 import * as warmups from './warmup';
 
 // type definitions
-import { Input, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas, emptyResult } from './exports';
+import { Input, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, emptyResult } from './exports';
 import type { Tensor, Tensor4D } from './tfjs/types';
 // type exports
 export * from './exports';
@@ -94,7 +92,15 @@ export class Human {
    * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}
    * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas
    */
-  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };
+  // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };
+  draw: typeof draw = draw;
+
+  /** Face Matching
+   * - similarity: compare two face descriptors and return similarity index
+   * - distance: compare two face descriptors and return raw calculated differences
+   * - find: compare face descriptor to array of face descriptors and return best match
+   */
+  match: typeof match = match;
 
   /** Currently loaded models
    * @internal
@@ -121,8 +127,6 @@ export class Human {
   #numTensors: number;
   #analyzeMemoryLeaks: boolean;
   #checkSanity: boolean;
-  /** WebGL debug info */
-  gl: Record<string, unknown>;
   // definition end
 
   /** Constructor for **Human** library that is futher used for all operations
@@ -153,28 +157,15 @@ export class Human {
     this.performance = {};
     this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;
     // object that contains all initialized models
-    this.models = new models.Models();
+    this.models = new models.Models(this);
     // reexport draw methods
     draw.init();
-    this.draw = {
-      options: draw.options,
-      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),
-      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),
-      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),
-      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),
-      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),
-      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),
-      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),
-      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),
-    };
     this.result = emptyResult();
     // export access to image processing
     this.process = { tensor: null, canvas: null };
     // export raw access to underlying models
     this.faceTriangulation = facemesh.triangulation;
     this.faceUVMap = facemesh.uvmap;
-    // set gl info
-    this.gl = humangl.config;
     // init model validation
     models.validateModel(this, null, '');
     // include platform info
@@ -227,18 +218,6 @@ export class Human {
     return msgs;
   }
 
-  /** Check model for invalid kernel ops for current backend */
-  check() {
-    return models.validate(this);
-  }
-
-  /** Exports face matching methods {@link match#similarity} */
-  public similarity = match.similarity;
-  /** Exports face matching methods {@link match#distance} */
-  public distance = match.distance;
-  /** Exports face matching methods {@link match#match} */
-  public match = match.match;
-
   /** Utility wrapper for performance.now() */
   now(): number { // eslint-disable-line class-methods-use-this
     return now();
@@ -273,16 +252,7 @@ export class Human {
     return tensor;
   }
 
-  /** Enhance method performs additional enhacements to face image previously detected for futher processing
+  /** Compare two input tensors for pixel similarity
-   *
-   * @param input - Tensor as provided in human.result.face[n].tensor
-   * @returns Tensor
-   */
-  enhance(input: Tensor): Tensor | null { // eslint-disable-line class-methods-use-this
-    return faceres.enhance(input);
-  }
-
-  /** Compare two input tensors for pixel simmilarity
    * - use `human.image` to process any valid input and get a tensor that can be used for compare
    * - when passing manually generated tensors:
    *  - both input tensors must be in format [1, height, width, 3]
@@ -325,18 +295,17 @@ export class Human {
       await tf.ready();
       if (this.env.browser) {
         if (this.config.debug) log('configuration:', this.config);
-        // @ts-ignore private property
         if (this.config.debug) log('tf flags:', this.tf.ENV.flags);
       }
     }
 
-    await models.load(this); // actually loads models
+    await this.models.load(); // actually loads models
     if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run
     this.env.initial = false;
 
     const loaded = Object.values(this.models).filter((model) => model).length;
     if (loaded !== count) { // number of loaded models changed
-      models.validate(this); // validate kernel ops used by model against current backend
+      this.models.validate(); // validate kernel ops used by model against current backend
       this.emit('load');
     }
 
@@ -359,9 +328,6 @@ export class Human {
     return interpolate.calc(result, this.config);
   }
 
-  /** get model loading/loaded stats */
-  getModelStats(): models.ModelStats { return models.getModelStats(this); }
-
   /** Warmup method pre-initializes all configured models for faster inference
    * - can take significant time on startup
    * - only used for `webgl` and `humangl` backends

src/models.ts

@@ -31,136 +31,10 @@ import { modelStats, ModelInfo } from './tfjs/load';
 import type { GraphModel } from './tfjs/types';
 import type { Human } from './human';
 
-/** Instances of all possible TFJS Graph Models used by Human
- * - loaded as needed based on configuration
- * - initialized explictly with `human.load()` method
- * - initialized implicity on first call to `human.detect()`
- * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading
- */
-export class Models {
-  ssrnetage: null | GraphModel | Promise<GraphModel> = null;
-  gear: null | GraphModel | Promise<GraphModel> = null;
-  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;
-  blazepose: null | GraphModel | Promise<GraphModel> = null;
-  centernet: null | GraphModel | Promise<GraphModel> = null;
-  efficientpose: null | GraphModel | Promise<GraphModel> = null;
-  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;
-  insightface: null | GraphModel | Promise<GraphModel> = null;
-  emotion: null | GraphModel | Promise<GraphModel> = null;
-  facedetect: null | GraphModel | Promise<GraphModel> = null;
-  faceiris: null | GraphModel | Promise<GraphModel> = null;
-  facemesh: null | GraphModel | Promise<GraphModel> = null;
-  faceres: null | GraphModel | Promise<GraphModel> = null;
-  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;
-  handpose: null | GraphModel | Promise<GraphModel> = null;
-  handskeleton: null | GraphModel | Promise<GraphModel> = null;
-  handtrack: null | GraphModel | Promise<GraphModel> = null;
-  liveness: null | GraphModel | Promise<GraphModel> = null;
-  meet: null | GraphModel | Promise<GraphModel> = null;
-  movenet: null | GraphModel | Promise<GraphModel> = null;
-  nanodet: null | GraphModel | Promise<GraphModel> = null;
-  posenet: null | GraphModel | Promise<GraphModel> = null;
-  selfie: null | GraphModel | Promise<GraphModel> = null;
-  rvm: null | GraphModel | Promise<GraphModel> = null;
-  antispoof: null | GraphModel | Promise<GraphModel> = null;
-}
-
-/** structure that holds global stats for currently loaded models */
-export interface ModelStats {
-  numLoadedModels: number,
-  numDefinedModels: number,
-  percentageLoaded: number,
-  totalSizeFromManifest: number,
-  totalSizeWeights: number,
-  totalSizeLoading: number,
-  totalSizeEnabled: undefined,
-  modelStats: ModelInfo[],
-}
-
-let instance: Human;
-
-export const getModelStats = (currentInstance: Human): ModelStats => {
-  if (currentInstance) instance = currentInstance;
-  if (!instance) log('instance not registred');
-  let totalSizeFromManifest = 0;
-  let totalSizeWeights = 0;
-  let totalSizeLoading = 0;
-  for (const m of Object.values(modelStats)) {
-    totalSizeFromManifest += m.sizeFromManifest;
-    totalSizeWeights += m.sizeLoadedWeights;
-    totalSizeLoading += m.sizeDesired;
-  }
-  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
-  return {
-    numLoadedModels: Object.values(modelStats).length,
-    numDefinedModels: Object.keys(instance.models).length,
-    percentageLoaded,
-    totalSizeFromManifest,
-    totalSizeWeights,
-    totalSizeLoading,
-    totalSizeEnabled: undefined,
-    modelStats: Object.values(modelStats),
-  };
-};
-
-export function reset(currentInstance: Human): void {
-  if (currentInstance) instance = currentInstance;
-  // if (instance.config.debug) log('resetting loaded models');
-  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;
-}
-
-/** Load method preloads all instance.configured models on-demand */
-export async function load(currentInstance: Human): Promise<void> {
-  if (currentInstance) instance = currentInstance;
-  if (!instance) log('instance not registred');
-  if (env.initial) reset(instance);
-  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole
-    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) {
-      [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);
-    }
-    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) {
-      [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);
-    }
-  }
-  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);
-  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector'].modelPath) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);
-  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);
-  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);
-  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);
-  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);
-  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);
-  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);
-  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);
-  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);
-  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);
-  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && (!instance.models.facemesh)) instance.models.facemesh = facemesh.load(instance.config);
-  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);
-  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);
-  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);
-  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);
-  if (instance.config.face.enabled && instance.config.face['insightface']?.enabled && !instance.models.insightface) instance.models.insightface = insightface.load(instance.config);
-  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);
-  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.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)) {
-    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') {
-      instance.models[model as keyof Models] = await instance.models[model as keyof Models];
-    }
-  }
-}
-
 export interface KernelOps { name: string, url: string, missing: string[], ops: string[] }
 
-export function validateModel(currentInstance: Human | null, model: GraphModel | null, name: string): KernelOps | null {
+export function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {
   if (!model) return null;
-  if (currentInstance) instance = currentInstance;
-  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', 'switch', 'shape', 'merge', 'split', 'broadcastto'];
@@ -193,15 +67,124 @@ export function validateModel(currentInstance: Human | null, model: GraphModel |
   return missing.length > 0 ? { name, missing, ops, url } : null;
 }
 
-export function validate(currentInstance: Human): { name: string, missing: string[] }[] {
+/** structure that holds global stats for currently loaded models */
-  if (currentInstance) instance = currentInstance;
+export interface ModelStats {
-  if (!instance) log('instance not registred');
+  numLoadedModels: number,
+  numDefinedModels: number,
+  percentageLoaded: number,
+  totalSizeFromManifest: number,
+  totalSizeWeights: number,
+  totalSizeLoading: number,
+  modelStats: ModelInfo[],
+}
+
+/** Models class used by Human
+ * - models: record of all GraphModels
+ * - list: returns list of configured models with their stats
+ * - loaded: returns array of loaded models
+ * - reset: unloads all models
+ * - validate: checks loaded models for valid kernel ops vs current backend
+ * - stats: live detailed model stats that can be checked during model load phase
+ */
+export class Models {
+  instance: Human;
+  models: Record<string, null | GraphModel>;
+
+  constructor(currentInstance: Human) {
+    this.models = {};
+    this.instance = currentInstance;
+  }
+
+  stats(): ModelStats {
+    let totalSizeFromManifest = 0;
+    let totalSizeWeights = 0;
+    let totalSizeLoading = 0;
+    for (const m of Object.values(modelStats)) {
+      totalSizeFromManifest += m.sizeFromManifest;
+      totalSizeWeights += m.sizeLoadedWeights;
+      totalSizeLoading += m.sizeDesired;
+    }
+    const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
+    return {
+      numLoadedModels: Object.values(modelStats).length,
+      numDefinedModels: Object.keys(this.models).length,
+      percentageLoaded,
+      totalSizeFromManifest,
+      totalSizeWeights,
+      totalSizeLoading,
+      modelStats: Object.values(modelStats),
+    };
+  }
+
+  reset(): void {
+    for (const model of Object.keys(this.models)) this.models[model] = null;
+  }
+
+  async load(): Promise<void> {
+    if (env.initial) this.reset();
+    const m: Record<string, null | GraphModel | Promise<GraphModel>> = {};
+    // face main models
+    m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;
+    m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;
+    m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;
+    m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;
+    m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;
+    m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;
+    m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;
+    // face alternatives
+    m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;
+    m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;
+    m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;
+    m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;
+    m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;
+    // body alterinatives
+    m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;
+    m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;
+    m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;
+    m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;
+    m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;
+    // hand alternatives
+    m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;
+    m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;
+    if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];
+    // object detection alternatives
+    m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;
+    m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;
+    // segmentation alternatives
+    m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;
+    m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;
+    m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;
+
+    // models are loaded in parallel asynchronously so lets wait until they are actually loaded
+    await Promise.all([...Object.values(m)]);
+    for (const model of Object.keys(m)) this.models[model] = m[model] as GraphModel || this.models[model] || null; // only update actually loaded models
+  }
+
+  list() {
+    const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));
+    for (const m of models) {
+      const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));
+      if (!stats) continue;
+      m.size = modelStats[stats].sizeLoadedWeights;
+      m.url = modelStats[stats].url;
+    }
+    return models;
+  }
+
+  loaded() {
+    const list = this.list();
+    const loaded = list.filter((model) => model.loaded).map((model) => model.name);
+    return loaded;
+  }
+
+  validate(): { name: string, missing: string[] }[] {
     const missing: KernelOps[] = [];
-  for (const defined of Object.keys(currentInstance.models)) {
+    for (const defined of Object.keys(this.models)) {
-    const model: GraphModel | null = currentInstance.models[defined as keyof Models] as GraphModel | null;
+      const model: GraphModel | null = this.models[defined as keyof Models];
       if (!model) continue;
-    const res = validateModel(currentInstance, model, defined);
+      const res = validateModel(this.instance, model, defined);
       if (res) missing.push(res);
     }
     return missing;
   }
+}

src/tfjs/humangl.ts

@@ -4,7 +4,6 @@ import * as tf from 'dist/tfjs.esm.js';
 import type { Human } from '../human';
 import { log } from '../util/util';
 import * as image from '../image/image';
-import * as models from '../models';
 import type { AnyCanvas } from '../exports';
 
 export const config = {
@@ -46,7 +45,7 @@ export function register(instance: Human): void {
   if (instance.config.backend !== 'humangl') return;
   if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {
     log('humangl error: backend invalid context');
-    models.reset(instance);
+    instance.models.reset();
     /*
     log('resetting humangl backend');
     await tf.removeBackend(config.name);

src/tfjs/load.ts

@@ -18,6 +18,7 @@ export interface ModelInfo {
   sizeDesired: number,
   sizeFromManifest: number,
   sizeLoadedWeights: number,
+  url: string,
 }
 
 export const modelStats: Record<string, ModelInfo> = {};
@@ -45,6 +46,7 @@ export async function loadModel(modelPath: string | undefined): Promise<GraphMod
     sizeLoadedWeights: 0,
     sizeDesired: modelsDefs[shortModelName],
     inCache: false,
+    url: '',
   };
   options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available
   let cachedModels = {};
@@ -54,8 +56,9 @@ export async function loadModel(modelPath: string | undefined): Promise<GraphMod
     options.cacheSupported = false;
   }
   modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache
+  modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;
   const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };
-  let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl
+  let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl
   let loaded = false;
   try {
     // @ts-ignore private function

src/warmup.ts

@@ -8,9 +8,9 @@ import * as sample from './sample';
 import * as image from './image/image';
 import * as backend from './tfjs/backend';
 import { env } from './util/env';
-import type { Config } from './config';
 import { emptyResult, Result } from './result';
-import { Human, models } from './human';
+import type { Config } from './config';
+import type { Human } from './human';
 import type { Tensor, DataType } from './tfjs/types';
 
 async function warmupBitmap(instance: Human): Promise<Result | undefined> {
@@ -161,7 +161,7 @@ export async function warmup(instance: Human, userConfig?: Partial<Config>): Pro
     return emptyResult();
   }
   return new Promise(async (resolve) => {
-    await models.load(instance);
+    await instance.models.load();
     await runCompile(instance);
     const res = await runInference(instance);
     const t1 = now();

test/build.log

@@ -1,105 +1,56 @@
-2022-11-17 10:11:23 DATA:  Build {"name":"@vladmandic/human","version":"3.0.0"} 
+2022-11-17 14:37:08 DATA:  Build {"name":"@vladmandic/human","version":"3.0.0"} 
-2022-11-17 10:11:23 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.0"} 
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-2022-11-17 10:11:37 INFO:  Done... 
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-2022-11-17 10:17:25 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"text/javascript","size":1917209,"url":"/dist/human.esm.js","remote":"::1"} 
-2022-11-17 10:17:25 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"application/octet-stream","size":9470,"url":"/demo/typescript/index.js.map","remote":"::1"} 
-2022-11-17 10:17:25 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"application/octet-stream","size":3385692,"url":"/dist/human.esm.js.map","remote":"::1"} 
-2022-11-17 10:17:25 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"application/manifest+json","size":304,"url":"/demo/manifest.webmanifest","remote":"::1"} 
-2022-11-17 10:17:25 DATA:  HTTPS: {"method":"GET","ver":"2.0","status":200,"mime":"image/png","size":142790,"url":"/assets/icon.png","remote":"::1"} 

test/test-browser-esm.js

@@ -75,7 +75,7 @@ async function testDefault(title, testConfig = {}) {
   await human.load();
   const models = Object.keys(human.models).map((model) => ({ name: model, loaded: (human.models[model] !== null) }));
   log('  models', models);
-  const ops = await human.check();
+  const ops = await human.models.validate();
   if (ops && ops.length > 0) log('  missing ops', ops);
   const img = await image('../../samples/in/ai-body.jpg');
   const input = await human.image(img); // process image
@@ -108,7 +108,7 @@ async function testMatch() {
   const similarity = await human.similarity(desc1, desc2);
   const descArray = [];
   for (let i = 0; i < 100; i++) descArray.push(desc2);
-  const match = await human.match(desc1, descArray);
+  const match = await human.match.find(desc1, descArray);
   log(`test similarity/${human.tf.getBackend()}`, match, similarity);
 }
 

test/test-node-load.js

@@ -9,22 +9,21 @@ const log = (status, ...data) => {
 async function main() {
   const human = new Human.Human(); // create instance of human using default configuration
   const startTime = new Date();
-  log('info', 'load start', { human: human.version, tf: tf.version_core, progress: human.getModelStats().percentageLoaded });
+  log('info', 'load start', { human: human.version, tf: tf.version_core, progress: human.models.stats().percentageLoaded });
 
   async function monitor() {
-    const progress = human.getModelStats().percentageLoaded;
+    const progress = human.models.stats().percentageLoaded;
     log('data', 'load interval', { elapsed: new Date() - startTime, progress });
     if (progress < 1) setTimeout(monitor, 10);
   }
 
   monitor();
-  // setInterval(() => log('interval', { elapsed: new Date() - startTime, progress: human.getModelStats().percentageLoaded }));
   const loadPromise = human.load();
   loadPromise
-    .then(() => log('state', 'passed', { progress: human.getModelStats().percentageLoaded }))
+    .then(() => log('state', 'passed', { progress: human.models.stats().percentageLoaded }))
     .catch(() => log('error', 'load promise'));
   await loadPromise;
-  log('info', 'load final', { progress: human.getModelStats().percentageLoaded });
+  log('info', 'load final', { progress: human.models.stats().percentageLoaded });
   await human.warmup(); // optional as model warmup is performed on-demand first time its executed
 }
 

test/test-node-main.js

@@ -138,7 +138,7 @@ async function testDetect(human, input, title, checkLeak = true) {
   lastOp = `testDetect ${title}`;
   log('state', 'start', title);
   await human.load(config);
-  const missing = human.check();
+  const missing = human.models.validate();
   for (const op of missing) log('warn', 'missing kernel ops', { title, model: op.name, url: op.url, missing: op.missing, backkend: human.tf.getBackend() });
   const tensors = human.tf.engine().state.numTensors;
   const image = input ? await getImage(human, input) : human.tf.randomNormal([1, 1024, 1024, 3]);
@@ -189,7 +189,7 @@ async function verifyDetails(human) {
   verify(res.face.length === 1, 'details face length', res.face.length);
   for (const face of res.face) {
     verify(face.score > 0.9 && face.boxScore > 0.9 && face.faceScore > 0.9, 'details face score', face.score, face.boxScore, face.faceScore);
-    verify(face.age > 23 && face.age < 30 && face.gender === 'female' && face.genderScore > 0.9 && face.iris > 0.5 && face.distance < 2.5, 'details face age/gender', face.age, face.gender, face.genderScore, face.distance);
+    verify(face.age > 23 && face.age < 30 && face.gender === 'female' && face.genderScore > 0.9 && face.distance > 0.5 && face.distance < 2.5, 'details face age/gender', face.age, face.gender, face.genderScore, face.distance);
     verify(face.box.length === 4 && face.boxRaw.length === 4 && face.mesh.length === 478 && face.meshRaw.length === 478 && face.embedding.length === 1024, 'details face arrays', face.box.length, face.mesh.length, face.embedding.length);
     verify(face.emotion.length >= 2 && face.emotion[0].score > 0.30 && face.emotion[0].emotion === 'angry', 'details face emotion', face.emotion.length, face.emotion[0]);
     verify(face.real > 0.55, 'details face anti-spoofing', face.real);
@@ -293,9 +293,9 @@ async function test(Human, inputConfig) {
   // test model loading
   log('info', 'test: model load');
   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 models = human.models.list();
-  const loaded = models.filter((model) => model.loaded);
+  const loaded = human.models.loaded();
-  if (models.length === 25 && loaded.length === 11) log('state', 'passed: models loaded', models.length, loaded.length, models);
+  if (models.length === 24 && 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 });
@@ -380,15 +380,15 @@ async function test(Human, inputConfig) {
   const desc3 = res3 && res3.face && res3.face[0] && res3.face[0].embedding ? [...res3.face[0].embedding] : null;
   if (!desc1 || !desc2 || !desc3 || desc1.length !== 1024 || desc2.length !== 1024 || desc3.length !== 1024) log('error', 'failed: face descriptor', desc1?.length, desc2?.length, desc3?.length);
   else log('state', 'passed: face descriptor');
-  res1 = human.similarity(desc1, desc1);
+  res1 = human.match.similarity(desc1, desc1);
-  res2 = human.similarity(desc1, desc2);
+  res2 = human.match.similarity(desc1, desc2);
-  res3 = human.similarity(desc1, desc3);
+  res3 = human.match.similarity(desc1, desc3);
   if (res1 < 1 || res2 < 0.40 || res3 < 0.40 || res2 > 0.75 || res3 > 0.75) log('error', 'failed: face similarity', { similarity: [res1, res2, res3], descriptors: [desc1?.length, desc2?.length, desc3?.length] });
   else log('state', 'passed: face similarity', { similarity: [res1, res2, res3], descriptors: [desc1?.length, desc2?.length, desc3?.length] });
 
   // test object detection
   log('info', 'test object');
-  config.object = { enabled: true, modelPath: 'mb3-centernet.json' };
+  config.object = { enabled: true, modelPath: 'centernet.json' };
   res = await testDetect(human, 'samples/in/ai-body.jpg', 'object');
   if (!res || res.object?.length < 1 || res.object[0]?.label !== 'person') log('error', 'failed: centernet', res.object);
   else log('state', 'passed: centernet');
@@ -461,9 +461,9 @@ async function test(Human, inputConfig) {
   const arr = db.map((rec) => rec.embedding);
   if (db.length < 20) log('error', 'failed: face database ', db.length);
   else log('state', 'passed: face database', db.length);
-  res1 = human.match(desc1, arr);
+  res1 = human.match.find(desc1, arr);
-  res2 = human.match(desc2, arr);
+  res2 = human.match.find(desc2, arr);
-  res3 = human.match(desc3, arr);
+  res3 = human.match.find(desc3, arr);
   if (res1.index !== 4 || res2.index !== 4 || res3.index !== 4) log('error', 'failed: face match', res1, res2, res3);
   else log('state', 'passed: face match', { first: { index: res1.index, similarity: res1.similarity } }, { second: { index: res2.index, similarity: res2.similarity } }, { third: { index: res3.index, similarity: res3.similarity } });
 

wiki

@@ -1 +1 @@
-Subproject commit 93e58e16b579922e2f19bc91c8ead0af0f326f5a
+Subproject commit 6ea5ea911dcf7ad598c8ee3777b103d7e531fec5