release rebuild

2.9.1
update tfjs
2022-07-25 08:33:07 -04:00 · 2022-07-25 08:30:38 -04:00 · 2022-07-25 08:30:34 -04:00 · 2022-07-23 14:45:40 -04:00 · 2022-07-21 13:06:13 -04:00 · 2022-07-21 12:53:10 -04:00
463 changed files with 240279 additions and 74255 deletions
--- a/.build.json
+++ b/.build.json
@ -12,10 +12,10 @@
    "clean": ["clean"]
  },
  "clean": {
-    "locations": ["dist/*", "types/*", "typedoc/*"]
+    "locations": ["dist/*", "types/lib/*", "typedoc/*"]
  },
  "lint": {
-    "locations": [ "**/*.json", "src/**/*.ts", "test/**/*.js", "demo/**/*.js", "**/*.md" ],
+    "locations": [ "*.json", "src/**/*.ts", "test/**/*.js", "demo/**/*.js" ],
    "rules": { }
  },
  "changelog": {
@ -24,8 +24,8 @@
  "serve": {
    "sslKey": "node_modules/@vladmandic/build/cert/https.key",
    "sslCrt": "node_modules/@vladmandic/build/cert/https.crt",
-    "httpPort": 8000,
+    "httpPort": 10030,
-    "httpsPort": 8001,
+    "httpsPort": 10031,
    "documentRoot": ".",
    "defaultFolder": "demo",
    "defaultFile": "index.html"
@ -39,13 +39,6 @@
      "banner": { "js": "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n" }
    },
    "targets": [
      { 
        "name": "tfjs/browser/version",
        "platform": "browser",
        "format": "esm",
        "input": "tfjs/tf-version.ts",
        "output": "dist/tfjs.version.js"
      },
      {
        "name": "tfjs/nodejs/cpu",
        "platform": "node",
@ -84,7 +77,6 @@
        "format": "cjs",
        "input": "tfjs/tf-node-wasm.ts",
        "output": "dist/tfjs.esm.js",
        "minify": false,
        "external": ["@tensorflow"]
      },
      {
@ -95,6 +87,13 @@
        "output": "dist/human.node-wasm.js",
        "external": ["@tensorflow"]
      },
      { 
        "name": "tfjs/browser/version",
        "platform": "browser",
        "format": "esm",
        "input": "tfjs/tf-version.ts",
        "output": "dist/tfjs.version.js"
      },
      {
        "name": "tfjs/browser/esm/nobundle",
        "platform": "browser",
@ -109,17 +108,17 @@
        "format": "esm",
        "input": "src/human.ts",
        "output": "dist/human.esm-nobundle.js",
-        "sourcemap": false,
+        "sourcemap": true,
        "external": ["@tensorflow"]
      },
      {
-        "name": "tfjs/browser/esm/bundle",
+        "name": "tfjs/browser/esm/custom",
        "platform": "browser",
        "format": "esm",
-        "input": "tfjs/tf-browser.ts",
+        "input": "tfjs/tf-custom.ts",
        "output": "dist/tfjs.esm.js",
        "sourcemap": false,
-        "minify": true
+        "minify": false
      },
      {
        "name": "human/browser/iife/bundle",
@ -160,15 +159,6 @@
        "output": "demo/faceid/index.js",
        "sourcemap": true,
        "external": ["*/human.esm.js"]
      },
      {
        "name": "demo/tracker",
        "platform": "browser",
        "format": "esm",
        "input": "demo/tracker/index.ts",
        "output": "demo/tracker/index.js",
        "sourcemap": true,
        "external": ["*/human.esm.js"]
      }
    ]
  },
--- a/.eslintrc.json
+++ b/.eslintrc.json
@ -1,204 +1,93 @@
 {
-  "globals": {
+  "globals": {},
  "env": {
      "browser": true,
      "commonjs": true,
      "node": true,
      "es2021": true
  },
-  "rules": {
+  "parser": "@typescript-eslint/parser",
-    "@typescript-eslint/no-require-imports":"off"
+  "parserOptions": {
    "ecmaVersion": 2021
  },
-  "overrides": [
+  "plugins": [
-    {
+    "@typescript-eslint",
-      "files": ["**/*.ts"],
+    "html"
-      "parser": "@typescript-eslint/parser",
+  ],
-      "parserOptions": { "ecmaVersion": "latest", "project": ["./tsconfig.json"] },
+  "extends": [
-      "plugins": ["@typescript-eslint"],
+    "airbnb-base",
-      "env": {
+    "eslint:recommended",
-        "browser": true,
+    "plugin:@typescript-eslint/eslint-recommended",
-        "commonjs": false,
+    "plugin:@typescript-eslint/recommended",
-        "node": false,
+    "plugin:import/errors",
-        "es2021": true
+    "plugin:import/warnings",
-      },
+    "plugin:json/recommended-with-comments",
-      "extends": [
+    "plugin:node/recommended",
-        "airbnb-base",
+    "plugin:promise/recommended"
        "eslint:recommended",
        "plugin:@typescript-eslint/strict",
        "plugin:@typescript-eslint/recommended",
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    {
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        "@typescript-eslint/no-unused-vars":"off",
        "@typescript-eslint/triple-slash-reference":"off",
        "import/newline-after-import":"off",
        "import/no-unresolved":"off",
        "no-console":"off",
        "no-global-assign":"off",
        "no-multi-spaces":"off",
        "no-restricted-globals":"off",
        "no-undef":"off",
        "no-unused-vars":"off",
        "node/no-missing-import":"off",
        "node/no-missing-require":"off",
        "promise/catch-or-return":"off"
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  "ignorePatterns": [
    "node_modules",
    "assets",
    "dist",
    "demo/helpers/*.js",
    "demo/typescript/*.js",
    "demo/faceid/*.js",
-    "demo/tracker/*.js",
+    "dist",
-    "typedoc"
+    "media",
-  ]
+    "models",
    "node_modules",
    "types/human.d.ts"
  ],
  "rules": {
    "@typescript-eslint/ban-ts-comment": "off",
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    "no-shadow": "off",
    "no-underscore-dangle": "off",
    "node/no-missing-import": ["error", { "tryExtensions": [".js", ".json", ".ts"] }],
    "node/no-unpublished-import": "off",
    "node/no-unpublished-require": "off",
    "node/no-unsupported-features/es-syntax": "off",
    "node/shebang": "off",
    "object-curly-newline": "off",
    "prefer-destructuring": "off",
    "prefer-template":"off",
    "promise/always-return": "off",
    "promise/catch-or-return": "off",
    "promise/no-nesting": "off",
    "radix": "off"
  }
 }
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@ -1,11 +0,0 @@
 github: [vladmandic]
 patreon: # Replace with a single Patreon username
 open_collective: # Replace with a single Open Collective username
 ko_fi: # Replace with a single Ko-fi username
 tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
 community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
 liberapay: # Replace with a single Liberapay username
 issuehunt: # Replace with a single IssueHunt username
 otechie: # Replace with a single Otechie username
 lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
 custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
--- a/.gitignore
+++ b/.gitignore
@ -1,9 +1,5 @@
 .vscode
 pnpm-lock.yaml
 *.swp
 node_modules/
 types/lib
 pnpm-lock.yaml
 package-lock.json
 *.swp
 samples/**/*.mp4
 samples/**/*.webm
 temp
 tmp
--- a/.markdownlint.json
+++ b/.markdownlint.json
@ -1,7 +1,6 @@
 {
  "MD012": false,
  "MD013": false,
  "MD029": false,
  "MD033": false,
  "MD036": false,
  "MD041": false
--- a/.npmrc
+++ b/.npmrc
@ -1,5 +1,4 @@
 force=true
-omit=dev
+production=true
 legacy-peer-deps=true
 strict-peer-dependencies=false
 node-options='--no-deprecation'
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@ -1,10 +0,0 @@
 {
  "search.exclude": {
    "dist/*": true,
    "node_modules/*": true,
    "types": true,
    "typedoc": true,
  },
  "search.useGlobalIgnoreFiles": true,
  "search.useParentIgnoreFiles": true
 }
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@ -1,6 +1,6 @@
 # @vladmandic/human  
-  Version: **3.3.6**  
+  Version: **2.9.1**  
  Description: **Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition**  
  Author: **Vladimir Mandic <mandic00@live.com>**  
@ -9,183 +9,11 @@
 ## Changelog
 ### **3.3.6** 2025/08/26 mandic00@live.com
 ### **origin/main** 2025/02/05 mandic00@live.com
 - full rebuild
 ### **3.3.5** 2025/02/05 mandic00@live.com
 - rebuild
 - add human.draw.tensor method
 ### **3.3.4** 2024/10/24 mandic00@live.com
 ### **3.3.3** 2024/10/14 mandic00@live.com
 - add loaded property to model stats and mark models not loaded correctly.
 - release build
 ### **3.3.2** 2024/09/11 mandic00@live.com
 - full rebuild
 ### **3.3.1** 2024/09/11 mandic00@live.com
 - add config.face.detector.square option
 - human 3.3 alpha test run
 - human 3.3 alpha with new build environment
 - release rebuild
 - fix flazeface tensor scale and update build platform
 ### **3.2.2** 2024/04/17 mandic00@live.com
 ### **release: 3.2.1** 2024/02/15 mandic00@live.com
 ### **3.2.1** 2024/02/15 mandic00@live.com
 ### **3.2.0** 2023/12/06 mandic00@live.com
 - set browser false when navigator object is empty
 - https://github.com/vladmandic/human/issues/402
 ### **release: 3.1.2** 2023/09/18 mandic00@live.com
 - full rebuild
 ### **3.1.2** 2023/09/18 mandic00@live.com
 - major toolkit upgrade
 - full rebuild
 - major toolkit upgrade
 ### **3.1.1** 2023/08/05 mandic00@live.com
 - fixes plus tfjs upgrade for new release
 ### **3.0.7** 2023/06/12 mandic00@live.com
 - full rebuild
 - fix memory leak in histogramequalization
 - initial work on tracker
 ### **3.0.6** 2023/03/21 mandic00@live.com
 - add optional crop to multiple models
 - fix movenet-multipose
 - add electron detection
 - fix gender-ssrnet-imdb
 - add movenet-multipose workaround
 - rebuild and publish
 - add face.detector.minsize configurable setting
 - add affectnet
 ### **3.0.5** 2023/02/02 mandic00@live.com
 - add gear-e models
 - detect react-native
 - redo blazeface annotations
 ### **3.0.4** 2023/01/29 mandic00@live.com
 - make naviator calls safe
 - fix facedetector-only configs
 ### **3.0.3** 2023/01/07 mandic00@live.com
 - full rebuild
 ### **3.0.2** 2023/01/06 mandic00@live.com
 - default face.rotation disabled
 ### **release: 3.0.1** 2022/11/22 mandic00@live.com
 ### **3.0.1** 2022/11/22 mandic00@live.com
 - support dynamic loads
 - polish demos
 - add facedetect demo and fix model async load
 - enforce markdown linting
 - cleanup git history
 - default empty result
 - refactor draw and models namespaces
 - refactor distance
 - add basic anthropometry
 - added webcam id specification
 - include external typedefs
 - prepare external typedefs
 - rebuild all
 - include project files for types
 - architectural improvements
 - refresh dependencies
 - add named exports
 - add draw label templates
 - reduce dev dependencies
 - tensor rank strong typechecks
 - rebuild dependencies
 ### **2.11.1** 2022/10/09 mandic00@live.com
 - add rvm segmentation model
 - add human.webcam methods
 - create funding.yml
 - fix rotation interpolation
 ### **2.10.3** 2022/09/21 mandic00@live.com
 - add human.video method
 ### **2.10.2** 2022/09/11 mandic00@live.com
 - add node.js esm compatibility (#292)
 - release
 ### **2.10.1** 2022/09/07 mandic00@live.com
 - release candidate
 - add config flags
 - test update
 - release preview
 - optimize startup sequence
 - reorder backend init code
 - test embedding
 - embedding test
 - add browser iife tests
 - minor bug fixes and increased test coverage
 - extend release tests
 - add model load exception handling
 - add softwarekernels config option
 - expand type safety
 - full eslint rule rewrite
 ### **2.9.4** 2022/08/20 mandic00@live.com
 - add browser test
 - add tensorflow library detection
 - fix wasm detection
 - enumerate additional models
 - release refresh
 ### **2.9.3** 2022/08/10 mandic00@live.com
 - rehault testing framework
 - release refresh
 - add insightface
 ### **2.9.2** 2022/08/08 mandic00@live.com
 - release rebuild
 ### **2.9.1** 2022/07/25 mandic00@live.com
 ### **origin/main** 2022/07/23 mandic00@live.com
 - full rebuild
 - release cleanup
 - tflite experiments
@ -229,6 +57,7 @@
 - enable precompile as part of warmup
 - prepare release beta
 - change default face crop
 - face attention model is available in human-models
 - beta release 2.7
 - refactor draw methods
 - implement face attention model
@ -540,6 +369,7 @@
 - implemented human.next global interpolation method
 - finished draw buffering and smoothing and enabled by default
 - implemented service worker
 - quantized centernet
 - release candidate
 - added usage restrictions
 - quantize handdetect model
--- a/README.md
+++ b/README.md
@ -1,9 +1,9 @@
 [![](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/vladmandic)
 ![Git Version](https://img.shields.io/github/package-json/v/vladmandic/human?style=flat-square&svg=true&label=git)
 ![NPM Version](https://img.shields.io/npm/v/@vladmandic/human.png?style=flat-square)
 ![Last Commit](https://img.shields.io/github/last-commit/vladmandic/human?style=flat-square&svg=true)
 ![License](https://img.shields.io/github/license/vladmandic/human?style=flat-square&svg=true)
 ![GitHub Status Checks](https://img.shields.io/github/checks-status/vladmandic/human/main?style=flat-square&svg=true)
 ![Vulnerabilities](https://img.shields.io/snyk/vulnerabilities/github/vladmandic/human?style=flat-square&svg=true)
 # Human Library
@ -13,45 +13,19 @@
 <br>
-## Highlights
+JavaScript module using TensorFlow/JS Machine Learning library  
- Compatible with most server-side and client-side environments and frameworks  
+- **Browser**:  
- Combines multiple machine learning models which can be switched on-demand depending on the use-case  
+  Compatible with both desktop and mobile platforms  
- Related models are executed in an attention pipeline to provide details when needed  
+  Compatible with *CPU*, *WebGL*, *WASM* backends  
- Optimized input pre-processing that can enhance image quality of any type of inputs  
+  Compatible with *WebWorker* execution  
- Detection of frame changes to trigger only required models for improved performance  
+  Compatible with *WebView*  
- Intelligent temporal interpolation to provide smooth results regardless of processing performance  
+- **NodeJS**:  
- Simple unified API  
+  Compatible with both software *tfjs-node* and  
- Built-in Image, Video and WebCam handling
+  GPU accelerated backends *tfjs-node-gpu* using CUDA libraries  
 [*Jump to Quick Start*](#quick-start)
 <br>
 ## Compatibility
 **Browser**:  
  - Compatible with both desktop and mobile platforms  
  - Compatible with *WebGPU*, *WebGL*, *WASM*, *CPU* backends  
  - Compatible with *WebWorker* execution  
  - Compatible with *WebView*  
  - Primary platform: *Chromium*-based browsers  
  - Secondary platform: *Firefox*, *Safari*
 **NodeJS**:  
  - Compatibile with *WASM* backend for executions on architectures where *tensorflow* binaries are not available  
  - Compatible with *tfjs-node* using software execution via *tensorflow* shared libraries  
  - Compatible with *tfjs-node* using GPU-accelerated execution via *tensorflow* shared libraries and nVidia CUDA  
  - Supported versions are from **14.x** to **22.x**  
  - NodeJS version **23.x** is not supported due to breaking changes and issues with `@tensorflow/tfjs`  
 <br>
 ## Releases
 - [Release Notes](https://github.com/vladmandic/human/releases)
 - [NPM Link](https://www.npmjs.com/package/@vladmandic/human)
 ## Demos
 *Check out [**Simple Live Demo**](https://vladmandic.github.io/human/demo/typescript/index.html) fully annotated app as a good start starting point ([html](https://github.com/vladmandic/human/blob/main/demo/typescript/index.html))([code](https://github.com/vladmandic/human/blob/main/demo/typescript/index.ts))*  
 *Check out [**Main Live Demo**](https://vladmandic.github.io/human/demo/index.html) app for advanced processing of of webcam, video stream or images static images with all possible tunable options*  
@ -59,53 +33,49 @@
 - To start video detection, simply press *Play*  
 - To process images, simply drag & drop in your Browser window  
 - Note: For optimal performance, select only models you'd like to use
- Note: If you have modern GPU, *WebGL* (default) backend is preferred, otherwise select *WASM* backend
+- Note: If you have modern GPU, WebGL (default) backend is preferred, otherwise select WASM backend
 <br>
 ## Releases
 - [Release Notes](https://github.com/vladmandic/human/releases)
 - [NPM Link](https://www.npmjs.com/package/@vladmandic/human)
 ## Demos
 - [**List of all Demo applications**](https://github.com/vladmandic/human/wiki/Demos)
 - [**Live Examples galery**](https://vladmandic.github.io/human/samples/index.html)
 ### Browser Demos
 *All browser demos are self-contained without any external dependencies*
 - **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/demo/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 Detect** [[*Live*]](https://vladmandic.github.io/human/demo/facedetect/index.html) [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facedetect): Extract faces from images and processes details
 - **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
 - **ElectronJS** [[*Details*]](https://github.com/vladmandic/human-electron): Use Human with TypeScript and ElectonJS to create standalone cross-platform apps
- **3D Analysis with BabylonJS** [[*Live*]](https://vladmandic.github.io/human-motion/src/index.html) [[*Details*]](https://github.com/vladmandic/human-motion): 3D tracking and visualization of heead, face, eye, body and hand
+- **3D Analysis** [[*Live*]](https://vladmandic.github.io/human-motion/src/index.html) [[*Details*]](https://github.com/vladmandic/human-motion): 3D tracking and visualization of heead, face, eye, body and hand
- **VRM Virtual Model Tracking with Three.JS** [[*Live*]](https://vladmandic.github.io/human-three-vrm/src/human-vrm.html) [[*Details*]](https://github.com/vladmandic/human-three-vrm): VR model with head, face, eye, body and hand tracking  
+- **Avatar Bone Mapping** [[*Live*]](https://vladmandic.github.io/human-vrm/src/human-avatar.html) [[*Details*]](https://github.com/vladmandic/human-avatar): Human skeleton with full bone mapping using look and inverse kinematics controllers
- **VRM Virtual Model Tracking with BabylonJS** [[*Live*]](https://vladmandic.github.io/human-bjs-vrm/src/index.html) [[*Details*]](https://github.com/vladmandic/human-bjs-vrm): VR model with head, face, eye, body and hand tracking  
+- **Virtual Model Tracking** [[*Live*]](https://vladmandic.github.io/human-vrm/src/human-vrm.html) [[*Details*]](https://github.com/vladmandic/human-vrm): VR model with head, face, eye, body and hand tracking  
 ### NodeJS Demos
-*NodeJS demos may require extra dependencies which are used to decode inputs*  
+- **Main** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Process images from files, folders or URLs using native methods  
-*See header of each demo to see its dependencies as they are not automatically installed with `Human`*
+- **Canvas** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Process image from file or URL and draw results to a new image file using `node-canvas`  
 - **Video** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Processing of video input using `ffmpeg`  
 - **WebCam** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Processing of webcam screenshots using `fswebcam`  
 - **Events** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Showcases usage of `Human` eventing to get notifications on processing
 - **Similarity** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Compares two input images for similarity of detected faces
 - **Face Match** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facematch): Parallel processing of face **match** in multiple child worker threads
 - **Multiple Workers** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Runs multiple parallel `human` by dispaching them to pool of pre-created worker processes  
 - **Main** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node.js): Process images from files, folders or URLs using native methods  
 - **Canvas** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node-canvas.js): Process image from file or URL and draw results to a new image file using `node-canvas`  
 - **Video** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node-video.js): Processing of video input using `ffmpeg`  
 - **WebCam** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node-webcam.js): Processing of webcam screenshots using `fswebcam`  
 - **Events** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node-event.js): Showcases usage of `Human` eventing to get notifications on processing
 - **Similarity** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs/node-similarity.js): Compares two input images for similarity of detected faces
 - **Face Match** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/facematch/node-match.js): Parallel processing of face **match** in multiple child worker threads
 - **Multiple Workers** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/multithread/node-multiprocess.js): Runs multiple parallel `human` by dispaching them to pool of pre-created worker processes  
 - **Dynamic Load** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Loads Human dynamically with multiple different desired backends  
 ## Project pages
 - [**Code Repository**](https://github.com/vladmandic/human)
 - [**NPM Package**](https://www.npmjs.com/package/@vladmandic/human)
 - [**Issues Tracker**](https://github.com/vladmandic/human/issues)
- [**TypeDoc API Specification - Main class**](https://vladmandic.github.io/human/typedoc/classes/Human.html)
+- [**TypeDoc API Specification**](https://vladmandic.github.io/human/typedoc/classes/Human.html)
 - [**TypeDoc API Specification - Full**](https://vladmandic.github.io/human/typedoc/)
 - [**Change Log**](https://github.com/vladmandic/human/blob/main/CHANGELOG.md)
 - [**Current To-do List**](https://github.com/vladmandic/human/blob/main/TODO.md)
@ -116,7 +86,6 @@
 - [**Usage & Functions**](https://github.com/vladmandic/human/wiki/Usage)
 - [**Configuration Details**](https://github.com/vladmandic/human/wiki/Config)
 - [**Result Details**](https://github.com/vladmandic/human/wiki/Result)
 - [**Customizing Draw Methods**](https://github.com/vladmandic/human/wiki/Draw)
 - [**Caching & Smoothing**](https://github.com/vladmandic/human/wiki/Caching)
 - [**Input Processing**](https://github.com/vladmandic/human/wiki/Image)
 - [**Face Recognition & Face Description**](https://github.com/vladmandic/human/wiki/Embedding)
@ -148,24 +117,27 @@
 <hr><br>
-## App Examples
+## Examples
-Visit [Examples gallery](https://vladmandic.github.io/human/samples/index.html) for more examples  
+Visit [Examples galery](https://vladmandic.github.io/human/samples/samples.html) for more examples  
-[<img src="assets/samples.jpg" width="640"/>](assets/samples.jpg)
+<https://vladmandic.github.io/human/samples/samples.html>
 ![samples](assets/samples.jpg)
 <br>
 ## Options
 All options as presented in the demo application...  
-[demo/index.html](demo/index.html)  
+> [demo/index.html](demo/index.html)  
-[<img src="assets/screenshot-menu.png"/>](assets/screenshot-menu.png)
+
 ![Options visible in demo](assets/screenshot-menu.png)
 <br>
 **Results Browser:**  
 [ *Demo -> Display -> Show Results* ]<br>
-[<img src="assets/screenshot-results.png"/>](assets/screenshot-results.png)
+![Results](assets/screenshot-results.png)
 <br>
@ -177,47 +149,37 @@ sorts them by similarity to selected face
 and optionally matches detected face with database of known people to guess their names
 > [demo/facematch](demo/facematch/index.html)  
-[<img src="assets/screenshot-facematch.jpg" width="640"/>](assets/screenshot-facematch.jpg)
+![Face Matching](assets/screenshot-facematch.jpg)
 2. **Face Detect:**  
 Extracts all detect faces from loaded images on-demand and highlights face details on a selected face  
 > [demo/facedetect](demo/facedetect/index.html)  
 [<img src="assets/screenshot-facedetect.jpg" width="640"/>](assets/screenshot-facedetect.jpg)
 3. **Face ID:**  
 Performs validation check on a webcam input to detect a real face and matches it to known faces stored in database
 > [demo/faceid](demo/faceid/index.html)  
 [<img src="assets/screenshot-faceid.jpg" width="640"/>](assets/screenshot-faceid.jpg)
 <br>
-4. **3D Rendering:**  
+2. **3D Rendering:**  
 > [human-motion](https://github.com/vladmandic/human-motion)
-[<img src="https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-face.jpg" width="640"/>](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-face.jpg)
+![Face3D](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-face.jpg)
-[<img src="https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-body.jpg" width="640"/>](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-body.jpg)
+![Body3D](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-body.jpg)
-[<img src="https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-hand.jpg" width="640"/>](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-hand.jpg)
+![Hand3D](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-hand.jpg)
 <br>
-5. **VR Model Tracking:**  
+3. **Avatar Bone Mapping:**  
-> [human-three-vrm](https://github.com/vladmandic/human-three-vrm)  
+> [human-avatar](https://github.com/vladmandic/human-avatar)
 > [human-bjs-vrm](https://github.com/vladmandic/human-bjs-vrm)  
-[<img src="https://github.com/vladmandic/human-three-vrm/raw/main/assets/human-vrm-screenshot.jpg" width="640"/>](https://github.com/vladmandic/human-three-vrm/raw/main/assets/human-vrm-screenshot.jpg)
+![Avatar](https://github.com/vladmandic/human-avatar/raw/main/assets/screenshot.jpg)
 <br>
-6. **Human as OS native application:**
+4. **VR Model Tracking:**  
-> [human-electron](https://github.com/vladmandic/human-electron)
+> [human-vrmmotion](https://github.com/vladmandic/human-vrm)
 ![VRM](https://github.com/vladmandic/human-vrm/raw/main/assets/human-vrm-screenshot.jpg)
 <br>
 **468-Point Face Mesh Defails:**  
 (view in full resolution to see keypoints)  
-[<img src="assets/facemesh.png" width="400"/>](assets/facemesh.png)
+![FaceMesh](assets/facemesh.png)
 <br><hr><br>
@ -227,25 +189,44 @@ Simply load `Human` (*IIFE version*) directly from a cloud CDN in your HTML file
 (pick one: `jsdelirv`, `unpkg` or `cdnjs`)
 ```html
 <!DOCTYPE HTML>
 <script src="https://cdn.jsdelivr.net/npm/@vladmandic/human/dist/human.js"></script>
 <script src="https://unpkg.dev/@vladmandic/human/dist/human.js"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/human/3.0.0/human.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/human/2.1.5/human.js"></script>
 ```
 For details, including how to use `Browser ESM` version or `NodeJS` version of `Human`, see [**Installation**](https://github.com/vladmandic/human/wiki/Install)
 <br>
-## Code Examples
+## Inputs
-Simple app that uses Human to process video input and  
+`Human` library can process all known input types:  
 - `Image`, `ImageData`, `ImageBitmap`, `Canvas`, `OffscreenCanvas`, `Tensor`,  
 - `HTMLImageElement`, `HTMLCanvasElement`, `HTMLVideoElement`, `HTMLMediaElement`
 Additionally, `HTMLVideoElement`, `HTMLMediaElement` can be a standard `<video>` tag that links to:
 - WebCam on user's system
 - Any supported video type  
  For example: `.mp4`, `.avi`, etc.
 - Additional video types supported via *HTML5 Media Source Extensions*  
  Live streaming examples:
  - **HLS** (*HTTP Live Streaming*) using `hls.js`
  - **DASH** (Dynamic Adaptive Streaming over HTTP) using `dash.js`
 - **WebRTC** media track using built-in support  
 <br>
 ## Example
 Example simple app that uses Human to process video input and  
 draw output on screen using internal draw helper functions
 ```js
 // create instance of human with simple configuration using default values
 const config = { backend: 'webgl' };
-const human = new Human.Human(config);
+const human = new Human(config);
 // select input HTMLVideoElement and output HTMLCanvasElement from page
 const inputVideo = document.getElementById('video-id');
 const outputCanvas = document.getElementById('canvas-id');
@ -264,7 +245,6 @@ function detectVideo() {
    human.draw.gesture(outputCanvas, result.gesture);
    // and loop immediate to the next frame
    requestAnimationFrame(detectVideo);
    return result;
  });
 }
@ -304,7 +284,7 @@ human.events.addEventListener('detect', () => { // event gets triggered when det
 function detectVideo() {
  human.detect(inputVideo) // run detection
-    .then(() => requestAnimationFrame(detectVideo)); // upon detect complete start processing of the next frame
+  .then(() => requestAnimationFrame(detectVideo)); // upon detect complete start processing of the next frame
 }
 detectVideo(); // start loop
@ -325,7 +305,7 @@ async function detectVideo() {
 async function drawVideo() {
  if (result) { // check if result is available
-    const interpolated = human.next(result); // get smoothened result using last-known results
+    const interpolated = human.next(result); // calculate next interpolated frame
    human.draw.all(outputCanvas, interpolated); // draw the frame
  }
  requestAnimationFrame(drawVideo); // run draw loop
@ -335,108 +315,26 @@ detectVideo(); // start detection loop
 drawVideo(); // start draw loop
 ```
 or same, but using built-in full video processing instead of running manual frame-by-frame loop:
 ```js
 const human = new Human(); // create instance of Human
 const inputVideo = document.getElementById('video-id');
 const outputCanvas = document.getElementById('canvas-id');
 async function drawResults() {
  const interpolated = human.next(); // get smoothened result using last-known results
  human.draw.all(outputCanvas, interpolated); // draw the frame
  requestAnimationFrame(drawResults); // run draw loop
 }
 human.video(inputVideo); // start detection loop which continously updates results
 drawResults(); // start draw loop
 ```
 or using built-in webcam helper methods that take care of video handling completely:
 ```js
 const human = new Human(); // create instance of Human
 const outputCanvas = document.getElementById('canvas-id');
 async function drawResults() {
  const interpolated = human.next(); // get smoothened result using last-known results
  human.draw.canvas(outputCanvas, human.webcam.element); // draw current webcam frame
  human.draw.all(outputCanvas, interpolated); // draw the frame detectgion results
  requestAnimationFrame(drawResults); // run draw loop
 }
 await human.webcam.start({ crop: true });
 human.video(human.webcam.element); // start detection loop which continously updates results
 drawResults(); // start draw loop
 ```
 And for even better results, you can run detection in a separate web worker thread
 <br><hr><br>
 ## Inputs
 `Human` library can process all known input types:  
 - `Image`, `ImageData`, `ImageBitmap`, `Canvas`, `OffscreenCanvas`, `Tensor`,  
 - `HTMLImageElement`, `HTMLCanvasElement`, `HTMLVideoElement`, `HTMLMediaElement`
 Additionally, `HTMLVideoElement`, `HTMLMediaElement` can be a standard `<video>` tag that links to:
 - WebCam on user's system
 - Any supported video type  
  e.g. `.mp4`, `.avi`, etc.
 - Additional video types supported via *HTML5 Media Source Extensions*  
  e.g.: **HLS** (*HTTP Live Streaming*) using `hls.js` or **DASH** (*Dynamic Adaptive Streaming over HTTP*) using `dash.js`
 - **WebRTC** media track using built-in support  
 <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/)
    ![typedoc](assets/screenshot-typedoc.png)
 <br><hr><br>
 ## TypeDefs
 `Human` is written using TypeScript strong typing and ships with full **TypeDefs** for all classes defined by the library bundled in `types/human.d.ts` and enabled by default  
 *Note*: This does not include embedded `tfjs`  
 If you want to use embedded `tfjs` inside `Human` (`human.tf` namespace) and still full **typedefs**, add this code:
 > import type * as tfjs from '@vladmandic/human/dist/tfjs.esm';  
 > const tf = human.tf as typeof tfjs;
 This is not enabled by default as `Human` does not ship with full **TFJS TypeDefs** due to size considerations  
 Enabling `tfjs` TypeDefs as above creates additional project (dev-only as only types are required) dependencies as defined in `@vladmandic/human/dist/tfjs.esm.d.ts`:
 > @tensorflow/tfjs-core, @tensorflow/tfjs-converter, @tensorflow/tfjs-backend-wasm, @tensorflow/tfjs-backend-webgl
 <br><hr><br>
 ## Default models
 Default models in Human library are:
- **Face Detection**: *MediaPipe BlazeFace Back variation*
+- **Face Detection**: MediaPipe BlazeFace Back variation
- **Face Mesh**: *MediaPipe FaceMesh*
+- **Face Mesh**: MediaPipe FaceMesh
- **Face Iris Analysis**: *MediaPipe Iris*
+- **Face Iris Analysis**: MediaPipe Iris
- **Face Description**: *HSE FaceRes*
+- **Face Description**: HSE FaceRes
- **Emotion Detection**: *Oarriaga Emotion*
+- **Emotion Detection**: Oarriaga Emotion
- **Body Analysis**: *MoveNet Lightning variation*
+- **Body Analysis**: MoveNet Lightning variation
- **Hand Analysis**: *HandTrack & MediaPipe HandLandmarks*
+- **Hand Analysis**: HandTrack & MediaPipe HandLandmarks
- **Body Segmentation**: *Google Selfie*
+- **Body Segmentation**: Google Selfie
- **Object Detection**: *CenterNet with MobileNet v3*
+- **Object Detection**: CenterNet with MobileNet v3
 Note that alternative models are provided and can be enabled via configuration  
-For example, body pose detection by default uses *MoveNet Lightning*, but can be switched to *MultiNet Thunder* for higher precision or *Multinet MultiPose* for multi-person detection or even *PoseNet*, *BlazePose* or *EfficientPose* depending on the use case  
+For example, `PoseNet` model can be switched for `BlazePose`, `EfficientPose` or `MoveNet` model depending on the use case  
 For more info, see [**Configuration Details**](https://github.com/vladmandic/human/wiki/Configuration) and [**List of Models**](https://github.com/vladmandic/human/wiki/Models)
@ -448,9 +346,9 @@ For more info, see [**Configuration Details**](https://github.com/vladmandic/hum
 <br><hr><br>
-`Human` library is written in [TypeScript](https://www.typescriptlang.org/docs/handbook/intro.html) **5.1** using [TensorFlow/JS](https://www.tensorflow.org/js/) **4.10** and conforming to latest `JavaScript` [ECMAScript version 2022](https://262.ecma-international.org/) standard  
+`Human` library is written in `TypeScript` [4.6](https://www.typescriptlang.org/docs/handbook/intro.html)  
-
+Conforming to latest `JavaScript` [ECMAScript version 2021](https://262.ecma-international.org/) standard  
-Build target for distributables is `JavaScript` [EMCAScript version 2018](https://262.ecma-international.org/9.0/)  
+Build target is `JavaScript` [EMCAScript version 2018](https://262.ecma-international.org/11.0/)  
 <br>
@ -459,7 +357,6 @@ and [**API Specification**](https://vladmandic.github.io/human/typedoc/classes/H
 <br>
 [![](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/vladmandic)
 ![Stars](https://img.shields.io/github/stars/vladmandic/human?style=flat-square&svg=true)
 ![Forks](https://badgen.net/github/forks/vladmandic/human)
 ![Code Size](https://img.shields.io/github/languages/code-size/vladmandic/human?style=flat-square&svg=true)
--- a/TODO.md
+++ b/TODO.md
@ -2,37 +2,50 @@
 ## Work-in-Progress
 N/A
 <hr><br>
-## Known Issues & Limitations
+## Exploring
 - Optical flow for intelligent temporal interpolation  
  <https://docs.opencv.org/3.3.1/db/d7f/tutorial_js_lucas_kanade.html>
 - Advanced histogram equalization for optimization of badly lit scenes  
  **Adaptive**, **Contrast Limited**, **CLAHE**
 - TFLite models  
  <https://js.tensorflow.org/api_tflite/0.0.1-alpha.4/>
 - Body segmentation with temporal analysis  
  <https://github.com/PeterL1n/RobustVideoMatting>
 <hr><br>
 ## Known Issues
 ### Face with Attention
 `FaceMesh-Landmarks` model is supported only with `CPU` and `WebGL` backends due to `TFJS` issues
-`FaceMesh-Attention` is not supported when using `WASM` backend due to missing kernel op in **TFJS**  
+### WASM
 No issues with default model `FaceMesh`  
-### Object Detection
+Support for **WASM SIMD** and **WASM MultiThreading** is still disabled by default in **Chromium** based browsers  
 Suggestion is to enable it manually for major performance boost  
 Enable via <chrome://flags/#enable-experimental-webassembly-features>
-`NanoDet` model is not supported when using `WASM` backend due to missing kernel op in **TFJS**  
+### WebGPU
 No issues with default model `MB3-CenterNet`  
-## Body Detection using MoveNet-MultiPose
+Experimental support only until support is officially added in Chromium  
-
+Enable via <chrome://flags/#enable-unsafe-webgpu>
 Model does not return valid detection scores (all other functionality is not impacted)  
 ### 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
+### Face Detection
-No support for running in **web workers** as Safari still does not support `OffscreenCanvas`  
+Enhanced rotation correction for face detection is not working in **NodeJS** due to missing kernel op in **TFJS**  
-[Details](https://developer.mozilla.org/en-US/docs/Web/API/OffscreenCanvas#browser_compatibility)  
+Feature is automatically disabled in **NodeJS** without user impact  
 ## React-Native
 `Human` support for **React-Native** is best-effort, but not part of the main development focus  
 <hr><br>
 ## Pending Release Changes
--- a/.api-extractor.json
+++ b/.api-extractor.json
@ -1,8 +1,9 @@
 {
  "$schema": "https://developer.microsoft.com/json-schemas/api-extractor/v7/api-extractor.schema.json",
  "mainEntryPointFilePath": "types/lib/src/human.d.ts",
  "bundledPackages": ["@types/offscreencanvas", "@tensorflow/tfjs-core", "@tensorflow/tfjs-converter", "@tensorflow/tfjs-data"],
  "compiler": {
-    "skipLibCheck": true
+    "skipLibCheck": false
  },
  "newlineKind": "lf",
  "dtsRollup": {
--- a/assets/screenshot-facedetect.jpg
+++ b/assets/screenshot-facedetect.jpg
--- a/assets/screenshot-faceid.jpg
+++ b/assets/screenshot-faceid.jpg
--- a/assets/screenshot-menu.png
+++ b/assets/screenshot-menu.png
--- a/assets/screenshot-results.png
+++ b/assets/screenshot-results.png
--- a/assets/screenshot-segmentation.jpg
+++ b/assets/screenshot-segmentation.jpg
--- a/assets/screenshot-typedoc.png
+++ b/assets/screenshot-typedoc.png
--- a/build.js
+++ b/build.js
@ -1,25 +1,15 @@
 const fs = require('fs');
-const path = require('path');
+const log = require('@vladmandic/pilogger');
-const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
+const Build = require('@vladmandic/build').Build;
-const Build = require('@vladmandic/build').Build; // eslint-disable-line node/no-unpublished-require
+const APIExtractor = require('@microsoft/api-extractor');
-const APIExtractor = require('@microsoft/api-extractor'); // eslint-disable-line node/no-unpublished-require
+const tf = require('@tensorflow/tfjs-node');
-const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
+const package = require('./package.json');
 const packageJSON = require('./package.json');
-const logFile = 'test/build.log';
+const modelsDir = '../human-models/models';
 const modelsOut = 'models/models.json';
 const modelsFolders = [
  './models',
  '../human-models/models',
  '../blazepose/model/',
  '../anti-spoofing/model',
  '../efficientpose/models',
  '../insightface/models',
  '../movenet/models',
  '../nanodet/models',
 ];
-const apiExtractorIgnoreList = [ // eslint-disable-line no-unused-vars
+// eslint-disable-next-line @typescript-eslint/no-unused-vars
 const apiExtractorIgnoreList = [
  'ae-missing-release-tag',
  'tsdoc-param-tag-missing-hyphen',
  'tsdoc-escape-right-brace',
@ -31,72 +21,36 @@ const apiExtractorIgnoreList = [ // eslint-disable-line no-unused-vars
  'tsdoc-unnecessary-backslash',
 ];
-const regEx = [
+function copy(src, dst) {
-  { search: 'types="@webgpu/types/dist"', replace: 'path="../src/types/webgpu.d.ts"' },
+  if (!fs.existsSync(src)) return;
  { search: 'types="offscreencanvas"', replace: 'path="../src/types/offscreencanvas.d.ts"' },
 ];
 function copyFile(src, dst) {
  if (!fs.existsSync(src)) {
    log.warn('Copy:', { input: src, output: dst });
    return;
  }
  log.state('Copy:', { input: src, output: dst });
  const buffer = fs.readFileSync(src);
  fs.writeFileSync(dst, buffer);
 }
 function writeFile(str, dst) {
  log.state('Write:', { output: dst });
  fs.writeFileSync(dst, str);
 }
 function regExFile(src, entries) {
  if (!fs.existsSync(src)) {
    log.warn('Filter:', { src });
    return;
  }
  log.state('Filter:', { input: src });
  for (const entry of entries) {
    const buffer = fs.readFileSync(src, 'UTF-8');
    const lines = buffer.split(/\r?\n/);
    const out = [];
    for (const line of lines) {
      if (line.includes(entry.search)) out.push(line.replace(entry.search, entry.replace));
      else out.push(line);
    }
    fs.writeFileSync(src, out.join('\n'));
  }
 }
 async function analyzeModels() {
-  log.info('Analyze models:', { folders: modelsFolders.length, result: modelsOut });
+  log.info('Analyze:', { modelsDir, modelsOut });
  let totalSize = 0;
  const models = {};
-  const allModels = [];
+  let dir;
-  for (const folder of modelsFolders) {
+  try {
-    try {
+    dir = fs.readdirSync(modelsDir);
-      if (!fs.existsSync(folder)) continue;
+  } catch {
-      const stat = fs.statSync(folder);
+    log.warn('Cannot enumerate:', modelsDir);
      if (!stat.isDirectory) continue;
      const dir = fs.readdirSync(folder);
      const found = dir.map((f) => `file://${folder}/${f}`).filter((f) => f.endsWith('json'));
      log.state('Models', { folder, models: found.length });
      allModels.push(...found);
    } catch {
      // log.warn('Cannot enumerate:', modelFolder);
    }
  }
-  for (const url of allModels) {
+  if (!dir || dir.length === 0) {
-    // if (!f.endsWith('.json')) continue;
+    log.warn('No models found:', modelsDir);
-    // const url = `file://${modelsDir}/${f}`;
+    return;
  }
  for (const f of dir) {
    if (!f.endsWith('.json')) continue;
    const url = `file://${modelsDir}/${f}`;
    const model = new tf.GraphModel(url); // create model prototype and decide if load from cache or from original modelurl
    model.findIOHandler();
    const artifacts = await model.handler.load();
    const size = artifacts?.weightData?.byteLength || 0;
    totalSize += size;
-    const name = path.basename(url).replace('.json', '');
+    const name = f.replace('.json', '');
-    if (!models[name]) models[name] = size;
+    models[name] = size;
  }
  const json = JSON.stringify(models, null, 2);
  fs.writeFileSync(modelsOut, json);
@ -104,50 +58,38 @@ async function analyzeModels() {
 }
 async function main() {
-  log.logFile(logFile);
+  log.data('Build', { name: package.name, version: package.version });
-  log.data('Build', { name: packageJSON.name, version: packageJSON.version });
+  // generate model signature
-
+  await analyzeModels();
  // run production build
  const build = new Build();
  await build.run('production');
  // patch tfjs typedefs
-  copyFile('node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts', 'types/tfjs-core.d.ts');
+  log.state('Copy:', { input: 'tfjs/tfjs.esm.d.ts' });
-  copyFile('node_modules/@vladmandic/tfjs/types/tfjs.d.ts', 'types/tfjs.esm.d.ts');
+  copy('tfjs/tfjs.esm.d.ts', 'types/lib/dist/tfjs.esm.d.ts');
  copyFile('src/types/tsconfig.json', 'types/tsconfig.json');
  copyFile('src/types/eslint.json', 'types/.eslintrc.json');
  copyFile('src/types/tfjs.esm.d.ts', 'dist/tfjs.esm.d.ts');
  regExFile('types/tfjs-core.d.ts', regEx);
  // run api-extractor to create typedef rollup
-  const extractorConfig = APIExtractor.ExtractorConfig.loadFileAndPrepare('.api-extractor.json');
+  const extractorConfig = APIExtractor.ExtractorConfig.loadFileAndPrepare('api-extractor.json');
-  try {
+  const extractorResult = APIExtractor.Extractor.invoke(extractorConfig, {
-    const extractorResult = APIExtractor.Extractor.invoke(extractorConfig, {
+    localBuild: true,
-      localBuild: true,
+    showVerboseMessages: false,
-      showVerboseMessages: false,
+    messageCallback: (msg) => {
-      messageCallback: (msg) => {
+      msg.handled = true;
-        msg.handled = true;
+      if (msg.logLevel === 'none' || msg.logLevel === 'verbose' || msg.logLevel === 'info') return;
-        if (msg.logLevel === 'none' || msg.logLevel === 'verbose' || msg.logLevel === 'info') return;
+      if (msg.sourceFilePath?.includes('/node_modules/')) return;
-        if (msg.sourceFilePath?.includes('/node_modules/')) return;
+      // if (apiExtractorIgnoreList.reduce((prev, curr) => prev || msg.messageId.includes(curr), false)) return; // those are external issues outside of human control
-        // if (apiExtractorIgnoreList.reduce((prev, curr) => prev || msg.messageId.includes(curr), false)) return; // those are external issues outside of human control
+      log.data('API', { level: msg.logLevel, category: msg.category, id: msg.messageId, file: msg.sourceFilePath, line: msg.sourceFileLine, text: msg.text });
-        log.data('API', { level: msg.logLevel, category: msg.category, id: msg.messageId, file: msg.sourceFilePath, line: msg.sourceFileLine, text: msg.text });
+    },
-      },
+  });
-    });
+  log.state('API-Extractor:', { succeeeded: extractorResult.succeeded, errors: extractorResult.errorCount, warnings: extractorResult.warningCount });
-    log.state('API-Extractor:', { succeeeded: extractorResult.succeeded, errors: extractorResult.errorCount, warnings: extractorResult.warningCount });
+  // distribute typedefs
-  } catch (err) {
+  log.state('Copy:', { input: 'types/human.d.ts' });
-    log.error('API-Extractor:', err);
+  copy('types/human.d.ts', 'dist/human.esm-nobundle.d.ts');
-  }
+  copy('types/human.d.ts', 'dist/human.esm.d.ts');
-  regExFile('types/human.d.ts', regEx);
+  copy('types/human.d.ts', 'dist/human.d.ts');
-  writeFile('export * from \'../types/human\';', 'dist/human.esm-nobundle.d.ts');
+  copy('types/human.d.ts', 'dist/human.node-gpu.d.ts');
-  writeFile('export * from \'../types/human\';', 'dist/human.esm.d.ts');
+  copy('types/human.d.ts', 'dist/human.node.d.ts');
-  writeFile('export * from \'../types/human\';', 'dist/human.d.ts');
+  copy('types/human.d.ts', 'dist/human.node-wasm.d.ts');
-  writeFile('export * from \'../types/human\';', 'dist/human.node-gpu.d.ts');
+  log.info('Human Build complete...');
  writeFile('export * from \'../types/human\';', 'dist/human.node.d.ts');
  writeFile('export * from \'../types/human\';', 'dist/human.node-wasm.d.ts');
  // generate model signature
  await analyzeModels();
  log.info('Human Build complete...', { logFile });
 }
 main();
--- a/build.log
+++ b/build.log
--- a/demo/README.md
+++ b/demo/README.md
@ -8,7 +8,6 @@ For details on other demos see Wiki: [**Demos**](https://github.com/vladmandic/h
 `index.html`: Full demo using `Human` ESM module running in Browsers,  
 Includes:
 - Selectable inputs:
  - Sample images
  - Image via drag & drop
@ -38,14 +37,12 @@ Includes:
  in `index.js:ui`
 ```js
 const ui = {
  console: true,      // log messages to browser console
  useWorker: true,    // use web workers for processing
  buffered: true,     // should output be buffered between frames
  interpolated: true, // should output be interpolated for smoothness between frames
  results: false,     // show results tree
  useWebRTC: false,   // use webrtc as camera source instead of local webcam
 };
 ```
 Demo implements several ways to use `Human` library,  
--- a/demo/benchmark/README.md
+++ b/demo/benchmark/README.md
@ -0,0 +1,4 @@
 # Human Benchmarks
 - `node.js` runs benchmark using `tensorflow` backend in **NodeJS**
 - `index.html` runs benchmark using `wasm`, `webgl`, `humangl` and `webgpu` backends in **Browser**
--- a/demo/benchmark/index.html
+++ b/demo/benchmark/index.html
@ -0,0 +1,121 @@
 <!DOCTYPE html>
 <html lang="en">
  <head>
    <meta charset="utf-8">
    <title>Human</title>
    <meta name="viewport" content="width=device-width" id="viewport">
    <meta name="keywords" content="Human">
    <meta name="application-name" content="Human">
    <meta name="description" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="msapplication-tooltip" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="theme-color" content="#000000">
    <link rel="manifest" href="../manifest.webmanifest">
    <link rel="shortcut icon" href="../../favicon.ico" type="image/x-icon">
    <link rel="apple-touch-icon" href="../../assets/icon.png">
    <style>
      @font-face { font-family: 'Lato'; font-display: swap; font-style: normal; font-weight: 100; src: local('Lato'), url('../../assets/lato-light.woff2') }
      html { font-family: 'Lato', 'Segoe UI'; font-size: 16px; font-variant: small-caps; }
      body { margin: 0; background: black; color: white; overflow-x: hidden; width: 100vw; height: 100vh; }
      body::-webkit-scrollbar { display: none; }
    </style>
  </head>
  <body>
    <img id="image" src="../../samples/in/group-1.jpg" alt="test image" style="display: none">
    <div id="log" style="position: absolute; top: 0; margin: 1rem; font-size: 1rem; line-height: 1.4rem; font-family: monospace;"></div>
    <script type="module">
      import Human from '../../dist/human.esm.js';
      const loop = 20;
      let initial = true;
      const backends = ['wasm', 'webgl', 'humangl', 'webgpu'];
      function str(...msg) {
        if (!Array.isArray(msg)) return msg;
        let line = '';
        for (const entry of msg) {
          if (typeof entry === 'object') line += JSON.stringify(entry).replace(/{|}|"|\[|\]/g, '').replace(/,/g, ', ');
          else line += entry;
        }
        return line;
      }
      function log(...msg) {
        const dt = new Date();
        const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
        // eslint-disable-next-line no-console
        if (msg) console.log(ts, 'Human:', ...msg);
        const el = document.getElementById('log');
        el.innerHTML += str(...msg) + '<br>';
      }
      const myConfig = {
        modelBasePath: 'https://vladmandic.github.io/human/models',
        // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.18.0/dist/',
        debug: true,
        async: true,
        cacheSensitivity: 0,
        filter: { enabled: false },
        face: {
          enabled: true,
          detector: { enabled: true, rotation: false },
          mesh: { enabled: true },
          iris: { enabled: true },
          description: { enabled: true },
          emotion: { enabled: true },
          antispoof: { enabled: true },
          liveness: { enabled: true },
        },
        hand: { enabled: true },
        body: { enabled: true },
        object: { enabled: true },
      };
      async function benchmark(backend) {
        myConfig.backend = backend;
        const human = new Human(myConfig);
        await human.init();
        await human.load();
        const loaded = Object.keys(human.models).filter((a) => human.models[a]);
        if (initial) {
          log('Human: ', human.version);
          log('TFJS: ', human.tf.version.tfjs);
          log('Environment: ', { platform: human.env.platform, agent: human.env.agent });
          log('Backends: ', human.env.backends);
          log('Support: ', { offscreen: human.env.offscreen, webgl: human.env.webgl.supported, webgpu: human.env.webgpu.supported, wasm: human.env.wasm.supported, simd: human.env.wasm.simd, multithread: human.env.wasm.multithread });
          log('Models: ', loaded);
          log('');
          initial = false;
        }
        const element = document.getElementById('image');
        const processed = await human.image(element);
        const t0 = human.now();
        await human.detect(processed.tensor, myConfig);
        const t1 = human.now();
        if (human.tf.getBackend().toLowerCase() === backend.toLowerCase()) {
          log('Backend: ', human.tf.getBackend());
          log('Memory state: ', human.tf.engine().memory());
          log('Initial inference: ', Math.round(t1 - t0));
          for (let i = 0; i < loop; i++) {
            const image = await human.image(element);
            human.tf.dispose(image.tensor);
          }
          const t2 = human.now();
          log('Input processing: ', Math.round(10 * (t2 - t1) / loop) / 10);
          for (let i = 0; i < loop; i++) await human.detect(processed.tensor, myConfig);
          const t3 = human.now();
          log('Average inference: ', Math.round((t3 - t1) / loop));
        } else {
          log('Backend error: ', { desired: backend, current: human.tf.getBackend().toLowerCase() });
        }
        log('');
      }
      log('Attempting backends: ', backends);
      async function main() {
        for (const backend of backends) await benchmark(backend);
      }
      window.onload = main;
    </script> 
  </body>
 </html>
--- a/demo/benchmark/node.js
+++ b/demo/benchmark/node.js
@ -0,0 +1,65 @@
 // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
 const tf = require('@tensorflow/tfjs-node-gpu');
 const log = require('@vladmandic/pilogger');
 const canvasJS = require('canvas');
 const Human = require('../../dist/human.node-gpu.js').default;
 const input = './samples/in/group-1.jpg';
 const loop = 20;
 const myConfig = {
  backend: 'tensorflow',
  modelBasePath: 'https://vladmandic.github.io/human/models',
  // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.18.0/dist/',
  debug: true,
  async: true,
  cacheSensitivity: 0,
  filter: { enabled: false },
  face: {
    enabled: true,
    detector: { enabled: true, rotation: false },
    mesh: { enabled: true },
    iris: { enabled: true },
    description: { enabled: true },
    emotion: { enabled: true },
    antispoof: { enabled: true },
    liveness: { enabled: true },
  },
  hand: { enabled: true },
  body: { enabled: true },
  object: { enabled: true },
 };
 async function getImage(human) {
  const img = await canvasJS.loadImage(input);
  const canvas = canvasJS.createCanvas(img.width, img.height);
  const ctx = canvas.getContext('2d');
  ctx.drawImage(img, 0, 0, img.width, img.height);
  const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
  const tensor = human.tf.tensor(Array.from(imageData.data), [canvas.height, canvas.width, 4], 'int32'); // create rgba image tensor from flat array
  log.info('Image:', input, tensor.shape);
  return tensor;
 }
 async function main() {
  log.header();
  const human = new Human(myConfig);
  await human.tf.ready();
  log.info('Human:', human.version);
  await human.load();
  const loaded = Object.keys(human.models).filter((a) => human.models[a]);
  log.info('Loaded:', loaded);
  log.info('Memory state:', human.tf.engine().memory());
  const tensor = await getImage(human);
  log.state('Processing:', tensor['shape']);
  const t0 = human.now();
  await human.detect(tensor, myConfig);
  const t1 = human.now();
  log.state('Backend:', human.tf.getBackend());
  log.data('Warmup:', Math.round(t1 - t0));
  for (let i = 0; i < loop; i++) await human.detect(tensor, myConfig);
  const t2 = human.now();
  log.data('Average:', Math.round((t2 - t1) / loop));
 }
 main();
--- a/demo/facedetect/facedetect.js
+++ b/demo/facedetect/facedetect.js
@ -1,160 +0,0 @@
 /**
 * Human demo for browsers
 *
 * Demo for face detection
 */
 /** @type {Human} */
 import { Human } from '../../dist/human.esm.js';
 let loader;
 const humanConfig = { // user configuration for human, used to fine-tune behavior
  cacheSensitivity: 0,
  debug: true,
  modelBasePath: 'https://vladmandic.github.io/human-models/models/',
  filter: { enabled: true, equalization: false, flip: false },
  face: {
    enabled: true,
    detector: { rotation: false, maxDetected: 100, minConfidence: 0.2, return: true, square: false },
    iris: { enabled: true },
    description: { enabled: true },
    emotion: { enabled: true },
    antispoof: { enabled: true },
    liveness: { enabled: true },
  },
  body: { enabled: false },
  hand: { enabled: false },
  object: { enabled: false },
  gesture: { enabled: false },
  segmentation: { enabled: false },
 };
 const human = new Human(humanConfig); // new instance of human
 export const showLoader = (msg) => { loader.setAttribute('msg', msg); loader.style.display = 'block'; };
 export const hideLoader = () => loader.style.display = 'none';
 class ComponentLoader extends HTMLElement { // watch for attributes
  message = document.createElement('div');
  static get observedAttributes() { return ['msg']; }
  attributeChangedCallback(_name, _prevVal, currVal) {
    this.message.innerHTML = currVal;
  }
  connectedCallback() { // triggered on insert
    this.attachShadow({ mode: 'open' });
    const css = document.createElement('style');
    css.innerHTML = `
      .loader-container { top: 450px; justify-content: center; position: fixed; width: 100%; }
      .loader-message { font-size: 1.5rem; padding: 1rem; }
      .loader { width: 300px; height: 300px; border: 3px solid transparent; border-radius: 50%; border-top: 4px solid #f15e41; animation: spin 4s linear infinite; position: relative; }
      .loader::before, .loader::after { content: ""; position: absolute; top: 6px; bottom: 6px; left: 6px; right: 6px; border-radius: 50%; border: 4px solid transparent; }
      .loader::before { border-top-color: #bad375; animation: 3s spin linear infinite; }
      .loader::after { border-top-color: #26a9e0; animation: spin 1.5s linear infinite; }
      @keyframes spin { from { transform: rotate(0deg); } to { transform: rotate(360deg); } }
    `;
    const container = document.createElement('div');
    container.id = 'loader-container';
    container.className = 'loader-container';
    loader = document.createElement('div');
    loader.id = 'loader';
    loader.className = 'loader';
    this.message.id = 'loader-message';
    this.message.className = 'loader-message';
    this.message.innerHTML = '';
    container.appendChild(this.message);
    container.appendChild(loader);
    this.shadowRoot?.append(css, container);
    loader = this; // eslint-disable-line @typescript-eslint/no-this-alias
  }
 }
 customElements.define('component-loader', ComponentLoader);
 function addFace(face, source) {
  const deg = (rad) => Math.round((rad || 0) * 180 / Math.PI);
  const canvas = document.createElement('canvas');
  const emotion = face.emotion?.map((e) => `${Math.round(100 * e.score)}% ${e.emotion}`) || [];
  const rotation = `pitch ${deg(face.rotation?.angle.pitch)}° | roll ${deg(face.rotation?.angle.roll)}°  | yaw ${deg(face.rotation?.angle.yaw)}°`;
  const gaze = `direction ${deg(face.rotation?.gaze.bearing)}° strength ${Math.round(100 * (face.rotation.gaze.strength || 0))}%`;
  canvas.title = `
    source: ${source}
    score: ${Math.round(100 * face.boxScore)}% detection ${Math.round(100 * face.faceScore)}% analysis
    age: ${face.age} years | gender: ${face.gender} score ${Math.round(100 * face.genderScore)}%
    emotion: ${emotion.join(' | ')}
    head rotation: ${rotation}
    eyes gaze: ${gaze}
    camera distance: ${face.distance}m | ${Math.round(100 * face.distance / 2.54)}in
    check: ${Math.round(100 * face.real)}% real ${Math.round(100 * face.live)}% live
  `.replace(/  /g, ' ');
  canvas.onclick = (e) => {
    e.preventDefault();
    document.getElementById('description').innerHTML = canvas.title;
  };
  human.draw.tensor(face.tensor, canvas);
  human.tf.dispose(face.tensor);
  return canvas;
 }
 async function addFaces(imgEl) {
  showLoader('human: busy');
  const faceEl = document.getElementById('faces');
  faceEl.innerHTML = '';
  const res = await human.detect(imgEl);
  console.log(res); // eslint-disable-line no-console
  document.getElementById('description').innerHTML = `detected ${res.face.length} faces`;
  for (const face of res.face) {
    const canvas = addFace(face, imgEl.src.substring(0, 64));
    faceEl.appendChild(canvas);
  }
  hideLoader();
 }
 function addImage(imageUri) {
  const imgEl = new Image(256, 256);
  imgEl.onload = () => {
    const images = document.getElementById('images');
    images.appendChild(imgEl); // add image if loaded ok
    images.scroll(images?.offsetWidth, 0);
  };
  imgEl.onerror = () => console.error('addImage', { imageUri }); // eslint-disable-line no-console
  imgEl.onclick = () => addFaces(imgEl);
  imgEl.title = imageUri.substring(0, 64);
  imgEl.src = encodeURI(imageUri);
 }
 async function initDragAndDrop() {
  const reader = new FileReader();
  reader.onload = async (e) => {
    if (e.target.result.startsWith('data:image')) await addImage(e.target.result);
  };
  document.body.addEventListener('dragenter', (evt) => evt.preventDefault());
  document.body.addEventListener('dragleave', (evt) => evt.preventDefault());
  document.body.addEventListener('dragover', (evt) => evt.preventDefault());
  document.body.addEventListener('drop', async (evt) => {
    evt.preventDefault();
    evt.dataTransfer.dropEffect = 'copy';
    for (const f of evt.dataTransfer.files) reader.readAsDataURL(f);
  });
  document.body.onclick = (e) => {
    if (e.target.localName !== 'canvas') document.getElementById('description').innerHTML = '';
  };
 }
 async function main() {
  showLoader('loading models');
  await human.load();
  showLoader('compiling models');
  await human.warmup();
  showLoader('loading images');
  const images = ['group-1.jpg', 'group-2.jpg', 'group-3.jpg', 'group-4.jpg', 'group-5.jpg', 'group-6.jpg', 'group-7.jpg', 'solvay1927.jpg', 'stock-group-1.jpg', 'stock-group-2.jpg', 'stock-models-6.jpg', 'stock-models-7.jpg'];
  const imageUris = images.map((a) => `../../samples/in/${a}`);
  for (let i = 0; i < imageUris.length; i++) addImage(imageUris[i]);
  initDragAndDrop();
  hideLoader();
 }
 window.onload = main;
--- a/demo/facedetect/index.html
+++ b/demo/facedetect/index.html
@ -1,43 +0,0 @@
 <!DOCTYPE html>
 <html lang="en">
  <head>
    <meta charset="utf-8">
    <title>Human</title>
    <!-- <meta http-equiv="content-type" content="text/html; charset=utf-8"> -->
    <meta name="viewport" content="width=device-width, shrink-to-fit=yes">
    <meta name="keywords" content="Human">
    <meta name="application-name" content="Human">
    <meta name="description" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="msapplication-tooltip" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="theme-color" content="#000000">
    <link rel="manifest" href="../manifest.webmanifest">
    <link rel="shortcut icon" href="../../favicon.ico" type="image/x-icon">
    <link rel="apple-touch-icon" href="../../assets/icon.png">
    <script src="./facedetect.js" type="module"></script>
    <style>
      img { object-fit: contain; }
      img:hover { filter: grayscale(1); transform: scale(1.08); transition : all 0.3s ease; }   
      @font-face { font-family: 'Lato'; font-display: swap; font-style: normal; font-weight: 100; src: local('Lato'), url('../../assets/lato-light.woff2') }
      html { font-family: 'Lato', 'Segoe UI'; font-size: 24px; font-variant: small-caps; }
      body { margin: 24px; background: black; color: white; overflow: hidden; text-align: -webkit-center; width: 100vw; height: 100vh; }
      ::-webkit-scrollbar { height: 8px; border: 0; border-radius: 0; }
      ::-webkit-scrollbar-thumb { background: grey }
      ::-webkit-scrollbar-track { margin: 3px; }
      canvas { width: 192px; height: 192px; margin: 2px; padding: 2px; cursor: grab; transform: scale(1.00); transition : all 0.3s ease; }
      canvas:hover { filter: grayscale(1); transform: scale(1.08); transition : all 0.3s ease; }   
    </style>
  </head>
  <body>
    <component-loader></component-loader>
    <div style="display: flex">
      <div>
        <div style="margin: 24px">select image to show detected faces<br>drag & drop to add your images</div>
        <div id="images" style="display: flex; width: 98vw; overflow-x: auto; overflow-y: hidden; scroll-behavior: smooth"></div>
      </div>
    </div>
    <div id="list" style="height: 10px"></div>
    <div style="margin: 24px">hover or click on face to show details</div>
    <div id="faces" style="overflow-y: auto"></div>
    <div id="description" style="white-space: pre;"></div>
  </body>
 </html>
--- a/demo/faceid/README.md
+++ b/demo/faceid/README.md
@ -38,5 +38,4 @@ designed to serve as a quick check when used together with other indicators:
 **FaceID** is compatible with
 - `faceres.json` (default) perfoms combined age/gender/descriptor analysis
 - `faceres-deep.json` higher resolution variation of `faceres`
 - `insightface` alternative model for face descriptor analysis
 - `mobilefacenet` alternative model for face descriptor analysis
--- a/demo/faceid/index.html
+++ b/demo/faceid/index.html
@ -19,23 +19,14 @@
      body { margin: 0; padding: 16px; background: black; color: white; overflow-x: hidden; width: 100vw; height: 100vh; }
      body::-webkit-scrollbar { display: none; }
      .button { padding: 2px; cursor: pointer; box-shadow: 2px 2px black; width: 64px; text-align: center; place-content: center; margin-left: 16px; height: 16px; display: none }
-      .ok { position: absolute; top: 64px; right: 20px; width: 150px; background-color: grey; padding: 4px; color: black; font-size: 14px }
+      .ok { position: absolute; top: 64px; right: 20px; width: 100px; background-color: grey; padding: 4px; color: black; font-size: 14px }
    </style>
  </head>
  <body>
    <div style="padding: 8px">
      <h1 style="margin: 0">faceid demo using human library</h1>
      look directly at camera and make sure that detection passes all of the required tests noted on the right hand side of the screen<br>
      if input does not satisfies tests within specific timeout, no image will be selected<br>
      once face image is approved, it will be compared with existing face database<br>
      you can also store face descriptor with label in a browser's indexdb for future usage<br>
      <br>
      <i>note: this is not equivalent to full faceid methods as used by modern mobile phones or windows hello<br>
        as they rely on additional infrared sensors and depth-sensing and not just camera image for additional levels of security</i>
    </div>
    <canvas id="canvas" style="padding: 8px"></canvas>
    <canvas id="source" style="padding: 8px"></canvas>
    <video id="video" playsinline style="display: none"></video>
    <pre id="fps" style="position: absolute; bottom: 16px; right: 20px; background-color: grey; padding: 8px; box-shadow: 2px 2px black"></pre>
    <pre id="log" style="padding: 8px"></pre>
    <div id="match" style="display: none; padding: 8px">
      <label for="name">name:</label>
@ -43,7 +34,7 @@
      <span id="save" class="button" style="background-color: royalblue">save</span>
      <span id="delete" class="button" style="background-color: lightcoral">delete</span>
    </div>
-    <div id="retry" class="button" style="background-color: darkslategray; width: 93%; margin-top: 32px; padding: 12px">retry</div>
+    <div id="retry" class="button" style="background-color: darkslategray; width: 350px; margin-top: 32px; padding: 4px">retry</div>
    <div id="ok"></div>
  </body>
 </html>
--- a/demo/faceid/index.js
+++ b/demo/faceid/index.js
--- a/demo/faceid/index.js.map
+++ b/demo/faceid/index.js.map
--- a/demo/faceid/index.ts
+++ b/demo/faceid/index.ts
@ -7,20 +7,17 @@
 * @license MIT
 */
-import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
+import { Human, TensorLike, FaceResult } from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
 import * as indexDb from './indexdb'; // methods to deal with indexdb
 const humanConfig = { // user configuration for human, used to fine-tune behavior
  cacheSensitivity: 0.01,
  modelBasePath: '../../models',
-  filter: { enabled: true, equalization: true }, // lets run with histogram equilizer
+  filter: { equalization: true }, // lets run with histogram equilizer
  debug: true,
  face: {
    enabled: true,
-    detector: { rotation: true, return: true, mask: false }, // return tensor is used to get detected face image
+    detector: { rotation: true, return: true, cropFactor: 1.6, mask: false }, // return tensor is used to get detected face image
    description: { enabled: true }, // default model for face descriptor extraction is faceres
-    // mobilefacenet: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model
+    mobilefacenet: { enabled: false, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model
    // insightface: { enabled: true, modelPath: 'https://vladmandic.github.io/insightface/models/insightface-mobilenet-swish.json' }, // alternative model
    iris: { enabled: true }, // needed to determine gaze direction
    emotion: { enabled: false }, // not needed
    antispoof: { enabled: true }, // enable optional antispoof module
@ -38,50 +35,29 @@ const matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; // for fa
 const options = {
  minConfidence: 0.6, // overal face confidence for box, face, gender, real, live
  minSize: 224, // min input to face descriptor model before degradation
-  maxTime: 30000, // max time before giving up
+  maxTime: 10000, // max time before giving up
  blinkMin: 10, // minimum duration of a valid blink
  blinkMax: 800, // maximum duration of a valid blink
  threshold: 0.5, // minimum similarity
  distanceMin: 0.4, // closest that face is allowed to be to the cammera in cm
  distanceMax: 1.0, // farthest that face is allowed to be to the cammera in cm
  mask: humanConfig.face.detector.mask,
  rotation: humanConfig.face.detector.rotation,
  cropFactor: humanConfig.face.detector.cropFactor,
  ...matchOptions,
 };
-const ok: Record<string, { status: boolean | undefined, val: number }> = { // must meet all rules
+const ok = { // must meet all rules
-  faceCount: { status: false, val: 0 },
+  faceCount: false,
-  faceConfidence: { status: false, val: 0 },
+  faceConfidence: false,
-  facingCenter: { status: false, val: 0 },
+  facingCenter: false,
-  lookingCenter: { status: false, val: 0 },
+  lookingCenter: false,
-  blinkDetected: { status: false, val: 0 },
+  blinkDetected: false,
-  faceSize: { status: false, val: 0 },
+  faceSize: false,
-  antispoofCheck: { status: false, val: 0 },
+  antispoofCheck: false,
-  livenessCheck: { status: false, val: 0 },
+  livenessCheck: false,
-  distance: { status: false, val: 0 },
+  elapsedMs: 0, // total time while waiting for valid face
  age: { status: false, val: 0 },
  gender: { status: false, val: 0 },
  timeout: { status: true, val: 0 },
  descriptor: { status: false, val: 0 },
  elapsedMs: { status: undefined, val: 0 }, // total time while waiting for valid face
  detectFPS: { status: undefined, val: 0 }, // mark detection fps performance
  drawFPS: { status: undefined, val: 0 }, // mark redraw fps performance
 };
-
+const allOk = () => ok.faceCount && ok.faceSize && ok.blinkDetected && ok.facingCenter && ok.lookingCenter && ok.faceConfidence && ok.antispoofCheck && ok.livenessCheck;
-const allOk = () => ok.faceCount.status
+const current: { face: FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record
  && ok.faceSize.status
  && ok.blinkDetected.status
  && ok.facingCenter.status
  && ok.lookingCenter.status
  && ok.faceConfidence.status
  && ok.antispoofCheck.status
  && ok.livenessCheck.status
  && ok.distance.status
  && ok.descriptor.status
  && ok.age.status
  && ok.gender.status;
 const current: { face: H.FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record
 const blink = { // internal timers for blink start/end/duration
  start: 0,
@ -90,9 +66,9 @@ const blink = { // internal timers for blink start/end/duration
 };
 // let db: Array<{ name: string, source: string, embedding: number[] }> = []; // holds loaded face descriptor database
-const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
+const human = new Human(humanConfig); // create instance of human with overrides from user configuration
-human.env.perfadd = false; // is performance data showing instant or total values
+human.env['perfadd'] = false; // is performance data showing instant or total values
 human.draw.options.font = 'small-caps 18px "Lato"'; // set font used to draw labels when using draw methods
 human.draw.options.lineHeight = 20;
@ -110,110 +86,97 @@ const dom = { // grab instances of dom objects so we dont have to look them up l
  ok: document.getElementById('ok') as HTMLDivElement,
 };
 const timestamp = { detect: 0, draw: 0 }; // holds information used to calculate performance and possible memory leaks
 const fps = { detect: 0, draw: 0 }; // holds calculated fps information for both detect and screen refresh
 let startTime = 0;
 const log = (...msg) => { // helper method to output messages
  dom.log.innerText += msg.join(' ') + '\n';
-  console.log(...msg); // eslint-disable-line no-console
+  // eslint-disable-next-line no-console
  console.log(...msg);
 };
 const printFPS = (msg) => dom.fps.innerText = msg; // print status element
 async function webCam() { // initialize webcam
  printFPS('starting webcam...');
  // @ts-ignore resizeMode is not yet defined in tslib
  const cameraOptions: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };
  const stream: MediaStream = await navigator.mediaDevices.getUserMedia(cameraOptions);
  const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });
  dom.video.srcObject = stream;
-  void dom.video.play();
+  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) log('video:', dom.video.videoWidth, dom.video.videoHeight, '|', stream.getVideoTracks()[0].label);
  dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click
-    if (dom.video.paused) void dom.video.play();
+    if (dom.video.paused) dom.video.play();
    else dom.video.pause();
  };
 }
 async function detectionLoop() { // main detection loop
  if (!dom.video.paused) {
-    if (current.face?.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor
+    if (current.face && current.face.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor
    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result
    const now = human.now();
-    ok.detectFPS.val = Math.round(10000 / (now - timestamp.detect)) / 10;
+    fps.detect = 1000 / (now - timestamp.detect);
    timestamp.detect = now;
    requestAnimationFrame(detectionLoop); // start new frame immediately
  }
 }
-function drawValidationTests() {
+async function validationLoop(): Promise<FaceResult> { // main screen refresh loop
  const interpolated = await human.next(human.result); // smoothen result using last-known results
  await human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen
  await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.
  const now = human.now();
  fps.draw = 1000 / (now - timestamp.draw);
  timestamp.draw = now;
  printFPS(`fps: ${fps.detect.toFixed(1).padStart(5, ' ')} detect | ${fps.draw.toFixed(1).padStart(5, ' ')} draw`); // write status
  ok.faceCount = human.result.face.length === 1; // must be exactly detected face
  if (ok.faceCount) { // skip the rest if no face
    const gestures: string[] = Object.values(human.result.gesture).map((gesture) => gesture.gesture); // flatten all gestures
    if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed
    if (blink.start > 0 && !gestures.includes('blink left eye') && !gestures.includes('blink right eye')) blink.end = human.now(); // if blink started how long until eyes are back open
    ok.blinkDetected = ok.blinkDetected || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);
    if (ok.blinkDetected && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);
    ok.facingCenter = gestures.includes('facing center');
    ok.lookingCenter = gestures.includes('looking center'); // must face camera and look at camera
    ok.faceConfidence = (human.result.face[0].boxScore || 0) > options.minConfidence && (human.result.face[0].faceScore || 0) > options.minConfidence && (human.result.face[0].genderScore || 0) > options.minConfidence;
    ok.antispoofCheck = (human.result.face[0].real || 0) > options.minConfidence;
    ok.livenessCheck = (human.result.face[0].live || 0) > options.minConfidence;
    ok.faceSize = human.result.face[0].box[2] >= options.minSize && human.result.face[0].box[3] >= options.minSize;
  }
  let y = 32;
  for (const [key, val] of Object.entries(ok)) {
    let el = document.getElementById(`ok-${key}`);
    if (!el) {
      el = document.createElement('div');
      el.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';
+    if (typeof val === 'boolean') el.style.backgroundColor = val ? 'lightgreen' : 'lightcoral';
-    const status = val.status ? 'ok' : 'fail';
+    else el.innerText = `${key}:${val}`;
    el.innerText = `${key}: ${val.val === 0 ? status : val.val}`;
    y += 28;
  }
-}
+  if (allOk()) { // all criteria met
 async function validationLoop(): Promise<H.FaceResult> { // main screen refresh loop
  const interpolated = human.next(human.result); // smoothen result using last-known results
  human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen
  await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.
  const now = human.now();
  ok.drawFPS.val = Math.round(10000 / (now - timestamp.draw)) / 10;
  timestamp.draw = now;
  ok.faceCount.val = human.result.face.length;
  ok.faceCount.status = ok.faceCount.val === 1; // must be exactly detected face
  if (ok.faceCount.status) { // skip the rest if no face
    const gestures: string[] = Object.values(human.result.gesture).map((gesture: H.GestureResult) => gesture.gesture); // flatten all gestures
    if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed
    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
    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'); // must face camera and look at camera
    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.distance.val = human.result.face[0].distance || 0;
    ok.distance.status = (ok.distance.val >= options.distanceMin) && (ok.distance.val <= options.distanceMax);
    ok.descriptor.val = human.result.face[0].embedding?.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;
  }
  // run again
  ok.timeout.status = ok.elapsedMs.val <= options.maxTime;
  drawValidationTests();
  if (allOk() || !ok.timeout.status) { // all criteria met
    dom.video.pause();
    return human.result.face[0];
  }
-  ok.elapsedMs.val = Math.trunc(human.now() - startTime);
+  if (ok.elapsedMs > options.maxTime) { // give up
-  return new Promise((resolve) => {
+    dom.video.pause();
-    setTimeout(async () => {
+    return human.result.face[0];
-      await validationLoop(); // run validation loop until conditions are met
+  } else { // run again
-      resolve(human.result.face[0]); // recursive promise resolve
+    ok.elapsedMs = Math.trunc(human.now() - startTime);
-    }, 30); // use to slow down refresh from max refresh rate to target of 30 fps
+    return new Promise((resolve) => {
-  });
+      setTimeout(async () => {
        const res = await validationLoop(); // run validation loop until conditions are met
        if (res) resolve(human.result.face[0]); // recursive promise resolve
      }, 30); // use to slow down refresh from max refresh rate to target of 30 fps
    });
  }
 }
 async function saveRecords() {
@ -221,8 +184,7 @@ async function saveRecords() {
    const image = dom.canvas.getContext('2d')?.getImageData(0, 0, dom.canvas.width, dom.canvas.height) as ImageData;
    const rec = { id: 0, name: dom.name.value, descriptor: current.face?.embedding as number[], image };
    await indexDb.save(rec);
-    log('saved face record:', rec.name, 'descriptor length:', current.face?.embedding?.length);
+    log('saved face record:', rec.name);
    log('known face records:', await indexDb.count());
  } else {
    log('invalid name');
  }
@ -235,21 +197,20 @@ async function deleteRecord() {
 }
 async function detectFace() {
  dom.canvas.style.height = '';
  dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);
-  if (!current?.face?.tensor || !current?.face?.embedding) return false;
+  if (!current.face || !current.face.tensor || !current.face.embedding) return false;
-  console.log('face record:', current.face); // eslint-disable-line no-console
+  // eslint-disable-next-line no-console
-  log(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${100 * (current.face.distance || 0)}cm/${Math.round(100 * (current.face.distance || 0) / 2.54)}in`);
+  console.log('face record:', current.face);
-  await human.draw.tensor(current.face.tensor, dom.canvas);
+  human.tf.browser.toPixels(current.face.tensor as unknown as TensorLike, dom.canvas);
  if (await indexDb.count() === 0) {
-    log('face database is empty: nothing to compare face with');
+    log('face database is empty');
    document.body.style.background = 'black';
    dom.delete.style.display = 'none';
    return false;
  }
  const db = await indexDb.load();
-  const descriptors = db.map((rec) => rec.descriptor).filter((desc) => desc.length > 0);
+  const descriptors = db.map((rec) => rec.descriptor);
-  const res = human.match.find(current.face.embedding, descriptors, matchOptions);
+  const res = await human.match(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}%`);
@ -262,20 +223,17 @@ async function detectFace() {
 }
 async function main() { // main entry point
-  ok.faceCount.status = false;
+  ok.faceCount = false;
-  ok.faceConfidence.status = false;
+  ok.faceConfidence = false;
-  ok.facingCenter.status = false;
+  ok.facingCenter = false;
-  ok.blinkDetected.status = false;
+  ok.blinkDetected = false;
-  ok.faceSize.status = false;
+  ok.faceSize = false;
-  ok.antispoofCheck.status = false;
+  ok.antispoofCheck = false;
-  ok.livenessCheck.status = false;
+  ok.livenessCheck = false;
-  ok.age.status = false;
+  ok.elapsedMs = 0;
  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(); // start detection loop
@ -293,21 +251,19 @@ async function main() { // main entry point
  if (!allOk()) { // is all criteria met?
    log('did not find valid face');
    return false;
  } else {
    return detectFace();
  }
  return detectFace();
 }
 async function init() {
  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);
  log('options:', JSON.stringify(options).replace(/{|}|"|\[|\]/g, '').replace(/,/g, ' '));
-  log('initializing webcam...');
+  printFPS('loading...');
  await webCam(); // start webcam
  log('loading human models...');
  await human.load(); // preload all models
  log('initializing human...');
  log('face embedding model:', humanConfig.face.description.enabled ? 'faceres' : '', humanConfig.face['mobilefacenet']?.enabled ? 'mobilefacenet' : '', humanConfig.face['insightface']?.enabled ? 'insightface' : '');
  log('loading face database...');
  log('known face records:', await indexDb.count());
  await webCam(); // start webcam
  await human.load(); // preload all models
  printFPS('initializing...');
  dom.retry.addEventListener('click', main);
  dom.save.addEventListener('click', saveRecords);
  dom.delete.addEventListener('click', deleteRecord);
--- a/demo/faceid/indexdb.ts
+++ b/demo/faceid/indexdb.ts
@ -3,9 +3,10 @@ let db: IDBDatabase; // instance of indexdb
 const database = 'human';
 const table = 'person';
-export interface FaceRecord { id: number, name: string, descriptor: number[], image: ImageData }
+export type FaceRecord = { id: number, name: string, descriptor: number[], image: ImageData };
-const log = (...msg) => console.log('indexdb', ...msg); // eslint-disable-line no-console
+// eslint-disable-next-line no-console
 const log = (...msg) => console.log('indexdb', ...msg);
 export async function open() {
  if (db) return true;
@ -18,7 +19,7 @@ export async function open() {
      db.createObjectStore(table, { keyPath: 'id', autoIncrement: true });
    };
    request.onsuccess = (evt) => { // open
-      db = (evt.target as IDBOpenDBRequest).result;
+      db = (evt.target as IDBOpenDBRequest).result as IDBDatabase;
      log('open:', db);
      resolve(true);
    };
@ -26,7 +27,7 @@ export async function open() {
 }
 export async function load(): Promise<FaceRecord[]> {
-  const faceDB: FaceRecord[] = [];
+  const faceDB: Array<FaceRecord> = [];
  if (!db) await open(); // open or create if not already done
  return new Promise((resolve) => {
    const cursor: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).openCursor(null, 'next');
--- a/demo/facematch/README.md
+++ b/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 similarity between them
+detect, extract and identify all faces plus calculate simmilarity between them
 It highlights functionality such as:
@ -72,13 +72,12 @@ Non-linear performance that increases with number of worker threads due to commu
 > node node-match
 <!-- eslint-skip -->
 ```js
-INFO:  options: { dbFile: './faces.json', dbMax: 10000, threadPoolSize: 6, workerSrc: './node-match-worker.js', debug: false, minThreshold: 0.9, descLength: 1024 }
+2021-10-13 07:53:36 INFO:  options: { dbFile: './faces.json', dbMax: 10000, threadPoolSize: 6, workerSrc: './node-match-worker.js', debug: false, minThreshold: 0.9, descLength: 1024 }
-DATA:  created shared buffer: { maxDescriptors: 10000, totalBytes: 40960000, totalElements: 10240000 }
+2021-10-13 07:53:36 DATA:  created shared buffer: { maxDescriptors: 10000, totalBytes: 40960000, totalElements: 10240000 }
-DATA:  db loaded: { existingRecords: 0, newRecords: 5700 }
+2021-10-13 07:53:36 DATA:  db loaded: { existingRecords: 0, newRecords: 5700 }
-INFO:  starting worker thread pool: { totalWorkers: 6, alreadyActive: 0 }
+2021-10-13 07:53:36 INFO:  starting worker thread pool: { totalWorkers: 6, alreadyActive: 0 }
-STATE: submitted: { matchJobs: 100, poolSize: 6, activeWorkers: 6 }
+2021-10-13 07:53:36 STATE: submitted: { matchJobs: 100, poolSize: 6, activeWorkers: 6 }
-STATE: { matchJobsFinished: 100, totalTimeMs: 1769, averageTimeMs: 17.69 }
+2021-10-13 07:53:38 STATE: { matchJobsFinished: 100, totalTimeMs: 1769, averageTimeMs: 17.69 }
-INFO:  closing workers: { poolSize: 6, activeWorkers: 6 }
+2021-10-13 07:53:38 INFO:  closing workers: { poolSize: 6, activeWorkers: 6 }
 ```
--- a/demo/facematch/facematch.js
+++ b/demo/facematch/facematch.js
@ -1,17 +1,18 @@
 // @ts-nocheck
 /**
 * Human demo for browsers
 *
- * Demo for face descriptor analysis and face similarity analysis
+ * Demo for face descriptor analysis and face simmilarity analysis
 */
 /** @type {Human} */
-import { Human } from '../../dist/human.esm.js';
+import Human from '../../dist/human.esm.js';
 const userConfig = {
  backend: 'humangl',
  async: true,
  warmup: 'none',
-  cacheSensitivity: 0.01,
+  cacheSensitivity: 0,
  debug: true,
  modelBasePath: '../../models/',
  deallocate: true,
@ -22,6 +23,7 @@ const userConfig = {
  },
  face: {
    enabled: true,
    // detector: { rotation: false, return: true, maxDetected: 50, iouThreshold: 0.206, minConfidence: 0.122 },
    detector: { return: true, rotation: true, maxDetected: 50, iouThreshold: 0.01, minConfidence: 0.2 },
    mesh: { enabled: true },
    iris: { enabled: false },
@ -44,7 +46,8 @@ const minScore = 0.4;
 function log(...msg) {
  const dt = new Date();
  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
-  console.log(ts, ...msg); // eslint-disable-line no-console
+  // eslint-disable-next-line no-console
  console.log(ts, ...msg);
 }
 function title(msg) {
@ -63,16 +66,25 @@ async function loadFaceMatchDB() {
  }
 }
-async function selectFaceCanvas(face) {
+async function SelectFaceCanvas(face) {
  // if we have face image tensor, enhance it and display it
  let embedding;
  document.getElementById('orig').style.filter = 'blur(16px)';
  if (face.tensor) {
    title('Sorting Faces by Similarity');
-    const c = document.getElementById('orig');
+    const enhanced = human.enhance(face);
-    await human.draw.tensor(face.tensor, c);
+    if (enhanced) {
      const c = document.getElementById('orig');
      const squeeze = human.tf.squeeze(enhanced);
      const normalize = human.tf.div(squeeze, 255);
      await human.tf.browser.toPixels(normalize, c);
      human.tf.dispose([enhanced, squeeze, normalize]);
      const ctx = c.getContext('2d');
      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.find(face.embedding, arr);
+    const res = await human.match(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 = `
@ -93,11 +105,11 @@ 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.match.similarity(face.embedding, current.embedding);
+    const similarity = human.similarity(face.embedding, current.embedding);
    canvas.tag.similarity = similarity;
    // get best match
    // draw the canvas
-    await human.draw.tensor(current.tensor, canvas);
+    await human.tf.browser.toPixels(current.tensor, canvas);
    const ctx = canvas.getContext('2d');
    ctx.font = 'small-caps 1rem "Lato"';
    ctx.fillStyle = 'rgba(0, 0, 0, 1)';
@ -110,7 +122,7 @@ 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.find(current.embedding, arr);
+    const res = await human.match(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);
  }
@ -125,7 +137,7 @@ async function selectFaceCanvas(face) {
  title('Selected Face');
 }
-async function addFaceCanvas(index, res, fileName) {
+async function AddFaceCanvas(index, res, fileName) {
  all[index] = res.face;
  for (const i in res.face) {
    if (!res.face[i].tensor) continue; // did not get valid results
@ -144,37 +156,35 @@ async function addFaceCanvas(index, res, fileName) {
      gender: ${Math.round(100 * res.face[i].genderScore)}% ${res.face[i].gender}
      emotion: ${emotion}
    `.replace(/  /g, ' ');
-    await human.draw.tensor(res.face[i].tensor, canvas);
+    await human.tf.browser.toPixels(res.face[i].tensor, canvas);
    const ctx = canvas.getContext('2d');
-    if (!ctx) return;
+    if (!ctx) return false;
    ctx.font = 'small-caps 0.8rem "Lato"';
    ctx.fillStyle = 'rgba(255, 255, 255, 1)';
    ctx.fillText(`${res.face[i].age}y ${(100 * (res.face[i].genderScore || 0)).toFixed(1)}% ${res.face[i].gender}`, 4, canvas.height - 6);
    const arr = db.map((rec) => rec.embedding);
-    const result = human.match.find(res.face[i].embedding, arr);
+    const result = human.match(res.face[i].embedding, arr);
    ctx.font = 'small-caps 1rem "Lato"';
    if (result.similarity && res.similarity > minScore) ctx.fillText(`${(100 * result.similarity).toFixed(1)}% ${db[result.index].name}`, 4, canvas.height - 30);
    document.getElementById('faces').appendChild(canvas);
    canvas.addEventListener('click', (evt) => {
      log('Select:', 'Image:', evt.target.tag.sample, 'Face:', evt.target.tag.face, 'Source:', evt.target.tag.source, all[evt.target.tag.sample][evt.target.tag.face]);
-      selectFaceCanvas(all[evt.target.tag.sample][evt.target.tag.face]);
+      SelectFaceCanvas(all[evt.target.tag.sample][evt.target.tag.face]);
    });
  }
 }
-async function addImageElement(index, image, length) {
+async function AddImageElement(index, image, length) {
  const faces = all.reduce((prev, curr) => prev += curr.length, 0);
  title(`Analyzing Input Images<br> ${Math.round(100 * index / length)}% [${index} / ${length}]<br>Found ${faces} Faces`);
  return new Promise((resolve) => {
    const img = new Image(128, 128);
    img.onload = () => { // must wait until image is loaded
      document.getElementById('images').appendChild(img); // and finally we can add it
-      human.detect(img, userConfig)
+      human.detect(img, userConfig).then((res) => {
-        .then((res) => { // eslint-disable-line promise/always-return
+        AddFaceCanvas(index, res, image); // then wait until image is analyzed
-          addFaceCanvas(index, res, image); // then wait until image is analyzed
+        resolve(true);
-          resolve(true);
+      });
        })
        .catch(() => log('human detect error'));
    };
    img.onerror = () => {
      log('Add image error:', index + 1, image);
@ -212,23 +222,18 @@ async function main() {
  // could not dynamically enumerate images so using static list
  if (images.length === 0) {
    images = [
-      'ai-face.jpg', 'ai-upper.jpg', 'ai-body.jpg', 'solvay1927.jpg',
+      'ai-body.jpg', 'solvay1927.jpg', 'ai-upper.jpg',
      'person-carolina.jpg', 'person-celeste.jpg', 'person-leila1.jpg', 'person-leila2.jpg', 'person-lexi.jpg', 'person-linda.jpg', 'person-nicole.jpg', 'person-tasia.jpg',
      'person-tetiana.jpg', 'person-vlado1.jpg', 'person-vlado5.jpg', 'person-vlado.jpg', 'person-christina.jpg', 'person-lauren.jpg',
      'group-1.jpg', 'group-2.jpg', 'group-3.jpg', 'group-4.jpg', 'group-5.jpg', 'group-6.jpg', 'group-7.jpg',
-      'person-celeste.jpg', 'person-christina.jpg', 'person-lauren.jpg', 'person-lexi.jpg', 'person-linda.jpg', 'person-nicole.jpg', 'person-tasia.jpg', 'person-tetiana.jpg', 'person-vlado.jpg', 'person-vlado1.jpg', 'person-vlado5.jpg',
+      'daz3d-brianna.jpg', 'daz3d-chiyo.jpg', 'daz3d-cody.jpg', 'daz3d-drew-01.jpg', 'daz3d-drew-02.jpg', 'daz3d-ella-01.jpg', 'daz3d-ella-02.jpg', 'daz3d-gillian.jpg',
-      'stock-group-1.jpg', 'stock-group-2.jpg',
+      'daz3d-hye-01.jpg', 'daz3d-hye-02.jpg', 'daz3d-kaia.jpg', 'daz3d-karen.jpg', 'daz3d-kiaria-01.jpg', 'daz3d-kiaria-02.jpg', 'daz3d-lilah-01.jpg', 'daz3d-lilah-02.jpg',
-      'stock-models-1.jpg', 'stock-models-2.jpg', 'stock-models-3.jpg', 'stock-models-4.jpg', 'stock-models-5.jpg', 'stock-models-6.jpg', 'stock-models-7.jpg', 'stock-models-8.jpg', 'stock-models-9.jpg',
+      'daz3d-lilah-03.jpg', 'daz3d-lila.jpg', 'daz3d-lindsey.jpg', 'daz3d-megah.jpg', 'daz3d-selina-01.jpg', 'daz3d-selina-02.jpg', 'daz3d-snow.jpg',
-      'stock-teen-1.jpg', 'stock-teen-2.jpg', 'stock-teen-3.jpg', 'stock-teen-4.jpg', 'stock-teen-5.jpg', 'stock-teen-6.jpg', 'stock-teen-7.jpg', 'stock-teen-8.jpg',
+      'daz3d-sunshine.jpg', 'daz3d-taia.jpg', 'daz3d-tuesday-01.jpg', 'daz3d-tuesday-02.jpg', 'daz3d-tuesday-03.jpg', 'daz3d-zoe.jpg', 'daz3d-ginnifer.jpg',
-      'stock-models-10.jpg', 'stock-models-11.jpg', 'stock-models-12.jpg', 'stock-models-13.jpg', 'stock-models-14.jpg', 'stock-models-15.jpg', 'stock-models-16.jpg',
+      'daz3d-_emotions01.jpg', 'daz3d-_emotions02.jpg', 'daz3d-_emotions03.jpg', 'daz3d-_emotions04.jpg', 'daz3d-_emotions05.jpg',
      'cgi-model-1.jpg', 'cgi-model-2.jpg', 'cgi-model-3.jpg', 'cgi-model-4.jpg', 'cgi-model-5.jpg', 'cgi-model-6.jpg', 'cgi-model-7.jpg', 'cgi-model-8.jpg', 'cgi-model-9.jpg',
      'cgi-model-10.jpg', 'cgi-model-11.jpg', 'cgi-model-12.jpg', 'cgi-model-13.jpg', 'cgi-model-14.jpg', 'cgi-model-15.jpg', 'cgi-model-18.jpg', 'cgi-model-19.jpg',
      'cgi-model-20.jpg', 'cgi-model-21.jpg', 'cgi-model-22.jpg', 'cgi-model-23.jpg', 'cgi-model-24.jpg', 'cgi-model-25.jpg', 'cgi-model-26.jpg', 'cgi-model-27.jpg', 'cgi-model-28.jpg', 'cgi-model-29.jpg',
      'cgi-model-30.jpg', 'cgi-model-31.jpg', 'cgi-model-33.jpg', 'cgi-model-34.jpg',
      'cgi-multiangle-1.jpg', 'cgi-multiangle-2.jpg', 'cgi-multiangle-3.jpg', 'cgi-multiangle-4.jpg', 'cgi-multiangle-6.jpg', 'cgi-multiangle-7.jpg', 'cgi-multiangle-8.jpg', 'cgi-multiangle-9.jpg', 'cgi-multiangle-10.jpg', 'cgi-multiangle-11.jpg',
      'stock-emotions-a-1.jpg', 'stock-emotions-a-2.jpg', 'stock-emotions-a-3.jpg', 'stock-emotions-a-4.jpg', 'stock-emotions-a-5.jpg', 'stock-emotions-a-6.jpg', 'stock-emotions-a-7.jpg', 'stock-emotions-a-8.jpg',
      'stock-emotions-b-1.jpg', 'stock-emotions-b-2.jpg', 'stock-emotions-b-3.jpg', 'stock-emotions-b-4.jpg', 'stock-emotions-b-5.jpg', 'stock-emotions-b-6.jpg', 'stock-emotions-b-7.jpg', 'stock-emotions-b-8.jpg',
    ];
    // add prefix for gitpages
-    images = images.map((a) => `../../samples/in/${a}`);
+    images = images.map((a) => `/human/samples/in/${a}`);
    log('Adding static image list:', images);
  } else {
    log('Discovered images:', images);
@ -237,7 +242,7 @@ async function main() {
  // images = ['/samples/in/person-lexi.jpg', '/samples/in/person-carolina.jpg', '/samples/in/solvay1927.jpg'];
  const t0 = human.now();
-  for (let i = 0; i < images.length; i++) await addImageElement(i, images[i], images.length);
+  for (let i = 0; i < images.length; i++) await AddImageElement(i, images[i], images.length);
  const t1 = human.now();
  // print stats
@ -251,7 +256,7 @@ async function main() {
  title('');
  log('Ready');
  human.validate(userConfig);
-  human.match.similarity([], []);
+  human.similarity([], []);
 }
 window.onload = main;
--- a/demo/facematch/index.html
+++ b/demo/facematch/index.html
@ -45,6 +45,6 @@
      <div id="list" style="height: 10px"></div>
      <div class="text">Select person to sort by similarity and get a known face match</div>
      <div id="faces" style="height: 50vh; overflow-y: auto"></div>
-    </div>
+  </div>
  </body>
 </html>
--- a/demo/facematch/node-match-worker.js
+++ b/demo/facematch/node-match-worker.js
@ -1,8 +1,3 @@
 /**
 * Runs in a worker thread started by `node-match` demo app
 *
 */
 const threads = require('worker_threads');
 let debug = false;
@ -38,8 +33,7 @@ function match(descBuffer, options = { order: 2, multiplier: 20 }) {
    if (best < threshold || best === 0) break; // short circuit
  }
  best = (options.order === 2) ? Math.sqrt(best) : best ** (1 / options.order);
-  const similarity = Math.round(100 * Math.max(0, 100 - best) / 100.0) / 100;
+  return { index, distance: best, similarity: Math.max(0, 100 - best) / 100.0 };
  return { index, distance: best, similarity };
 }
 threads.parentPort?.on('message', (msg) => {
@ -53,7 +47,7 @@ threads.parentPort?.on('message', (msg) => {
  if (msg instanceof SharedArrayBuffer) { // called only once to receive reference to shared array buffer
    buffer = msg;
    view = new Float32Array(buffer); // initialize f64 view into buffer
-    if (debug) threads.parentPort?.postMessage(`buffer: ${buffer.byteLength}`);
+    if (debug) threads.parentPort?.postMessage(`buffer: ${buffer?.byteLength}`);
  }
  if (typeof msg.records !== 'undefined') { // recived every time when number of records changes
    records = msg.records;
@ -61,15 +55,15 @@ threads.parentPort?.on('message', (msg) => {
  }
  if (typeof msg.debug !== 'undefined') { // set verbose logging
    debug = msg.debug;
-    // if (debug) threads.parentPort?.postMessage(`debug: ${debug}`);
+    if (debug) threads.parentPort?.postMessage(`debug: ${debug}`);
  }
  if (typeof msg.threshold !== 'undefined') { // set minimum similarity threshold
    threshold = msg.threshold;
-    // if (debug) threads.parentPort?.postMessage(`threshold: ${threshold}`);
+    if (debug) threads.parentPort?.postMessage(`threshold: ${threshold}`);
  }
  if (typeof msg.shutdown !== 'undefined') { // got message to close worker
    if (debug) threads.parentPort?.postMessage('shutting down');
-    process.exit(0); // eslint-disable-line no-process-exit
+    process.exit(0);
  }
 });
--- a/demo/facematch/node-match.js
+++ b/demo/facematch/node-match.js
@ -1,13 +1,7 @@
 /**
 * Human demo app for NodeJS that generates random facial descriptors
 * and uses NodeJS multi-threading to start multiple threads for face matching
 * uses `node-match-worker.js` to perform actual face matching analysis
 */
 const fs = require('fs');
 const path = require('path');
 const log = require('@vladmandic/pilogger');
 const threads = require('worker_threads');
 const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
 // global optinos
 const options = {
@ -15,7 +9,7 @@ const options = {
  dbMax: 10000, // maximum number of records to hold in memory
  threadPoolSize: 12, // number of worker threads to create in thread pool
  workerSrc: './node-match-worker.js', // code that executes in the worker thread
-  debug: true, // verbose messages
+  debug: false, // verbose messages
  minThreshold: 0.5, // match returns first record that meets the similarity threshold, set to 0 to always scan all records
  descLength: 1024, // descriptor length
 };
@ -160,7 +154,7 @@ async function createBuffer() {
  data.buffer = new SharedArrayBuffer(4 * options.dbMax * options.descLength); // preallocate max number of records as sharedarraybuffers cannot grow
  data.view = new Float32Array(data.buffer); // create view into buffer
  data.labels.length = 0;
-  log.data('created shared buffer:', { maxDescriptors: (data.view.length || 0) / options.descLength, totalBytes: data.buffer.byteLength, totalElements: data.view.length });
+  log.data('created shared buffer:', { maxDescriptors: (data.view?.length || 0) / options.descLength, totalBytes: data.buffer.byteLength, totalElements: data.view?.length });
 }
 async function main() {
@ -176,7 +170,7 @@ async function main() {
    data.requestID++; // increase request id
    if (testOptions.fuzDescriptors) match(fuzDescriptor(descriptor)); // fuz descriptor for harder match
    else match(descriptor);
-    if (options.debug) log.debug('submited job', data.requestID); // we already know what we're searching for so we can compare results
+    if (options.debug) log.info('submited job', data.requestID); // we already know what we're searching for so we can compare results
  }
  log.state('submitted:', { matchJobs: testOptions.maxJobs, poolSize: data.workers.length, activeWorkers: data.workers.filter((worker) => !!worker).length });
 }
--- a/demo/helpers/gl-bench.js
+++ b/demo/helpers/gl-bench.js
@ -1,3 +1,4 @@
 // @ts-nocheck
 // based on: https://github.com/munrocket/gl-bench
 const UICSS = `
@ -42,7 +43,9 @@ class GLBench {
  constructor(gl, settings = {}) {
    this.css = UICSS;
    this.svg = UISVG;
    // eslint-disable-next-line @typescript-eslint/no-empty-function
    this.paramLogger = () => {};
    // eslint-disable-next-line @typescript-eslint/no-empty-function
    this.chartLogger = () => {};
    this.chartLen = 20;
    this.chartHz = 20;
@ -89,6 +92,7 @@ class GLBench {
      const addProfiler = (fn, self, target) => {
        const t = self.now();
        // eslint-disable-next-line prefer-rest-params
        fn.apply(target, arguments);
        if (self.trackGPU) self.finished.push(glFinish(t, self.activeAccums.slice(0)));
      };
@ -103,11 +107,13 @@ class GLBench {
      if (gl[fn]) {
        gl[fn] = addProfiler(gl[fn], this, gl);
      } else {
        // eslint-disable-next-line no-console
        console.log('bench: cannot attach to webgl function');
      }
      /*
      gl.getExtension = ((fn, self) => {
        // eslint-disable-next-line prefer-rest-params
        const ext = fn.apply(gl, arguments);
        if (ext) {
          ['drawElementsInstancedANGLE', 'drawBuffersWEBGL'].forEach((fn2) => {
@ -142,6 +148,7 @@ class GLBench {
        return (i, cpu, gpu, mem, fps, totalTime, frameId) => {
          nodes['gl-cpu'][i].style.strokeDasharray = (cpu * 0.27).toFixed(0) + ' 100';
          nodes['gl-gpu'][i].style.strokeDasharray = (gpu * 0.27).toFixed(0) + ' 100';
          // eslint-disable-next-line no-nested-ternary
          nodes['gl-mem'][i].innerHTML = names[i] ? names[i] : (mem ? 'mem: ' + mem.toFixed(0) + 'mb' : '');
          nodes['gl-fps'][i].innerHTML = 'FPS: ' + fps.toFixed(1);
          logger(names[i], cpu, gpu, mem, fps, totalTime, frameId);
--- a/demo/helpers/jsonview.js
+++ b/demo/helpers/jsonview.js
@ -64,7 +64,9 @@ function createNode() {
    hideChildren() {
      if (Array.isArray(this.children)) {
        this.children.forEach((item) => {
          // @ts-ignore
          item['elem']['classList'].add('hide');
          // @ts-ignore
          if (item['expanded']) item.hideChildren();
        });
      }
@ -72,7 +74,9 @@ function createNode() {
    showChildren() {
      if (Array.isArray(this.children)) {
        this.children.forEach((item) => {
          // @ts-ignore
          item['elem']['classList'].remove('hide');
          // @ts-ignore
          if (item['expanded']) item.showChildren();
        });
      }
--- a/demo/helpers/menu.js
+++ b/demo/helpers/menu.js
@ -119,6 +119,7 @@ class Menu {
    this.menu.appendChild(this.container);
    if (typeof parent === 'object') parent.appendChild(this.menu);
    // @ts-ignore undefined
    else document.getElementById(parent).appendChild(this.menu);
  }
@ -183,6 +184,7 @@ class Menu {
      this.hidden = !this.hidden;
      const all = document.getElementsByClassName('menu');
      for (const item of all) {
        // @ts-ignore
        item.style.display = this.hidden ? 'none' : 'block';
      }
    });
@ -239,6 +241,7 @@ class Menu {
    el.addEventListener('change', (evt) => {
      if (evt.target) {
        object[variable] = parseInt(evt.target['value']) === parseFloat(evt.target['value']) ? parseInt(evt.target['value']) : parseFloat(evt.target['value']);
        // @ts-ignore
        evt.target.setAttribute('value', evt.target['value']);
        if (callback) callback(evt.target['value']);
      }
@ -283,6 +286,7 @@ class Menu {
    return el;
  }
  // eslint-disable-next-line class-methods-use-this
  updateValue(title, val, suffix = '') {
    const el = document.getElementById(`menu-val-${title}`);
    if (el) el.innerText = `${title}: ${val}${suffix}`;
@ -299,9 +303,11 @@ class Menu {
    return el;
  }
  // eslint-disable-next-line class-methods-use-this
  async updateChart(id, values) {
    if (!values || (values.length === 0)) return;
    /** @type {HTMLCanvasElement} */ 
    // @ts-ignore undefined
    const canvas = document.getElementById(`menu-canvas-${id}`);
    if (!canvas) return;
    const ctx = canvas.getContext('2d');
--- a/demo/helpers/webrtc.js
+++ b/demo/helpers/webrtc.js
@ -4,7 +4,8 @@ async function log(...msg) {
  if (debug) {
    const dt = new Date();
    const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
-    console.log(ts, 'webrtc', ...msg); // eslint-disable-line no-console
+    // eslint-disable-next-line no-console
    console.log(ts, 'webrtc', ...msg);
  }
 }
--- a/demo/index-pwa.js
+++ b/demo/index-pwa.js
@ -2,7 +2,6 @@
 * PWA Service Worker for Human main demo
 */
 /* eslint-disable no-restricted-globals */
 /// <reference lib="webworker" />
 const skipCaching = false;
@ -20,7 +19,8 @@ const stats = { hit: 0, miss: 0 };
 const log = (...msg) => {
  const dt = new Date();
  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
-  console.log(ts, 'pwa', ...msg); // eslint-disable-line no-console
+  // eslint-disable-next-line no-console
  console.log(ts, 'pwa', ...msg);
 };
 async function updateCached(req) {
@ -31,7 +31,7 @@ async function updateCached(req) {
        caches
          .open(cacheName)
          .then((cache) => cache.put(req, update))
-          .catch((err) => log('cache update error', err)); // eslint-disable-line promise/no-nesting
+          .catch((err) => log('cache update error', err));
      }
      return true;
    })
@ -75,13 +75,14 @@ async function getCached(evt) {
 }
 function cacheInit() {
  // eslint-disable-next-line promise/catch-or-return
  caches.open(cacheName)
    // eslint-disable-next-line promise/no-nesting
    .then((cache) => cache.addAll(cacheFiles)
-      .then( // eslint-disable-line promise/no-nesting
+      .then(
        () => log('cache refresh:', cacheFiles.length, 'files'),
        (err) => log('cache error', err),
-      ))
+      ));
    .catch(() => log('cache error'));
 }
 if (!listening) {
@ -98,12 +99,14 @@ if (!listening) {
  self.addEventListener('install', (evt) => {
    log('install');
    // @ts-ignore scope for self is ServiceWorkerGlobalScope not Window
    self.skipWaiting();
    evt.waitUntil(cacheInit);
  });
  self.addEventListener('activate', (evt) => {
    log('activate');
    // @ts-ignore scope for self is ServiceWorkerGlobalScope not Window
    evt.waitUntil(self.clients.claim());
  });
@ -111,7 +114,7 @@ if (!listening) {
    const uri = new URL(evt.request.url);
    // if (uri.pathname === '/') { log('cache skip /', evt.request); return; } // skip root access requests
    if (evt.request.cache === 'only-if-cached' && evt.request.mode !== 'same-origin') return; // required due to chrome bug
-    if (uri.origin !== self.location.origin) return; // skip non-local requests
+    if (uri.origin !== location.origin) return; // skip non-local requests
    if (evt.request.method !== 'GET') return; // only cache get requests
    if (evt.request.url.includes('/api/')) return; // don't cache api requests, failures are handled at the time of call
@ -126,7 +129,7 @@ if (!listening) {
    log(`PWA: ${evt.type}`);
    if (refreshed) return;
    refreshed = true;
-    self.location.reload();
+    location.reload();
  });
  listening = true;
--- a/demo/index-worker.js
+++ b/demo/index-worker.js
@ -6,9 +6,10 @@
 /// <reference lib="webworker"/>
 // load Human using IIFE script as Chome Mobile does not support Modules as Workers
-self.importScripts('../dist/human.js'); // eslint-disable-line no-restricted-globals
+self.importScripts('../dist/human.js');
 let busy = false;
 // @ts-ignore
 // eslint-disable-next-line new-cap, no-undef
 const human = new Human.default();
--- a/demo/index.html
+++ b/demo/index.html
@ -89,9 +89,9 @@
  <body>
    <div id="play" class="play icon-play"></div>
    <div id="background">
-      <div class="wave one"></div>
+      <div class='wave one'></div>
-      <div class="wave two"></div>
+      <div class='wave two'></div>
-      <div class="wave three"></div>         
+      <div class='wave three'></div>         
    </div>
    <div id="loader" class="loader"></div>
    <div id="status" class="status"></div>
@ -110,6 +110,10 @@
      <canvas id="compare-canvas" width="200" height="200"></canvas>
      <div id="similarity"></div>
    </div>
    <div id="segmentation-container" class="compare-image">
      <canvas id="segmentation-mask" width="256" height="256" style="width: 256px; height: 256px;"></canvas>
      <canvas id="segmentation-canvas" width="256" height="256" style="width: 256px; height: 256px;"></canvas>
    </div>
    <div id="samples-container" class="samples-container"></div>
    <div id="hint" class="hint"></div>
    <div id="log" class="log"></div>
--- a/demo/index.js
+++ b/demo/index.js
@ -18,12 +18,11 @@
 * ui={}: contains all variables exposed in the UI
 */
-// WARNING!!!
+// test url <https://human.local/?worker=false&async=false&bench=false&draw=true&warmup=full&backend=humangl>
 // This demo is written using older code style and a lot of manual setup
 // Newer versions of Human have richer functionality allowing for much cleaner & easier usage
 // It is recommended to use other demos such as `demo/typescript` for usage examples
-import { Human } from '../dist/human.esm.js'; // equivalent of @vladmandic/human
+// @ts-nocheck // typescript checks disabled as this is pure javascript
 import Human from '../dist/human.esm.js'; // equivalent of @vladmandic/human
 import Menu from './helpers/menu.js';
 import GLBench from './helpers/gl-bench.js';
 import webRTC from './helpers/webrtc.js';
@ -37,7 +36,7 @@ let userConfig = {
  // hand: { enabled: false },
  /*
  warmup: 'none',
-  backend: 'webgl',
+  backend: 'humangl',
  debug: true,
  wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.9.0/dist/',
  async: false,
@ -86,7 +85,7 @@ const ui = {
  facing: true, // camera facing front or back
  baseBackground: 'rgba(50, 50, 50, 1)', // 'grey'
  columns: 2, // when processing sample images create this many columns
-  useWorker: false, // use web workers for processing
+  useWorker: true, // use web workers for processing
  worker: 'index-worker.js',
  maxFPSframes: 10, // keep fps history for how many frames
  modelsPreload: false, // preload human models on startup
@ -114,6 +113,7 @@ 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
@ -153,7 +153,7 @@ let bench;
 let lastDetectedResult = {};
 // helper function: async pause
-// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
+// eslint-disable-next-line @typescript-eslint/no-unused-vars, no-unused-vars
 const delay = (ms) => new Promise((resolve) => { setTimeout(resolve, ms); });
 // helper function: translates json to human readable string
@ -171,7 +171,8 @@ function str(...msg) {
 function log(...msg) {
  const dt = new Date();
  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
-  if (ui.console) console.log(ts, ...msg); // eslint-disable-line no-console
+  // eslint-disable-next-line no-console
  if (ui.console) console.log(ts, ...msg);
 }
 let prevStatus = '';
@ -185,7 +186,7 @@ function status(msg) {
    prevStatus = msg;
  } else {
    const video = document.getElementById('video');
-    const playing = isLive(video) && !video.paused; // eslint-disable-line no-use-before-define
+    const playing = (video.srcObject !== null) && !video.paused;
    document.getElementById('play').style.display = playing ? 'none' : 'block';
    document.getElementById('loader').style.display = 'none';
    div.innerText = '';
@ -195,6 +196,7 @@ function status(msg) {
 async function videoPlay(videoElement = document.getElementById('video')) {
  document.getElementById('btnStartText').innerHTML = 'pause video';
  await videoElement.play();
  // status();
 }
 async function videoPause() {
@ -222,13 +224,21 @@ async function calcSimmilarity(result) {
    compare.original = result;
    log('setting face compare baseline:', result.face[0]);
    if (result.face[0].tensor) {
-      const c = document.getElementById('orig');
+      const enhanced = human.enhance(result.face[0]);
-      human.draw.tensor(result.face[0].tensor, c);
+      if (enhanced) {
        const c = document.getElementById('orig');
        const squeeze = human.tf.squeeze(enhanced);
        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.match.similarity(compare.original.face[0].embedding, result.face[0].embedding);
+  const similarity = human.similarity(compare.original.face[0].embedding, result.face[0].embedding);
  document.getElementById('similarity').innerText = `similarity: ${Math.trunc(1000 * similarity) / 10}%`;
 }
@ -256,7 +266,21 @@ async function drawResults(input) {
  // draw fps chart
  await menu.process.updateChart('FPS', ui.detectFPS);
-  if (!result.canvas || ui.buffered) { // refresh with input if using buffered output or if missing canvas
+  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
    const image = await human.image(input, false);
    result.canvas = image.canvas;
    human.tf.dispose(image.tensor);
@ -325,10 +349,12 @@ async function drawResults(input) {
      videoPause();
      ui.drawThread = null;
    }
-  } else if (ui.drawThread) {
+  } else {
-    log('stopping buffered refresh');
+    if (ui.drawThread) {
-    cancelAnimationFrame(ui.drawThread);
+      log('stopping buffered refresh');
-    ui.drawThread = null;
+      cancelAnimationFrame(ui.drawThread);
      ui.drawThread = null;
    }
  }
 }
@ -419,7 +445,8 @@ async function setupCamera() {
  ui.menuWidth.input.setAttribute('value', video.videoWidth);
  ui.menuHeight.input.setAttribute('value', video.videoHeight);
  if (live || ui.autoPlay) await videoPlay();
-  if ((live || ui.autoPlay) && !ui.detectThread) runHumanDetect(video, canvas); // eslint-disable-line no-use-before-define
+  // eslint-disable-next-line no-use-before-define
  if ((live || ui.autoPlay) && !ui.detectThread) runHumanDetect(video, canvas);
  return 'camera stream ready';
 }
@ -473,7 +500,8 @@ function webWorker(input, image, canvas, timestamp) {
      ui.framesDetect++;
      if (!ui.drawThread) drawResults(input);
      if (isLive(input)) {
-        ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now)); // eslint-disable-line no-use-before-define
+        // eslint-disable-next-line no-use-before-define
        ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));
      }
    });
  }
@ -510,28 +538,36 @@ function runHumanDetect(input, canvas, timestamp) {
    // perform detection in worker
    webWorker(input, data, canvas, timestamp);
  } else {
-    human.detect(input, userConfig)
+    human.detect(input, userConfig).then((result) => {
-      .then((result) => {
+      status();
-        status();
+      /*
-        if (result.performance && result.performance.total) ui.detectFPS.push(1000 / result.performance.total);
+      setTimeout(async () => { // simulate gl context lost 2sec after initial detection
-        if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();
+        const ext = human.gl && human.gl.gl ? human.gl.gl.getExtension('WEBGL_lose_context') : {};
-        if (ui.bench) {
+        if (ext && ext.loseContext) {
-          if (!bench) initPerfMonitor();
+          log('simulate context lost:', human.env.webgl, human.gl, ext);
-          bench.nextFrame(timestamp);
+          human.gl.gl.getExtension('WEBGL_lose_context').loseContext();
          await videoPause();
          status('Exception: WebGL');
        }
-        if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';
+      }, 2000);
-        if (result.error) {
+      */
-          log(result.error);
+      if (result.performance && result.performance.total) ui.detectFPS.push(1000 / result.performance.total);
-          document.getElementById('log').innerText += `\nHuman error: ${result.error}`;
+      if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();
-        } else {
+      if (ui.bench) {
-          lastDetectedResult = result;
+        if (!bench) initPerfMonitor();
-          if (!ui.drawThread) drawResults(input);
+        bench.nextFrame(timestamp);
-          ui.framesDetect++;
+      }
-          ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));
+      if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';
-        }
+      if (result.error) {
-        return result;
+        log(result.error);
-      })
+        document.getElementById('log').innerText += `\nHuman error: ${result.error}`;
-      .catch(() => log('human detect error'));
+      } else {
        lastDetectedResult = result;
        if (!ui.drawThread) drawResults(input);
        ui.framesDetect++;
        ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));
      }
    });
  }
 }
@ -578,7 +614,8 @@ async function processImage(input, title) {
        // copy to clipboard on click
        if (typeof ClipboardItem !== 'undefined' && navigator.clipboard) {
          evt.target.toBlob((blob) => {
-            const item = new ClipboardItem({ 'image/png': blob }); // eslint-disable-line no-undef
+            // eslint-disable-next-line no-undef
            const item = new ClipboardItem({ 'image/png': blob });
            navigator.clipboard.write([item]);
            log('copied image to clipboard');
          });
@ -606,15 +643,21 @@ async function processImage(input, title) {
 async function processVideo(input, title) {
  status(`processing video: ${title}`);
  // const video = document.getElementById('video-file') || document.createElement('video');
  const video = document.getElementById('video');
  const canvas = document.getElementById('canvas');
  // video.id = 'video-file';
  // video.controls = true;
  // video.loop = true;
  // video.style.display = 'none';
  // document.body.appendChild(video);
  video.addEventListener('error', () => status(`video loading error: ${video.error.message}`));
  video.addEventListener('canplay', async () => {
    for (const m of Object.values(menu)) m.hide();
    document.getElementById('samples-container').style.display = 'none';
    canvas.style.display = 'block';
    await videoPlay();
-    runHumanDetect(video, canvas);
+    if (!ui.detectThread) runHumanDetect(video, canvas);
  });
  video.srcObject = null;
  video.src = input;
@ -627,8 +670,9 @@ async function detectVideo() {
  const canvas = document.getElementById('canvas');
  canvas.style.display = 'block';
  cancelAnimationFrame(ui.detectThread);
-  if (isLive(video) && !video.paused) {
+  if ((video.srcObject !== null) && !video.paused) {
    await videoPause();
    // if (ui.drawThread) cancelAnimationFrame(ui.drawThread);
  } else {
    const cameraError = await setupCamera();
    if (!cameraError) {
@ -704,6 +748,7 @@ 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 type="file" id="file-input" class="input-file"></input> &nbsp input');
  menu.image.addHTML('<input type="file" id="file-background" class="input-file"></input> &nbsp 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);
@ -751,6 +796,8 @@ function setupMenu() {
  menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
  menu.models.addBool('gestures', userConfig.gesture, 'enabled', (val) => userConfig.gesture.enabled = val);
  menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
  menu.models.addBool('body segmentation', userConfig.segmentation, 'enabled', (val) => userConfig.segmentation.enabled = val);
  menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
  menu.models.addBool('object detection', userConfig.object, 'enabled', (val) => userConfig.object.enabled = val);
  menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
  menu.models.addBool('face compare', compare, 'enabled', (val) => {
@ -770,7 +817,6 @@ function setupMenu() {
 async function resize() {
  window.onresize = null;
  log('resize');
  // best setting for mobile, ignored for desktop
  // can set dynamic value such as Math.min(1, Math.round(100 * window.innerWidth / 960) / 100);
  const viewportScale = 0.7;
@ -819,12 +865,42 @@ 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());
@ -862,10 +938,10 @@ async function pwaRegister() {
      const regs = await navigator.serviceWorker.getRegistrations();
      for (const reg of regs) {
        log('pwa found:', reg.scope);
-        if (reg.scope.startsWith(window.location.origin)) found = reg;
+        if (reg.scope.startsWith(location.origin)) found = reg;
      }
      if (!found) {
-        const reg = await navigator.serviceWorker.register(pwa.scriptFile, { scope: window.location.pathname });
+        const reg = await navigator.serviceWorker.register(pwa.scriptFile, { scope: location.pathname });
        found = reg;
        log('pwa registered:', reg.scope);
      }
@ -897,7 +973,8 @@ async function main() {
      if (ui.detectThread) cancelAnimationFrame(ui.detectThread);
      if (ui.drawThread) cancelAnimationFrame(ui.drawThread);
      const msg = evt.reason.message || evt.reason || evt;
-      console.error(msg); // eslint-disable-line no-console
+      // eslint-disable-next-line no-console
      console.error(msg);
      document.getElementById('log').innerHTML = msg;
      status(`exception: ${msg}`);
      evt.preventDefault();
@ -920,7 +997,7 @@ async function main() {
  await pwaRegister();
  // parse url search params
-  const params = new URLSearchParams(window.location.search);
+  const params = new URLSearchParams(location.search);
  log('url options:', params.toString());
  if (params.has('worker')) {
    ui.useWorker = JSON.parse(params.get('worker'));
@ -963,8 +1040,10 @@ async function main() {
  // we've merged human defaults with user config and now lets store it back so it can be accessed by methods such as menu
  userConfig = human.config;
  if (typeof tf !== 'undefined') {
-    log('TensorFlow external version:', tf.version); // eslint-disable-line no-undef
+    // eslint-disable-next-line no-undef
-    human.tf = tf; // eslint-disable-line no-undef
+    log('TensorFlow external version:', tf.version);
    // eslint-disable-next-line no-undef
    human.tf = tf; // use externally loaded version of tfjs
  }
  log('tfjs version:', human.tf.version.tfjs);
@ -977,7 +1056,8 @@ async function main() {
  if (ui.modelsPreload && !ui.useWorker) {
    status('loading');
    await human.load(userConfig); // this is not required, just pre-loads all models
-    log('demo loaded models:', human.models.loaded());
+    const loaded = Object.keys(human.models).filter((a) => human.models[a]);
    log('demo loaded models:', loaded);
  } else {
    await human.init();
  }
@ -999,6 +1079,9 @@ async function main() {
  // init drag & drop
  await dragAndDrop();
  // init segmentation
  await runSegmentation();
  if (params.has('image')) {
    try {
      const image = JSON.parse(params.get('image'));
@ -1019,7 +1102,7 @@ async function main() {
  }
  if (human.config.debug) log('environment:', human.env);
-  if (human.config.backend === 'webgl' && human.config.debug) log('backend:', human.gl);
+  if (human.config.backend === 'humangl' && human.config.debug) log('backend:', human.gl);
 }
 window.onload = main;
--- a/demo/multithread/README.md
+++ b/demo/multithread/README.md
@ -16,7 +16,6 @@
 node demo/nodejs/node-multiprocess.js
 ```
 <!-- eslint-skip -->
 ```json
 2021-06-01 08:54:19 INFO:  @vladmandic/human version 2.0.0
 2021-06-01 08:54:19 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.0.0
--- a/demo/multithread/index.html
+++ b/demo/multithread/index.html
@ -9,10 +9,10 @@
    <meta name="description" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="msapplication-tooltip" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="theme-color" content="#000000">
-    <link rel="manifest" href="../../manifest.webmanifest">
+    <link rel="manifest" href="../manifest.webmanifest">
    <link rel="shortcut icon" href="../../favicon.ico" type="image/x-icon">
    <link rel="apple-touch-icon" href="../../assets/icon.png">
-    <script src="../multithread/index.js" type="module"></script>
+    <script src="./index.js" type="module"></script>
    <style>
      @font-face { font-family: 'Lato'; font-display: swap; font-style: normal; font-weight: 100; src: local('Lato'), url('../../assets/lato-light.woff2') }
      html { font-family: 'Lato', 'Segoe UI'; font-size: 16px; font-variant: small-caps; }
--- a/demo/multithread/index.js
+++ b/demo/multithread/index.js
@ -5,15 +5,15 @@
 *
 */
-import { Human } from '../../dist/human.esm.js'; // equivalent of @vladmandic/human
+import Human from '../../dist/human.esm.js'; // equivalent of @vladmandic/human
 import GLBench from '../helpers/gl-bench.js';
-const workerJS = '../multithread/worker.js';
+const workerJS = './worker.js';
 const config = {
  main: { // processes input and runs gesture analysis
    warmup: 'none',
-    backend: 'webgl',
+    backend: 'humangl',
    modelBasePath: '../../models/',
    async: false,
    filter: { enabled: true },
@ -26,7 +26,7 @@ const config = {
  },
  face: { // runs all face models
    warmup: 'none',
-    backend: 'webgl',
+    backend: 'humangl',
    modelBasePath: '../../models/',
    async: false,
    filter: { enabled: false },
@ -39,7 +39,7 @@ const config = {
  },
  body: { // runs body model
    warmup: 'none',
-    backend: 'webgl',
+    backend: 'humangl',
    modelBasePath: '../../models/',
    async: false,
    filter: { enabled: false },
@ -52,7 +52,7 @@ const config = {
  },
  hand: { // runs hands model
    warmup: 'none',
-    backend: 'webgl',
+    backend: 'humangl',
    modelBasePath: '../../models/',
    async: false,
    filter: { enabled: false },
@ -65,7 +65,7 @@ const config = {
  },
  object: { // runs object model
    warmup: 'none',
-    backend: 'webgl',
+    backend: 'humangl',
    modelBasePath: '../../models/',
    async: false,
    filter: { enabled: false },
@ -130,7 +130,8 @@ const result = { // initialize empty result object which will be partially fille
 function log(...msg) {
  const dt = new Date();
  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
-  console.log(ts, ...msg); // eslint-disable-line no-console
+  // eslint-disable-next-line no-console
  console.log(ts, ...msg);
 }
 async function drawResults() {
@ -202,9 +203,7 @@ async function setupCamera() {
    },
  };
  // enumerate devices for diag purposes
-  navigator.mediaDevices.enumerateDevices()
+  navigator.mediaDevices.enumerateDevices().then((devices) => log('enumerated devices:', devices));
    .then((devices) => log('enumerated devices:', devices))
    .catch(() => log('mediaDevices error'));
  log('camera constraints', constraints);
  try {
    stream = await navigator.mediaDevices.getUserMedia(constraints);
@ -231,7 +230,7 @@ async function setupCamera() {
    };
  });
  // attach input to video element
-  if (stream && video) video.srcObject = stream;
+  if (stream && video) video['srcObject'] = stream;
  return promise;
 }
--- a/demo/multithread/node-multiprocess-worker.js
+++ b/demo/multithread/node-multiprocess-worker.js
@ -6,10 +6,11 @@
 */
 const fs = require('fs');
-const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
+const log = require('@vladmandic/pilogger');
 // workers actual import tfjs and human modules
-const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
+// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
 const tf = require('@tensorflow/tfjs-node');
 const Human = require('../../dist/human.node.js').default; // or const Human = require('../dist/human.node-gpu.js').default;
 let human = null;
@ -63,9 +64,11 @@ async function main() {
  // on worker start first initialize message handler so we don't miss any messages
  process.on('message', (msg) => {
-    // if main told worker to exit
+    // @ts-ignore
-    if (msg.exit && process.exit) process.exit(); // eslint-disable-line no-process-exit
+    if (msg.exit && process.exit) process.exit(); // if main told worker to exit
    // @ts-ignore
    if (msg.test && process.send) process.send({ test: true });
    // @ts-ignore
    if (msg.image) detect(msg.image); // if main told worker to process image
    log.data('Worker received message:', process.pid, msg); // generic log
  });
--- a/demo/multithread/node-multiprocess.js
+++ b/demo/multithread/node-multiprocess.js
@ -8,12 +8,13 @@
 const fs = require('fs');
 const path = require('path');
-const childProcess = require('child_process'); // eslint-disable-line camelcase
+// eslint-disable-next-line import/no-extraneous-dependencies, node/no-unpublished-require
-const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
+const log = require('@vladmandic/pilogger'); // this is my simple logger with few extra features
 const child_process = require('child_process');
 // note that main process does not import human or tfjs at all, it's all done from worker process
-const workerFile = 'demo/multithread/node-multiprocess-worker.js';
+const workerFile = 'demo/nodejs/node-multiprocess-worker.js';
-const imgPathRoot = './samples/in'; // modify to include your sample images
+const imgPathRoot = './assets'; // modify to include your sample images
 const numWorkers = 4; // how many workers will be started
 const workers = []; // this holds worker processes
 const images = []; // this holds queue of enumerated images
@ -22,7 +23,7 @@ let numImages;
 // trigered by main when worker sends ready message
 // if image pool is empty, signal worker to exit otherwise dispatch image to worker and remove image from queue
-async function submitDetect(worker) {
+async function detect(worker) {
  if (!t[2]) t[2] = process.hrtime.bigint(); // first time do a timestamp so we can measure initial latency
  if (images.length === numImages) worker.send({ test: true }); // for first image in queue just measure latency
  if (images.length === 0) worker.send({ exit: true }); // nothing left in queue
@ -73,13 +74,13 @@ async function main() {
  // manage worker processes
  for (let i = 0; i < numWorkers; i++) {
    // create worker process
-    workers[i] = await childProcess.fork(workerFile, ['special']);
+    workers[i] = await child_process.fork(workerFile, ['special']);
    // parse message that worker process sends back to main
    // if message is ready, dispatch next image in queue
    // if message is processing result, just print how many faces were detected
    // otherwise it's an unknown message
    workers[i].on('message', (msg) => {
-      if (msg.ready) submitDetect(workers[i]);
+      if (msg.ready) detect(workers[i]);
      else if (msg.image) log.data('Main: worker finished:', workers[i].pid, 'detected faces:', msg.detected.face?.length, 'bodies:', msg.detected.body?.length, 'hands:', msg.detected.hand?.length, 'objects:', msg.detected.object?.length);
      else if (msg.test) measureLatency();
      else log.data('Main: worker message:', workers[i].pid, msg);
--- a/demo/multithread/worker.js
+++ b/demo/multithread/worker.js
@ -1,7 +1,7 @@
 /// <reference lib="webworker" />
 // load Human using IIFE script as Chome Mobile does not support Modules as Workers
-self.importScripts('../../dist/human.js'); // eslint-disable-line no-restricted-globals
+self.importScripts('../../dist/human.js');
 let human;
@ -9,8 +9,9 @@ onmessage = async (msg) => {
  // received from index.js using:
  // worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, config }, [image.data.buffer]);
-  // Human is registered as global namespace using IIFE script
+  // @ts-ignore // Human is registered as global namespace using IIFE script
-  if (!human) human = new Human.default(msg.data.config); // eslint-disable-line no-undef, new-cap
+  // eslint-disable-next-line no-undef, new-cap
  if (!human) human = new Human.default(msg.data.config);
  const image = new ImageData(new Uint8ClampedArray(msg.data.image), msg.data.width, msg.data.height);
  let result = {};
  result = await human.detect(image, msg.data.config);
--- a/demo/nodejs/README.md
+++ b/demo/nodejs/README.md
@ -28,8 +28,7 @@ or you can pass a path to image to analyze, either on local filesystem or using
 node demo/nodejs/node.js
 ```
-<!-- eslint-skip -->
+```json
 ```js
 2021-06-01 08:52:15 INFO:  @vladmandic/human version 2.0.0
 2021-06-01 08:52:15 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.0.0
 2021-06-01 08:52:15 INFO:  Current folder: /home/vlado/dev/human
@ -83,7 +82,7 @@ node demo/nodejs/node.js
    detector: { modelPath: 'handdetect.json' },
    skeleton: { modelPath: 'handskeleton.json' }
  },
-  object: { enabled: true, modelPath: 'centernet.json', minConfidence: 0.2, iouThreshold: 0.4, maxDetected: 10, skipFrames: 19 }
+  object: { enabled: true, modelPath: 'mb3-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
@ -97,7 +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/centernet.json
+08:52:15.957 Human: load model: file://models/mb3-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 ]
--- a/demo/nodejs/node-age-gender.js
+++ b/demo/nodejs/node-age-gender.js
@ -1,70 +0,0 @@
 const fs = require('fs');
 const process = require('process');
 const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
 const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
 const Human = require('../../dist/human.node.js');
 const humanConfig = {
  debug: false,
  face: {
    enabled: true,
    detector: { enabled: true, modelPath: 'blazeface.json' },
    description: { enabled: true, modelPath: 'faceres.json' },
    // gear: { enabled: true, modelPath: '/home/vlado/dev/human-models/models/gear.json' },
    // ssrnet: { enabled: true, modelPathAge: '/home/vlado/dev/human-models/models/age.json', modelPathGender: '/home/vlado/dev/human-models/models/gender.json' },
    emotion: { enabled: false },
    mesh: { enabled: false },
    iris: { enabled: false },
    antispoof: { enabled: false },
    liveness: { enabled: false },
  },
  body: { enabled: false },
  hand: { enabled: false },
  gesture: { enabled: false },
 };
 const human = new Human.Human(humanConfig);
 const ageThreshold = 18;
 async function detect(inputFile) {
  try {
    const buffer = fs.readFileSync(inputFile);
    const tensor = human.tf.node.decodeImage(buffer);
    const result = await human.detect(tensor);
    human.tf.dispose(tensor);
    if (!result || !result.face || result.face.length === 0) return false;
    let msg = ` file=${inputFile} resolution=${tensor.shape}`;
    for (const face of result.face) {
      msg = ` file=${inputFile} resolution=${tensor.shape} age=${face.age} gender=${face.gender} confidence=${face.genderScore}`;
      if (face.age < ageThreshold) {
        log.warn('fail:' + msg);
        return true;
      }
    }
    log.info('pass: ' + msg);
    return false;
  } catch (err) {
    log.error(`error: file=${inputFile}: ${err}`);
  }
  return false;
 }
 async function main() {
  log.info(`Human: version=${human.version} tf=${tf.version_core}`);
  process.noDeprecation = true;
  if (process.argv.length < 3) return;
  await human.load();
  await human.warmup();
  const t0 = performance.now();
  const args = process.argv.slice(2);
  let pass = 0;
  let fail = 0;
  for (const arg of args) {
    const ok = await detect(arg);
    if (ok) pass++;
    else fail++;
  }
  const t1 = performance.now();
  log.info(`Human: files=${args.length} pass=${pass} fail=${fail} time=${Math.round(t1 - t0)} fps=${Math.round(10000 * args.length / (t1 - t0)) / 10}`);
 }
 main();
--- a/demo/nodejs/node-bench.js
+++ b/demo/nodejs/node-bench.js
@ -1,66 +0,0 @@
 /**
 * Human simple demo for NodeJS
 */
 const childProcess = require('child_process'); // eslint-disable-line camelcase
 const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
 const canvas = require('canvas'); // eslint-disable-line node/no-unpublished-require
 const config = {
  cacheSensitivity: 0.01,
  wasmPlatformFetch: true,
  modelBasePath: 'https://vladmandic.github.io/human-models/models/',
 };
 const count = 10;
 async function loadImage(input) {
  const inputImage = await canvas.loadImage(input);
  const inputCanvas = new canvas.Canvas(inputImage.width, inputImage.height);
  const inputCtx = inputCanvas.getContext('2d');
  inputCtx.drawImage(inputImage, 0, 0);
  const imageData = inputCtx.getImageData(0, 0, inputCanvas.width, inputCanvas.height);
  process.send({ input, resolution: [inputImage.width, inputImage.height] });
  return imageData;
 }
 async function runHuman(module, backend) {
  if (backend === 'wasm') require('@tensorflow/tfjs-backend-wasm'); // eslint-disable-line node/no-unpublished-require, global-require
  const Human = require('../../dist/' + module); // eslint-disable-line global-require, import/no-dynamic-require
  config.backend = backend;
  const human = new Human.Human(config);
  human.env.Canvas = canvas.Canvas;
  human.env.Image = canvas.Image;
  human.env.ImageData = canvas.ImageData;
  process.send({ human: human.version, module });
  await human.init();
  process.send({ desired: human.config.backend, wasm: human.env.wasm, tfjs: human.tf.version.tfjs, tensorflow: human.env.tensorflow });
  const imageData = await loadImage('samples/in/ai-body.jpg');
  const t0 = human.now();
  await human.load();
  const t1 = human.now();
  await human.warmup();
  const t2 = human.now();
  for (let i = 0; i < count; i++) await human.detect(imageData);
  const t3 = human.now();
  process.send({ backend: human.tf.getBackend(), load: Math.round(t1 - t0), warmup: Math.round(t2 - t1), detect: Math.round(t3 - t2), count, memory: human.tf.memory().numBytes });
 }
 async function executeWorker(args) {
  return new Promise((resolve) => {
    const worker = childProcess.fork(process.argv[1], args);
    worker.on('message', (msg) => log.data(msg));
    worker.on('exit', () => resolve(true));
  });
 }
 async function main() {
  if (process.argv[2]) {
    await runHuman(process.argv[2], process.argv[3]);
  } else {
    await executeWorker(['human.node.js', 'tensorflow']);
    await executeWorker(['human.node-gpu.js', 'tensorflow']);
    await executeWorker(['human.node-wasm.js', 'wasm']);
  }
 }
 main();
--- a/demo/nodejs/node-canvas.js
+++ b/demo/nodejs/node-canvas.js
@ -1,22 +1,21 @@
 /**
 * Human demo for NodeJS using Canvas library
 *
 * Requires [canvas](https://www.npmjs.com/package/canvas) to provide Canvas functionality in NodeJS environment
 */
 const fs = require('fs');
 const process = require('process');
-const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
+const log = require('@vladmandic/pilogger');
-// in nodejs environments tfjs-node is required to be loaded before human
+const canvas = require('canvas');
-const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
+
-const canvas = require('canvas'); // eslint-disable-line node/no-unpublished-require
+// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
 const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
 // const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
 const config = { // just enable all and leave default settings
  debug: false,
-  face: { enabled: true, detector: { maxDetected: 10 } }, // includes mesh, iris, emotion, descriptor
+  face: { enabled: true }, // includes mesh, iris, emotion, descriptor
-  hand: { enabled: true, maxDetected: 20, minConfidence: 0.5, detector: { modelPath: 'handtrack.json' } }, // use alternative hand model
+  hand: { enabled: true, maxDetected: 2, minConfidence: 0.5, detector: { modelPath: 'handtrack.json' } }, // use alternative hand model
  body: { enabled: true },
  object: { enabled: true },
  gestures: { enabled: true },
@ -24,7 +23,6 @@ const config = { // just enable all and leave default settings
 async function main() {
  log.header();
  process.noDeprecation = true;
  globalThis.Canvas = canvas.Canvas; // patch global namespace with canvas library
  globalThis.ImageData = canvas.ImageData; // patch global namespace with canvas library
@ -33,16 +31,15 @@ async function main() {
  // init
  const human = new Human.Human(config); // create instance of human
-  log.info('Human:', human.version, 'TF:', tf.version_core);
+  log.info('Human:', human.version);
  await human.load(); // pre-load models
-  log.info('Loaded models:', human.models.loaded());
+  log.info('Loaded models:', Object.keys(human.models).filter((a) => human.models[a]));
  log.info('Memory state:', human.tf.engine().memory());
  // parse cmdline
  const input = process.argv[2];
-  let output = process.argv[3];
+  const output = process.argv[3];
  if (!output.toLowerCase().endsWith('.jpg')) output += '.jpg';
  if (process.argv.length !== 4) log.error('Parameters: <input-image> <output-image> missing');
  else if (!fs.existsSync(input) && !input.startsWith('http')) log.error(`File not found: ${process.argv[2]}`);
  else {
@ -56,6 +53,9 @@ async function main() {
    // run detection
    const result = await human.detect(imageData);
    // run segmentation
    // const seg = await human.segmentation(inputCanvas);
    // log.data('Segmentation:', { data: seg.data.length, alpha: typeof seg.alpha, canvas: typeof seg.canvas });
    // print results summary
    const persons = result.persons; // invoke persons getter, only used to print summary on console
@ -63,7 +63,7 @@ async function main() {
      const face = persons[i].face;
      const faceTxt = face ? `score:${face.score} age:${face.age} gender:${face.gender} iris:${face.iris}` : null;
      const body = persons[i].body;
-      const bodyTxt = body ? `score:${body.score} keypoints:${body.keypoints.length}` : null;
+      const bodyTxt = body ? `score:${body.score} keypoints:${body.keypoints?.length}` : null;
      log.data(`Detected: #${i}: Face:${faceTxt} Body:${bodyTxt} LeftHand:${persons[i].hands.left ? 'yes' : 'no'} RightHand:${persons[i].hands.right ? 'yes' : 'no'} Gestures:${persons[i].gestures.length}`);
    }
@ -71,6 +71,7 @@ async function main() {
    const outputCanvas = new canvas.Canvas(inputImage.width, inputImage.height); // create canvas
    const outputCtx = outputCanvas.getContext('2d');
    outputCtx.drawImage(result.canvas || inputImage, 0, 0); // draw input image onto canvas
    // @ts-ignore canvas is not checked for typedefs
    human.draw.all(outputCanvas, result); // use human build-in method to draw results as overlays on canvas
    const outFile = fs.createWriteStream(output); // write canvas to new image file
    outFile.on('finish', () => log.state('Output image:', output, outputCanvas.width, outputCanvas.height));
--- a/demo/nodejs/node-event.js
+++ b/demo/nodejs/node-event.js
@ -2,12 +2,14 @@
 * Human demo for NodeJS
 */
 const log = require('@vladmandic/pilogger');
 const fs = require('fs');
 const process = require('process');
-const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
+let fetch; // fetch is dynamically imported later
-// in nodejs environments tfjs-node is required to be loaded before human
+
-const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
+// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
 const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
 // const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
@ -36,13 +38,13 @@ async function detect(input) {
  let buffer;
  log.info('Loading image:', input);
  if (input.startsWith('http:') || input.startsWith('https:')) {
    fetch = (await import('node-fetch')).default;
    const res = await fetch(input);
-    if (res && res.ok) buffer = Buffer.from(await res.arrayBuffer());
+    if (res && res.ok) buffer = await res.buffer();
    else log.error('Invalid image URL:', input, res.status, res.statusText, res.headers.get('content-type'));
  } else {
    buffer = fs.readFileSync(input);
  }
  log.data('Image bytes:', buffer?.length, 'buffer:', buffer?.slice(0, 32));
  // decode image using tfjs-node so we don't need external depenencies
  if (!buffer) return;
@ -57,7 +59,6 @@ async function main() {
  log.header();
  human = new Human.Human(myConfig);
  log.info('Human:', human.version, 'TF:', tf.version_core);
  if (human.events) {
    human.events.addEventListener('warmup', () => {
@ -65,7 +66,8 @@ async function main() {
    });
    human.events.addEventListener('load', () => {
-      log.info('Event Loaded:', human.models.loaded(), human.tf.engine().memory());
+      const loaded = Object.keys(human.models).filter((a) => human.models[a]);
      log.info('Event Loaded:', loaded, human.tf.engine().memory());
    });
    human.events.addEventListener('image', () => {
@ -77,7 +79,7 @@ async function main() {
      const persons = human.result.persons;
      for (let i = 0; i < persons.length; i++) {
        const face = persons[i].face;
-        const faceTxt = face ? `score:${face.score} age:${face.age} gender:${face.gender} iris:${face.distance}` : null;
+        const faceTxt = face ? `score:${face.score} age:${face.age} gender:${face.gender} iris:${face.iris}` : null;
        const body = persons[i].body;
        const bodyTxt = body ? `score:${body.score} keypoints:${body.keypoints?.length}` : null;
        log.data(`  #${i}: Face:${faceTxt} Body:${bodyTxt} LeftHand:${persons[i].hands.left ? 'yes' : 'no'} RightHand:${persons[i].hands.right ? 'yes' : 'no'} Gestures:${persons[i].gestures.length}`);
--- a/demo/nodejs/node-fetch.js
+++ b/demo/nodejs/node-fetch.js
@ -1,13 +1,7 @@
 /**
 * Human demo for NodeJS using http fetch to get image file
 *
 * Requires [node-fetch](https://www.npmjs.com/package/node-fetch) to provide `fetch` functionality in NodeJS environment
 */
 const fs = require('fs');
 const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-// in nodejs environments tfjs-node is required to be loaded before human
+// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
-const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
+const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
 // const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
@ -16,15 +10,16 @@ const humanConfig = {
 };
 async function main(inputFile) {
-  global.fetch = (await import('node-fetch')).default; // eslint-disable-line node/no-unpublished-import, import/no-unresolved, node/no-missing-import, node/no-extraneous-import
+  // @ts-ignore
  global.fetch = (await import('node-fetch')).default;
  const human = new Human.Human(humanConfig); // create instance of human using default configuration
  log.info('Human:', human.version, 'TF:', tf.version_core);
  await human.load(); // optional as models would be loaded on-demand first time they are required
  await human.warmup(); // optional as model warmup is performed on-demand first time its executed
  const buffer = fs.readFileSync(inputFile); // read file data into buffer
  const tensor = human.tf.node.decodeImage(buffer); // decode jpg data
  const result = await human.detect(tensor); // run detection; will initialize backend and on-demand load models
-  log.data(result.gesture);
+  // eslint-disable-next-line no-console
  console.log(result.gesture);
 }
 main('samples/in/ai-body.jpg');
--- a/demo/nodejs/node-similarity.js
+++ b/demo/nodejs/node-similarity.js
@ -2,12 +2,12 @@
 * Human Person Similarity test for NodeJS
 */
 const log = require('@vladmandic/pilogger');
 const fs = require('fs');
 const process = require('process');
-const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
+// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
-// in nodejs environments tfjs-node is required to be loaded before human
+const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
 const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
 // const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
@ -25,19 +25,21 @@ const myConfig = {
 async function init() {
  human = new Human.Human(myConfig);
  await human.tf.ready();
-  log.info('Human:', human.version, 'TF:', tf.version_core);
+  log.info('Human:', human.version);
  await human.load();
-  log.info('Loaded:', human.models.loaded());
+  const loaded = Object.keys(human.models).filter((a) => human.models[a]);
  log.info('Loaded:', loaded);
  log.info('Memory state:', human.tf.engine().memory());
 }
 async function detect(input) {
  if (!fs.existsSync(input)) {
-    throw new Error('Cannot load image:', input);
+    log.error('Cannot load image:', input);
    process.exit(1);
  }
  const buffer = fs.readFileSync(input);
  const tensor = human.tf.node.decodeImage(buffer, 3);
-  log.state('Loaded image:', input, tensor.shape);
+  log.state('Loaded image:', input, tensor['shape']);
  const result = await human.detect(tensor, myConfig);
  human.tf.dispose(tensor);
  log.state('Detected faces:', result.face.length);
@ -45,20 +47,20 @@ async function detect(input) {
 }
 async function main() {
  process.noDeprecation = true;
  log.configure({ inspect: { breakLength: 265 } });
  log.header();
  if (process.argv.length !== 4) {
    log.error('Parameters: <first image> <second image> missing');
-    return;
+    process.exit(1);
  }
  await init();
  const res1 = await detect(process.argv[2]);
  const res2 = await detect(process.argv[3]);
  if (!res1 || !res1.face || res1.face.length === 0 || !res2 || !res2.face || res2.face.length === 0) {
-    throw new Error('Could not detect face descriptors');
+    log.error('Could not detect face descriptors');
    process.exit(1);
  }
-  const similarity = human.match.similarity(res1.face[0].embedding, res2.face[0].embedding, { order: 2 });
+  const similarity = human.similarity(res1.face[0].embedding, res2.face[0].embedding, { order: 2 });
  log.data('Similarity: ', similarity);
 }
--- a/demo/nodejs/node-simple.js
+++ b/demo/nodejs/node-simple.js
@ -1,32 +1,26 @@
 /**
 * Human simple demo for NodeJS
 */
 const fs = require('fs');
 const process = require('process');
-// in nodejs environments tfjs-node is required to be loaded before human
+// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
-const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
+const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
 // const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
 const humanConfig = {
  // add any custom config here
  debug: true,
  body: { enabled: false },
 };
 async function detect(inputFile) {
  process.noDeprecation = true;
  const human = new Human.Human(humanConfig); // create instance of human using default configuration
  console.log('Human:', human.version, 'TF:', tf.version_core); // eslint-disable-line no-console
  await human.load(); // optional as models would be loaded on-demand first time they are required
  await human.warmup(); // optional as model warmup is performed on-demand first time its executed
  const buffer = fs.readFileSync(inputFile); // read file data into buffer
  const tensor = human.tf.node.decodeImage(buffer); // decode jpg data
-  console.log('loaded input file:', inputFile, 'resolution:', tensor.shape); // eslint-disable-line no-console
+  // eslint-disable-next-line no-console
  console.log('loaded input file:', inputFile, 'resolution:', tensor.shape);
  const result = await human.detect(tensor); // run detection; will initialize backend and on-demand load models
-  console.log(result); // eslint-disable-line no-console
+  // eslint-disable-next-line no-console
  console.log(result);
 }
 if (process.argv.length === 3) detect(process.argv[2]); // if input file is provided as cmdline parameter use it
--- a/demo/nodejs/node-video.js
+++ b/demo/nodejs/node-video.js
@ -7,24 +7,24 @@
 * If you want process at specific intervals, set output fps to some value
 * If you want to process an input stream, set real-time flag and set input as required
 *
- * Note that [pipe2jpeg](https://www.npmjs.com/package/pipe2jpeg) is not part of Human dependencies and should be installed manually
+ * Note that pipe2jpeg is not part of Human dependencies and should be installed manually
- * Working version of `ffmpeg` must be present on the system
+ * Working version of ffmpeg must be present on the system
 */
 const process = require('process');
 const spawn = require('child_process').spawn;
-const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
+const log = require('@vladmandic/pilogger');
-// in nodejs environments tfjs-node is required to be loaded before human
+// @ts-ignore pipe2jpeg is not installed by default
-// const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
+// eslint-disable-next-line node/no-missing-require
-// const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
+const Pipe2Jpeg = require('pipe2jpeg');
-const Pipe2Jpeg = require('pipe2jpeg'); // eslint-disable-line node/no-missing-require, import/no-unresolved
+
 // eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
 const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
 // const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
 let count = 0; // counter
 let busy = false; // busy flag
-let inputFile = './test.mp4';
+const inputFile = './test.mp4';
 if (process.argv.length === 3) inputFile = process.argv[2];
 const humanConfig = {
  modelBasePath: 'file://models/',
@ -61,27 +61,24 @@ const ffmpegParams = [
  'pipe:1', // output to unix pipe that is then captured by pipe2jpeg
 ];
-async function detect(jpegBuffer) {
+async function process(jpegBuffer) {
  if (busy) return; // skip processing if busy
  busy = true;
  const tensor = human.tf.node.decodeJpeg(jpegBuffer, 3); // decode jpeg buffer to raw tensor
  log.state('input frame:', ++count, 'size:', jpegBuffer.length, 'decoded shape:', tensor.shape);
  const res = await human.detect(tensor);
-  human.tf.dispose(tensor); // must dispose tensor
+  log.data('gesture', JSON.stringify(res.gesture));
-  // start custom processing here
+  // do processing here
-  log.data('frame', { frame: ++count, size: jpegBuffer.length, shape: tensor.shape, face: res?.face?.length, body: res?.body?.length, hand: res?.hand?.length, gesture: res?.gesture?.length });
+  tf.dispose(tensor); // must dispose tensor
  if (res?.face?.[0]) log.data('person', { score: [res.face[0].boxScore, res.face[0].faceScore], age: res.face[0].age || 0, gender: [res.face[0].genderScore || 0, res.face[0].gender], emotion: res.face[0].emotion?.[0] });
  // at the of processing mark loop as not busy so it can process next frame
  busy = false;
 }
 async function main() {
  log.header();
  process.noDeprecation = true;
  await human.tf.ready();
  // pre-load models
-  log.info({ human: human.version, tf: human.tf.version_core });
+  log.info('human:', human.version);
-  log.info({ input: inputFile });
+  pipe2jpeg.on('jpeg', (jpegBuffer) => process(jpegBuffer));
  pipe2jpeg.on('data', (jpegBuffer) => detect(jpegBuffer));
  const ffmpeg = spawn('ffmpeg', ffmpegParams, { stdio: ['ignore', 'pipe', 'ignore'] });
  ffmpeg.on('error', (error) => log.error('ffmpeg error:', error));
--- a/demo/nodejs/node-webcam.js
+++ b/demo/nodejs/node-webcam.js
@ -2,16 +2,18 @@
 * Human demo for NodeJS
 * Unsupported sample of using external utility fswebcam to capture screenshot from attached webcam in regular intervals and process it using Human
 *
- * Note that [node-webcam](https://www.npmjs.com/package/node-webcam) is not part of Human dependencies and should be installed manually
+ * Note that node-webcam is not part of Human dependencies and should be installed manually
- * Working version of `fswebcam` must be present on the system
+ * Working version of fswebcam must be present on the system
 */
 let initial = true; // remember if this is the first run to print additional details
-const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
+const log = require('@vladmandic/pilogger');
-const nodeWebCam = require('node-webcam'); // eslint-disable-line import/no-unresolved, node/no-missing-require
+// @ts-ignore node-webcam is not installed by default
 // eslint-disable-next-line node/no-missing-require
 const nodeWebCam = require('node-webcam');
-// in nodejs environments tfjs-node is required to be loaded before human
+// eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
-const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
+const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
 // const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
@ -27,7 +29,6 @@ const camera = nodeWebCam.create(optionsCamera);
 const optionsHuman = {
  modelBasePath: 'file://models/',
 };
 const human = new Human.Human(optionsHuman);
 function buffer2tensor(buffer) {
@ -60,20 +61,18 @@ async function detect() {
    } else {
      const tensor = buffer2tensor(data); // create tensor from image buffer
      if (initial) log.data('input tensor:', tensor.shape);
-      human.detect(tensor) // eslint-disable-line promise/no-promise-in-callback
+      // eslint-disable-next-line promise/no-promise-in-callback
-        .then((result) => {
+      human.detect(tensor).then((result) => {
-          if (result && result.face && result.face.length > 0) {
+        if (result && result.face && result.face.length > 0) {
-            for (let i = 0; i < result.face.length; i++) {
+          for (let i = 0; i < result.face.length; i++) {
-              const face = result.face[i];
+            const face = result.face[i];
-              const emotion = face.emotion?.reduce((prev, curr) => (prev.score > curr.score ? prev : curr));
+            const emotion = face.emotion?.reduce((prev, curr) => (prev.score > curr.score ? prev : curr));
-              log.data(`detected face: #${i} boxScore:${face.boxScore} faceScore:${face.faceScore} age:${face.age} genderScore:${face.genderScore} gender:${face.gender} emotionScore:${emotion?.score} emotion:${emotion?.emotion} iris:${face.iris}`);
+            log.data(`detected face: #${i} boxScore:${face.boxScore} faceScore:${face.faceScore} age:${face.age} genderScore:${face.genderScore} gender:${face.gender} emotionScore:${emotion?.score} emotion:${emotion?.emotion} iris:${face.iris}`);
            }
          } else {
            log.data('  Face: N/A');
          }
-          return result;
+        } else {
-        })
+          log.data('  Face: N/A');
-        .catch(() => log.error('human detect error'));
+        }
      });
    }
    initial = false;
  });
@ -82,7 +81,6 @@ async function detect() {
 }
 async function main() {
  log.info('human:', human.version, 'tf:', tf.version_core);
  camera.list((list) => {
    log.data('detected camera:', list);
  });
--- a/demo/nodejs/node.js
+++ b/demo/nodejs/node.js
@ -1,14 +1,16 @@
 /**
 * Human demo for NodeJS
-*/
+ */
 const log = require('@vladmandic/pilogger');
 const fs = require('fs');
 const path = require('path');
 const process = require('process');
 const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
-// in nodejs environments tfjs-node is required to be loaded before human
+let fetch; // fetch is dynamically imported later
-const tf = require('@tensorflow/tfjs-node'); // eslint-disable-line node/no-unpublished-require
+
 // eslint-disable-next-line import/no-extraneous-dependencies, no-unused-vars, @typescript-eslint/no-unused-vars
 const tf = require('@tensorflow/tfjs-node'); // in nodejs environments tfjs-node is required to be loaded before human
 // const human = require('@vladmandic/human'); // use this when human is installed as module (majority of use cases)
 const Human = require('../../dist/human.node.js'); // use this when using human in dev mode
@ -44,14 +46,14 @@ async function init() {
  human = new Human.Human(myConfig);
  // wait until tf is ready
  await human.tf.ready();
  log.info('human:', human.version, 'tf:', tf.version_core);
  // pre-load models
  log.info('Human:', human.version);
  // log.info('Active Configuration', human.config);
  await human.load();
-  log.info('Loaded:', human.models.loaded());
+  const loaded = Object.keys(human.models).filter((a) => human.models[a]);
  log.info('Loaded:', loaded);
  // log.info('Memory state:', human.tf.engine().memory());
-  log.data(tf.backend().binding ? tf.backend().binding.TF_Version : null);
+  log.data(tf.backend()['binding'] ? tf.backend()['binding']['TF_Version'] : null);
 }
 async function detect(input) {
@ -60,12 +62,11 @@ async function detect(input) {
  log.info('Loading image:', input);
  if (input.startsWith('http:') || input.startsWith('https:')) {
    const res = await fetch(input);
-    if (res && res.ok) buffer = Buffer.from(await res.arrayBuffer());
+    if (res && res.ok) buffer = await res.buffer();
    else log.error('Invalid image URL:', input, res.status, res.statusText, res.headers.get('content-type'));
  } else {
    buffer = fs.readFileSync(input);
  }
  log.data('Image bytes:', buffer?.length, 'buffer:', buffer?.slice(0, 32));
  // decode image using tfjs-node so we don't need external depenencies
  // can also be done using canvas.js or some other 3rd party image library
@ -85,14 +86,14 @@ async function detect(input) {
  });
  // image shape contains image dimensions and depth
-  log.state('Processing:', tensor.shape);
+  log.state('Processing:', tensor['shape']);
  // run actual detection
  let result;
  try {
    result = await human.detect(tensor, myConfig);
  } catch (err) {
-    log.error('caught', err);
+    log.error('caught');
  }
  // dispose image tensor as we no longer need it
@ -104,7 +105,7 @@ async function detect(input) {
    for (let i = 0; i < result.face.length; i++) {
      const face = result.face[i];
      const emotion = face.emotion.reduce((prev, curr) => (prev.score > curr.score ? prev : curr));
-      log.data(`  Face: #${i} boxScore:${face.boxScore} faceScore:${face.faceScore} age:${face.age} genderScore:${face.genderScore} gender:${face.gender} emotionScore:${emotion.score} emotion:${emotion.emotion} distance:${face.distance}`);
+      log.data(`  Face: #${i} boxScore:${face.boxScore} faceScore:${face.faceScore} age:${face.age} genderScore:${face.genderScore} gender:${face.gender} emotionScore:${emotion.score} emotion:${emotion.emotion} iris:${face.iris}`);
    }
  } else {
    log.data('  Face: N/A');
@ -187,8 +188,8 @@ async function test() {
 async function main() {
  log.configure({ inspect: { breakLength: 265 } });
  log.header();
  process.noDeprecation = true;
  log.info('Current folder:', process.env.PWD);
  fetch = (await import('node-fetch')).default;
  await init();
  const f = process.argv[2];
  if (process.argv.length !== 3) {
@ -196,18 +197,20 @@ async function main() {
    await test();
  } else if (!fs.existsSync(f) && !f.startsWith('http')) {
    log.error(`File not found: ${process.argv[2]}`);
-  } else if (fs.existsSync(f)) {
+  } else {
-    const stat = fs.statSync(f);
+    if (fs.existsSync(f)) {
-    if (stat.isDirectory()) {
+      const stat = fs.statSync(f);
-      const dir = fs.readdirSync(f);
+      if (stat.isDirectory()) {
-      for (const file of dir) {
+        const dir = fs.readdirSync(f);
-        await detect(path.join(f, file));
+        for (const file of dir) {
          await detect(path.join(f, file));
        }
      } else {
        await detect(f);
      }
    } else {
      await detect(f);
    }
  } else {
    await detect(f);
  }
 }
--- a/demo/nodejs/process-folder.js
+++ b/demo/nodejs/process-folder.js
@ -1,120 +1,78 @@
 /**
 * Human demo for NodeJS
 *
 * Takes input and output folder names parameters and processes all images
 * found in input folder and creates annotated images in output folder
 *
 * Requires [canvas](https://www.npmjs.com/package/canvas) to provide Canvas functionality in NodeJS environment
 */
 const fs = require('fs');
 const path = require('path');
 const process = require('process');
-const log = require('@vladmandic/pilogger'); // eslint-disable-line node/no-unpublished-require
+const log = require('@vladmandic/pilogger');
-const canvas = require('canvas'); // eslint-disable-line node/no-unpublished-require
+const canvas = require('canvas');
-// for nodejs, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
+// const tf = require('@tensorflow/tfjs-node-gpu'); // for nodejs, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
 const tf = require('@tensorflow/tfjs-node-gpu'); // eslint-disable-line node/no-unpublished-require
 const Human = require('../../dist/human.node-gpu.js'); // this is 'const Human = require('../dist/human.node-gpu.js').default;'
 const config = { // just enable all and leave default settings
  modelBasePath: 'file://models',
  debug: true,
-  softwareKernels: true, // slower but enhanced precision since face rotation can work in software mode in nodejs environments
+  async: false,
-  cacheSensitivity: 0.01,
+  cacheSensitivity: 0,
-  face: { enabled: true, detector: { maxDetected: 100, minConfidence: 0.1 } },
+  face: { enabled: true, detector: { maxDetected: 20 } },
-  object: { enabled: true, maxDetected: 100, minConfidence: 0.1 },
+  object: { enabled: true },
  gesture: { enabled: true },
-  hand: { enabled: true, maxDetected: 100, minConfidence: 0.2 },
+  hand: { enabled: true },
-  body: { enabled: true, maxDetected: 100, minConfidence: 0.1, modelPath: 'https://vladmandic.github.io/human-models/models/movenet-multipose.json' },
+  body: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/movenet-multipose.json' },
 };
 const poolSize = 4;
 const human = new Human.Human(config); // create instance of human
 async function saveFile(shape, buffer, result, outFile) {
  return new Promise(async (resolve, reject) => { // eslint-disable-line no-async-promise-executor
    const outputCanvas = new canvas.Canvas(shape[2], shape[1]); // create canvas
    const outputCtx = outputCanvas.getContext('2d');
    const inputImage = await canvas.loadImage(buffer); // load image using canvas library
    outputCtx.drawImage(inputImage, 0, 0); // draw input image onto canvas
    human.draw.all(outputCanvas, result); // use human build-in method to draw results as overlays on canvas
    const outStream = fs.createWriteStream(outFile); // write canvas to new image file
    outStream.on('finish', () => {
      log.data('Output image:', outFile, outputCanvas.width, outputCanvas.height);
      resolve();
    });
    outStream.on('error', (err) => {
      log.error('Output error:', outFile, err);
      reject();
    });
    const stream = outputCanvas.createJPEGStream({ quality: 0.5, progressive: true, chromaSubsampling: true });
    stream.pipe(outStream);
  });
 }
 async function processFile(image, inFile, outFile) {
  const buffer = fs.readFileSync(inFile);
  const tensor = tf.tidy(() => {
    const decode = tf.node.decodeImage(buffer, 3);
    const expand = tf.expandDims(decode, 0);
    const cast = tf.cast(expand, 'float32');
    return cast;
  });
  log.state('Loaded image:', inFile, tensor.shape);
  const result = await human.detect(tensor);
  human.tf.dispose(tensor);
  log.data(`Detected: ${image}:`, 'Face:', result.face.length, 'Body:', result.body.length, 'Hand:', result.hand.length, 'Objects:', result.object.length, 'Gestures:', result.gesture.length);
  if (outFile) await saveFile(tensor.shape, buffer, result, outFile);
 }
 async function main() {
  log.header();
  globalThis.Canvas = canvas.Canvas; // patch global namespace with canvas library
  globalThis.ImageData = canvas.ImageData; // patch global namespace with canvas library
-  log.info('Human:', human.version, 'TF:', tf.version_core);
+  const human = new Human.Human(config); // create instance of human
-  process.noDeprecation = true;
+  log.info('Human:', human.version);
  const configErrors = await human.validate();
  if (configErrors.length > 0) log.error('Configuration errors:', configErrors);
  await human.load(); // pre-load models
-  log.info('Loaded models:', human.models.loaded());
+  log.info('Loaded models:', Object.keys(human.models).filter((a) => human.models[a]));
  const inDir = process.argv[2];
  const outDir = process.argv[3];
-  if (!inDir) {
+  if (process.argv.length !== 4) {
-    log.error('Parameters: <input-directory> missing');
+    log.error('Parameters: <input-directory> <output-directory> missing');
    return;
  }
-  if (inDir && (!fs.existsSync(inDir) || !fs.statSync(inDir).isDirectory())) {
+  if (!fs.existsSync(inDir) || !fs.statSync(inDir).isDirectory() || !fs.existsSync(outDir) || !fs.statSync(outDir).isDirectory()) {
-    log.error('Invalid input directory:', fs.existsSync(inDir) ?? fs.statSync(inDir).isDirectory());
+    log.error('Invalid directory specified:', 'input:', fs.existsSync(inDir) ?? fs.statSync(inDir).isDirectory(), 'output:', fs.existsSync(outDir) ?? fs.statSync(outDir).isDirectory());
    return;
  }
  if (!outDir) {
    log.info('Parameters: <output-directory> missing, images will not be saved');
  }
  if (outDir && (!fs.existsSync(outDir) || !fs.statSync(outDir).isDirectory())) {
    log.error('Invalid output directory:', fs.existsSync(outDir) ?? fs.statSync(outDir).isDirectory());
    return;
  }
  const dir = fs.readdirSync(inDir);
  const images = dir.filter((f) => fs.statSync(path.join(inDir, f)).isFile() && (f.toLocaleLowerCase().endsWith('.jpg') || f.toLocaleLowerCase().endsWith('.jpeg')));
  log.info(`Processing folder: ${inDir} entries:`, dir.length, 'images', images.length);
-  const t0 = performance.now();
+  for (const image of images) {
-  const promises = [];
+    const inFile = path.join(inDir, image);
-  for (let i = 0; i < images.length; i++) {
+    const buffer = fs.readFileSync(inFile);
-    const inFile = path.join(inDir, images[i]);
+    const tensor = human.tf.tidy(() => {
-    const outFile = outDir ? path.join(outDir, images[i]) : null;
+      const decode = human.tf.node.decodeImage(buffer, 3);
-    promises.push(processFile(images[i], inFile, outFile));
+      const expand = human.tf.expandDims(decode, 0);
-    if (i % poolSize === 0) await Promise.all(promises);
+      const cast = human.tf.cast(expand, 'float32');
      return cast;
    });
    log.state('Loaded image:', inFile, tensor.shape);
    const result = await human.detect(tensor);
    human.tf.dispose(tensor);
    log.data(`Detected: ${image}:`, 'Face:', result.face.length, 'Body:', result.body.length, 'Hand:', result.hand.length, 'Objects:', result.object.length, 'Gestures:', result.gesture.length);
    const outputCanvas = new canvas.Canvas(tensor.shape[2], tensor.shape[1]); // create canvas
    const outputCtx = outputCanvas.getContext('2d');
    const inputImage = await canvas.loadImage(buffer); // load image using canvas library
    outputCtx.drawImage(inputImage, 0, 0); // draw input image onto canvas
    // @ts-ignore
    human.draw.all(outputCanvas, result); // use human build-in method to draw results as overlays on canvas
    const outFile = path.join(outDir, image);
    const outStream = fs.createWriteStream(outFile); // write canvas to new image file
    outStream.on('finish', () => log.state('Output image:', outFile, outputCanvas.width, outputCanvas.height));
    outStream.on('error', (err) => log.error('Output error:', outFile, err));
    const stream = outputCanvas.createJPEGStream({ quality: 0.5, progressive: true, chromaSubsampling: true });
    // @ts-ignore
    stream.pipe(outStream);
  }
  await Promise.all(promises);
  const t1 = performance.now();
  log.info(`Processed ${images.length} images in ${Math.round(t1 - t0)} ms`);
 }
 main();
--- a/demo/offline.html
+++ b/demo/offline.html
@ -24,13 +24,13 @@
      a:hover { color: lightskyblue; text-decoration: none; }
      .row { width: 90vw; margin: auto; margin-top: 100px; text-align: center; }
    </style>
-  </head>
+</head>
-  <body>
+<body>
-    <div class="row text-center">
+  <div class="row text-center">
-      <h1>
+    <h1>
-        <a href="/">Human: Offline</a><br>
+      <a href="/">Human: Offline</a><br>
-        <img alt="icon" src="../assets/icon.png">
+      <img alt="icon" src="../assets/icon.png">
-      </h1>
+    </h1>
-    </div>
+  </div>
-  </body>
+</body>
 </html>
--- a/demo/segmentation/index.html
+++ b/demo/segmentation/index.html
@ -1,61 +0,0 @@
 <!DOCTYPE html>
 <html lang="en">
  <head>
    <meta charset="utf-8">
    <meta http-equiv="content-type" content="text/html; charset=utf-8">
    <title>Human Demo</title>
    <meta name="viewport" content="width=device-width, shrink-to-fit=yes">
    <meta name="mobile-web-app-capable" content="yes">
    <meta name="application-name" content="Human Demo">
    <meta name="keywords" content="Human Demo">
    <meta name="description" content="Human Demo; Author: Vladimir Mandic <mandic00@live.com>">
    <link rel="manifest" href="../manifest.webmanifest">
    <link rel="shortcut icon" href="../favicon.ico" type="image/x-icon">
    <link rel="icon" sizes="256x256" href="../assets/icons/dash-256.png">
    <link rel="apple-touch-icon" href="../assets/icons/dash-256.png">
    <link rel="apple-touch-startup-image" href="../assets/icons/dash-256.png">
    <style>
      @font-face { font-family: 'CenturyGothic'; font-display: swap; font-style: normal; font-weight: 400; src: local('CenturyGothic'), url('../assets/century-gothic.ttf') format('truetype'); }
      html { font-size: 18px; }
      body { font-size: 1rem; font-family: "CenturyGothic", "Segoe UI", sans-serif; font-variant: small-caps; width: -webkit-fill-available; height: 100%; background: black; color: white; overflow: hidden; margin: 0; }
      select { font-size: 1rem; font-family: "CenturyGothic", "Segoe UI", sans-serif; font-variant: small-caps; background: gray; color: white; border: none; }
    </style>
    <script src="../segmentation/index.js" type="module"></script>
  </head>
  <body>
    <noscript><h1>javascript is required</h1></noscript>
    <nav>
      <div id="nav" class="nav"></div>
    </nav>
    <header>
      <div id="header" class="header" style="position: fixed; top: 0; right: 0; padding: 4px; margin: 16px; background: rgba(0, 0, 0, 0.5); z-index: 10; line-height: 2rem;">
        <label for="mode">mode</label>
        <select id="mode" name="mode">
          <option value="default">remove background</option>
          <option value="alpha">draw alpha channel</option>
          <option value="foreground">full foreground</option>
          <option value="state">recurrent state</option>
        </select><br>
        <label for="composite">composite</label>
        <select id="composite" name="composite"></select><br>
        <label for="ratio">downsample ratio</label>
        <input type="range" name="ratio" id="ratio" min="0.1" max="1" value="0.5" step="0.05">
        <div id="fps" style="margin-top: 8px"></div>
      </div>
    </header>
    <main>
      <div id="main" class="main">
        <video id="webcam" style="position: fixed; top: 0; left: 0; width: 50vw; height: 50vh"></video>
        <img id="background" alt="background" style="position: fixed; top: 0; right: 0; width: 50vw; height: 50vh" controls></img>
        <canvas id="output" style="position: fixed; bottom: 0; left: 0; height: 50vh"></canvas>
        <canvas id="merge" style="position: fixed; bottom: 0; right: 0; height: 50vh"></canvas>
      </div>
    </main>
    <footer>
      <div id="footer" class="footer"></div>
    </footer>
    <aside>
      <div id="aside" class="aside"></div>
    </aside>
  </body>
 </html>
--- a/demo/segmentation/index.js
+++ b/demo/segmentation/index.js
@ -1,99 +0,0 @@
 /**
 * Human demo for browsers
 * @default Human Library
 * @summary <https://github.com/vladmandic/human>
 * @author <https://github.com/vladmandic>
 * @copyright <https://github.com/vladmandic>
 * @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
  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 backgroundImage = '../../samples/in/background.jpg';
 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 = {
    background: document.getElementById('background'),
    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.background.src = backgroundImage;
  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) => `<option value="${gco}">${gco}</option>`).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.models.stats());
  log('models loaded:', human.models.loaded());
  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: window.innerWidth / 2, height: window.innerHeight / 2 }); // 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.draw.tensor(rgba, dom.output); // draw raw output
    human.tf.dispose(rgba); // dispose tensors
    ctxMerge.globalCompositeOperation = 'source-over';
    ctxMerge.drawImage(dom.background, 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;
--- a/demo/tracker/README.md
+++ b/demo/tracker/README.md
@ -1,28 +0,0 @@
 ## Tracker
 ### Based on
 <https://github.com/opendatacam/node-moving-things-tracker>  
 ### Build
 - remove reference to `lodash`:  
  > `isEqual` in <tracker.js>  
 - replace external lib:  
  > curl https://raw.githubusercontent.com/ubilabs/kd-tree-javascript/master/kdTree.js -o lib/kdTree-min.js
 - build with `esbuild`:  
  > node_modules/.bin/esbuild --bundle tracker.js --format=esm --platform=browser --target=esnext --keep-names --tree-shaking=false --analyze --outfile=/home/vlado/dev/human/demo/tracker/tracker.js --banner:js="/* eslint-disable */"
 ### Usage
    computeDistance(item1, item2)
    disableKeepInMemory()
    enableKeepInMemory()
    getAllTrackedItems()
    getJSONDebugOfTrackedItems(roundInt = true)
    getJSONOfAllTrackedItems()
    getJSONOfTrackedItems(roundInt = true)
    getTrackedItemsInMOTFormat(frameNb)
    reset()
    setParams(newParams)
    updateTrackedItemsWithNewFrame(detectionsOfThisFrame, frameNb)
--- a/demo/tracker/index.html
+++ b/demo/tracker/index.html
@ -1,65 +0,0 @@
 <!DOCTYPE html>
 <html lang="en">
  <head>
    <meta charset="utf-8">
    <title>Human</title>
    <meta name="viewport" content="width=device-width" id="viewport">
    <meta name="keywords" content="Human">
    <meta name="application-name" content="Human">
    <meta name="description" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="msapplication-tooltip" content="Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <meta name="theme-color" content="#000000">
    <link rel="manifest" href="../manifest.webmanifest">
    <link rel="shortcut icon" href="../../favicon.ico" type="image/x-icon">
    <link rel="apple-touch-icon" href="../../assets/icon.png">
    <script src="./index.js" type="module"></script>
    <style>
      html { font-family: 'Segoe UI'; font-size: 16px; font-variant: small-caps; }
      body { margin: 0; background: black; color: white; overflow-x: hidden; width: 100vw; height: 100vh; }
      body::-webkit-scrollbar { display: none; }
      input[type="file"] { font-family: 'Segoe UI'; font-size: 14px; font-variant: small-caps; }
      ::-webkit-file-upload-button { background: #333333; color: white; border: 0; border-radius: 0; padding: 6px 16px; box-shadow: 4px 4px 4px #222222; font-family: 'Segoe UI'; font-size: 14px; font-variant: small-caps; }
    </style>
  </head>
  <body>
    <div style="display: flex">
      <video id="video" playsinline style="width: 25vw" controls controlslist="nofullscreen nodownload noremoteplayback" disablepictureinpicture loop></video>
      <canvas id="canvas" style="width: 75vw"></canvas> 
    </div>
    <div class="uploader" style="padding: 8px">
      <input type="file" name="inputvideo" id="inputvideo" accept="video/*"></input>
      <input type="checkbox" id="interpolation" name="interpolation"></input>
      <label for="tracker">interpolation</label>
    </div>
    <form id="config" style="padding: 8px; line-height: 1.6rem;">
      tracker | 
      <input type="checkbox" id="tracker" name="tracker" checked></input>
      <label for="tracker">enabled</label> | 
      <input type="checkbox" id="keepInMemory" name="keepInMemory"></input>
      <label for="keepInMemory">keepInMemory</label> | 
      <br>
      tracker source | 
      <input type="radio" id="box-face" name="box" value="face" checked>
      <label for="box-face">face</label> |
      <input type="radio" id="box-body" name="box" value="body">
      <label for="box-face">body</label> |
      <input type="radio" id="box-object" name="box" value="object">
      <label for="box-face">object</label> |
      <br>
      tracker config | 
      <input type="range" id="unMatchedFramesTolerance" name="unMatchedFramesTolerance" min="0" max="300" step="1", value="60"></input>
      <label for="unMatchedFramesTolerance">unMatchedFramesTolerance</label> |
      <input type="range" id="iouLimit" name="unMatchedFramesTolerance" min="0" max="1" step="0.01", value="0.1"></input>
      <label for="iouLimit">iouLimit</label> |
      <input type="range" id="distanceLimit" name="unMatchedFramesTolerance" min="0" max="1" step="0.01", value="0.1"></input>
      <label for="distanceLimit">distanceLimit</label> |
      <input type="radio" id="matchingAlgorithm-kdTree" name="matchingAlgorithm" value="kdTree" checked>
      <label for="matchingAlgorithm-kdTree">kdTree</label> |
      <input type="radio" id="matchingAlgorithm-munkres" name="matchingAlgorithm" value="munkres">
      <label for="matchingAlgorithm-kdTree">munkres</label> |
    </form>
    <pre id="status" style="position: absolute; top: 12px; right: 20px; background-color: grey; padding: 8px; box-shadow: 2px 2px black"></pre>
    <pre id="log" style="padding: 8px"></pre>
    <div id="performance" style="position: absolute; bottom: 0; width: 100%; padding: 8px; font-size: 0.8rem;"></div>
  </body>
 </html>
--- a/demo/tracker/index.js
+++ b/demo/tracker/index.js
--- a/demo/tracker/index.js.map
+++ b/demo/tracker/index.js.map
--- a/demo/tracker/index.ts
+++ b/demo/tracker/index.ts
@ -1,208 +0,0 @@
 /**
 * Human demo for browsers
 * @default Human Library
 * @summary <https://github.com/vladmandic/human>
 * @author <https://github.com/vladmandic>
 * @copyright <https://github.com/vladmandic>
 * @license MIT
 */
 import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
 import tracker from './tracker.js';
 const humanConfig: Partial<H.Config> = { // user configuration for human, used to fine-tune behavior
  debug: true,
  backend: 'webgl',
  // cacheSensitivity: 0,
  // cacheModels: false,
  // warmup: 'none',
  modelBasePath: 'https://vladmandic.github.io/human-models/models',
  filter: { enabled: true, equalization: false, flip: false },
  face: {
    enabled: true,
    detector: { rotation: false, maxDetected: 10, minConfidence: 0.3 },
    mesh: { enabled: true },
    attention: { enabled: false },
    iris: { enabled: false },
    description: { enabled: false },
    emotion: { enabled: false },
    antispoof: { enabled: false },
    liveness: { enabled: false },
  },
  body: { enabled: false, maxDetected: 6, modelPath: 'movenet-multipose.json' },
  hand: { enabled: false },
  object: { enabled: false, maxDetected: 10 },
  segmentation: { enabled: false },
  gesture: { enabled: false },
 };
 interface TrackerConfig {
  unMatchedFramesTolerance: number, // number of frame when an object is not matched before considering it gone; ignored if fastDelete is set
  iouLimit: number, // exclude things from beeing matched if their IOU less than; 1 means total overlap; 0 means no overlap
  fastDelete: boolean, // remove new objects immediately if they could not be matched in the next frames; if set, ignores unMatchedFramesTolerance
  distanceLimit: number, // distance limit for matching; if values need to be excluded from matching set their distance to something greater than the distance limit
  matchingAlgorithm: 'kdTree' | 'munkres', // algorithm used to match tracks with new detections
 }
 interface TrackerResult {
  id: number,
  confidence: number,
  bearing: number,
  isZombie: boolean,
  name: string,
  x: number,
  y: number,
  w: number,
  h: number,
 }
 const trackerConfig: TrackerConfig = {
  unMatchedFramesTolerance: 100,
  iouLimit: 0.05,
  fastDelete: false,
  distanceLimit: 1e4,
  matchingAlgorithm: 'kdTree',
 };
 const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
 const dom = { // grab instances of dom objects so we dont have to look them up later
  video: document.getElementById('video') as HTMLVideoElement,
  canvas: document.getElementById('canvas') as HTMLCanvasElement,
  log: document.getElementById('log') as HTMLPreElement,
  fps: document.getElementById('status') as HTMLPreElement,
  tracker: document.getElementById('tracker') as HTMLInputElement,
  interpolation: document.getElementById('interpolation') as HTMLInputElement,
  config: document.getElementById('config') as HTMLFormElement,
  ctx: (document.getElementById('canvas') as HTMLCanvasElement).getContext('2d') as CanvasRenderingContext2D,
 };
 const timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks
 const fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh
 const log = (...msg) => { // helper method to output messages
  dom.log.innerText += msg.join(' ') + '\n';
  console.log(...msg); // eslint-disable-line no-console
 };
 const status = (msg) => dom.fps.innerText = msg; // print status element
 async function detectionLoop() { // main detection loop
  if (!dom.video.paused && dom.video.readyState >= 2) {
    if (timestamp.start === 0) timestamp.start = human.now();
    // log('profiling data:', await human.profile(dom.video));
    await human.detect(dom.video, humanConfig); // actual detection; were not capturing output in a local variable as it can also be reached via human.result
    const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks
    if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak
    timestamp.tensors = tensors;
    fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;
    fps.frames++;
    fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;
  }
  timestamp.detect = human.now();
  requestAnimationFrame(detectionLoop); // start new frame immediately
 }
 function drawLoop() { // main screen refresh loop
  if (!dom.video.paused && dom.video.readyState >= 2) {
    const res: H.Result = dom.interpolation.checked ? human.next(human.result) : human.result; // interpolate results if enabled
    let tracking: H.FaceResult[] | H.BodyResult[] | H.ObjectResult[] = [];
    if (human.config.face.enabled) tracking = res.face;
    else if (human.config.body.enabled) tracking = res.body;
    else if (human.config.object.enabled) tracking = res.object;
    else log('unknown object type');
    let data: TrackerResult[] = [];
    if (dom.tracker.checked) {
      const items = tracking.map((obj) => ({
        x: obj.box[0] + obj.box[2] / 2,
        y: obj.box[1] + obj.box[3] / 2,
        w: obj.box[2],
        h: obj.box[3],
        name: obj.label || (human.config.face.enabled ? 'face' : 'body'),
        confidence: obj.score,
      }));
      tracker.updateTrackedItemsWithNewFrame(items, fps.frames);
      data = tracker.getJSONOfTrackedItems(true) as TrackerResult[];
    }
    human.draw.canvas(dom.video, dom.canvas); // copy input video frame to output canvas
    for (let i = 0; i < tracking.length; i++) {
      // @ts-ignore
      const name = tracking[i].label || (human.config.face.enabled ? 'face' : 'body');
      dom.ctx.strokeRect(tracking[i].box[0], tracking[i].box[1], tracking[i].box[1], tracking[i].box[2]);
      dom.ctx.fillText(`id: ${tracking[i].id} ${Math.round(100 * tracking[i].score)}% ${name}`, tracking[i].box[0] + 4, tracking[i].box[1] + 16);
      if (data[i]) {
        dom.ctx.fillText(`t: ${data[i].id} ${Math.round(100 * data[i].confidence)}% ${data[i].name} ${data[i].isZombie ? 'zombie' : ''}`, tracking[i].box[0] + 4, tracking[i].box[1] + 34);
      }
    }
  }
  const now = human.now();
  fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;
  timestamp.draw = now;
  status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status
  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps
 }
 async function handleVideo(file: File) {
  const url = URL.createObjectURL(file);
  dom.video.src = url;
  await dom.video.play();
  log('loaded video:', file.name, 'resolution:', [dom.video.videoWidth, dom.video.videoHeight], 'duration:', dom.video.duration);
  dom.canvas.width = dom.video.videoWidth;
  dom.canvas.height = dom.video.videoHeight;
  dom.ctx.strokeStyle = 'white';
  dom.ctx.fillStyle = 'white';
  dom.ctx.font = '16px Segoe UI';
  dom.video.playbackRate = 0.25;
 }
 function initInput() {
  document.body.addEventListener('dragenter', (evt) => evt.preventDefault());
  document.body.addEventListener('dragleave', (evt) => evt.preventDefault());
  document.body.addEventListener('dragover', (evt) => evt.preventDefault());
  document.body.addEventListener('drop', async (evt) => {
    evt.preventDefault();
    if (evt.dataTransfer) evt.dataTransfer.dropEffect = 'copy';
    const file = evt.dataTransfer?.files?.[0];
    if (file) await handleVideo(file);
    log(dom.video.readyState);
  });
  (document.getElementById('inputvideo') as HTMLInputElement).onchange = async (evt) => {
    evt.preventDefault();
    const file = evt.target?.['files']?.[0];
    if (file) await handleVideo(file);
  };
  dom.config.onchange = () => {
    trackerConfig.distanceLimit = (document.getElementById('distanceLimit') as HTMLInputElement).valueAsNumber;
    trackerConfig.iouLimit = (document.getElementById('iouLimit') as HTMLInputElement).valueAsNumber;
    trackerConfig.unMatchedFramesTolerance = (document.getElementById('unMatchedFramesTolerance') as HTMLInputElement).valueAsNumber;
    trackerConfig.unMatchedFramesTolerance = (document.getElementById('unMatchedFramesTolerance') as HTMLInputElement).valueAsNumber;
    trackerConfig.matchingAlgorithm = (document.getElementById('matchingAlgorithm-kdTree') as HTMLInputElement).checked ? 'kdTree' : 'munkres';
    tracker.setParams(trackerConfig);
    if ((document.getElementById('keepInMemory') as HTMLInputElement).checked) tracker.enableKeepInMemory();
    else tracker.disableKeepInMemory();
    tracker.reset();
    log('tracker config change', JSON.stringify(trackerConfig));
    humanConfig.face!.enabled = (document.getElementById('box-face') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion
    humanConfig.body!.enabled = (document.getElementById('box-body') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion
    humanConfig.object!.enabled = (document.getElementById('box-object') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion
  };
  dom.tracker.onchange = (evt) => {
    log('tracker', (evt.target as HTMLInputElement).checked ? 'enabled' : 'disabled');
    tracker.setParams(trackerConfig);
    tracker.reset();
  };
 }
 async function main() { // main entry point
  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(); // preload all models
  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);
  log('models loaded:', human.models.loaded());
  status('initializing...');
  await human.warmup(); // warmup function to initialize backend for future faster detection
  initInput(); // initialize input
  await detectionLoop(); // start detection loop
  drawLoop(); // start draw loop
 }
 window.onload = main;
--- a/demo/tracker/tracker.js
+++ b/demo/tracker/tracker.js
--- a/demo/typescript/index.html
+++ b/demo/typescript/index.html
@ -21,10 +21,10 @@
    </style>
  </head>
  <body>
-    <canvas id="canvas" style="margin: 0 auto; width: 100vw"></canvas>
+    <canvas id="canvas" style="margin: 0 auto; width: 100%"></canvas>
    <video id="video" playsinline style="display: none"></video>
    <pre id="status" style="position: absolute; top: 12px; right: 20px; background-color: grey; padding: 8px; box-shadow: 2px 2px black"></pre>
    <pre id="log" style="padding: 8px"></pre>
-    <div id="performance" style="position: absolute; bottom: 0; width: 100%; padding: 8px; font-size: 0.8rem;"></div>
+    <div id="performance" style="position: absolute; bottom: 1rem; width: 100%; padding: 8px; font-size: 0.8rem;"></div>
  </body>
 </html>
--- a/demo/typescript/index.js
+++ b/demo/typescript/index.js
@ -4,6 +4,14 @@
  author: <https://github.com/vladmandic>'
 */
-import*as m from"../../dist/human.esm.js";var v=1920,b={debug:!0,backend:"webgl",modelBasePath:"https://vladmandic.github.io/human-models/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},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},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;e.draw.options.drawPoints=!0;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(" ")+`
+import{Human as p}from"../../dist/human.esm.js";var w={async:!0,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},gesture:{enabled:!0}},t=new p(w);t.env.perfadd=!1;t.draw.options.font='small-caps 18px "Lato"';t.draw.options.lineHeight=20;var e={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},o={detect:0,draw:0,tensors:0},d={detect:0,draw:0},s=(...a)=>{e.log.innerText+=a.join(" ")+`
-`,console.log(...t)},i=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 f(){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(f)}async function u(){var d,r,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 p={bodyLabels:`person confidence [score] and ${(c=(r=(d=e.result)==null?void 0:d.body)==null?void 0:r[0])==null?void 0:c.keypoints.length} keypoints`};await e.draw.all(a.canvas,l,p),g(l.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,i(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(u,30)}async function h(){let d=(await e.webcam.enumerate())[0].deviceId,r=await e.webcam.start({element:a.video,crop:!1,width:v,id:d});o(r),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),i("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.models.stats()),o("models loaded:",e.models.loaded()),o("environment",e.env),i("initializing..."),await e.warmup(),await h(),await f(),await u()}window.onload=y;
+`,console.log(...a)},r=a=>e.fps.innerText=a,b=a=>e.perf.innerText="tensors:"+t.tf.memory().numTensors+" | performance: "+JSON.stringify(a).replace(/"|{|}/g,"").replace(/,/g," | ");async function h(){r("starting webcam...");let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth},height:{ideal:document.body.clientHeight}}},n=await navigator.mediaDevices.getUserMedia(a),m=new Promise(u=>{e.video.onloadeddata=()=>u(!0)});e.video.srcObject=n,e.video.play(),await m,e.canvas.width=e.video.videoWidth,e.canvas.height=e.video.videoHeight;let i=n.getVideoTracks()[0],f=i.getCapabilities?i.getCapabilities():"",v=i.getSettings?i.getSettings():"",g=i.getConstraints?i.getConstraints():"";s("video:",e.video.videoWidth,e.video.videoHeight,i.label,{stream:n,track:i,settings:v,constraints:g,capabilities:f}),e.canvas.onclick=()=>{e.video.paused?e.video.play():e.video.pause()}}async function c(){if(!e.video.paused){await t.detect(e.video);let n=t.tf.memory().numTensors;n-o.tensors!==0&&s("allocated tensors:",n-o.tensors),o.tensors=n}let a=t.now();d.detect=1e3/(a-o.detect),o.detect=a,requestAnimationFrame(c)}async function l(){if(!e.video.paused){let n=await t.next(t.result);t.config.filter.flip?await t.draw.canvas(n.canvas,e.canvas):await t.draw.canvas(e.video,e.canvas),await t.draw.all(e.canvas,n),b(n.performance)}let a=t.now();d.draw=1e3/(a-o.draw),o.draw=a,r(e.video.paused?"paused":`fps: ${d.detect.toFixed(1).padStart(5," ")} detect | ${d.draw.toFixed(1).padStart(5," ")} draw`),setTimeout(l,30)}async function y(){s("human version:",t.version,"| tfjs version:",t.tf.version["tfjs-core"]),s("platform:",t.env.platform,"| agent:",t.env.agent),r("loading..."),await t.load(),s("backend:",t.tf.getBackend(),"| available:",t.env.backends),s("models stats:",t.getModelStats()),s("models loaded:",Object.values(t.models).filter(a=>a!==null).length),r("initializing..."),await t.warmup(),await h(),await c(),await l()}window.onload=y;
 /**
 * Human demo for browsers
 * @default Human Library
 * @summary <https://github.com/vladmandic/human>
 * @author <https://github.com/vladmandic>
 * @copyright <https://github.com/vladmandic>
 * @license MIT
 */
 //# sourceMappingURL=index.js.map
--- a/demo/typescript/index.js.map
+++ b/demo/typescript/index.js.map
--- a/demo/typescript/index.ts
+++ b/demo/typescript/index.ts
@ -7,33 +7,27 @@
 * @license MIT
 */
-import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
+import { Human, Config } from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
-const width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations
+const humanConfig: Partial<Config> = { // user configuration for human, used to fine-tune behavior
-
+  // backend: 'wasm' as const,
-const humanConfig: Partial<H.Config> = { // user configuration for human, used to fine-tune behavior
+  // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.18.0/dist/',
  debug: true,
  backend: 'webgl',
  // cacheSensitivity: 0,
-  // cacheModels: false,
+  async: true,
-  // warmup: 'none',
+  modelBasePath: '../../models',
  // modelBasePath: '../../models',
  modelBasePath: 'https://vladmandic.github.io/human-models/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 }, antispoof: { enabled: true }, liveness: { enabled: true } },
+  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },
-  body: { enabled: false },
+  body: { enabled: true },
-  hand: { enabled: false },
+  hand: { enabled: true },
  object: { enabled: false },
  segmentation: { enabled: false },
  gesture: { enabled: true },
 };
-const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
+const human = new Human(humanConfig); // create instance of human with overrides from user configuration
-human.env.perfadd = false; // is performance data showing instant or total values
+human.env['perfadd'] = false; // is performance data showing instant or total values
 human.draw.options.font = 'small-caps 18px "Lato"'; // set font used to draw labels when using draw methods
 human.draw.options.lineHeight = 20;
 human.draw.options.drawPoints = true; // draw points on face mesh
 // human.draw.options.fillPolygons = true;
 const dom = { // grab instances of dom objects so we dont have to look them up later
@ -43,72 +37,77 @@ const dom = { // grab instances of dom objects so we dont have to look them up l
  fps: document.getElementById('status') as HTMLPreElement,
  perf: document.getElementById('performance') as HTMLDivElement,
 };
-const timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks
+const timestamp = { detect: 0, draw: 0, tensors: 0 }; // holds information used to calculate performance and possible memory leaks
-const fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh
+const fps = { detect: 0, draw: 0 }; // holds calculated fps information for both detect and screen refresh
 const log = (...msg) => { // helper method to output messages
  dom.log.innerText += msg.join(' ') + '\n';
-  console.log(...msg); // eslint-disable-line no-console
+  // eslint-disable-next-line no-console
  console.log(...msg);
 };
 const status = (msg) => dom.fps.innerText = msg; // print status element
-const perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/"|{|}/g, '').replace(/,/g, ' | '); // print performance element
+const perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors + ' | performance: ' + JSON.stringify(msg).replace(/"|{|}/g, '').replace(/,/g, ' | '); // print performance element
 async function webCam() { // initialize webcam
  status('starting webcam...');
  // @ts-ignore resizeMode is not yet defined in tslib
  const options: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth }, height: { ideal: document.body.clientHeight } } };
  const stream: MediaStream = await navigator.mediaDevices.getUserMedia(options);
  const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });
  dom.video.srcObject = stream;
  dom.video.play();
  await ready;
  dom.canvas.width = dom.video.videoWidth;
  dom.canvas.height = dom.video.videoHeight;
  const track: MediaStreamTrack = stream.getVideoTracks()[0];
  const capabilities: MediaTrackCapabilities | string = track.getCapabilities ? track.getCapabilities() : '';
  const settings: MediaTrackSettings | string = track.getSettings ? track.getSettings() : '';
  const constraints: MediaTrackConstraints | string = track.getConstraints ? track.getConstraints() : '';
  log('video:', dom.video.videoWidth, dom.video.videoHeight, track.label, { stream, track, settings, constraints, capabilities });
  dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click
    if (dom.video.paused) dom.video.play();
    else dom.video.pause();
  };
 }
 async function detectionLoop() { // main detection loop
  if (!dom.video.paused) {
-    if (timestamp.start === 0) timestamp.start = human.now();
+    // console.log('profiling data:', await human.profile(dom.video));
    // log('profiling data:', await human.profile(dom.video));
    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result
    const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks
    if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak
    timestamp.tensors = tensors;
    fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;
    fps.frames++;
    fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;
    if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });
  }
-  timestamp.detect = human.now();
+  const now = human.now();
  fps.detect = 1000 / (now - timestamp.detect);
  timestamp.detect = now;
  requestAnimationFrame(detectionLoop); // start new frame immediately
 }
 async function drawLoop() { // main screen refresh loop
  if (!dom.video.paused) {
-    const interpolated = human.next(human.result); // smoothen result using last-known results
+    const interpolated = await human.next(human.result); // smoothen result using last-known results
-    const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters
+    if (human.config.filter.flip) await human.draw.canvas(interpolated.canvas as HTMLCanvasElement, dom.canvas); // draw processed image to screen canvas
-    human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);
+    else await 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
-
+    await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.
    const opt: Partial<H.DrawOptions> = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };
    await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.
    perf(interpolated.performance); // write performance data
  }
  const now = human.now();
-  fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;
+  fps.draw = 1000 / (now - timestamp.draw);
  timestamp.draw = now;
-  status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status
+  status(dom.video.paused ? 'paused' : `fps: ${fps.detect.toFixed(1).padStart(5, ' ')} detect | ${fps.draw.toFixed(1).padStart(5, ' ')} draw`); // write status
  // requestAnimationFrame(drawLoop); // refresh at screen refresh rate
  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps
 }
 async function webCam() {
  const devices = await human.webcam.enumerate();
  const id = devices[0].deviceId; // use first available video source
  const webcamStatus = await human.webcam.start({ element: dom.video, crop: false, width, id }); // use human webcam helper methods and associate webcam stream with a dom element
  log(webcamStatus);
  dom.canvas.width = human.webcam.width;
  dom.canvas.height = human.webcam.height;
  dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click
    if (human.webcam.paused) await human.webcam.play();
    else human.webcam.pause();
  };
 }
 async function main() { // main entry point
  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(); // preload all models
  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);
-  log('models stats:', human.models.stats());
+  log('models stats:', human.getModelStats());
-  log('models loaded:', human.models.loaded());
+  log('models loaded:', Object.values(human.models).filter((model) => model !== null).length);
  log('environment', human.env);
  status('initializing...');
  await human.warmup(); // warmup function to initialize backend for future faster detection
  await webCam(); // start webcam
--- a/demo/video/index.html
+++ b/demo/video/index.html
@ -1,58 +0,0 @@
 <!DOCTYPE html>
 <html lang="en">
  <head>
    <meta charset="utf-8">
    <title>Human</title>
    <meta name="viewport" content="width=device-width" id="viewport">
    <meta name="keywords" content="Human">
    <meta name="description" content="Human: Demo; Author: Vladimir Mandic <https://github.com/vladmandic>">
    <link rel="manifest" href="../manifest.webmanifest">
    <link rel="shortcut icon" href="../../favicon.ico" type="image/x-icon">
    <style>
      @font-face { font-family: 'Lato'; font-display: swap; font-style: normal; font-weight: 100; src: local('Lato'), url('../../assets/lato-light.woff2') }
      body { font-family: 'Lato', 'Segoe UI'; font-size: 16px; font-variant: small-caps; margin: 0; background: black; color: white; overflow: hidden; width: 100vw; height: 100vh; }
    </style>
  </head>
  <body>
    <canvas id="canvas" style="margin: 0 auto; width: 100%"></canvas>
    <pre id="log" style="padding: 8px; position: fixed; bottom: 0"></pre>
    <script type="module">
      import * as H from '../../dist/human.esm.js'; // equivalent of import @vladmandic/Human
      const humanConfig = { // user configuration for human, used to fine-tune behavior
        modelBasePath: '../../models', // models can be loaded directly from cdn as well
        filter: { enabled: true, equalization: true, 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 },
        gesture: { enabled: true },
        object: { enabled: false },
        segmentation: { enabled: false },
      };
      const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
      const canvas = document.getElementById('canvas'); // output canvas to draw both webcam and detection results
      async function drawLoop() { // main screen refresh loop
        const interpolated = human.next(); // get smoothened result using last-known results which are continously updated based on input webcam video
        human.draw.canvas(human.webcam.element, canvas); // draw webcam video to screen canvas // better than using procesed image as this loop happens faster than processing loop
        await human.draw.all(canvas, interpolated); // draw labels, boxes, lines, etc.
        setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 1000/30 ~ 30 fps
      }
      async function main() { // main entry point
        document.getElementById('log').innerHTML = `human version: ${human.version} | tfjs version: ${human.tf.version['tfjs-core']}<br>platform: ${human.env.platform} | agent ${human.env.agent}`;
        await human.webcam.start({ crop: true }); // find webcam and start it
        human.video(human.webcam.element); // instruct human to continously detect video frames
        canvas.width = human.webcam.width; // set canvas resolution to input webcam native resolution
        canvas.height = human.webcam.height;
        canvas.onclick = async () => { // pause when clicked on screen and resume on next click
          if (human.webcam.paused) await human.webcam.play();
          else human.webcam.pause();
        };
        await drawLoop(); // start draw loop
      }
      window.onload = main;
    </script>  
  </body>
 </html>
--- a/dist/human.d.ts
+++ b/dist/human.d.ts
--- a/dist/human.esm-nobundle.d.ts
+++ b/dist/human.esm-nobundle.d.ts
--- a/dist/human.esm-nobundle.js
+++ b/dist/human.esm-nobundle.js
--- a/dist/human.esm-nobundle.js.map
+++ b/dist/human.esm-nobundle.js.map
--- a/dist/human.esm.d.ts
+++ b/dist/human.esm.d.ts
--- a/dist/human.esm.js
+++ b/dist/human.esm.js
--- a/dist/human.esm.js.map
+++ b/dist/human.esm.js.map
--- a/dist/human.js
+++ b/dist/human.js
--- a/dist/human.node-gpu.d.ts
+++ b/dist/human.node-gpu.d.ts
--- a/dist/human.node-gpu.js
+++ b/dist/human.node-gpu.js
--- a/dist/human.node-wasm.d.ts
+++ b/dist/human.node-wasm.d.ts
--- a/dist/human.node-wasm.js
+++ b/dist/human.node-wasm.js
--- a/dist/human.node.d.ts
+++ b/dist/human.node.d.ts
--- a/dist/human.node.js
+++ b/dist/human.node.js
--- a/dist/tfjs.esm.d.ts
+++ b/dist/tfjs.esm.d.ts
@ -1,25 +0,0 @@
 /* eslint-disable import/no-unresolved */
 /* eslint-disable import/no-extraneous-dependencies */
 export * from 'types/tfjs.esm';
 export declare const version: {
  'tfjs-core': string;
  'tfjs-backend-cpu': string;
  'tfjs-backend-webgl': string;
  'tfjs-data': string;
  'tfjs-layers': string;
  'tfjs-converter': string;
  tfjs: string;
 };
 export * from '@tensorflow/tfjs-core';
 export * from '@tensorflow/tfjs-converter';
 export * from '@tensorflow/tfjs-data';
 export * from '@tensorflow/tfjs-layers';
 export * from '@tensorflow/tfjs-backend-cpu';
 export * from '@tensorflow/tfjs-backend-wasm';
 export * from '@tensorflow/tfjs-backend-webgl';
 export * from '@tensorflow/tfjs-backend-webgpu';
 export * from '@tensorflow/tfjs-node';
 export * from '@tensorflow/tfjs-node-gpu';
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
--- a/dist/tfjs.version.js
+++ b/dist/tfjs.version.js
@ -4,4 +4,4 @@
  author: <https://github.com/vladmandic>'
 */
-var e="4.22.0";var s="4.22.0";var t="4.22.0";var n="4.22.0";var r="4.22.0";var i="4.22.0";var h={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":r,"tfjs-backend-webgpu":i};export{h as version};
+var e="3.19.0";var s="3.19.0";var t="3.19.0";var i="3.19.0";var n="3.19.0";var r="3.19.0";var l="3.19.0";var V={tfjs:e,"tfjs-core":s,"tfjs-data":t,"tfjs-layers":i,"tfjs-converter":n,"tfjs-backend-webgl":r,"tfjs-backend-wasm":l};export{V as version};
--- a/human.service
+++ b/human.service
@ -5,7 +5,7 @@ After=network.target network-online.target
 [Service]
 Type=simple
 Environment="NODE_ENV=production"
-ExecStart=<path-to-node> <your-project-folder>/node_modules/@vladmandic/build/src/build.js --profile serve
+ExecStart=<path-to-node> <your-project-folder>/node_modules/@vladmandic/build/src/build.js --profile development
 WorkingDirectory=<your-project-folder>
 StandardOutput=inherit
 StandardError=inherit
--- a/models/README.md
+++ b/models/README.md
@ -3,3 +3,308 @@
 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' ]
 }
 ```
--- a/models/facemesh-attention.bin
+++ b/models/facemesh-attention.bin
--- a/models/facemesh-attention.json
+++ b/models/facemesh-attention.json
--- a/models/handlandmark-full.bin
+++ b/models/handlandmark-full.bin
--- a/models/handlandmark-full.json
+++ b/models/handlandmark-full.json
--- a/models/handlandmark-lite.bin
+++ b/models/handlandmark-lite.bin
--- a/Show More
+++ b/Show More