major release

Switched to using the utility function that works in both nodejs and browser environments

.build.json

@@ -0,0 +1,169 @@
+{
+  "log": {
+    "enabled": true,
+    "debug": false,
+    "console": true,
+    "output": "test/build.log"
+  },
+  "profiles": {
+    "production": ["clean", "compile", "typings", "typedoc", "lint", "changelog"],
+    "development": ["serve", "watch", "compile"],
+    "serve": ["serve"]
+  },
+  "clean": {
+    "locations": ["dist/*", "types/lib/*", "typedoc/*"]
+  },
+  "lint": {
+    "locations": [ "*.json", "src/**/*.ts", "test/**/*.js", "demo/**/*.js" ],
+    "rules": { }
+  },
+  "changelog": {
+    "log": "CHANGELOG.md"
+  },
+  "serve": {
+    "sslKey": "node_modules/@vladmandic/build/cert/https.key",
+    "sslCrt": "node_modules/@vladmandic/build/cert/https.crt",
+    "httpPort": 10030,
+    "httpsPort": 10031,
+    "documentRoot": ".",
+    "defaultFolder": "demo",
+    "defaultFile": "index.html"
+    },
+  "build": {
+    "global": {
+      "target": "es2018",
+      "sourcemap": false,
+      "treeShaking": true,
+      "ignoreAnnotations": true,
+      "banner": { "js": "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n" }
+    },
+    "targets": [
+      {
+        "name": "tfjs/nodejs/cpu",
+        "platform": "node",
+        "format": "cjs",
+        "input": "tfjs/tf-node.ts",
+        "output": "dist/tfjs.esm.js",
+        "external": ["@tensorflow"]
+      },
+      {
+        "name": "human/nodejs/cpu",
+        "platform": "node",
+        "format": "cjs",
+        "input": "src/human.ts",
+        "output": "dist/human.node.js",
+        "external": ["@tensorflow"]
+      },
+      {
+        "name": "tfjs/nodejs/gpu",
+        "platform": "node",
+        "format": "cjs",
+        "input": "tfjs/tf-node-gpu.ts",
+        "output": "dist/tfjs.esm.js",
+        "external": ["@tensorflow"]
+      },
+      {
+        "name": "human/nodejs/gpu",
+        "platform": "node",
+        "format": "cjs",
+        "input": "src/human.ts",
+        "output": "dist/human.node-gpu.js",
+        "external": ["@tensorflow"]
+      },
+      {
+        "name": "tfjs/nodejs/wasm",
+        "platform": "node",
+        "format": "cjs",
+        "input": "tfjs/tf-node-wasm.ts",
+        "output": "dist/tfjs.esm.js",
+        "external": ["@tensorflow"]
+      },
+      {
+        "name": "human/nodejs/wasm",
+        "platform": "node",
+        "format": "cjs",
+        "input": "src/human.ts",
+        "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",
+        "format": "esm",
+        "input": "tfjs/tf-browser.ts",
+        "output": "dist/tfjs.esm.js",
+        "external": ["@tensorflow"]
+      },
+      {
+        "name": "human/browser/esm/nobundle",
+        "platform": "browser",
+        "format": "esm",
+        "input": "src/human.ts",
+        "output": "dist/human.esm-nobundle.js",
+        "sourcemap": true,
+        "external": ["@tensorflow"]
+      },
+      {
+        "name": "tfjs/browser/esm/custom",
+        "platform": "browser",
+        "format": "esm",
+        "input": "tfjs/tf-custom.ts",
+        "output": "dist/tfjs.esm.js",
+        "sourcemap": false
+      },
+      {
+        "name": "human/browser/iife/bundle",
+        "platform": "browser",
+        "format": "iife",
+        "input": "src/human.ts",
+        "output": "dist/human.js",
+        "minify": true,
+        "globalName": "Human",
+        "external": ["@tensorflow"]
+      },
+      {
+        "name": "human/browser/esm/bundle",
+        "platform": "browser",
+        "format": "esm",
+        "input": "src/human.ts",
+        "output": "dist/human.esm.js",
+        "sourcemap": true,
+        "minify": false,
+        "external": ["@tensorflow"],
+        "typings": "types/lib",
+        "typedoc": "typedoc"
+      },
+      {
+        "name": "demo/typescript",
+        "platform": "browser",
+        "format": "esm",
+        "input": "demo/typescript/index.ts",
+        "output": "demo/typescript/index.js",
+        "sourcemap": true,
+        "external": ["*/human.esm.js"]
+      },
+      {
+        "name": "demo/faceid",
+        "platform": "browser",
+        "format": "esm",
+        "input": "demo/faceid/index.ts",
+        "output": "demo/faceid/index.js",
+        "sourcemap": true,
+        "external": ["*/human.esm.js"]
+      }
+    ]
+  },
+  "watch": {
+    "locations": [ "src/**/*", "tfjs/**/*", "demo/**/*.ts" ]
+  },
+  "typescript": {
+    "allowJs": false
+  }
+}

.eslintrc.json

@@ -0,0 +1,93 @@
+{
+  "globals": {},
+  "env": {
+      "browser": true,
+      "commonjs": true,
+      "node": true,
+      "es2021": true
+  },
+  "parser": "@typescript-eslint/parser",
+  "parserOptions": {
+    "ecmaVersion": 2021
+  },
+  "plugins": [
+    "@typescript-eslint",
+    "html"
+  ],
+  "extends": [
+    "airbnb-base",
+    "eslint:recommended",
+    "plugin:@typescript-eslint/eslint-recommended",
+    "plugin:@typescript-eslint/recommended",
+    "plugin:import/errors",
+    "plugin:import/warnings",
+    "plugin:json/recommended-with-comments",
+    "plugin:node/recommended",
+    "plugin:promise/recommended"
+  ],
+  "ignorePatterns": [
+    "assets",
+    "demo/helpers/*.js",
+    "demo/typescript/*.js",
+    "demo/faceid/*.js",
+    "dist",
+    "media",
+    "models",
+    "node_modules",
+    "types/human.d.ts"
+  ],
+  "rules": {
+    "@typescript-eslint/ban-ts-comment": "off",
+    "@typescript-eslint/explicit-module-boundary-types": "off",
+    "@typescript-eslint/no-shadow": "error",
+    "@typescript-eslint/no-var-requires": "off",
+    "@typescript-eslint/prefer-as-const": "off",
+    "@typescript-eslint/triple-slash-reference": "off",
+    "@typescript-eslint/no-inferrable-types": "off",
+    "@typescript-eslint/no-empty-interface": ["error", { "allowSingleExtends": true }],
+    "camelcase": "off",
+    "class-methods-use-this": "off",
+    "dot-notation": "off",
+    "func-names": "off",
+    "guard-for-in": "off",
+    "import/extensions": "off",
+    "import/named": "off",
+    "import/no-extraneous-dependencies": "off",
+    "import/no-named-as-default": "off",
+    "import/no-unresolved": "off",
+    "import/prefer-default-export": "off",
+    "lines-between-class-members": "off",
+    "max-len": [1, 275, 3],
+    "newline-per-chained-call": "off",
+    "no-async-promise-executor": "off",
+    "no-await-in-loop": "off",
+    "no-bitwise": "off",
+    "no-case-declarations":"off",
+    "no-continue": "off",
+    "no-else-return": "off",
+    "no-lonely-if": "off",
+    "no-loop-func": "off",
+    "no-mixed-operators": "off",
+    "no-param-reassign":"off",
+    "no-plusplus": "off",
+    "no-process-exit": "off",
+    "no-regex-spaces": "off",
+    "no-restricted-globals": "off",
+    "no-restricted-syntax": "off",
+    "no-return-assign": "off",
+    "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"
+  }
+}

.github/ISSUE_TEMPLATE/issue.md

@@ -0,0 +1,35 @@
+---
+name: Issue
+about: Issue
+title: ''
+labels: ''
+assignees: vladmandic
+
+---
+
+**Issue Description**
+
+**Steps to Reproduce**
+
+**Expected Behavior**
+
+**Environment**
+
+- Human library version?
+- Built-in demo or custom code?
+- Type of module used (e.g. `js`, `esm`, `esm-nobundle`)?
+- TensorFlow/JS version (if not using bundled module)?
+- Browser or NodeJS and version (e.g. *NodeJS 14.15* or *Chrome 89*)?
+- OS and Hardware platform (e.g. *Windows 10*, *Ubuntu Linux on x64*, *Android 10*)?
+- Packager (if any) (e.g, *webpack*, *rollup*, *parcel*, *esbuild*, etc.)?
+- Framework (if any) (e.g. *React*, *NextJS*, etc.)?
+
+**Diagnostics**
+
+- Check out any applicable [diagnostic steps](https://github.com/vladmandic/human/wiki/Diag)
+
+**Additional**
+
+- For installation or startup issues include your `package.json`
+- For usage issues, it is recommended to post your code as [gist](https://gist.github.com/)
+- For general questions, create a [discussion topic](https://github.com/vladmandic/human/discussions)

.github/PULL_REQUEST_TEMPLATE/pull_request_template.md

@@ -0,0 +1,3 @@
+# Pull Request Template
+
+<br>

.github/workflows/codeql-analysis.yml

@@ -0,0 +1,67 @@
+# For most projects, this workflow file will not need changing; you simply need
+# to commit it to your repository.
+#
+# You may wish to alter this file to override the set of languages analyzed,
+# or to provide custom queries or build logic.
+#
+# ******** NOTE ********
+# We have attempted to detect the languages in your repository. Please check
+# the `language` matrix defined below to confirm you have the correct set of
+# supported CodeQL languages.
+#
+name: "CodeQL"
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    # The branches below must be a subset of the branches above
+    branches: [ main ]
+  schedule:
+    - cron: '16 14 * * 6'
+
+jobs:
+  analyze:
+    name: Analyze
+    runs-on: ubuntu-latest
+
+    strategy:
+      fail-fast: false
+      matrix:
+        language: [ 'javascript' ]
+        # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
+        # Learn more:
+        # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v2
+
+    # Initializes the CodeQL tools for scanning.
+    - name: Initialize CodeQL
+      uses: github/codeql-action/init@v1
+      with:
+        languages: ${{ matrix.language }}
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file.
+        # Prefix the list here with "+" to use these queries and those in the config file.
+        # queries: ./path/to/local/query, your-org/your-repo/queries@main
+
+    # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
+    # If this step fails, then you should remove it and run the build manually (see below)
+    - name: Autobuild
+      uses: github/codeql-action/autobuild@v1
+
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 https://git.io/JvXDl
+
+    # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
+    #    and modify them (or add more) to build your code if your project
+    #    uses a compiled language
+
+    #- run: |
+    #   make bootstrap
+    #   make release
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v1

.gitignore

@@ -0,0 +1,5 @@
+.vscode
+pnpm-lock.yaml
+*.swp
+node_modules/
+types/lib

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "wiki"]
+	path = wiki
+	url = https://github.com/vladmandic/human.wiki.git

.hintrc

@@ -0,0 +1,16 @@
+{
+  "extends": [
+    "web-recommended"
+  ],
+  "browserslist": [
+    "chrome >= 90",
+    "edge >= 90",
+    "firefox >= 100",
+    "android >= 90",
+    "safari >= 15"
+  ],
+  "hints": {
+    "no-inline-styles": "off",
+    "meta-charset-utf-8": "off"
+  }
+}

.markdownlint.json

@@ -0,0 +1,7 @@
+{
+  "MD012": false,
+  "MD013": false,
+  "MD033": false,
+  "MD036": false,
+  "MD041": false
+}

.npmignore

@@ -0,0 +1,7 @@
+node_modules
+pnpm-lock.yaml
+samples
+typedoc
+test
+wiki
+types/lib

.npmrc

@@ -0,0 +1 @@
+force = true

CODE_OF_CONDUCT

@@ -0,0 +1,33 @@
+# Code of Conduct
+
+Use your best judgement  
+If it will possibly make others uncomfortable, do not post it
+
+- Be respectful  
+  Disagreement is not an opportunity to attack someone else's thoughts or opinions  
+  Although views may differ, remember to approach every situation with patience and care  
+- Be considerate  
+  Think about how your contribution will affect others in the community  
+- Be open minded  
+  Embrace new people and new ideas. Our community is continually evolving and we welcome positive change  
+
+Be mindful of your language  
+Any of the following behavior is unacceptable:
+
+- Offensive comments of any kind
+- Threats or intimidation
+- Sexually explicit material
+- Or any other kinds of harassment
+
+If you believe someone is violating the code of conduct, we ask that you report it
+
+Participants asked to stop any harassing behavior are expected to comply immediately  
+
+<br>
+
+## Usage Restrictions
+
+`Human` library does not alow for usage in following scenarios:
+
+- Any life-critical decisions
+- Any form of surveillance without consent of the user is explicitly out of scope

CONTRIBUTING

@@ -0,0 +1,22 @@
+# Contributing Guidelines
+
+Pull requests from everyone are welcome
+
+Procedure for contributing:
+
+- Create a fork of the repository on github  
+  In a top right corner of a GitHub, select "Fork"
+  Its recommended to fork latest version from main branch to avoid any possible conflicting code updates
+- Clone your forked repository to your local system  
+  `git clone https://github.com/<your-username>/<your-fork>
+- Make your changes  
+- Test your changes against code guidelines  
+  `npm run lint`
+- Test your changes in Browser and NodeJS  
+  `npm run dev` and naviate to https://localhost:10031  
+  `node test/test-node.js`  
+- Push changes to your fork  
+  Exclude files in `/dist', '/types', '/typedoc' from the commit as they are dynamically generated during build
+- Submit a PR (pull request)
+
+Your pull request will be reviewed and pending review results, merged into main branch

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2020 Vladimir Mandic
+Copyright (c) Vladimir Mandic
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -0,0 +1,365 @@
+![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
+
+**AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition,**  
+**Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis,**  
+**Age & Gender & Emotion Prediction, Gaze Tracking, Gesture Recognition, Body Segmentation**  
+
+<br>
+
+JavaScript module using TensorFlow/JS Machine Learning library  
+
+- **Browser**:  
+  Compatible with both desktop and mobile platforms  
+  Compatible with *CPU*, *WebGL*, *WASM* backends  
+  Compatible with *WebWorker* execution
+- **NodeJS**:  
+  Compatible with both software *tfjs-node* and  
+  GPU accelerated backends *tfjs-node-gpu* using CUDA libraries  
+
+<br>
+
+*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*  
+
+- 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
+
+<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
+
+- **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
+- **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 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** [[*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
+- **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
+- **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
+
+- **Main** [[*Details*]](https://github.com/vladmandic/human/tree/main/demo/nodejs): Process images from files, folders or URLs using native methods  
+- **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  
+
+
+## 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**](https://vladmandic.github.io/human/typedoc/classes/Human.html)
+- [**Change Log**](https://github.com/vladmandic/human/blob/main/CHANGELOG.md)
+- [**Current To-do List**](https://github.com/vladmandic/human/blob/main/TODO.md)
+
+## Wiki pages
+
+- [**Home**](https://github.com/vladmandic/human/wiki)
+- [**Installation**](https://github.com/vladmandic/human/wiki/Install)
+- [**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)
+- [**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)
+- [**Gesture Recognition**](https://github.com/vladmandic/human/wiki/Gesture)
+- [**Common Issues**](https://github.com/vladmandic/human/wiki/Issues)
+- [**Background and Benchmarks**](https://github.com/vladmandic/human/wiki/Background)
+
+## Additional notes
+
+- [**Comparing Backends**](https://github.com/vladmandic/human/wiki/Backends)
+- [**Development Server**](https://github.com/vladmandic/human/wiki/Development-Server)
+- [**Build Process**](https://github.com/vladmandic/human/wiki/Build-Process)
+- [**Adding Custom Modules**](https://github.com/vladmandic/human/wiki/Module)
+- [**Performance Notes**](https://github.com/vladmandic/human/wiki/Performance)
+- [**Performance Profiling**](https://github.com/vladmandic/human/wiki/Profiling)
+- [**Platform Support**](https://github.com/vladmandic/human/wiki/Platforms)
+- [**Diagnostic and Performance trace information**](https://github.com/vladmandic/human/wiki/Diag)
+- [**Dockerize Human applications**](https://github.com/vladmandic/human/wiki/Docker)
+- [**List of Models & Credits**](https://github.com/vladmandic/human/wiki/Models)
+- [**Models Download Repository**](https://github.com/vladmandic/human-models)
+- [**Security & Privacy Policy**](https://github.com/vladmandic/human/blob/main/SECURITY.md)
+- [**License & Usage Restrictions**](https://github.com/vladmandic/human/blob/main/LICENSE)
+
+<br>
+
+*See [**issues**](https://github.com/vladmandic/human/issues?q=) and [**discussions**](https://github.com/vladmandic/human/discussions) for list of known limitations and planned enhancements*  
+
+*Suggestions are welcome!*  
+
+<hr><br>
+
+## Examples
+
+Visit [Examples galery](https://vladmandic.github.io/human/samples/samples.html) for more examples  
+<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)  
+
+![Options visible in demo](assets/screenshot-menu.png)
+
+<br>
+
+**Results Browser:**  
+[ *Demo -> Display -> Show Results* ]<br>
+![Results](assets/screenshot-results.png)
+
+<br>
+
+## Advanced Examples
+
+1. **Face Similarity Matching:**  
+Extracts all faces from provided input images,  
+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)  
+
+![Face Matching](assets/screenshot-facematch.jpg)
+
+<br>
+
+2. **3D Rendering:**  
+> [human-motion](https://github.com/vladmandic/human-motion)
+
+![Face3D](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-face.jpg)
+![Body3D](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-body.jpg)
+![Hand3D](https://github.com/vladmandic/human-motion/raw/main/assets/screenshot-hand.jpg)
+
+<br>
+
+3. **Avatar Bone Mapping:**  
+> [human-avatar](https://github.com/vladmandic/human-avatar)
+
+![Avatar](https://github.com/vladmandic/human-avatar/raw/main/assets/screenshot.jpg)
+
+<br>
+
+4. **VR Model Tracking:**  
+> [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)  
+
+![FaceMesh](assets/facemesh.png)
+
+<br><hr><br>
+
+## Quick Start
+
+Simply load `Human` (*IIFE version*) directly from a cloud CDN in your HTML file:  
+(pick one: `jsdelirv`, `unpkg` or `cdnjs`)
+
+```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/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>
+
+## 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  
+  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(config);
+// select input HTMLVideoElement and output HTMLCanvasElement from page
+const inputVideo = document.getElementById('video-id');
+const outputCanvas = document.getElementById('canvas-id');
+
+function detectVideo() {
+  // perform processing using default configuration
+  human.detect(inputVideo).then((result) => {
+    // result object will contain detected details
+    // as well as the processed canvas itself
+    // so lets first draw processed frame on canvas
+    human.draw.canvas(result.canvas, outputCanvas);
+    // then draw results on the same canvas
+    human.draw.face(outputCanvas, result.face);
+    human.draw.body(outputCanvas, result.body);
+    human.draw.hand(outputCanvas, result.hand);
+    human.draw.gesture(outputCanvas, result.gesture);
+    // and loop immediate to the next frame
+    requestAnimationFrame(detectVideo);
+  });
+}
+
+detectVideo();
+```
+
+or using `async/await`:
+
+```js
+// create instance of human with simple configuration using default values
+const config = { backend: 'webgl' };
+const human = new Human(config); // create instance of Human
+const inputVideo = document.getElementById('video-id');
+const outputCanvas = document.getElementById('canvas-id');
+
+async function detectVideo() {
+  const result = await human.detect(inputVideo); // run detection
+  human.draw.all(outputCanvas, result); // draw all results
+  requestAnimationFrame(detectVideo); // run loop
+}
+
+detectVideo(); // start loop
+```
+
+or using `Events`:
+
+```js
+// create instance of human with simple configuration using default values
+const config = { backend: 'webgl' };
+const human = new Human(config); // create instance of Human
+const inputVideo = document.getElementById('video-id');
+const outputCanvas = document.getElementById('canvas-id');
+
+human.events.addEventListener('detect', () => { // event gets triggered when detect is complete
+  human.draw.all(outputCanvas, human.result); // draw all results
+});
+
+function detectVideo() {
+  human.detect(inputVideo) // run detection
+  .then(() => requestAnimationFrame(detectVideo)); // upon detect complete start processing of the next frame
+}
+
+detectVideo(); // start loop
+```
+
+or using interpolated results for smooth video processing by separating detection and drawing loops:
+
+```js
+const human = new Human(); // create instance of Human
+const inputVideo = document.getElementById('video-id');
+const outputCanvas = document.getElementById('canvas-id');
+let result;
+
+async function detectVideo() {
+  result = await human.detect(inputVideo); // run detection
+  requestAnimationFrame(detectVideo); // run detect loop
+}
+
+async function drawVideo() {
+  if (result) { // check if result is available
+    const interpolated = human.next(result); // calculate next interpolated frame
+    human.draw.all(outputCanvas, interpolated); // draw the frame
+  }
+  requestAnimationFrame(drawVideo); // run draw loop
+}
+
+detectVideo(); // start detection loop
+drawVideo(); // start draw loop
+```
+
+And for even better results, you can run detection in a separate web worker thread
+
+<br><hr><br>
+
+## Default models
+
+Default models in Human library are:
+
+- **Face Detection**: MediaPipe BlazeFace Back variation
+- **Face Mesh**: MediaPipe FaceMesh
+- **Face Iris Analysis**: MediaPipe Iris
+- **Face Description**: HSE FaceRes
+- **Emotion Detection**: Oarriaga Emotion
+- **Body Analysis**: MoveNet Lightning variation
+- **Hand Analysis**: HandTrack & MediaPipe HandLandmarks
+- **Body Segmentation**: Google Selfie
+- **Object Detection**: CenterNet with MobileNet v3
+
+Note that alternative models are provided and can be enabled via configuration  
+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)
+
+<br><hr><br>
+
+## Diagnostics
+
+- [How to get diagnostic information or performance trace information](https://github.com/vladmandic/human/wiki/Diag)
+
+<br><hr><br>
+
+`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 is `JavaScript` [EMCAScript version 2018](https://262.ecma-international.org/11.0/)  
+
+<br>
+
+For details see [**Wiki Pages**](https://github.com/vladmandic/human/wiki)  
+and [**API Specification**](https://vladmandic.github.io/human/typedoc/classes/Human.html)
+
+<br>
+
+![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)
+![CDN](https://data.jsdelivr.com/v1/package/npm/@vladmandic/human/badge)<br>
+![Downloads](https://img.shields.io/npm/dw/@vladmandic/human.png?style=flat-square)
+![Downloads](https://img.shields.io/npm/dm/@vladmandic/human.png?style=flat-square)
+![Downloads](https://img.shields.io/npm/dy/@vladmandic/human.png?style=flat-square)

SECURITY.md

@@ -0,0 +1,32 @@
+# Security & Privacy Policy
+
+<br>
+
+## Issues
+
+All issues are tracked publicly on GitHub: <https://github.com/vladmandic/human/issues>  
+
+<br>
+
+## Vulnerabilities
+
+`Human` library code base and indluded dependencies are automatically scanned against known security vulnerabilities  
+Any code commit is validated before merge  
+
+- [Dependencies](https://github.com/vladmandic/human/security/dependabot)
+- [Scanning Alerts](https://github.com/vladmandic/human/security/code-scanning)
+
+<br>
+
+## Privacy
+
+`Human` library and included demo apps:
+
+- Are fully self-contained and does not send or share data of any kind with external targets
+- Do not store any user or system data tracking, user provided inputs (images, video) or detection results
+- Do not utilize any analytic services (such as Google Analytics)
+
+`Human` library can establish external connections *only* for following purposes and *only* when explicitly configured by user:
+
+- Load models from externally hosted site (e.g. CDN)
+- Load inputs for detection from *http & https* sources

TODO.md

@@ -0,0 +1,31 @@
+# To-Do list for Human library
+
+## Work in Progress
+
+### Exploring
+
+- Optical flow: <https://docs.opencv.org/3.3.1/db/d7f/tutorial_js_lucas_kanade.html>
+- Advanced histogram equalization: Adaptive, Contrast Limited, CLAHE
+- TFLite models: <https://js.tensorflow.org/api_tflite/0.0.1-alpha.4/>
+- Body segmentation: `robust-video-matting`
+
+<br><hr><br>
+
+## Known Issues
+
+#### WebGPU
+
+Experimental support only until support is officially added in Chromium
+
+### Face Detection
+
+Enhanced rotation correction for face detection is not working in NodeJS due to missing kernel op in TFJS  
+Feature is automatically disabled in NodeJS without user impact  
+
+- Backend NodeJS missing kernel op `RotateWithOffset`  
+  <https://github.com/tensorflow/tfjs/issues/5473>  
+
+<br><hr><br>
+
+## Pending Release Notes
+

api-extractor.json

@@ -0,0 +1,28 @@
+{
+  "$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": false
+  },
+  "newlineKind": "lf",
+  "dtsRollup": {
+    "enabled": true,
+    "untrimmedFilePath": "types/human.d.ts"
+  },
+  "docModel": { "enabled": false },
+  "tsdocMetadata": { "enabled": false },
+  "apiReport": { "enabled": false },
+  "messages": {
+    "compilerMessageReporting": {
+      "default": { "logLevel": "warning" }
+    },
+    "extractorMessageReporting": {
+      "default": { "logLevel": "warning" },
+      "ae-missing-release-tag": { "logLevel": "none" }
+    },
+    "tsdocMessageReporting": {
+      "default": { "logLevel": "warning" }
+    }
+  }
+}

assets/README.md

@@ -0,0 +1,4 @@
+# Human Library: Static Assets
+
+Static assets used by `Human` library demos and/or referenced by Wiki pages
+

build.js

@@ -0,0 +1,57 @@
+const fs = require('fs');
+const log = require('@vladmandic/pilogger');
+const Build = require('@vladmandic/build').Build;
+const APIExtractor = require('@microsoft/api-extractor');
+
+function copy(src, dst) {
+  if (!fs.existsSync(src)) return;
+  const buffer = fs.readFileSync(src);
+  fs.writeFileSync(dst, buffer);
+}
+
+// eslint-disable-next-line @typescript-eslint/no-unused-vars
+const apiExtractorIgnoreList = [
+  'ae-missing-release-tag',
+  'tsdoc-param-tag-missing-hyphen',
+  'tsdoc-escape-right-brace',
+  'tsdoc-undefined-tag',
+  'tsdoc-escape-greater-than',
+  'ae-unresolved-link',
+  'ae-forgotten-export',
+  'tsdoc-malformed-inline-tag',
+  'tsdoc-unnecessary-backslash',
+];
+
+async function main() {
+  // run production build
+  const build = new Build();
+  await build.run('production');
+  // patch tfjs typedefs
+  log.state('Copy:', { input: 'tfjs/tfjs.esm.d.ts' });
+  copy('tfjs/tfjs.esm.d.ts', 'types/lib/dist/tfjs.esm.d.ts');
+  // run api-extractor to create typedef rollup
+  const extractorConfig = APIExtractor.ExtractorConfig.loadFileAndPrepare('api-extractor.json');
+  const extractorResult = APIExtractor.Extractor.invoke(extractorConfig, {
+    localBuild: true,
+    showVerboseMessages: false,
+    messageCallback: (msg) => {
+      msg.handled = true;
+      if (msg.logLevel === 'none' || msg.logLevel === 'verbose' || msg.logLevel === 'info') 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
+      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 });
+  // distribute typedefs
+  log.state('Copy:', { input: 'types/human.d.ts' });
+  copy('types/human.d.ts', 'dist/human.esm-nobundle.d.ts');
+  copy('types/human.d.ts', 'dist/human.esm.d.ts');
+  copy('types/human.d.ts', 'dist/human.d.ts');
+  copy('types/human.d.ts', 'dist/human.node-gpu.d.ts');
+  copy('types/human.d.ts', 'dist/human.node.d.ts');
+  copy('types/human.d.ts', 'dist/human.node-wasm.d.ts');
+  log.info('Human Build complete...');
+}
+
+main();

demo/README.md

@@ -0,0 +1,64 @@
+# Human Library: Demos
+
+For details on other demos see Wiki: [**Demos**](https://github.com/vladmandic/human/wiki/Demos)
+
+## Main Demo
+
+
+`index.html`: Full demo using `Human` ESM module running in Browsers,  
+
+Includes:
+- Selectable inputs:
+  - Sample images
+  - Image via drag & drop
+  - Image via URL param
+  - WebCam input
+  - Video stream
+  - WebRTC stream
+- Selectable active `Human` modules
+  - With interactive module params
+- Interactive `Human` image filters
+- Selectable interactive `results` browser
+- Selectable `backend`
+- Multiple execution methods:
+  - Sync vs Async
+  - in main thread or web worker
+  - live on git pages, on user-hosted web server or via included [**micro http2 server**](https://github.com/vladmandic/human/wiki/Development-Server)
+
+### Demo Options
+
+- General `Human` library options  
+  in `index.js:userConfig`
+- General `Human` `draw` options  
+  in `index.js:drawOptions`
+- Demo PWA options  
+  in `index.js:pwa`
+- Demo specific options  
+  in `index.js:ui`
+
+```js
+  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,  
+
+### URL Params
+
+Demo app can use URL parameters to override configuration values  
+For example:
+
+- Force using `WASM` as backend: <https://vladmandic.github.io/human/demo/index.html?backend=wasm>
+- Enable `WebWorkers`: <https://vladmandic.github.io/human/demo/index.html?worker=true>
+- Skip pre-loading and warming up: <https://vladmandic.github.io/human/demo/index.html?preload=false&warmup=false>
+
+### WebRTC
+
+Note that WebRTC connection requires a WebRTC server that provides a compatible media track such as H.264 video track  
+For such a WebRTC server implementation see <https://github.com/vladmandic/stream-rtsp> project  
+that implements a connection to IP Security camera using RTSP protocol and transcodes it to WebRTC  
+ready to be consumed by a client such as `Human`

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**

demo/benchmark/index.html

@@ -0,0 +1,86 @@
+<!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; }
+      .status { position: absolute; width: 100vw; bottom: 10%; text-align: center; font-size: 3rem; font-weight: 100; text-shadow: 2px 2px #303030; }
+      .log { position: absolute; bottom: 0; margin: 0.4rem 0.4rem 0 0.4rem; font-size: 0.9rem; }
+    </style>
+  </head>
+  <body>
+    <div id="status" class="status"></div>
+    <img id="image" src="../../samples/in/group-1.jpg" alt="test image" style="display: none">
+    <div id="log" class="log"></div>
+    <script type="module">
+      import Human from '../../dist/human.esm.js';
+
+      const loop = 20;
+      const backends = ['wasm', 'webgl', 'humangl', 'webgpu'];
+
+      // eslint-disable-next-line no-console
+      const log = (...msg) => console.log(...msg);
+
+      const myConfig = {
+        modelBasePath: 'https://vladmandic.github.io/human/models',
+        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: false },
+          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.tf.ready();
+        log('Human:', human.version);
+        await human.load();
+        const loaded = Object.keys(human.models).filter((a) => human.models[a]);
+        log('Loaded:', loaded);
+        log('Memory state:', human.tf.engine().memory());
+        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();
+        log('Backend:', human.tf.getBackend());
+        log('Warmup:', Math.round(t1 - t0));
+        for (let i = 0; i < loop; i++) await human.detect(processed.tensor, myConfig);
+        const t2 = human.now();
+        log('Average:', Math.round((t2 - t1) / loop));
+      }
+
+      async function main() {
+        for (const backend of backends) await benchmark(backend);
+      }
+
+      window.onload = main;
+    </script> 
+  </body>
+</html>

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@3.9.0/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();

demo/faceid/README.md

@@ -0,0 +1,41 @@
+# Human Face Recognition: FaceID
+
+`faceid` runs multiple checks to validate webcam input before performing face match  
+Detected face image and descriptor are stored in client-side IndexDB  
+
+## Workflow
+- Starts webcam  
+- Waits until input video contains validated face or timeout is reached  
+  - Number of people
+  - Face size
+  - Face and gaze direction
+  - Detection scores
+  - Blink detection (including temporal check for blink speed) to verify live input
+  - Runs `antispoofing` optional module
+  - Runs `liveness` optional module
+- Runs match against database of registered faces and presents best match with scores
+
+## Notes
+
+Both `antispoof` and `liveness` models are tiny and
+designed to serve as a quick check when used together with other indicators:
+- size below 1MB
+- very quick inference times as they are very simple (11 ops for antispoof and 23 ops for liveness)
+- trained on low-resolution inputs
+
+### Anti-spoofing Module
+- Checks if input is realistic (e.g. computer generated faces)
+- Configuration: `human.config.face.antispoof`.enabled
+- Result: `human.result.face[0].real` as score
+
+### Liveness Module
+- Checks if input has obvious artifacts due to recording (e.g. playing back phone recording of a face)
+- Configuration: `human.config.face.liveness`.enabled
+- Result: `human.result.face[0].live` as score
+
+### Models
+
+**FaceID** is compatible with
+- `faceres.json` (default) perfoms combined age/gender/descriptor analysis
+- `faceres-deep.json` higher resolution variation of `faceres`
+- `mobilefacenet` alternative model for face descriptor analysis

demo/faceid/index.html

@@ -0,0 +1,40 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <title>Human: Face Recognition</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>
+      @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; 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: 100px; background-color: grey; padding: 4px; color: black; font-size: 14px }
+    </style>
+  </head>
+  <body>
+    <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>
+      <input id="name" type="text" value="" style="height: 16px; border: none; padding: 2px; margin-left: 8px">
+      <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: 350px; margin-top: 32px; padding: 4px">retry</div>
+    <div id="ok"></div>
+  </body>
+</html>

demo/faceid/index.js

@@ -0,0 +1,17 @@
+/*
+  Human
+  homepage: <https://github.com/vladmandic/human>
+  author: <https://github.com/vladmandic>'
+*/
+
+import{Human as H}from"../../dist/human.esm.js";var d,R="human",m="person",g=(...t)=>console.log("indexdb",...t);async function b(){return d?!0:new Promise(t=>{let i=indexedDB.open(R,1);i.onerror=s=>g("error:",s),i.onupgradeneeded=s=>{g("create:",s.target),d=s.target.result,d.createObjectStore(m,{keyPath:"id",autoIncrement:!0})},i.onsuccess=s=>{d=s.target.result,g("open:",d),t(!0)}})}async function C(){let t=[];return d||await b(),new Promise(i=>{let s=d.transaction([m],"readwrite").objectStore(m).openCursor(null,"next");s.onerror=o=>g("load error:",o),s.onsuccess=o=>{o.target.result?(t.push(o.target.result.value),o.target.result.continue()):i(t)}})}async function k(){return d||await b(),new Promise(t=>{let i=d.transaction([m],"readwrite").objectStore(m).count();i.onerror=s=>g("count error:",s),i.onsuccess=()=>t(i.result)})}async function x(t){d||await b();let i={name:t.name,descriptor:t.descriptor,image:t.image};d.transaction([m],"readwrite").objectStore(m).put(i),g("save:",i)}async function D(t){d||await b(),d.transaction([m],"readwrite").objectStore(m).delete(t.id),g("delete:",t)}var v={modelBasePath:"../../models",filter:{equalization:!0},face:{enabled:!0,detector:{rotation:!0,return:!0,cropFactor:1.6,mask:!1},description:{enabled:!0},mobilefacenet:{enabled:!1,modelPath:"https://vladmandic.github.io/human-models/models/mobilefacenet.json"},iris:{enabled:!0},emotion:{enabled:!1},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},gesture:{enabled:!0}},I={order:2,multiplier:25,min:.2,max:.8},c={minConfidence:.6,minSize:224,maxTime:1e4,blinkMin:10,blinkMax:800,threshold:.5,mask:v.face.detector.mask,rotation:v.face.detector.rotation,cropFactor:v.face.detector.cropFactor,...I},n={faceCount:!1,faceConfidence:!1,facingCenter:!1,lookingCenter:!1,blinkDetected:!1,faceSize:!1,antispoofCheck:!1,livenessCheck:!1,elapsedMs:0},M=()=>n.faceCount&&n.faceSize&&n.blinkDetected&&n.facingCenter&&n.lookingCenter&&n.faceConfidence&&n.antispoofCheck&&n.livenessCheck,r={face:null,record:null},l={start:0,end:0,time:0},a=new H(v);a.env.perfadd=!1;a.draw.options.font='small-caps 18px "Lato"';a.draw.options.lineHeight=20;var e={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("fps"),match:document.getElementById("match"),name:document.getElementById("name"),save:document.getElementById("save"),delete:document.getElementById("delete"),retry:document.getElementById("retry"),source:document.getElementById("source"),ok:document.getElementById("ok")},h={detect:0,draw:0},y={detect:0,draw:0},E=0,p=(...t)=>{e.log.innerText+=t.join(" ")+`
+`,console.log(...t)},w=t=>e.fps.innerText=t;async function S(){w("starting webcam...");let t={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth}}},i=await navigator.mediaDevices.getUserMedia(t),s=new Promise(o=>{e.video.onloadeddata=()=>o(!0)});e.video.srcObject=i,e.video.play(),await s,e.canvas.width=e.video.videoWidth,e.canvas.height=e.video.videoHeight,a.env.initial&&p("video:",e.video.videoWidth,e.video.videoHeight,"|",i.getVideoTracks()[0].label),e.canvas.onclick=()=>{e.video.paused?e.video.play():e.video.pause()}}async function T(){if(!e.video.paused){r.face&&r.face.tensor&&a.tf.dispose(r.face.tensor),await a.detect(e.video);let t=a.now();y.detect=1e3/(t-h.detect),h.detect=t,requestAnimationFrame(T)}}async function L(){let t=await a.next(a.result);await a.draw.canvas(e.video,e.canvas),await a.draw.all(e.canvas,t);let i=a.now();if(y.draw=1e3/(i-h.draw),h.draw=i,w(`fps: ${y.detect.toFixed(1).padStart(5," ")} detect | ${y.draw.toFixed(1).padStart(5," ")} draw`),n.faceCount=a.result.face.length===1,n.faceCount){let o=Object.values(a.result.gesture).map(f=>f.gesture);(o.includes("blink left eye")||o.includes("blink right eye"))&&(l.start=a.now()),l.start>0&&!o.includes("blink left eye")&&!o.includes("blink right eye")&&(l.end=a.now()),n.blinkDetected=n.blinkDetected||Math.abs(l.end-l.start)>c.blinkMin&&Math.abs(l.end-l.start)<c.blinkMax,n.blinkDetected&&l.time===0&&(l.time=Math.trunc(l.end-l.start)),n.facingCenter=o.includes("facing center"),n.lookingCenter=o.includes("looking center"),n.faceConfidence=(a.result.face[0].boxScore||0)>c.minConfidence&&(a.result.face[0].faceScore||0)>c.minConfidence&&(a.result.face[0].genderScore||0)>c.minConfidence,n.antispoofCheck=(a.result.face[0].real||0)>c.minConfidence,n.livenessCheck=(a.result.face[0].live||0)>c.minConfidence,n.faceSize=a.result.face[0].box[2]>=c.minSize&&a.result.face[0].box[3]>=c.minSize}let s=32;for(let[o,f]of Object.entries(n)){let u=document.getElementById(`ok-${o}`);u||(u=document.createElement("div"),u.innerText=o,u.className="ok",u.style.top=`${s}px`,e.ok.appendChild(u)),typeof f=="boolean"?u.style.backgroundColor=f?"lightgreen":"lightcoral":u.innerText=`${o}:${f}`,s+=28}return M()||n.elapsedMs>c.maxTime?(e.video.pause(),a.result.face[0]):(n.elapsedMs=Math.trunc(a.now()-E),new Promise(o=>{setTimeout(async()=>{await L()&&o(a.result.face[0])},30)}))}async function P(){var t,i;if(e.name.value.length>0){let s=(t=e.canvas.getContext("2d"))==null?void 0:t.getImageData(0,0,e.canvas.width,e.canvas.height),o={id:0,name:e.name.value,descriptor:(i=r.face)==null?void 0:i.embedding,image:s};await x(o),p("saved face record:",o.name)}else p("invalid name")}async function z(){r.record&&r.record.id>0&&await D(r.record)}async function j(){var o,f;if((o=e.canvas.getContext("2d"))==null||o.clearRect(0,0,c.minSize,c.minSize),!r.face||!r.face.tensor||!r.face.embedding)return!1;if(console.log("face record:",r.face),a.tf.browser.toPixels(r.face.tensor,e.canvas),await k()===0)return p("face database is empty"),document.body.style.background="black",e.delete.style.display="none",!1;let t=await C(),i=t.map(u=>u.descriptor),s=await a.match(r.face.embedding,i,I);return r.record=t[s.index]||null,r.record&&(p(`best match: ${r.record.name} | id: ${r.record.id} | similarity: ${Math.round(1e3*s.similarity)/10}%`),e.name.value=r.record.name,e.source.style.display="",(f=e.source.getContext("2d"))==null||f.putImageData(r.record.image,0,0)),document.body.style.background=s.similarity>c.threshold?"darkgreen":"maroon",s.similarity>c.threshold}async function B(){var t,i,s,o;return n.faceCount=!1,n.faceConfidence=!1,n.facingCenter=!1,n.blinkDetected=!1,n.faceSize=!1,n.antispoofCheck=!1,n.livenessCheck=!1,n.elapsedMs=0,e.match.style.display="none",e.retry.style.display="none",e.source.style.display="none",document.body.style.background="black",await S(),await T(),E=a.now(),r.face=await L(),e.canvas.width=((i=(t=r.face)==null?void 0:t.tensor)==null?void 0:i.shape[1])||c.minSize,e.canvas.height=((o=(s=r.face)==null?void 0:s.tensor)==null?void 0:o.shape[0])||c.minSize,e.source.width=e.canvas.width,e.source.height=e.canvas.height,e.canvas.style.width="",e.match.style.display="flex",e.save.style.display="flex",e.delete.style.display="flex",e.retry.style.display="block",M()?j():(p("did not find valid face"),!1)}async function q(){p("human version:",a.version,"| tfjs version:",a.tf.version["tfjs-core"]),p("options:",JSON.stringify(c).replace(/{|}|"|\[|\]/g,"").replace(/,/g," ")),w("loading..."),p("known face records:",await k()),await S(),await a.load(),w("initializing..."),e.retry.addEventListener("click",B),e.save.addEventListener("click",P),e.delete.addEventListener("click",z),await a.warmup(),await B()}window.onload=q;
+/**
+ * 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

demo/faceid/index.ts

@@ -0,0 +1,274 @@
+/**
+ * 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 { 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
+  modelBasePath: '../../models',
+  filter: { equalization: true }, // lets run with histogram equilizer
+  face: {
+    enabled: true,
+    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: false, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model
+    iris: { enabled: true }, // needed to determine gaze direction
+    emotion: { enabled: false }, // not needed
+    antispoof: { enabled: true }, // enable optional antispoof module
+    liveness: { enabled: true }, // enable optional liveness module
+  },
+  body: { enabled: false },
+  hand: { enabled: false },
+  object: { enabled: false },
+  gesture: { enabled: true }, // parses face and iris gestures
+};
+
+// const matchOptions = { order: 2, multiplier: 1000, min: 0.0, max: 1.0 }; // for embedding model
+const matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; // for faceres model
+
+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: 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
+  mask: humanConfig.face.detector.mask,
+  rotation: humanConfig.face.detector.rotation,
+  cropFactor: humanConfig.face.detector.cropFactor,
+  ...matchOptions,
+};
+
+const ok = { // must meet all rules
+  faceCount: false,
+  faceConfidence: false,
+  facingCenter: false,
+  lookingCenter: false,
+  blinkDetected: false,
+  faceSize: false,
+  antispoofCheck: false,
+  livenessCheck: false,
+  elapsedMs: 0, // total time while waiting for valid face
+};
+const allOk = () => ok.faceCount && ok.faceSize && ok.blinkDetected && ok.facingCenter && ok.lookingCenter && ok.faceConfidence && ok.antispoofCheck && ok.livenessCheck;
+const current: { face: 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,
+  end: 0,
+  time: 0,
+};
+
+// let db: Array<{ name: string, source: string, embedding: number[] }> = []; // holds loaded face descriptor database
+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.draw.options.font = 'small-caps 18px "Lato"'; // set font used to draw labels when using draw methods
+human.draw.options.lineHeight = 20;
+
+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('fps') as HTMLPreElement,
+  match: document.getElementById('match') as HTMLDivElement,
+  name: document.getElementById('name') as HTMLInputElement,
+  save: document.getElementById('save') as HTMLSpanElement,
+  delete: document.getElementById('delete') as HTMLSpanElement,
+  retry: document.getElementById('retry') as HTMLDivElement,
+  source: document.getElementById('source') as HTMLCanvasElement,
+  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';
+  // 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;
+  dom.video.play();
+  await ready;
+  dom.canvas.width = dom.video.videoWidth;
+  dom.canvas.height = dom.video.videoHeight;
+  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) dom.video.play();
+    else dom.video.pause();
+  };
+}
+
+async function detectionLoop() { // main detection loop
+  if (!dom.video.paused) {
+    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();
+    fps.detect = 1000 / (now - timestamp.detect);
+    timestamp.detect = now;
+    requestAnimationFrame(detectionLoop); // start new frame immediately
+  }
+}
+
+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.innerText = key;
+      el.className = 'ok';
+      el.style.top = `${y}px`;
+      dom.ok.appendChild(el);
+    }
+    if (typeof val === 'boolean') el.style.backgroundColor = val ? 'lightgreen' : 'lightcoral';
+    else el.innerText = `${key}:${val}`;
+    y += 28;
+  }
+  if (allOk()) { // all criteria met
+    dom.video.pause();
+    return human.result.face[0];
+  }
+  if (ok.elapsedMs > options.maxTime) { // give up
+    dom.video.pause();
+    return human.result.face[0];
+  } else { // run again
+    ok.elapsedMs = Math.trunc(human.now() - startTime);
+    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() {
+  if (dom.name.value.length > 0) {
+    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);
+  } else {
+    log('invalid name');
+  }
+}
+
+async function deleteRecord() {
+  if (current.record && current.record.id > 0) {
+    await indexDb.remove(current.record);
+  }
+}
+
+async function detectFace() {
+  dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);
+  if (!current.face || !current.face.tensor || !current.face.embedding) return false;
+  // eslint-disable-next-line no-console
+  console.log('face record:', current.face);
+  human.tf.browser.toPixels(current.face.tensor as unknown as TensorLike, dom.canvas);
+  if (await indexDb.count() === 0) {
+    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);
+  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}%`);
+    dom.name.value = current.record.name;
+    dom.source.style.display = '';
+    dom.source.getContext('2d')?.putImageData(current.record.image, 0, 0);
+  }
+  document.body.style.background = res.similarity > options.threshold ? 'darkgreen' : 'maroon';
+  return res.similarity > options.threshold;
+}
+
+async function main() { // main entry point
+  ok.faceCount = false;
+  ok.faceConfidence = false;
+  ok.facingCenter = false;
+  ok.blinkDetected = false;
+  ok.faceSize = false;
+  ok.antispoofCheck = false;
+  ok.livenessCheck = false;
+  ok.elapsedMs = 0;
+  dom.match.style.display = 'none';
+  dom.retry.style.display = 'none';
+  dom.source.style.display = 'none';
+  document.body.style.background = 'black';
+  await webCam();
+  await detectionLoop(); // start detection loop
+  startTime = human.now();
+  current.face = await validationLoop(); // start validation loop
+  dom.canvas.width = current.face?.tensor?.shape[1] || options.minSize;
+  dom.canvas.height = current.face?.tensor?.shape[0] || options.minSize;
+  dom.source.width = dom.canvas.width;
+  dom.source.height = dom.canvas.height;
+  dom.canvas.style.width = '';
+  dom.match.style.display = 'flex';
+  dom.save.style.display = 'flex';
+  dom.delete.style.display = 'flex';
+  dom.retry.style.display = 'block';
+  if (!allOk()) { // is all criteria met?
+    log('did not find valid face');
+    return false;
+  } else {
+    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, ' '));
+  printFPS('loading...');
+  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);
+  await human.warmup(); // warmup function to initialize backend for future faster detection
+  await main();
+}
+
+window.onload = init;

demo/faceid/indexdb.ts

@@ -0,0 +1,66 @@
+let db: IDBDatabase; // instance of indexdb
+
+const database = 'human';
+const table = 'person';
+
+export type FaceRecord = { id: number, name: string, descriptor: number[], image: ImageData };
+
+// eslint-disable-next-line no-console
+const log = (...msg) => console.log('indexdb', ...msg);
+
+export async function open() {
+  if (db) return true;
+  return new Promise((resolve) => {
+    const request: IDBOpenDBRequest = indexedDB.open(database, 1);
+    request.onerror = (evt) => log('error:', evt);
+    request.onupgradeneeded = (evt: IDBVersionChangeEvent) => { // create if doesnt exist
+      log('create:', evt.target);
+      db = (evt.target as IDBOpenDBRequest).result;
+      db.createObjectStore(table, { keyPath: 'id', autoIncrement: true });
+    };
+    request.onsuccess = (evt) => { // open
+      db = (evt.target as IDBOpenDBRequest).result as IDBDatabase;
+      log('open:', db);
+      resolve(true);
+    };
+  });
+}
+
+export async function load(): Promise<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');
+    cursor.onerror = (evt) => log('load error:', evt);
+    cursor.onsuccess = (evt) => {
+      if ((evt.target as IDBRequest).result) {
+        faceDB.push((evt.target as IDBRequest).result.value);
+        (evt.target as IDBRequest).result.continue();
+      } else {
+        resolve(faceDB);
+      }
+    };
+  });
+}
+
+export async function count(): Promise<number> {
+  if (!db) await open(); // open or create if not already done
+  return new Promise((resolve) => {
+    const store: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).count();
+    store.onerror = (evt) => log('count error:', evt);
+    store.onsuccess = () => resolve(store.result);
+  });
+}
+
+export async function save(faceRecord: FaceRecord) {
+  if (!db) await open(); // open or create if not already done
+  const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; // omit id as its autoincrement
+  db.transaction([table], 'readwrite').objectStore(table).put(newRecord);
+  log('save:', newRecord);
+}
+
+export async function remove(faceRecord: FaceRecord) {
+  if (!db) await open(); // open or create if not already done
+  db.transaction([table], 'readwrite').objectStore(table).delete(faceRecord.id); // delete based on id
+  log('delete:', faceRecord);
+}

demo/facematch/README.md

@@ -0,0 +1,83 @@
+# Human Face Recognition & Matching
+
+- **Browser** demo: `index.html` & `facematch.js`:  
+  Loads sample images, extracts faces and runs match and similarity analysis  
+- **NodeJS** demo `node-match.js` and `node-match-worker.js`  
+  Advanced multithreading demo that runs number of worker threads to process high number of matches
+- Sample face database: `faces.json`
+
+<br>
+
+## Browser Face Recognition Demo
+
+- `demo/facematch`: Demo for Browsers that uses all face description and embedding features to  
+detect, extract and identify all faces plus calculate simmilarity between them
+
+It highlights functionality such as:
+
+- Loading images
+- Extracting faces from images
+- Calculating face embedding descriptors
+- Finding face similarity and sorting them by similarity
+- Finding best face match based on a known list of faces and printing matches
+
+<br>
+
+## NodeJS Multi-Threading Match Solution
+
+### Methods and Properties in `node-match`
+
+- `createBuffer`: create shared buffer array  
+  single copy of data regardless of number of workers  
+  fixed size based on `options.dbMax`
+- `appendRecord`: add additional batch of descriptors to buffer  
+  can append batch of records to buffer at anytime  
+  workers are informed of the new content after append has been completed  
+- `workersStart`: start or expand pool of `threadPoolSize` workers  
+  each worker runs `node-match-worker` and listens for messages from main thread  
+  can shutdown workers or create additional worker threads on-the-fly  
+  safe against workers that exit  
+- `workersClose`: close workers in a pool  
+  first request workers to exit then terminate after timeout
+- `match`: dispach a match job to a worker  
+  returns first match that satisfies `minThreshold`  
+  assigment to workers using round-robin  
+  since timing for each job is near-fixed and predictable  
+- `getDescriptor`: get descriptor array for a given id from a buffer
+- `fuzDescriptor`: small randomize descriptor content for harder match
+- `getLabel`: fetch label for resolved descriptor index  
+- `loadDB`: load face database from a JSON file `dbFile`  
+  extracts descriptors and adds them to buffer  
+  extracts labels and maintains them in main thread  
+  for test purposes loads same database `dbFact` times to create a very large database
+
+`node-match` runs in a listens for messages from workers until `maxJobs` have been reached
+
+### Performance
+
+Linear performance decrease that depends on number of records in database  
+Non-linear performance that increases with number of worker threads due to communication overhead
+
+- Face dataase with 10k records:
+  > threadPoolSize: 1 =>  ~60 ms / match job  
+  > threadPoolSize: 6 =>  ~25 ms / match job
+- Face database with 50k records:
+  > threadPoolSize: 1 => ~300 ms / match job  
+  > threadPoolSize: 6 => ~100 ms / match job
+- Face database with 100k records:
+  > threadPoolSize: 1 => ~600 ms / match job  
+  > threadPoolSize: 6 => ~200 ms / match job
+
+### Example
+
+> node node-match
+
+```js
+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 }
+2021-10-13 07:53:36 DATA:  created shared buffer: { maxDescriptors: 10000, totalBytes: 40960000, totalElements: 10240000 }
+2021-10-13 07:53:36 DATA:  db loaded: { existingRecords: 0, newRecords: 5700 }
+2021-10-13 07:53:36 INFO:  starting worker thread pool: { totalWorkers: 6, alreadyActive: 0 }
+2021-10-13 07:53:36 STATE: submitted: { matchJobs: 100, poolSize: 6, activeWorkers: 6 }
+2021-10-13 07:53:38 STATE: { matchJobsFinished: 100, totalTimeMs: 1769, averageTimeMs: 17.69 }
+2021-10-13 07:53:38 INFO:  closing workers: { poolSize: 6, activeWorkers: 6 }
+```

demo/facematch/facematch.js

@@ -0,0 +1,262 @@
+// @ts-nocheck
+/**
+ * Human demo for browsers
+ *
+ * Demo for face descriptor analysis and face simmilarity analysis
+ */
+
+/** @type {Human} */
+import Human from '../../dist/human.esm.js';
+
+const userConfig = {
+  backend: 'humangl',
+  async: true,
+  warmup: 'none',
+  cacheSensitivity: 0,
+  debug: true,
+  modelBasePath: '../../models/',
+  deallocate: true,
+  filter: {
+    enabled: true,
+    equalization: true,
+    width: 0,
+  },
+  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 },
+    emotion: { enabled: true },
+    description: { enabled: true },
+  },
+  hand: { enabled: false },
+  gesture: { enabled: false },
+  body: { enabled: false },
+  segmentation: { enabled: false },
+};
+
+const human = new Human(userConfig); // new instance of human
+
+const all = []; // array that will hold all detected faces
+let db = []; // array that holds all known faces
+
+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')}`;
+  // eslint-disable-next-line no-console
+  console.log(ts, ...msg);
+}
+
+function title(msg) {
+  document.getElementById('title').innerHTML = msg;
+}
+
+async function loadFaceMatchDB() {
+  // download db with known faces
+  try {
+    let res = await fetch('/demo/facematch/faces.json');
+    if (!res || !res.ok) res = await fetch('/human/demo/facematch/faces.json');
+    db = (res && res.ok) ? await res.json() : [];
+    log('Loaded Faces DB:', db);
+  } catch (err) {
+    log('Could not load faces database', err);
+  }
+}
+
+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 enhanced = human.enhance(face);
+    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(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 = `
+      source: ${face.fileName}<br>
+      match: ${Math.round(1000 * res.similarity) / 10}% ${db[res.index].name}<br>
+      score: ${Math.round(100 * face.boxScore)}% detection ${Math.round(100 * face.faceScore)}% analysis<br>
+      age: ${face.age} years<br>
+      gender: ${Math.round(100 * face.genderScore)}% ${face.gender}<br>
+      emotion: ${emotion}<br>
+    `;
+    embedding = face.embedding.map((a) => parseFloat(a.toFixed(4)));
+    navigator.clipboard.writeText(`{"name":"unknown", "source":"${face.fileName}", "embedding":[${embedding}]},`);
+  }
+
+  // loop through all canvases that contain faces
+  const canvases = document.getElementsByClassName('face');
+  let time = 0;
+  for (const canvas of canvases) {
+    // calculate similarity from selected face to current one in the loop
+    const current = all[canvas.tag.sample][canvas.tag.face];
+    const similarity = human.similarity(face.embedding, current.embedding);
+    canvas.tag.similarity = similarity;
+    // get best match
+    // draw the canvas
+    await human.tf.browser.toPixels(current.tensor, canvas);
+    const ctx = canvas.getContext('2d');
+    ctx.font = 'small-caps 1rem "Lato"';
+    ctx.fillStyle = 'rgba(0, 0, 0, 1)';
+    ctx.fillText(`${(100 * similarity).toFixed(1)}%`, 3, 23);
+    ctx.fillStyle = 'rgba(255, 255, 255, 1)';
+    ctx.fillText(`${(100 * similarity).toFixed(1)}%`, 4, 24);
+    ctx.font = 'small-caps 0.8rem "Lato"';
+    ctx.fillText(`${current.age}y ${(100 * (current.genderScore || 0)).toFixed(1)}% ${current.gender}`, 4, canvas.height - 6);
+    // identify person
+    ctx.font = 'small-caps 1rem "Lato"';
+    const start = human.now();
+    const arr = db.map((rec) => rec.embedding);
+    const res = await human.match(current.embedding, arr);
+    time += (human.now() - start);
+    if (res.similarity > minScore) ctx.fillText(`DB: ${(100 * res.similarity).toFixed(1)}% ${db[res.index].name}`, 4, canvas.height - 30);
+  }
+
+  log('Analyzed:', 'Face:', canvases.length, 'DB:', db.length, 'Time:', time);
+  // sort all faces by similarity
+  const sorted = document.getElementById('faces');
+  [...sorted.children]
+    .sort((a, b) => parseFloat(b.tag.similarity) - parseFloat(a.tag.similarity))
+    .forEach((canvas) => sorted.appendChild(canvas));
+  document.getElementById('orig').style.filter = 'blur(0)';
+  title('Selected Face');
+}
+
+async function AddFaceCanvas(index, res, fileName) {
+  all[index] = res.face;
+  for (const i in res.face) {
+    if (!res.face[i].tensor) continue; // did not get valid results
+    if ((res.face[i].faceScore || 0) < human.config.face.detector.minConfidence) continue; // face analysis score too low
+    all[index][i].fileName = fileName;
+    const canvas = document.createElement('canvas');
+    canvas.tag = { sample: index, face: i, source: fileName };
+    canvas.width = 200;
+    canvas.height = 200;
+    canvas.className = 'face';
+    const emotion = res.face[i].emotion[0] ? `${Math.round(100 * res.face[i].emotion[0].score)}% ${res.face[i].emotion[0].emotion}` : 'N/A';
+    canvas.title = `
+      source: ${res.face[i].fileName}
+      score: ${Math.round(100 * res.face[i].boxScore)}% detection ${Math.round(100 * res.face[i].faceScore)}% analysis
+      age: ${res.face[i].age} years
+      gender: ${Math.round(100 * res.face[i].genderScore)}% ${res.face[i].gender}
+      emotion: ${emotion}
+    `.replace(/  /g, ' ');
+    await human.tf.browser.toPixels(res.face[i].tensor, canvas);
+    const ctx = canvas.getContext('2d');
+    if (!ctx) return false;
+    ctx.font = 'small-caps 0.8rem "Lato"';
+    ctx.fillStyle = 'rgba(255, 255, 255, 1)';
+    ctx.fillText(`${res.face[i].age}y ${(100 * (res.face[i].genderScore || 0)).toFixed(1)}% ${res.face[i].gender}`, 4, canvas.height - 6);
+    const arr = db.map((rec) => rec.embedding);
+    const result = human.match(res.face[i].embedding, arr);
+    ctx.font = 'small-caps 1rem "Lato"';
+    if (result.similarity && res.similarity > minScore) ctx.fillText(`${(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]);
+    });
+  }
+}
+
+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).then((res) => {
+        AddFaceCanvas(index, res, image); // then wait until image is analyzed
+        resolve(true);
+      });
+    };
+    img.onerror = () => {
+      log('Add image error:', index + 1, image);
+      resolve(false);
+    };
+    img.title = image;
+    img.src = encodeURI(image);
+  });
+}
+
+function createFaceMatchDB() {
+  log('Creating Faces DB...');
+  for (const image of all) {
+    for (const face of image) db.push({ name: 'unknown', source: face.fileName, embedding: face.embedding });
+  }
+  log(db);
+}
+
+async function main() {
+  // pre-load human models
+  await human.load();
+
+  title('Loading Face Match Database');
+  let images = [];
+  let dir = [];
+  // load face descriptor database
+  await loadFaceMatchDB();
+
+  // enumerate all sample images in /assets
+  title('Enumerating Input Images');
+  const res = await fetch('/samples/in');
+  dir = (res && res.ok) ? await res.json() : [];
+  images = images.concat(dir.filter((img) => (img.endsWith('.jpg') && img.includes('sample'))));
+
+  // could not dynamically enumerate images so using static list
+  if (images.length === 0) {
+    images = [
+      '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',
+      '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',
+      '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',
+      'daz3d-lilah-03.jpg', 'daz3d-lila.jpg', 'daz3d-lindsey.jpg', 'daz3d-megah.jpg', 'daz3d-selina-01.jpg', 'daz3d-selina-02.jpg', 'daz3d-snow.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',
+      'daz3d-_emotions01.jpg', 'daz3d-_emotions02.jpg', 'daz3d-_emotions03.jpg', 'daz3d-_emotions04.jpg', 'daz3d-_emotions05.jpg',
+    ];
+    // add prefix for gitpages
+    images = images.map((a) => `/human/samples/in/${a}`);
+    log('Adding static image list:', images);
+  } else {
+    log('Discovered images:', images);
+  }
+
+  // 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);
+  const t1 = human.now();
+
+  // print stats
+  const num = all.reduce((prev, cur) => prev += cur.length, 0);
+  log('Extracted faces:', num, 'from images:', all.length, 'time:', Math.round(t1 - t0));
+  log(human.tf.engine().memory());
+
+  // if we didn't download db, generate it from current faces
+  if (!db || db.length === 0) createFaceMatchDB();
+
+  title('');
+  log('Ready');
+  human.validate(userConfig);
+  human.similarity([], []);
+}
+
+window.onload = main;

demo/facematch/index.html

@@ -0,0 +1,50 @@
+<!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="./facematch.js" type="module"></script>
+    <style>
+      img { object-fit: contain; }
+      @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; min-height: 100%; max-height: 100%; }
+      ::-webkit-scrollbar { height: 8px; border: 0; border-radius: 0; }
+      ::-webkit-scrollbar-thumb { background: grey }
+      ::-webkit-scrollbar-track { margin: 3px; }
+      .orig { width: 200px; height: 200px; padding-bottom: 20px; filter: blur(16px); transition : all 0.5s ease; }
+      .text { margin: 24px; }
+      .face { width: 128px; height: 128px; margin: 2px; padding: 2px; cursor: grab; transform: scale(1.00); transition : all 0.3s ease; }
+      .face:hover { filter: grayscale(1); transform: scale(1.08); transition : all 0.3s ease; }   
+    </style>
+  </head>
+  <body>
+    <div style="display: block">
+      <div style="display: flex">
+        <div style="min-width: 400px">
+          <div class="text" id="title"></div>
+          <canvas id="orig" class="orig"></canvas>
+          <div id="desc" style="font-size: 0.8rem; text-align: left;"></div>
+        </div>
+        <div style="width: 20px"></div>
+        <div>
+          <div class="text">Input Images</div>
+          <div id="images" style="display: flex; width: 60vw; overflow-x: auto; overflow-y: hidden; scroll-behavior: smooth"></div>
+        </div>
+      </div>
+      <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>
+  </body>
+</html>

demo/facematch/node-match-worker.js

@@ -0,0 +1,70 @@
+const threads = require('worker_threads');
+
+let debug = false;
+
+/** @type SharedArrayBuffer */
+let buffer;
+/** @type Float32Array */
+let view;
+let threshold = 0;
+let records = 0;
+
+const descLength = 1024; // descriptor length in bytes
+
+function distance(descBuffer, index, options = { order: 2, multiplier: 20 }) {
+  const descriptor = new Float32Array(descBuffer);
+  let sum = 0;
+  for (let i = 0; i < descriptor.length; i++) {
+    const diff = (options.order === 2) ? (descriptor[i] - view[index * descLength + i]) : (Math.abs(descriptor[i] - view[index * descLength + i]));
+    sum += (options.order === 2) ? (diff * diff) : (diff ** options.order);
+  }
+  return (options.multiplier || 20) * sum;
+}
+
+function match(descBuffer, options = { order: 2, multiplier: 20 }) {
+  let best = Number.MAX_SAFE_INTEGER;
+  let index = -1;
+  for (let i = 0; i < records; i++) {
+    const res = distance(descBuffer, i, { order: options.order, multiplier: options.multiplier });
+    if (res < best) {
+      best = res;
+      index = i;
+    }
+    if (best < threshold || best === 0) break; // short circuit
+  }
+  best = (options.order === 2) ? Math.sqrt(best) : best ** (1 / options.order);
+  return { index, distance: best, similarity: Math.max(0, 100 - best) / 100.0 };
+}
+
+threads.parentPort?.on('message', (msg) => {
+  if (typeof msg.descriptor !== 'undefined') { // actual work order to find a match
+    const t0 = performance.now();
+    const result = match(msg.descriptor);
+    const t1 = performance.now();
+    threads.parentPort?.postMessage({ request: msg.request, time: Math.trunc(t1 - t0), ...result });
+    return; // short circuit
+  }
+  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 (typeof msg.records !== 'undefined') { // recived every time when number of records changes
+    records = msg.records;
+    if (debug) threads.parentPort?.postMessage(`records: ${records}`);
+  }
+  if (typeof msg.debug !== 'undefined') { // set verbose logging
+    debug = msg.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 (typeof msg.shutdown !== 'undefined') { // got message to close worker
+    if (debug) threads.parentPort?.postMessage('shutting down');
+    process.exit(0);
+  }
+});
+
+if (debug) threads.parentPort?.postMessage('started');

demo/facematch/node-match.js

@@ -0,0 +1,178 @@
+const fs = require('fs');
+const path = require('path');
+const log = require('@vladmandic/pilogger');
+const threads = require('worker_threads');
+
+// global optinos
+const options = {
+  dbFile: 'demo/facematch/faces.json', // sample face db
+  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: 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
+};
+
+// test options
+const testOptions = {
+  dbFact: 175, // load db n times to fake huge size
+  maxJobs: 200, // exit after processing this many jobs
+  fuzDescriptors: true, // randomize descriptor content before match for harder jobs
+};
+
+// global data structures
+const data = {
+  /** @type string[] */
+  labels: [], // array of strings, length of array serves as overal number of records so has to be maintained carefully
+  /** @type SharedArrayBuffer | null */
+  buffer: null,
+  /** @type Float32Array | null */
+  view: null,
+  /** @type threads.Worker[] */
+  workers: [], // holds instance of workers. worker can be null if exited
+  requestID: 0, // each request should increment this counter as its used for round robin assignment
+};
+
+let t0 = process.hrtime.bigint(); // used for perf counters
+
+const appendRecords = (labels, descriptors) => {
+  if (!data.view) return 0;
+  if (descriptors.length !== labels.length) {
+    log.error('append error:', { descriptors: descriptors.length, labels: labels.length });
+  }
+  // if (options.debug) log.state('appending:', { descriptors: descriptors.length, labels: labels.length });
+  for (let i = 0; i < descriptors.length; i++) {
+    for (let j = 0; j < descriptors[i].length; j++) {
+      data.view[data.labels.length * descriptors[i].length + j] = descriptors[i][j]; // add each descriptors element to buffer
+    }
+    data.labels.push(labels[i]); // finally add to labels
+  }
+  for (const worker of data.workers) { // inform all workers how many records we have
+    if (worker) worker.postMessage({ records: data.labels.length });
+  }
+  return data.labels.length;
+};
+
+const getLabel = (index) => data.labels[index];
+
+const getDescriptor = (index) => {
+  if (!data.view) return [];
+  const descriptor = [];
+  for (let i = 0; i < 1024; i++) descriptor.push(data.view[index * options.descLength + i]);
+  return descriptor;
+};
+
+const fuzDescriptor = (descriptor) => {
+  for (let i = 0; i < descriptor.length; i++) descriptor[i] += Math.random() - 0.5;
+  return descriptor;
+};
+
+const delay = (ms) => new Promise((resolve) => { setTimeout(resolve, ms); });
+
+async function workersClose() {
+  const current = data.workers.filter((worker) => !!worker).length;
+  log.info('closing workers:', { poolSize: data.workers.length, activeWorkers: current });
+  for (const worker of data.workers) {
+    if (worker) worker.postMessage({ shutdown: true }); // tell worker to exit
+  }
+  await delay(250); // wait a little for threads to exit on their own
+  const remaining = data.workers.filter((worker) => !!worker).length;
+  if (remaining > 0) {
+    log.info('terminating remaining workers:', { remaining: current, pool: data.workers.length });
+    for (const worker of data.workers) {
+      if (worker) worker.terminate(); // if worker did not exit cleany terminate it
+    }
+  }
+}
+
+const workerMessage = (index, msg) => {
+  if (msg.request) {
+    if (options.debug) log.data('message:', { worker: index, request: msg.request, time: msg.time, label: getLabel(msg.index), similarity: msg.similarity });
+    if (msg.request >= testOptions.maxJobs) {
+      const t1 = process.hrtime.bigint();
+      const elapsed = Math.round(Number(t1 - t0) / 1000 / 1000);
+      log.state({ matchJobsFinished: testOptions.maxJobs, totalTimeMs: elapsed, averageTimeMs: Math.round(100 * elapsed / testOptions.maxJobs) / 100 });
+      workersClose();
+    }
+  } else {
+    log.data('message:', { worker: index, msg });
+  }
+};
+
+async function workerClose(id, code) {
+  const previous = data.workers.filter((worker) => !!worker).length;
+  delete data.workers[id];
+  const current = data.workers.filter((worker) => !!worker).length;
+  if (options.debug) log.state('worker exit:', { id, code, previous, current });
+}
+
+async function workersStart(numWorkers) {
+  const previous = data.workers.filter((worker) => !!worker).length;
+  log.info('starting worker thread pool:', { totalWorkers: numWorkers, alreadyActive: previous });
+  for (let i = 0; i < numWorkers; i++) {
+    if (!data.workers[i]) { // worker does not exist, so create it
+      const worker = new threads.Worker(path.join(__dirname, options.workerSrc));
+      worker.on('message', (msg) => workerMessage(i, msg));
+      worker.on('error', (err) => log.error('worker error:', { err }));
+      worker.on('exit', (code) => workerClose(i, code));
+      worker.postMessage(data.buffer); // send buffer to worker
+      data.workers[i] = worker;
+    }
+    data.workers[i]?.postMessage({ records: data.labels.length, threshold: options.minThreshold, debug: options.debug }); // inform worker how many records there are
+  }
+  await delay(100); // just wait a bit for everything to settle down
+}
+
+const match = (descriptor) => {
+  // const arr = Float32Array.from(descriptor);
+  const buffer = new ArrayBuffer(options.descLength * 4);
+  const view = new Float32Array(buffer);
+  view.set(descriptor);
+  const available = data.workers.filter((worker) => !!worker).length; // find number of available workers
+  if (available > 0) data.workers[data.requestID % available].postMessage({ descriptor: buffer, request: data.requestID }, [buffer]); // round robin to first available worker
+  else log.error('no available workers');
+};
+
+async function loadDB(count) {
+  const previous = data.labels.length;
+  if (!fs.existsSync(options.dbFile)) {
+    log.error('db file does not exist:', options.dbFile);
+    return;
+  }
+  t0 = process.hrtime.bigint();
+  for (let i = 0; i < count; i++) { // test loop: load entire face db from array of objects n times into buffer
+    const db = JSON.parse(fs.readFileSync(options.dbFile).toString());
+    const names = db.map((record) => record.name);
+    const descriptors = db.map((record) => record.embedding);
+    appendRecords(names, descriptors);
+  }
+  log.data('db loaded:', { existingRecords: previous, newRecords: data.labels.length });
+}
+
+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 });
+}
+
+async function main() {
+  log.header();
+  log.info('options:', options);
+
+  await createBuffer(); // create shared buffer array
+  await loadDB(testOptions.dbFact); // loadDB is a test method that calls actual addRecords
+  await workersStart(options.threadPoolSize); // can be called at anytime to modify worker pool size
+  for (let i = 0; i < testOptions.maxJobs; i++) {
+    const idx = Math.trunc(data.labels.length * Math.random()); // grab a random descriptor index that we'll search for
+    const descriptor = getDescriptor(idx); // grab a descriptor at index
+    data.requestID++; // increase request id
+    if (testOptions.fuzDescriptors) match(fuzDescriptor(descriptor)); // fuz descriptor for harder match
+    else match(descriptor);
+    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 });
+}
+
+main();

demo/helpers/README.md

@@ -0,0 +1,3 @@
+# Helper libraries
+
+Used by main `Human` demo app

demo/helpers/gl-bench.js

@@ -0,0 +1,277 @@
+// @ts-nocheck
+// based on: https://github.com/munrocket/gl-bench
+
+const UICSS = `
+  #gl-bench { position: absolute; right: 1rem; bottom: 1rem; z-index:1000; -webkit-user-select: none; -moz-user-select: none; user-select: none; }
+  #gl-bench div { position: relative; display: block; margin: 4px; padding: 0 2px 0 2px; background: #303030; border-radius: 0.1rem; cursor: pointer; opacity: 0.9; }
+  #gl-bench svg { height: 60px; margin: 0 0px 0px 4px; }
+  #gl-bench text { font-size: 16px; font-family: 'Lato', 'Segoe UI'; dominant-baseline: middle; text-anchor: middle; }
+  #gl-bench .gl-mem { font-size: 12px; fill: white; }
+  #gl-bench .gl-fps { font-size: 13px; fill: white; }
+  #gl-bench line { stroke-width: 5; stroke: white; stroke-linecap: round; }
+  #gl-bench polyline { fill: none; stroke: white; stroke-linecap: round; stroke-linejoin: round; stroke-width: 3.5; }
+  #gl-bench rect { fill: black; }
+  #gl-bench .opacity { stroke: black; }
+  `;
+
+const UISVG = `
+  <div class="gl-box">
+    <svg viewBox="0 0 60 60">
+      <text x="27" y="56" class="gl-fps">00 FPS</text>
+      <text x="30" y="8" class="gl-mem"></text>
+      <rect x="0" y="14" rx="4" ry="4" width="60" height="32"></rect>
+      <polyline class="gl-chart"></polyline>
+    </svg>
+    <svg viewBox="0 0 14 60" class="gl-cpu-svg">
+      <line x1="7" y1="38" x2="7" y2="11" class="opacity"/>
+      <line x1="7" y1="38" x2="7" y2="11" class="gl-cpu" stroke-dasharray="0 27"/>
+      <path d="M5.35 43c-.464 0-.812.377-.812.812v1.16c-.783.1972-1.421.812-1.595 1.624h-1.16c-.435 0-.812.348-.812.812s.348.812.812.812h1.102v1.653H1.812c-.464 0-.812.377-.812.812 0 .464.377.812.812.812h1.131c.1943.783.812 1.392 1.595 1.595v1.131c0 .464.377.812.812.812.464 0 .812-.377.812-.812V53.15h1.653v1.073c0 .464.377.812.812.812.464 0 .812-.377.812-.812v-1.131c.783-.1943 1.392-.812 1.595-1.595h1.131c.464 0 .812-.377.812-.812 0-.464-.377-.812-.812-.812h-1.073V48.22h1.102c.435 0 .812-.348.812-.812s-.348-.812-.812-.812h-1.16c-.1885-.783-.812-1.421-1.595-1.624v-1.131c0-.464-.377-.812-.812-.812-.464 0-.812.377-.812.812v1.073H6.162v-1.073c0-.464-.377-.812-.812-.812zm.58 3.48h2.088c.754 0 1.363.609 1.363 1.363v2.088c0 .754-.609 1.363-1.363 1.363H5.93c-.754 0-1.363-.609-1.363-1.363v-2.088c0-.754.609-1.363 1.363-1.363z" style="fill: grey"></path>
+    </svg>
+    <svg viewBox="0 0 14 60" class="gl-gpu-svg">
+      <line x1="7" y1="38" x2="7" y2="11" class="opacity"/>
+      <line x1="7" y1="38" x2="7" y2="11" class="gl-gpu" stroke-dasharray="0 27"/>
+      <path d="M1.94775 43.3772a.736.736 0 10-.00416 1.472c.58535.00231.56465.1288.6348.3197.07015.18975.04933.43585.04933.43585l-.00653.05405v8.671a.736.736 0 101.472 0v-1.4145c.253.09522.52785.1495.81765.1495h5.267c1.2535 0 2.254-.9752 2.254-2.185v-3.105c0-1.2075-1.00625-2.185-2.254-2.185h-5.267c-.28865 0-.5635.05405-.8165.1495.01806-.16445.04209-.598-.1357-1.0787-.22425-.6072-.9499-1.2765-2.0125-1.2765zm2.9095 3.6455c.42435 0 .7659.36225.7659.8119v2.9785c0 .44965-.34155.8119-.7659.8119s-.7659-.36225-.7659-.8119v-2.9785c0-.44965.34155-.8119.7659-.8119zm4.117 0a2.3 2.3 0 012.3 2.3 2.3 2.3 0 01-2.3 2.3 2.3 2.3 0 01-2.3-2.3 2.3 2.3 0 012.3-2.3z" style="fill: grey"></path>
+    </svg>
+  </div>
+  `;
+
+class GLBench {
+  /** GLBench constructor
+   * @param { WebGLRenderingContext | WebGL2RenderingContext | null } gl context
+   * @param { Object | undefined } settings additional settings
+   */
+  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;
+
+    this.names = [];
+    this.cpuAccums = [];
+    this.gpuAccums = [];
+    this.activeAccums = [];
+    this.chart = new Array(this.chartLen);
+    this.now = () => ((performance && performance.now) ? performance.now() : Date.now());
+    this.updateUI = () => {
+      [].forEach.call(this.nodes['gl-gpu-svg'], (node) => node.style.display = this.trackGPU ? 'inline' : 'none');
+    };
+
+    Object.assign(this, settings);
+    this.detected = 0;
+    this.finished = [];
+    this.isFramebuffer = 0;
+    this.frameId = 0;
+
+    // 120hz device detection
+    let rafId; let n = 0; let
+      t0;
+    const loop = (t) => {
+      if (++n < 20) {
+        rafId = requestAnimationFrame(loop);
+      } else {
+        this.detected = Math.ceil(1e3 * n / (t - t0) / 70);
+        cancelAnimationFrame(rafId);
+      }
+      if (!t0) t0 = t;
+    };
+    requestAnimationFrame(loop);
+
+    // attach gpu profilers
+    if (gl) {
+      const glFinish = async (t, activeAccums) => Promise.resolve(setTimeout(() => {
+        gl.getError();
+        const dt = this.now() - t;
+        activeAccums.forEach((active, i) => {
+          if (active) this.gpuAccums[i] += dt;
+        });
+      }, 0));
+
+      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)));
+      };
+
+      /* ['drawArrays', 'drawElements', 'drawArraysInstanced', 'drawBuffers', 'drawElementsInstanced', 'drawRangeElements'].forEach((fn) => {
+        if (gl[fn]) {
+          gl[fn] = addProfiler(gl[fn], this, gl);
+        }
+      });
+      */
+      const fn = 'drawElements';
+      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) => {
+            if (ext[fn2]) {
+              ext[fn2] = addProfiler(ext[fn2], self, ext);
+            }
+          });
+        }
+        return ext;
+      })(gl.getExtension, this);
+      */
+    }
+
+    // init ui and ui loggers
+    if (!this.withoutUI) {
+      if (!this.dom) this.dom = document.body;
+      const elm = document.createElement('div');
+      elm.id = 'gl-bench';
+      this.dom.appendChild(elm);
+      this.dom.insertAdjacentHTML('afterbegin', '<style id="gl-bench-style">' + this.css + '</style>');
+      this.dom = elm;
+      this.dom.addEventListener('click', () => {
+        this.trackGPU = !this.trackGPU;
+        this.updateUI();
+      });
+
+      this.paramLogger = ((logger, dom, names) => {
+        const classes = ['gl-cpu', 'gl-gpu', 'gl-mem', 'gl-fps', 'gl-gpu-svg', 'gl-chart'];
+        const nodes = { ...classes };
+        classes.forEach((c) => nodes[c] = dom.getElementsByClassName(c));
+        this.nodes = nodes;
+        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);
+        };
+      })(this.paramLogger, this.dom, this.names);
+
+      this.chartLogger = ((logger, dom) => {
+        const nodes = { 'gl-chart': dom.getElementsByClassName('gl-chart') };
+        return (i, chart, circularId) => {
+          let points = '';
+          const len = chart.length;
+          for (let j = 0; j < len; j++) {
+            const id = (circularId + j + 1) % len;
+            if (chart[id] !== undefined) points = points + ' ' + (60 * j / (len - 1)).toFixed(1) + ',' + (45 - chart[id] * 0.5 / this.detected).toFixed(1);
+          }
+          nodes['gl-chart'][i].setAttribute('points', points);
+          logger(this.names[i], chart, circularId);
+        };
+      })(this.chartLogger, this.dom);
+    }
+  }
+
+  /**
+   * Explicit UI add
+   * @param { string | undefined } name
+   */
+  addUI(name) {
+    if (this.names.indexOf(name) === -1) {
+      this.names.push(name);
+      if (this.dom) {
+        this.dom.insertAdjacentHTML('beforeend', this.svg);
+        this.updateUI();
+      }
+      this.cpuAccums.push(0);
+      this.gpuAccums.push(0);
+      this.activeAccums.push(false);
+    }
+  }
+
+  /**
+   * Increase frameID
+   * @param { number | undefined } now
+   */
+  nextFrame(now) {
+    this.frameId++;
+    const t = now || this.now();
+
+    // params
+    if (this.frameId <= 1) {
+      this.paramFrame = this.frameId;
+      this.paramTime = t;
+    } else {
+      const duration = t - this.paramTime;
+      if (duration >= 1e3) {
+        const frameCount = this.frameId - this.paramFrame;
+        const fps = frameCount / duration * 1e3;
+        for (let i = 0; i < this.names.length; i++) {
+          const cpu = this.cpuAccums[i] / duration * 100;
+          const gpu = this.gpuAccums[i] / duration * 100;
+          const mem = (performance && performance.memory) ? performance.memory.usedJSHeapSize / (1 << 20) : 0;
+          this.paramLogger(i, cpu, gpu, mem, fps, duration, frameCount);
+          this.cpuAccums[i] = 0;
+          Promise.all(this.finished).then(() => {
+            this.gpuAccums[i] = 0;
+            this.finished = [];
+          });
+        }
+        this.paramFrame = this.frameId;
+        this.paramTime = t;
+      }
+    }
+
+    // chart
+    if (!this.detected || !this.chartFrame) {
+      this.chartFrame = this.frameId;
+      this.chartTime = t;
+      this.circularId = 0;
+    } else {
+      const timespan = t - this.chartTime;
+      let hz = this.chartHz * timespan / 1e3;
+      while (--hz > 0 && this.detected) {
+        const frameCount = this.frameId - this.chartFrame;
+        const fps = frameCount / timespan * 1e3;
+        this.chart[this.circularId % this.chartLen] = fps;
+        for (let i = 0; i < this.names.length; i++) this.chartLogger(i, this.chart, this.circularId);
+        this.circularId++;
+        this.chartFrame = this.frameId;
+        this.chartTime = t;
+      }
+    }
+  }
+
+  /**
+   * Begin named measurement
+   * @param { string | undefined } name
+   */
+  begin(name) {
+    this.updateAccums(name);
+  }
+
+  /**
+   * End named measure
+   * @param { string | undefined } name
+   */
+  end(name) {
+    this.updateAccums(name);
+  }
+
+  updateAccums(name) {
+    let nameId = this.names.indexOf(name);
+    if (nameId === -1) {
+      nameId = this.names.length;
+      this.addUI(name);
+    }
+
+    const t = this.now();
+    const dt = t - this.t0;
+    for (let i = 0; i < nameId + 1; i++) {
+      if (this.activeAccums[i]) this.cpuAccums[i] += dt;
+    }
+    this.activeAccums[nameId] = !this.activeAccums[nameId];
+    this.t0 = t;
+  }
+}
+
+export default GLBench;

demo/helpers/jsonview.js

@@ -0,0 +1,161 @@
+let callbackFunction = null;
+
+function createElement(type, config) {
+  const htmlElement = document.createElement(type);
+  if (config === undefined) return htmlElement;
+  if (config.className) htmlElement.className = config.className;
+  if (config.content) htmlElement.textContent = config.content;
+  if (config.style) htmlElement.style = config.style;
+  if (config.children) config.children.forEach((el) => !el || htmlElement.appendChild(el));
+  return htmlElement;
+}
+
+function createExpandedElement(node) {
+  const iElem = createElement('i');
+  if (node.expanded) { iElem.className = 'fas fa-caret-down'; } else { iElem.className = 'fas fa-caret-right'; }
+  const caretElem = createElement('div', { style: 'width: 18px; text-align: center; cursor: pointer', children: [iElem] });
+  const handleClick = node.toggle.bind(node);
+  caretElem.addEventListener('click', handleClick);
+  const indexElem = createElement('div', { className: 'json json-index', content: node.key });
+  indexElem.addEventListener('click', handleClick);
+  const typeElem = createElement('div', { className: 'json json-type', content: node.type });
+  const keyElem = createElement('div', { className: 'json json-key', content: node.key });
+  keyElem.addEventListener('click', handleClick);
+  const sizeElem = createElement('div', { className: 'json json-size' });
+  sizeElem.addEventListener('click', handleClick);
+  if (node.type === 'array') {
+    sizeElem.innerText = `[${node.children.length} items]`;
+  } else if (node.type === 'object') {
+    const size = node.children.find((item) => item.key === 'size');
+    sizeElem.innerText = size ? `{${size.value.toLocaleString()} bytes}` : `{${node.children.length} properties}`;
+  }
+  let lineChildren;
+  if (node.key === null) lineChildren = [caretElem, typeElem, sizeElem];
+  else if (node.parent.type === 'array') lineChildren = [caretElem, indexElem, sizeElem];
+  else lineChildren = [caretElem, keyElem, sizeElem];
+  const lineElem = createElement('div', { className: 'json-line', children: lineChildren });
+  if (node.depth > 0) lineElem.style = `margin-left: ${node.depth * 24}px;`;
+  return lineElem;
+}
+
+function createNotExpandedElement(node) {
+  const caretElem = createElement('div', { style: 'width: 18px' });
+  const keyElem = createElement('div', { className: 'json json-key', content: node.key });
+  const separatorElement = createElement('div', { className: 'json-separator', content: ':' });
+  const valueType = ` json-${typeof node.value}`;
+  const valueContent = node.value.toLocaleString();
+  const valueElement = createElement('div', { className: `json json-value${valueType}`, content: valueContent });
+  const lineElem = createElement('div', { className: 'json-line', children: [caretElem, keyElem, separatorElement, valueElement] });
+  if (node.depth > 0) lineElem.style = `margin-left: ${node.depth * 24}px;`;
+  return lineElem;
+}
+
+function createNode() {
+  return {
+    key: '',
+    parent: {},
+    value: null,
+    expanded: false,
+    type: '',
+    children: [],
+    elem: {},
+    depth: 0,
+
+    hideChildren() {
+      if (Array.isArray(this.children)) {
+        this.children.forEach((item) => {
+          // @ts-ignore
+          item['elem']['classList'].add('hide');
+          // @ts-ignore
+          if (item['expanded']) item.hideChildren();
+        });
+      }
+    },
+    showChildren() {
+      if (Array.isArray(this.children)) {
+        this.children.forEach((item) => {
+          // @ts-ignore
+          item['elem']['classList'].remove('hide');
+          // @ts-ignore
+          if (item['expanded']) item.showChildren();
+        });
+      }
+    },
+    toggle() {
+      if (this.expanded) {
+        this.hideChildren();
+        const icon = this.elem?.querySelector('.fas');
+        icon.classList.replace('fa-caret-down', 'fa-caret-right');
+        if (callbackFunction !== null) callbackFunction(null);
+      } else {
+        this.showChildren();
+        const icon = this.elem?.querySelector('.fas');
+        icon.classList.replace('fa-caret-right', 'fa-caret-down');
+        if (this.type === 'object') {
+          if (callbackFunction !== null) callbackFunction(`${this.parent?.key}/${this.key}`);
+        }
+      }
+      this.expanded = !this.expanded;
+    },
+  };
+}
+
+function getType(val) {
+  let type
+  if (Array.isArray(val)) type = 'array';
+  else if (val === null) type = 'null';
+  else type = typeof val;
+  return type;
+}
+
+function traverseObject(obj, parent, filter) {
+  for (const key in obj) {
+    const child = createNode();
+    child.parent = parent;
+    child.key = key;
+    child.type = getType(obj[key]);
+    child.depth = parent.depth + 1;
+    child.expanded = false;
+    if (Array.isArray(filter)) {
+      for (const filtered of filter) {
+        if (key === filtered) return;
+      }
+    }
+    if (typeof obj[key] === 'object') {
+      child.children = [];
+      parent.children.push(child);
+      traverseObject(obj[key], child, filter);
+      child.elem = createExpandedElement(child);
+    } else {
+      child.value = obj[key];
+      child.elem = createNotExpandedElement(child);
+      parent.children.push(child);
+    }
+  }
+}
+
+function createTree(obj, title, filter) {
+  const tree = createNode();
+  tree.type = title;
+  tree.key = title;
+  tree.children = [];
+  tree.expanded = true;
+  traverseObject(obj, tree, filter);
+  tree.elem = createExpandedElement(tree);
+  return tree;
+}
+
+function traverseTree(node, callback) {
+  callback(node);
+  if (node.children !== null) node.children.forEach((item) => traverseTree(item, callback));
+}
+
+async function jsonView(json, element, title = '', filter = []) {
+  const tree = createTree(json, title, filter);
+  traverseTree(tree, (node) => {
+    if (!node.expanded) node.hideChildren();
+    element.appendChild(node.elem);
+  });
+}
+
+export default jsonView;

demo/helpers/menu.js

@@ -0,0 +1,333 @@
+let instance = 0;
+let CSScreated = false;
+
+let theme = {
+  background: '#303030',
+  hover: '#505050',
+  itemBackground: 'black',
+  itemColor: 'white',
+  buttonBackground: 'lightblue',
+  buttonHover: 'lightgreen',
+  checkboxOn: 'lightgreen',
+  checkboxOff: 'lightcoral',
+  rangeBackground: 'lightblue',
+  rangeLabel: 'white',
+  chartColor: 'lightblue',
+};
+
+function createCSS() {
+  if (CSScreated) return;
+  const css = `
+  :root { --rounded: 0.1rem; }
+  .menu { position: absolute; top: 0rem; right: 0; min-width: 180px; width: max-content; padding: 0.2rem 0.8rem 0 0.8rem; line-height: 1.8rem; z-index: 10; background: ${theme.background}; border: none }
+  .button { text-shadow: none; }
+
+  .menu-container { display: block; max-height: 100vh; }
+  .menu-container-fadeout { max-height: 0; overflow: hidden; transition: max-height, 0.5s ease; }
+  .menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; }
+  .menu-item { display: flex; white-space: nowrap; padding: 0.2rem; cursor: default; width: 100%; }
+  .menu-item:hover { background: ${theme.hover} }
+  .menu-title { cursor: pointer; }
+  .menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }
+  .menu-label { padding: 0; font-weight: 800; }
+
+  .menu-list { margin-right: 0.8rem; }
+  select:focus { outline: none; }
+  .menu-list-item { background: ${theme.itemBackground}; color: ${theme.itemColor}; border: none; padding: 0.2rem; font-family: inherit;
+    font-variant: inherit; border-radius: var(--rounded); font-weight: 800; }
+
+  .menu-chart-title { padding: 0; font-size: 0.8rem; font-weight: 800; align-items: center}
+  .menu-chart-canvas { background: transparent; margin: 0.2rem 0 0.2rem 0.6rem; }
+  
+  .menu-button { border: 0; background: ${theme.buttonBackground}; width: -webkit-fill-available; padding: 8px; margin: 8px; cursor: pointer;
+    border-radius: var(--rounded); justify-content: center; font-family: inherit; font-variant: inherit; font-size: 1rem; font-weight: 800; }
+  .menu-button:hover { background: ${theme.buttonHover}; box-shadow: 4px 4px 4px 0 black; }
+  .menu-button:focus { outline: none; }
+
+  .menu-checkbox { width: 2.6rem; height: 1rem; background: ${theme.itemBackground}; margin: 0.5rem 1.0rem 0 0; position: relative; border-radius: var(--rounded); }
+  .menu-checkbox:after { content: 'OFF'; color: ${theme.checkboxOff}; position: absolute; right: 0.2rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }
+  .menu-checkbox:before { content: 'ON'; color: ${theme.checkboxOn}; position: absolute; left: 0.3rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }
+  .menu-checkbox-label { width: 1.3rem; height: 1rem; cursor: pointer; position: absolute; top: 0; left: 0rem; z-index: 1; background: ${theme.checkboxOff};
+    border-radius: var(--rounded); transition: left 0.6s ease; }
+
+  input[type=checkbox] { visibility: hidden; }
+  input[type=checkbox]:checked + label { left: 1.4rem; background: ${theme.checkboxOn}; }
+
+  .menu-range { margin: 0.2rem 1.0rem 0 0; width: 5rem; background: transparent; color: ${theme.rangeBackground}; }
+  .menu-range:before { color: ${theme.rangeLabel}; margin: 0 0.4rem 0 0; font-weight: 800; font-size: 0.6rem; position: relative; top: 0.3rem; content: attr(value); }
+
+  input[type=range] { -webkit-appearance: none; }
+  input[type=range]::-webkit-slider-runnable-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }
+  input[type=range]::-moz-range-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }
+  input[type=range]::-webkit-slider-thumb { border: 1px solid #000000; margin-top: 0; height: 1rem; width: 0.6rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }
+  input[type=range]::-moz-range-thumb { border: 1px solid #000000; margin-top: 0rem; height: 1rem; width: 0.6rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }
+
+  .svg-background { fill:#303030; cursor:pointer; opacity: 0.6; }
+  .svg-foreground { fill:white; cursor:pointer; opacity: 0.8; }
+  `;
+  const el = document.createElement('style');
+  el.innerHTML = css;
+  document.getElementsByTagName('head')[0].appendChild(el);
+  CSScreated = true;
+}
+
+class Menu {
+  constructor(parent, title, position, userTheme) {
+    if (userTheme) theme = { ...theme, ...userTheme };
+    createCSS();
+    this.createMenu(parent, title, position);
+    this.id = 0;
+    this.instance = instance;
+    instance++;
+    this._maxFPS = 0;
+    this.hidden = false;
+  }
+
+  createMenu(parent, title = '', position = { top: null, left: null, bottom: null, right: null }) {
+    /** @type {HTMLDivElement} */
+    this.menu = document.createElement('div');
+    this.menu.id = `menu-${instance}`;
+    this.menu.className = 'menu';
+    if (position) {
+      if (position.top) this.menu.style.top = `${position.top}`;
+      if (position.bottom) this.menu.style.bottom = `${position.bottom}`;
+      if (position.left) this.menu.style.left = `${position.left}`;
+      if (position.right) this.menu.style.right = `${position.right}`;
+    }
+
+    this.container = document.createElement('div');
+    this.container.id = `menu-container-${instance}`;
+    this.container.className = 'menu-container menu-container-fadein';
+
+    // set menu title with pulldown arrow
+    const elTitle = document.createElement('div');
+    elTitle.className = 'menu-title';
+    elTitle.id = `menu-title-${instance}`;
+    const svg = `<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" style="width: 2rem; height: 2rem; vertical-align: top;">
+        <path d="M400 32H48A48 48 0 0 0 0 80v352a48 48 0 0 0 48 48h352a48 48 0 0 0 48-48V80a48 48 0 0 0-48-48zm-51.37 182.31L232.06 348.16a10.38 10.38 0 0 1-16.12 0L99.37 214.31C92.17 206 97.28 192 107.43 192h233.14c10.15 0 15.26 14 8.06 22.31z" class="svg-background"/>
+        <path d="M348.63 214.31L232.06 348.16a10.38 10.38 0 0 1-16.12 0L99.37 214.31C92.17 206 97.28 192 107.43 192h233.14c10.15 0 15.26 14 8.06 22.31z" class="svg-foreground"/>
+      </svg>`;
+    if (title) elTitle.innerHTML = `${title}${svg}`;
+    this.menu.appendChild(elTitle);
+    elTitle.addEventListener('click', () => {
+      if (this.container && this.menu) {
+        this.container.classList.toggle('menu-container-fadeout');
+        this.container.classList.toggle('menu-container-fadein');
+        // this.menu.style.borderStyle = this.container.classList.contains('menu-container-fadeout') ? 'none' : 'solid';
+      }
+    });
+
+    this.menu.appendChild(this.container);
+    if (typeof parent === 'object') parent.appendChild(this.menu);
+    // @ts-ignore undefined
+    else document.getElementById(parent).appendChild(this.menu);
+  }
+
+  get newID() {
+    this.id++;
+    return `menu-${this.instance}-${this.id}`;
+  }
+
+  get ID() {
+    return `menu-${this.instance}-${this.id}`;
+  }
+
+  get width() {
+    return this.menu ? this.menu.offsetWidth : 0;
+  }
+
+  get height() {
+    return this.menu ? this.menu.offsetHeight : 0;
+  }
+
+  hide() {
+    if (this.container && this.container.classList.contains('menu-container-fadein')) {
+      this.container.classList.toggle('menu-container-fadeout');
+      this.container.classList.toggle('menu-container-fadein');
+    }
+  }
+
+  visible() {
+    return (this.container ? this.container.classList.contains('menu-container-fadein') : false);
+  }
+
+  toggle(evt) {
+    if (this.container && this.menu) {
+      this.container.classList.toggle('menu-container-fadeout');
+      this.container.classList.toggle('menu-container-fadein');
+      /*
+      if (this.container.classList.contains('menu-container-fadein') && evt) {
+        const x = evt.x || (evt.touches && evt.touches[0] ? evt.touches[0].pageX : null);
+        // const y = evt.y || (evt.touches && evt.touches[0] ? evt.touches[0].pageY : null);
+        if (x) this.menu.style.left = `${x - (this.menu.offsetWidth / 2)}px`;
+        // if (y) this.menu.style.top = '5.5rem'; // `${evt.y + 55}px`;
+        if (this.menu.offsetLeft < 0) this.menu.style.left = '0';
+        if ((this.menu.offsetLeft + this.menu.offsetWidth) > window.innerWidth) {
+          this.menu.style.left = '';
+          this.menu.style.right = '0';
+        }
+        // this.menu.style.borderStyle = 'solid';
+      } else {
+        // this.menu.style.borderStyle = 'none';
+      }
+      */
+    }
+  }
+
+  addTitle(title) {
+    const el = document.createElement('div');
+    el.className = 'menu-title';
+    el.id = this.newID;
+    el.innerHTML = title;
+    if (this.menu) this.menu.appendChild(el);
+    el.addEventListener('click', () => {
+      this.hidden = !this.hidden;
+      const all = document.getElementsByClassName('menu');
+      for (const item of all) {
+        // @ts-ignore
+        item.style.display = this.hidden ? 'none' : 'block';
+      }
+    });
+    return el;
+  }
+
+  addLabel(title) {
+    const el = document.createElement('div');
+    el.className = 'menu-item menu-label';
+    el.id = this.newID;
+    el.innerHTML = title;
+    if (this.container) this.container.appendChild(el);
+    return el;
+  }
+
+  addBool(title, object, variable, callback) {
+    const el = document.createElement('div');
+    el.className = 'menu-item';
+    el.innerHTML = `<div class="menu-checkbox"><input class="menu-checkbox" type="checkbox" id="${this.newID}" ${object[variable] ? 'checked' : ''}/><label class="menu-checkbox-label" for="${this.ID}"></label></div>${title}`;
+    if (this.container) this.container.appendChild(el);
+    el.addEventListener('change', (evt) => {
+      if (evt.target) {
+        object[variable] = evt.target['checked'];
+        if (callback) callback(evt.target['checked']);
+      }
+    });
+    return el;
+  }
+
+  async addList(title, items, selected, callback) {
+    const el = document.createElement('div');
+    el.className = 'menu-item';
+    let options = '';
+    for (const item of items) {
+      const def = item === selected ? 'selected' : '';
+      options += `<option value="${item}" ${def}>${item}</option>`;
+    }
+    el.innerHTML = `<div class="menu-list"><select name="${this.ID}" title="${title}" class="menu-list-item">${options}</select><label for="${this.ID}"></label></div>${title}`;
+    el.style.fontFamily = document.body.style.fontFamily;
+    el.style.fontSize = document.body.style.fontSize;
+    el.style.fontVariant = document.body.style.fontVariant;
+    if (this.container) this.container.appendChild(el);
+    el.addEventListener('change', (evt) => {
+      if (callback && evt.target) callback(items[evt.target['selectedIndex']]);
+    });
+    return el;
+  }
+
+  addRange(title, object, variable, min, max, step, callback) {
+    const el = document.createElement('div');
+    el.className = 'menu-item';
+    el.innerHTML = `<input class="menu-range" type="range" title="${title}" id="${this.newID}" min="${min}" max="${max}" step="${step}" value="${object[variable]}">${title}`;
+    if (this.container) this.container.appendChild(el);
+    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']);
+      }
+    });
+    el['input'] = el.children[0];
+    return el;
+  }
+
+  addHTML(html) {
+    const el = document.createElement('div');
+    el.className = 'menu-item';
+    el.id = this.newID;
+    if (html) el.innerHTML = html;
+    if (this.container) this.container.appendChild(el);
+    return el;
+  }
+
+  addButton(titleOn, titleOff, callback) {
+    const el = document.createElement('button');
+    el.className = 'menu-item menu-button';
+    el.style.fontFamily = document.body.style.fontFamily;
+    el.style.fontSize = document.body.style.fontSize;
+    el.style.fontVariant = document.body.style.fontVariant;
+    el.type = 'button';
+    el.id = this.newID;
+    el.innerText = titleOn;
+    if (this.container) this.container.appendChild(el);
+    el.addEventListener('click', () => {
+      if (el.innerText === titleOn) el.innerText = titleOff;
+      else el.innerText = titleOn;
+      if (callback) callback(el.innerText !== titleOn);
+    });
+    return el;
+  }
+
+  addValue(title, val, suffix = '') {
+    const el = document.createElement('div');
+    el.className = 'menu-item';
+    el.id = `menu-val-${title}`;
+    el.innerText = `${title}: ${val}${suffix}`;
+    if (this.container) this.container.appendChild(el);
+    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}`;
+    else this.addValue(title, val);
+  }
+
+  addChart(title, id, width = 150, height = 40, color) {
+    if (color) theme.chartColor = color;
+    const el = document.createElement('div');
+    el.className = 'menu-item menu-chart-title';
+    el.id = this.newID;
+    el.innerHTML = `<font color=${theme.chartColor}>${title}</font><canvas id="menu-canvas-${id}" class="menu-chart-canvas" width="${width}px" height="${height}px"></canvas>`;
+    if (this.container) this.container.appendChild(el);
+    return el;
+  }
+
+  // eslint-disable-next-line class-methods-use-this
+  async updateChart(id, values) {
+    if (!values || (values.length === 0)) return;
+    /** @type {HTMLCanvasElement} */ 
+    // @ts-ignore undefined
+    const canvas = document.getElementById(`menu-canvas-${id}`);
+    if (!canvas) return;
+    const ctx = canvas.getContext('2d');
+    if (!ctx) return;
+    ctx.fillStyle = theme.background;
+    ctx.fillRect(0, 0, canvas.width, canvas.height);
+    const width = canvas.width / values.length;
+    const max = 1 + Math.max(...values);
+    const height = canvas.height / max;
+    for (let i = 0; i < values.length; i++) {
+      const gradient = ctx.createLinearGradient(0, (max - values[i]) * height, 0, 0);
+      gradient.addColorStop(0.1, theme.chartColor);
+      gradient.addColorStop(0.4, theme.background);
+      ctx.fillStyle = gradient;
+      ctx.fillRect(i * width, 0, width - 4, canvas.height);
+      ctx.fillStyle = theme.background;
+      ctx.font = `${width / 1.5}px "Segoe UI"`;
+      ctx.fillText(Math.round(values[i]).toString(), i * width + 1, canvas.height - 1, width - 1);
+    }
+  }
+}
+
+export default Menu;

demo/helpers/webrtc.js

@@ -0,0 +1,86 @@
+const debug = true;
+
+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')}`;
+    // eslint-disable-next-line no-console
+    console.log(ts, 'webrtc', ...msg);
+  }
+}
+
+/**
+ * helper implementation of webrtc 
+ * performs:
+ * - discovery
+ * - handshake
+ * - connct to webrtc stream
+ * - assign webrtc stream to video element
+ *
+ * for development purposes i'm using test webrtc server that reads rtsp stream from a security camera:
+ * <https://github.com/vladmandic/stream-rtsp>
+ * 
+* @param {string} server
+* @param {string} streamName
+* @param {HTMLVideoElement} elementName
+* @return {promise}
+*/
+async function webRTC(server, streamName, elementName) {
+  const suuid = streamName;
+  log('client starting');
+  log(`server: ${server} stream: ${suuid}`);
+  const stream = new MediaStream();
+  const connection = new RTCPeerConnection();
+  connection.oniceconnectionstatechange = () => log('connection', connection.iceConnectionState);
+  connection.onnegotiationneeded = async () => {
+    let offer;
+    if (connection.localDescription) {
+      offer = await connection.createOffer();
+      await connection.setLocalDescription(offer);
+      const res = await fetch(`${server}/stream/receiver/${suuid}`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8' },
+        body: new URLSearchParams({
+          suuid: `${suuid}`,
+          data: `${btoa(connection.localDescription.sdp || '')}`,
+        }),
+      });
+    }
+    const data = res && res.ok ? await res.text() : '';
+    if (data.length === 0 || !offer) {
+      log('cannot connect:', server);
+    } else {
+      connection.setRemoteDescription(new RTCSessionDescription({
+        type: 'answer',
+        sdp: atob(data),
+      }));
+      log('negotiation start:', offer);
+    }
+  };
+  connection.ontrack = (event) => {
+    stream.addTrack(event.track);
+    const video = (typeof elementName === 'string') ? document.getElementById(elementName) : elementName;
+    if (video instanceof HTMLVideoElement) video.srcObject = stream;
+    else log('element is not a video element:', elementName);
+    // video.onloadeddata = async () => log('resolution:', video.videoWidth, video.videoHeight);
+    log('received track:', event.track);
+  };
+
+  const res = await fetch(`${server}/stream/codec/${suuid}`);
+  const streams = res && res.ok ? await res.json() : [];
+  if (streams.length === 0) log('received no streams');
+  else log('received streams:', streams);
+  for (const s of streams) connection.addTransceiver(s.Type, { direction: 'sendrecv' });
+
+  const channel = connection.createDataChannel(suuid, { maxRetransmits: 10 });
+  channel.onmessage = (e) => log('channel message:', channel.label, 'payload', e.data);
+  channel.onerror = (e) => log('channel error:', channel.label, 'payload', e);
+  // channel.onbufferedamountlow = (e) => log('channel buffering:', channel.label, 'payload', e);
+  channel.onclose = () => log('channel close', channel.label);
+  channel.onopen = () => {
+    log('channel open', channel.label);
+    setInterval(() => channel.send('ping'), 1000); // send ping becouse PION doesn't handle RTCSessionDescription.close()
+  };
+}
+
+export default webRTC;

demo/index-pwa.js

@@ -0,0 +1,136 @@
+/**
+ * PWA Service Worker for Human main demo
+ */
+
+/// <reference lib="webworker" />
+
+const skipCaching = false;
+
+const cacheName = 'Human';
+const cacheFiles = ['/favicon.ico', 'manifest.webmanifest']; // assets and models are cached on first access
+
+let cacheModels = true; // *.bin; *.json
+let cacheWASM = true; // *.wasm
+let cacheOther = false; // *
+
+let listening = false;
+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')}`;
+  // eslint-disable-next-line no-console
+  console.log(ts, 'pwa', ...msg);
+};
+
+async function updateCached(req) {
+  fetch(req)
+    .then((update) => {
+      // update cache if request is ok
+      if (update.ok) {
+        caches
+          .open(cacheName)
+          .then((cache) => cache.put(req, update))
+          .catch((err) => log('cache update error', err));
+      }
+      return true;
+    })
+    .catch((err) => {
+      log('fetch error', err);
+      return false;
+    });
+}
+
+async function getCached(evt) {
+  // just fetch
+  if (skipCaching) return fetch(evt.request);
+
+  // get from cache or fetch if not in cache
+  let found = await caches.match(evt.request);
+  if (found && found.ok) {
+    stats.hit += 1;
+  } else {
+    stats.miss += 1;
+    found = await fetch(evt.request);
+  }
+
+  // if still don't have it, return offline page
+  if (!found || !found.ok) {
+    found = await caches.match('offline.html');
+  }
+
+  // update cache in the background
+  if (found && found.type === 'basic' && found.ok) {
+    const uri = new URL(evt.request.url);
+    if (uri.pathname.endsWith('.bin') || uri.pathname.endsWith('.json')) {
+      if (cacheModels) updateCached(evt.request);
+    } else if (uri.pathname.endsWith('.wasm')) {
+      if (cacheWASM) updateCached(evt.request);
+    } else if (cacheOther) {
+      updateCached(evt.request);
+    }
+  }
+
+  return found;
+}
+
+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(
+        () => log('cache refresh:', cacheFiles.length, 'files'),
+        (err) => log('cache error', err),
+      ));
+}
+
+if (!listening) {
+  // get messages from main app to update configuration
+  self.addEventListener('message', (evt) => {
+    log('event message:', evt.data);
+    switch (evt.data.key) {
+      case 'cacheModels': cacheModels = evt.data.val; break;
+      case 'cacheWASM': cacheWASM = evt.data.val; break;
+      case 'cacheOther': cacheOther = evt.data.val; break;
+      default:
+    }
+  });
+
+  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());
+  });
+
+  self.addEventListener('fetch', (evt) => {
+    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 !== 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
+
+    const response = getCached(evt);
+    if (response) evt.respondWith(response);
+    else log('fetch response missing');
+  });
+
+  // only trigger controllerchange once
+  let refreshed = false;
+  self.addEventListener('controllerchange', (evt) => {
+    log(`PWA: ${evt.type}`);
+    if (refreshed) return;
+    refreshed = true;
+    location.reload();
+  });
+
+  listening = true;
+}

demo/index-worker.js

@@ -0,0 +1,38 @@
+/**
+ * Web worker used by main demo app
+ * Loaded from index.js
+ */
+
+/// <reference lib="webworker"/>
+
+// load Human using IIFE script as Chome Mobile does not support Modules as Workers
+self.importScripts('../dist/human.js');
+
+let busy = false;
+// @ts-ignore
+// eslint-disable-next-line new-cap, no-undef
+const human = new Human.default();
+
+onmessage = async (msg) => { // receive message from main thread
+  if (busy) return;
+  busy = true;
+  // received from index.js using:
+  // worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, config }, [image.data.buffer]);
+  const image = new ImageData(new Uint8ClampedArray(msg.data.image), msg.data.width, msg.data.height);
+  let result = {};
+  result = await human.detect(image, msg.data.userConfig);
+  result.tensors = human.tf.engine().state.numTensors; // append to result object so main thread get info
+  result.backend = human.tf.getBackend(); // append to result object so main thread get info
+  if (result.canvas) { // convert canvas to imageData and send it by reference
+    const canvas = new OffscreenCanvas(result.canvas.width, result.canvas.height);
+    const ctx = canvas.getContext('2d');
+    if (ctx) ctx.drawImage(result.canvas, 0, 0);
+    const img = ctx ? ctx.getImageData(0, 0, result.canvas.width, result.canvas.height) : null;
+    result.canvas = null; // must strip original canvas from return value as it cannot be transfered from worker thread
+    if (img) postMessage({ result, image: img.data.buffer, width: msg.data.width, height: msg.data.height }, [img.data.buffer]);
+    else postMessage({ result }); // send message back to main thread with canvas
+  } else {
+    postMessage({ result }); // send message back to main thread without canvas
+  }
+  busy = false;
+};

demo/index.html

@@ -0,0 +1,122 @@
+<!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">
+    <link rel="stylesheet" type="text/css" href="./icons.css">
+    <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; }
+      body { margin: 0; background: black; color: white; overflow-x: hidden; width: 100vw; height: 100vh; }
+      body::-webkit-scrollbar { display: none; }
+      hr { width: 100%; }
+      .play { position: absolute; width: 256px; height: 256px; z-index: 9; bottom: 15%; left: 50%; margin-left: -125px; display: none; filter: grayscale(1); }
+      .play:hover { filter: grayscale(0); }
+      .btn-background { fill:grey; cursor: pointer; opacity: 0.6; }
+      .btn-background:hover { opacity: 1; }
+      .btn-foreground { fill:white; cursor: pointer; opacity: 0.8; }
+      .btn-foreground:hover { opacity: 1; }
+      .status { position: absolute; width: 100vw; bottom: 10%; text-align: center; font-size: 3rem; font-weight: 100; text-shadow: 2px 2px #303030; }
+      .thumbnail { margin: 8px; box-shadow: 0 0 4px 4px dimgrey; }
+      .thumbnail:hover { box-shadow: 0 0 8px 8px dimgrey; filter: grayscale(1); }
+      .log { position: absolute; bottom: 0; margin: 0.4rem 0.4rem 0 0.4rem; font-size: 0.9rem; }
+      .menubar { width: 100vw; background: #303030; display: flex; justify-content: space-evenly; text-align: center; padding: 8px; cursor: pointer; }
+      .samples-container { display: flex; flex-wrap: wrap; }
+      .video { display: none; }
+      .canvas { margin: 0 auto; }
+      .bench { position: absolute; right: 0; bottom: 0; }
+      .compare-image { width: 256px; position: absolute; top: 150px; left: 30px; box-shadow: 0 0 2px 2px black; background: black; display: none; }
+      .loader { width: 300px; height: 300px; border: 3px solid transparent; border-radius: 50%; border-top: 4px solid #f15e41; animation: spin 4s linear infinite; position: absolute; bottom: 15%; left: 50%; margin-left: -150px; z-index: 15; }
+      .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); }
+      }
+      .wave { position: fixed; top: 0; left: -90%; width: 100vw; height: 100vh; border-radius: 10%; opacity: .3; z-index: -1; }
+      .wave.one { animation: rotate 10000ms infinite linear; background: #2F4F4F; }
+      .wave.two { animation: rotate 15000ms infinite linear; background: #1F3F3F; }
+      .wave.three { animation: rotate 20000ms infinite linear; background: #0F1F1F; }
+      @keyframes rotate {
+        from { transform: rotate(0deg); }
+        from { transform: rotate(360deg); }
+      }
+      .button { text-shadow: 2px 2px black; display: flex; }
+      .button:hover { text-shadow: -2px -2px black; color: lightblue; }
+      .button::before { display: inline-block; font-style: normal; font-variant: normal; text-rendering: auto; -webkit-font-smoothing: antialiased; font-family: "FA"; font-weight: 900; font-size: 2.4rem; margin-right: 0.4rem; }
+      .button-display::before { content: "\f8c4"; }
+      .button-image::before { content: "\f3f2"; }
+      .button-process::before { content: "\f3f0"; }
+      .button-model::before { content: "\f2c2"; }
+      .button-start::before { content: "\f144"; }
+      .button-stop::before { content: "\f28b"; }
+
+      .icon { width: 180px; text-align: -webkit-center; text-align: -moz-center; filter: grayscale(1); }
+      .icon:hover { background: #505050; filter: grayscale(0); }
+      .hint { opacity: 0; transition-duration: 0.5s; transition-property: opacity; font-style: italic; position: fixed; top: 5rem; padding: 8px; margin: 8px; box-shadow: 0 0 2px 2px #303030; }
+      .input-file { align-self: center; width: 5rem; }
+
+      .results { position: absolute; left: 0; top: 5rem; background: #303030; width: 20rem; height: 90%; font-size: 0.8rem; overflow-y: auto; display: none }
+      .results::-webkit-scrollbar { background-color: #303030; }
+      .results::-webkit-scrollbar-thumb { background: black; border-radius: 10px; }
+      .json-line { margin: 4px 0; display: flex; justify-content: flex-start; }
+      .json {	margin-right: 8px; margin-left: 8px; }
+      .json-type { color: lightyellow; }
+      .json-key {	color: white; }
+      .json-index { color: lightcoral; }
+      .json-value {	margin-left: 20px; }
+      .json-number { color: lightgreen; }
+      .json-boolean { color: lightyellow; }
+      .json-string { color: lightblue; }
+      .json-size { color: gray; }
+      .hide {	display: none; }
+      .fas { display: inline-block;	width: 0;	height: 0; border-style: solid; }
+      .fa-caret-down { border-width: 10px 8px 0 8px; border-color: white transparent }
+      .fa-caret-right {	border-width: 10px 0 8px 10px; border-color: transparent transparent transparent white; }
+    </style>
+  </head>
+  <body>
+    <div id="play" class="play icon-play"></div>
+    <div id="background">
+      <div class='wave one'></div>
+      <div class='wave two'></div>
+      <div class='wave three'></div>         
+    </div>
+    <div id="loader" class="loader"></div>
+    <div id="status" class="status"></div>
+    <div id="menubar" class="menubar">
+      <div id="btnDisplay" class="icon"><div class="icon-binoculars"> </div>display</div>
+      <div id="btnImage" class="icon"><div class="icon-brush"></div>input</div>
+      <div id="btnProcess" class="icon"><div class="icon-stats"></div>options</div>
+      <div id="btnModel" class="icon"><div class="icon-games"></div>models</div>
+      <div id="btnStart" class="icon"><div class="icon-webcam"></div><span id="btnStartText">start video</span></div>
+    </div>
+    <div id="media">
+      <canvas id="canvas" class="canvas"></canvas>
+      <video id="video" playsinline class="video"></video>
+    </div>
+    <div id="compare-container" class="compare-image">
+      <canvas id="compare-canvas" width="256" height="256"></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>
+    <div id="results" class="results"></div>
+  </body>
+</html>

demo/manifest.webmanifest

@@ -0,0 +1,10 @@
+{
+  "name": "Human Library",
+  "short_name": "Human",
+  "icons": [{ "src": "../assets/icon.png", "sizes": "512x512", "type": "image/png", "purpose": "any maskable" }],
+  "start_url": "./index.html",
+  "scope": "/",
+  "display": "standalone",
+  "background_color": "#000000",
+  "theme_color": "#000000"
+}

demo/multithread/README.md

@@ -0,0 +1,70 @@
+# Human Multithreading Demos
+
+- **Browser** demo `multithread` & `worker`
+  Runs each `human` module in a separate web worker for highest possible performance  
+- **NodeJS** demo `node-multiprocess` & `node-multiprocess-worker`  
+  Runs multiple parallel `human` by dispaching them to pool of pre-created worker processes  
+
+<br><hr><br>
+
+## NodeJS Multi-process Demo
+
+`nodejs/node-multiprocess.js` and `nodejs/node-multiprocess-worker.js`: Demo using NodeJS with CommonJS module  
+  Demo that starts n child worker processes for parallel execution
+
+```shell
+node demo/nodejs/node-multiprocess.js
+```
+
+```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
+2021-06-01 08:54:19 INFO:  FaceAPI multi-process test
+2021-06-01 08:54:19 STATE: Enumerated images: ./assets 15
+2021-06-01 08:54:19 STATE: Main: started worker: 130362
+2021-06-01 08:54:19 STATE: Main: started worker: 130363
+2021-06-01 08:54:19 STATE: Main: started worker: 130369
+2021-06-01 08:54:19 STATE: Main: started worker: 130370
+2021-06-01 08:54:20 STATE: Worker: PID: 130370 TensorFlow/JS 3.6.0 Human 2.0.0 Backend: tensorflow
+2021-06-01 08:54:20 STATE: Worker: PID: 130362 TensorFlow/JS 3.6.0 Human 2.0.0 Backend: tensorflow
+2021-06-01 08:54:20 STATE: Worker: PID: 130369 TensorFlow/JS 3.6.0 Human 2.0.0 Backend: tensorflow
+2021-06-01 08:54:20 STATE: Worker: PID: 130363 TensorFlow/JS 3.6.0 Human 2.0.0 Backend: tensorflow
+2021-06-01 08:54:21 STATE: Main: dispatching to worker: 130370
+2021-06-01 08:54:21 INFO:  Latency: worker initializtion:  1348 message round trip: 0
+2021-06-01 08:54:21 DATA:  Worker received message: 130370 { test: true }
+2021-06-01 08:54:21 STATE: Main: dispatching to worker: 130362
+2021-06-01 08:54:21 DATA:  Worker received message: 130362 { image: 'samples/ai-face.jpg' }
+2021-06-01 08:54:21 DATA:  Worker received message: 130370 { image: 'samples/ai-body.jpg' }
+2021-06-01 08:54:21 STATE: Main: dispatching to worker: 130369
+2021-06-01 08:54:21 STATE: Main: dispatching to worker: 130363
+2021-06-01 08:54:21 DATA:  Worker received message: 130369 { image: 'assets/human-sample-upper.jpg' }
+2021-06-01 08:54:21 DATA:  Worker received message: 130363 { image: 'assets/sample-me.jpg' }
+2021-06-01 08:54:24 DATA:  Main: worker finished: 130362 detected faces: 1 bodies: 1 hands: 0 objects: 1
+2021-06-01 08:54:24 STATE: Main: dispatching to worker: 130362
+2021-06-01 08:54:24 DATA:  Worker received message: 130362 { image: 'assets/sample1.jpg' }
+2021-06-01 08:54:25 DATA:  Main: worker finished: 130369 detected faces: 1 bodies: 1 hands: 0 objects: 1
+2021-06-01 08:54:25 STATE: Main: dispatching to worker: 130369
+2021-06-01 08:54:25 DATA:  Main: worker finished: 130370 detected faces: 1 bodies: 1 hands: 0 objects: 1
+2021-06-01 08:54:25 STATE: Main: dispatching to worker: 130370
+2021-06-01 08:54:25 DATA:  Worker received message: 130369 { image: 'assets/sample2.jpg' }
+2021-06-01 08:54:25 DATA:  Main: worker finished: 130363 detected faces: 1 bodies: 1 hands: 0 objects: 2
+2021-06-01 08:54:25 STATE: Main: dispatching to worker: 130363
+2021-06-01 08:54:25 DATA:  Worker received message: 130370 { image: 'assets/sample3.jpg' }
+2021-06-01 08:54:25 DATA:  Worker received message: 130363 { image: 'assets/sample4.jpg' }
+2021-06-01 08:54:30 DATA:  Main: worker finished: 130362 detected faces: 3 bodies: 1 hands: 0 objects: 7
+2021-06-01 08:54:30 STATE: Main: dispatching to worker: 130362
+2021-06-01 08:54:30 DATA:  Worker received message: 130362 { image: 'assets/sample5.jpg' }
+2021-06-01 08:54:31 DATA:  Main: worker finished: 130369 detected faces: 3 bodies: 1 hands: 0 objects: 5
+2021-06-01 08:54:31 STATE: Main: dispatching to worker: 130369
+2021-06-01 08:54:31 DATA:  Worker received message: 130369 { image: 'assets/sample6.jpg' }
+2021-06-01 08:54:31 DATA:  Main: worker finished: 130363 detected faces: 4 bodies: 1 hands: 2 objects: 2
+2021-06-01 08:54:31 STATE: Main: dispatching to worker: 130363
+2021-06-01 08:54:39 STATE: Main: worker exit: 130370 0
+2021-06-01 08:54:39 DATA:  Main: worker finished: 130362 detected faces: 1 bodies: 1 hands: 0 objects: 1
+2021-06-01 08:54:39 DATA:  Main: worker finished: 130369 detected faces: 1 bodies: 1 hands: 1 objects: 3
+2021-06-01 08:54:39 STATE: Main: worker exit: 130362 0
+2021-06-01 08:54:39 STATE: Main: worker exit: 130369 0
+2021-06-01 08:54:41 DATA:  Main: worker finished: 130363 detected faces: 9 bodies: 1 hands: 0 objects: 10
+2021-06-01 08:54:41 STATE: Main: worker exit: 130363 0
+2021-06-01 08:54:41 INFO:  Processed: 15 images in total: 22006 ms working: 20658 ms average: 1377 ms
+```

demo/multithread/index.html

@@ -0,0 +1,33 @@
+<!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>
+      @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; }
+      .status { position: absolute; width: 100vw; bottom: 10%; text-align: center; font-size: 3rem; font-weight: 100; text-shadow: 2px 2px #303030; }
+      .log { position: absolute; bottom: 0; margin: 0.4rem 0.4rem 0 0.4rem; font-size: 0.9rem; }
+      .video { display: none; }
+      .canvas { margin: 0 auto; }
+    </style>
+  </head>
+  <body>
+    <div id="status" class="status"></div>
+    <canvas id="canvas" class="canvas"></canvas>
+    <video id="video" playsinline class="video"></video>
+    <div id="log" class="log"></div>
+  </body>
+</html>

demo/multithread/index.js

@@ -0,0 +1,263 @@
+/**
+ * Human demo for browsers
+ *
+ * @description Demo app that enables all Human modules and runs them in separate worker threads
+ *
+ */
+
+import Human from '../../dist/human.esm.js'; // equivalent of @vladmandic/human
+import GLBench from '../helpers/gl-bench.js';
+
+const workerJS = './worker.js';
+
+const config = {
+  main: { // processes input and runs gesture analysis
+    warmup: 'none',
+    backend: 'humangl',
+    modelBasePath: '../../models/',
+    async: false,
+    filter: { enabled: true },
+    face: { enabled: false },
+    object: { enabled: false },
+    gesture: { enabled: true },
+    hand: { enabled: false },
+    body: { enabled: false },
+    segmentation: { enabled: false },
+  },
+  face: { // runs all face models
+    warmup: 'none',
+    backend: 'humangl',
+    modelBasePath: '../../models/',
+    async: false,
+    filter: { enabled: false },
+    face: { enabled: true },
+    object: { enabled: false },
+    gesture: { enabled: false },
+    hand: { enabled: false },
+    body: { enabled: false },
+    segmentation: { enabled: false },
+  },
+  body: { // runs body model
+    warmup: 'none',
+    backend: 'humangl',
+    modelBasePath: '../../models/',
+    async: false,
+    filter: { enabled: false },
+    face: { enabled: false },
+    object: { enabled: false },
+    gesture: { enabled: false },
+    hand: { enabled: false },
+    body: { enabled: true },
+    segmentation: { enabled: false },
+  },
+  hand: { // runs hands model
+    warmup: 'none',
+    backend: 'humangl',
+    modelBasePath: '../../models/',
+    async: false,
+    filter: { enabled: false },
+    face: { enabled: false },
+    object: { enabled: false },
+    gesture: { enabled: false },
+    hand: { enabled: true },
+    body: { enabled: false },
+    segmentation: { enabled: false },
+  },
+  object: { // runs object model
+    warmup: 'none',
+    backend: 'humangl',
+    modelBasePath: '../../models/',
+    async: false,
+    filter: { enabled: false },
+    face: { enabled: false },
+    object: { enabled: true },
+    gesture: { enabled: false },
+    hand: { enabled: false },
+    body: { enabled: false },
+    segmentation: { enabled: false },
+  },
+};
+
+let human;
+let canvas;
+let video;
+let bench;
+
+const busy = {
+  face: false,
+  hand: false,
+  body: false,
+  object: false,
+};
+
+const workers = {
+  /** @type {Worker | null} */
+  face: null,
+  /** @type {Worker | null} */
+  body: null,
+  /** @type {Worker | null} */
+  hand: null,
+  /** @type {Worker | null} */
+  object: null,
+};
+
+const time = {
+  main: 0,
+  draw: 0,
+  face: '[warmup]',
+  body: '[warmup]',
+  hand: '[warmup]',
+  object: '[warmup]',
+};
+
+const start = {
+  main: 0,
+  draw: 0,
+  face: 0,
+  body: 0,
+  hand: 0,
+  object: 0,
+};
+
+const result = { // initialize empty result object which will be partially filled with results from each thread
+  performance: {},
+  hand: [],
+  body: [],
+  face: [],
+  object: [],
+};
+
+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
+  console.log(ts, ...msg);
+}
+
+async function drawResults() {
+  start.draw = human.now();
+  const interpolated = human.next(result);
+  await human.draw.all(canvas, interpolated);
+  time.draw = Math.round(1 + human.now() - start.draw);
+  const fps = Math.round(10 * 1000 / time.main) / 10;
+  const draw = Math.round(10 * 1000 / time.draw) / 10;
+  const div = document.getElementById('log');
+  if (div) div.innerText = `Human: version ${human.version} | Performance: Main ${time.main}ms Face: ${time.face}ms Body: ${time.body}ms Hand: ${time.hand}ms Object ${time.object}ms | FPS: ${fps} / ${draw}`;
+  requestAnimationFrame(drawResults);
+}
+
+async function receiveMessage(msg) {
+  result[msg.data.type] = msg.data.result;
+  busy[msg.data.type] = false;
+  time[msg.data.type] = Math.round(human.now() - start[msg.data.type]);
+}
+
+async function runDetection() {
+  start.main = human.now();
+  if (!bench) {
+    bench = new GLBench(null, { trackGPU: false, chartHz: 20, chartLen: 20 });
+    bench.begin('human');
+  }
+  const ctx = canvas.getContext('2d');
+  ctx.drawImage(video, 0, 0, canvas.width, canvas.height);
+  const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
+  if (!busy.face) {
+    busy.face = true;
+    start.face = human.now();
+    if (workers.face) workers.face.postMessage({ image: imageData.data.buffer, width: canvas.width, height: canvas.height, config: config.face, type: 'face' }, [imageData.data.buffer.slice(0)]);
+  }
+  if (!busy.body) {
+    busy.body = true;
+    start.body = human.now();
+    if (workers.body) workers.body.postMessage({ image: imageData.data.buffer, width: canvas.width, height: canvas.height, config: config.body, type: 'body' }, [imageData.data.buffer.slice(0)]);
+  }
+  if (!busy.hand) {
+    busy.hand = true;
+    start.hand = human.now();
+    if (workers.hand) workers.hand.postMessage({ image: imageData.data.buffer, width: canvas.width, height: canvas.height, config: config.hand, type: 'hand' }, [imageData.data.buffer.slice(0)]);
+  }
+  if (!busy.object) {
+    busy.object = true;
+    start.object = human.now();
+    if (workers.object) workers.object.postMessage({ image: imageData.data.buffer, width: canvas.width, height: canvas.height, config: config.object, type: 'object' }, [imageData.data.buffer.slice(0)]);
+  }
+
+  time.main = Math.round(human.now() - start.main);
+
+  bench.nextFrame();
+  requestAnimationFrame(runDetection);
+}
+
+async function setupCamera() {
+  video = document.getElementById('video');
+  canvas = document.getElementById('canvas');
+  const output = document.getElementById('log');
+  let stream;
+  const constraints = {
+    audio: false,
+    video: {
+      facingMode: 'user',
+      resizeMode: 'crop-and-scale',
+      width: { ideal: document.body.clientWidth },
+      aspectRatio: document.body.clientWidth / document.body.clientHeight,
+    },
+  };
+  // enumerate devices for diag purposes
+  navigator.mediaDevices.enumerateDevices().then((devices) => log('enumerated devices:', devices));
+  log('camera constraints', constraints);
+  try {
+    stream = await navigator.mediaDevices.getUserMedia(constraints);
+  } catch (err) {
+    if (output) output.innerText += `\n${err.name}: ${err.message}`;
+    log('camera error:', err);
+  }
+  if (stream) {
+    const tracks = stream.getVideoTracks();
+    log('enumerated viable tracks:', tracks);
+    const track = stream.getVideoTracks()[0];
+    const settings = track.getSettings();
+    log('selected video source:', track, settings);
+  } else {
+    log('missing video stream');
+  }
+  const promise = !stream || new Promise((resolve) => {
+    video.onloadeddata = () => {
+      canvas.style.height = '100vh';
+      canvas.width = video.videoWidth;
+      canvas.height = video.videoHeight;
+      video.play();
+      resolve(true);
+    };
+  });
+  // attach input to video element
+  if (stream && video) video['srcObject'] = stream;
+  return promise;
+}
+
+async function startWorkers() {
+  if (!workers.face) workers.face = new Worker(workerJS);
+  if (!workers.body) workers.body = new Worker(workerJS);
+  if (!workers.hand) workers.hand = new Worker(workerJS);
+  if (!workers.object) workers.object = new Worker(workerJS);
+  workers.face.onmessage = receiveMessage;
+  workers.body.onmessage = receiveMessage;
+  workers.hand.onmessage = receiveMessage;
+  workers.object.onmessage = receiveMessage;
+}
+
+async function main() {
+  if (typeof Worker === 'undefined' || typeof OffscreenCanvas === 'undefined') {
+    return;
+  }
+
+  human = new Human(config.main);
+  const div = document.getElementById('log');
+  if (div) div.innerText = `Human: version ${human.version}`;
+
+  await startWorkers();
+  await setupCamera();
+  runDetection();
+  drawResults();
+}
+
+window.onload = main;

demo/multithread/node-multiprocess-worker.js

@@ -0,0 +1,88 @@
+/**
+ * Human demo for NodeJS
+ *
+ * Used by node-multiprocess.js as an on-demand started worker process
+ * Receives messages from parent process and sends results
+ */
+
+const fs = require('fs');
+const log = require('@vladmandic/pilogger');
+
+// workers actual import tfjs and faceapi modules
+// 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;
+
+const myConfig = {
+  // backend: 'tensorflow',
+  modelBasePath: 'file://models/',
+  debug: false,
+  async: true,
+  face: {
+    enabled: true,
+    detector: { enabled: true, rotation: false },
+    mesh: { enabled: true },
+    iris: { enabled: true },
+    description: { enabled: true },
+    emotion: { enabled: true },
+  },
+  hand: {
+    enabled: true,
+  },
+  // body: { modelPath: 'blazepose.json', enabled: true },
+  body: { enabled: true },
+  object: { enabled: true },
+};
+
+// read image from a file and create tensor to be used by faceapi
+// this way we don't need any monkey patches
+// you can add any pre-proocessing here such as resizing, etc.
+async function image(img) {
+  const buffer = fs.readFileSync(img);
+  const tensor = human.tf.tidy(() => human.tf.node.decodeImage(buffer).toFloat().expandDims());
+  return tensor;
+}
+
+// actual faceapi detection
+async function detect(img) {
+  const tensor = await image(img);
+  const result = await human.detect(tensor);
+  if (process.send) { // check if ipc exists
+    process.send({ image: img, detected: result }); // send results back to main
+    process.send({ ready: true }); // send signal back to main that this worker is now idle and ready for next image
+  }
+  tf.dispose(tensor);
+}
+
+async function main() {
+  process.on('unhandledRejection', (err) => {
+    // @ts-ignore // no idea if exception message is compelte
+    log.error(err?.message || err || 'no error message');
+  });
+
+  // on worker start first initialize message handler so we don't miss any messages
+  process.on('message', (msg) => {
+    // @ts-ignore
+    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
+  });
+
+  // create instance of human
+  human = new Human(myConfig);
+  // wait until tf is ready
+  await human.tf.ready();
+  // pre-load models
+  log.state('Worker: PID:', process.pid, `TensorFlow/JS ${human.tf.version['tfjs-core']} Human ${human.version} Backend: ${human.tf.getBackend()}`);
+  await human.load();
+
+  // now we're ready, so send message back to main that it knows it can use this worker
+  if (process.send) process.send({ ready: true });
+}
+
+main();

demo/multithread/node-multiprocess.js

@@ -0,0 +1,98 @@
+/**
+ * Human demo for NodeJS
+ *
+ * Uses NodeJS fork functionality with inter-processing-messaging
+ * Starts a pool of worker processes and dispatch work items to each worker when they are available
+ * Uses node-multiprocess-worker.js for actual processing
+ */
+
+const fs = require('fs');
+const path = require('path');
+// eslint-disable-next-line import/no-extraneous-dependencies, 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/nodejs/node-multiprocess-worker.js';
+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
+const t = []; // timers
+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 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
+  else {
+    log.state('Main: dispatching to worker:', worker.pid);
+    worker.send({ image: images[0] });
+    images.shift();
+  }
+}
+
+// loop that waits for all workers to complete
+function waitCompletion() {
+  const activeWorkers = workers.reduce((any, worker) => (any += worker.connected ? 1 : 0), 0);
+  if (activeWorkers > 0) setImmediate(() => waitCompletion());
+  else {
+    t[1] = process.hrtime.bigint();
+    log.info('Processed:', numImages, 'images in', 'total:', Math.trunc(Number(t[1] - t[0]) / 1000000), 'ms', 'working:', Math.trunc(Number(t[1] - t[2]) / 1000000), 'ms', 'average:', Math.trunc(Number(t[1] - t[2]) / numImages / 1000000), 'ms');
+  }
+}
+
+function measureLatency() {
+  t[3] = process.hrtime.bigint();
+  const latencyInitialization = Math.trunc(Number(t[2] - t[0]) / 1000 / 1000);
+  const latencyRoundTrip = Math.trunc(Number(t[3] - t[2]) / 1000 / 1000);
+  log.info('Latency: worker initializtion: ', latencyInitialization, 'message round trip:', latencyRoundTrip);
+}
+
+async function main() {
+  process.on('unhandledRejection', (err) => {
+    // @ts-ignore // no idea if exception message is compelte
+    log.error(err?.message || err || 'no error message');
+  });
+
+  log.header();
+  log.info('FaceAPI multi-process test');
+
+  // enumerate all images into queue
+  const dir = fs.readdirSync(imgPathRoot);
+  for (const imgFile of dir) {
+    if (imgFile.toLocaleLowerCase().endsWith('.jpg')) images.push(path.join(imgPathRoot, imgFile));
+  }
+  numImages = images.length;
+  log.state('Enumerated images:', imgPathRoot, numImages);
+
+  t[0] = process.hrtime.bigint();
+  t[1] = process.hrtime.bigint();
+  t[2] = process.hrtime.bigint();
+  // manage worker processes
+  for (let i = 0; i < numWorkers; i++) {
+    // create worker process
+    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) 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);
+    });
+    // just log when worker exits
+    workers[i].on('exit', (msg) => log.state('Main: worker exit:', workers[i].pid, msg));
+    // just log which worker was started
+    log.state('Main: started worker:', workers[i].pid);
+  }
+
+  // wait for all workers to complete
+  waitCompletion();
+}
+
+main();

demo/multithread/worker.js

@@ -0,0 +1,19 @@
+/// <reference lib="webworker" />
+
+// load Human using IIFE script as Chome Mobile does not support Modules as Workers
+self.importScripts('../../dist/human.js');
+
+let human;
+
+onmessage = async (msg) => {
+  // received from index.js using:
+  // worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, config }, [image.data.buffer]);
+
+  // @ts-ignore // Human is registered as global namespace using IIFE script
+  // 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);
+  postMessage({ result: result[msg.data.type], type: msg.data.type });
+};

demo/nodejs/README.md

@@ -0,0 +1,120 @@
+# Human Demos for NodeJS
+
+- `node`: Process images from files, folders or URLs  
+  uses native methods for image loading and decoding without external dependencies  
+- `node-canvas`: Process image from file or URL and draw results to a new image file using `node-canvas`  
+  uses `node-canvas` library to load and decode images from files, draw detection results and write output to a new image file
+- `node-video`: Processing of video input using `ffmpeg`  
+  uses `ffmpeg` to decode video input (can be a file, stream or device such as webcam) and  
+  output results in a pipe that are captured by demo app as frames and processed by `Human` library
+- `node-webcam`: Processing of webcam screenshots using `fswebcam`  
+  uses `fswebcam` to connect to web cam and take screenshots at regular interval which are then processed by `Human` library
+- `node-event`: Showcases usage of `Human` eventing to get notifications on processing
+- `node-similarity`: Compares two input images for similarity of detected faces
+- `process-folder`: Processing all images in input folder and creates output images  
+  interally used to generate samples gallery  
+
+<br>
+
+## Main Demo
+
+`nodejs/node.js`: Demo using NodeJS with CommonJS module  
+Simple demo that can process any input image
+
+Note that you can run demo as-is and it will perform detection on provided sample images,  
+or you can pass a path to image to analyze, either on local filesystem or using URL
+
+```shell
+node demo/nodejs/node.js
+```
+
+```json
+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
+2021-06-01 08:52:15 INFO:  Human: 2.0.0
+2021-06-01 08:52:15 INFO:  Active Configuration {
+  backend: 'tensorflow',
+  modelBasePath: 'file://models/',
+  wasmPath: '../node_modules/@tensorflow/tfjs-backend-wasm/dist/',
+  debug: true,
+  async: false,
+  warmup: 'full',
+  cacheSensitivity: 0.75,
+  filter: {
+    enabled: true,
+    width: 0,
+    height: 0,
+    flip: true,
+    return: true,
+    brightness: 0,
+    contrast: 0,
+    sharpness: 0,
+    blur: 0,
+    saturation: 0,
+    hue: 0,
+    negative: false,
+    sepia: false,
+    vintage: false,
+    kodachrome: false,
+    technicolor: false,
+    polaroid: false,
+    pixelate: 0
+  },
+  gesture: { enabled: true },
+  face: {
+    enabled: true,
+    detector: { modelPath: 'blazeface.json', rotation: false, maxDetected: 10, skipFrames: 15, minConfidence: 0.2, iouThreshold: 0.1, return: false, enabled: true },
+    mesh: { enabled: true, modelPath: 'facemesh.json' },
+    iris: { enabled: true, modelPath: 'iris.json' },
+    description: { enabled: true, modelPath: 'faceres.json', skipFrames: 16, minConfidence: 0.1 },
+    emotion: { enabled: true, minConfidence: 0.1, skipFrames: 17, modelPath: 'emotion.json' }
+  },
+  body: { enabled: true, modelPath: 'movenet-lightning.json', maxDetected: 1, minConfidence: 0.2 },
+  hand: {
+    enabled: true,
+    rotation: true,
+    skipFrames: 18,
+    minConfidence: 0.1,
+    iouThreshold: 0.1,
+    maxDetected: 2,
+    landmarks: true,
+    detector: { modelPath: 'handdetect.json' },
+    skeleton: { modelPath: 'handskeleton.json' }
+  },
+  object: { enabled: true, modelPath: 'mb3-centernet.json', minConfidence: 0.2, iouThreshold: 0.4, maxDetected: 10, skipFrames: 19 }
+}
+08:52:15.673 Human: version: 2.0.0
+08:52:15.674 Human: tfjs version: 3.6.0
+08:52:15.674 Human: platform: linux x64
+08:52:15.674 Human: agent: NodeJS v16.0.0
+08:52:15.674 Human: setting backend: tensorflow
+08:52:15.710 Human: load model: file://models/blazeface.json
+08:52:15.743 Human: load model: file://models/facemesh.json
+08:52:15.744 Human: load model: file://models/iris.json
+08:52:15.760 Human: load model: file://models/emotion.json
+08:52:15.847 Human: load model: file://models/handdetect.json
+08:52:15.847 Human: load model: file://models/handskeleton.json
+08:52:15.914 Human: load model: file://models/movenet-lightning.json
+08:52:15.957 Human: load model: file://models/mb3-centernet.json
+08:52:16.015 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 ]
+2021-06-01 08:52:16 INFO:  Memory state: { unreliable: true, numTensors: 1318, numDataBuffers: 1318, numBytes: 50796152 }
+2021-06-01 08:52:16 INFO:  Loading image: private/daz3d/daz3d-kiaria-02.jpg
+2021-06-01 08:52:16 STATE: Processing: [ 1, 1300, 1000, 3, [length]: 4 ]
+2021-06-01 08:52:17 DATA:  Results:
+2021-06-01 08:52:17 DATA:    Face: #0 boxScore:0.88 faceScore:1 age:16.3 genderScore:0.97 gender:female emotionScore:0.85 emotion:happy iris:61.05
+2021-06-01 08:52:17 DATA:    Body: #0 score:0.82 keypoints:17
+2021-06-01 08:52:17 DATA:    Hand: #0 score:0.89
+2021-06-01 08:52:17 DATA:    Hand: #1 score:0.97
+2021-06-01 08:52:17 DATA:    Gesture: face#0 gesture:facing left
+2021-06-01 08:52:17 DATA:    Gesture: body#0 gesture:leaning right
+2021-06-01 08:52:17 DATA:    Gesture: hand#0 gesture:pinky forward middlefinger up
+2021-06-01 08:52:17 DATA:    Gesture: hand#1 gesture:pinky forward middlefinger up
+2021-06-01 08:52:17 DATA:    Gesture: iris#0 gesture:looking left
+2021-06-01 08:52:17 DATA:    Object: #0 score:0.55 label:person
+2021-06-01 08:52:17 DATA:    Object: #1 score:0.23 label:bottle
+2021-06-01 08:52:17 DATA:  Persons:
+2021-06-01 08:52:17 DATA:    #0: Face:score:1 age:16.3 gender:female iris:61.05 Body:score:0.82 keypoints:17 LeftHand:no RightHand:yes Gestures:4
+```

demo/nodejs/node-canvas.js

@@ -0,0 +1,84 @@
+/**
+ * Human demo for NodeJS using Canvas library
+ */
+
+const fs = require('fs');
+const process = require('process');
+const log = require('@vladmandic/pilogger');
+const canvas = require('canvas');
+
+// 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 faceapi = require('@vladmandic/face-api'); // 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 }, // includes mesh, iris, emotion, descriptor
+  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 },
+};
+
+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
+  // human.env.Canvas = canvas.Canvas; // alternatively monkey-patch human to use external canvas library
+  // human.env.ImageData = canvas.ImageData; // alternatively monkey-patch human to use external canvas library
+
+  // init
+  const human = new Human.Human(config); // create instance of human
+  log.info('Human:', human.version);
+
+  await human.load(); // pre-load models
+  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];
+  const output = process.argv[3];
+  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: ${process.argv[2]}`);
+  else {
+    // everything seems ok
+    const inputImage = await canvas.loadImage(input); // load image using canvas library
+    log.info('Loaded image', input, inputImage.width, inputImage.height);
+    const inputCanvas = new canvas.Canvas(inputImage.width, inputImage.height); // create canvas
+    const inputCtx = inputCanvas.getContext('2d');
+    inputCtx.drawImage(inputImage, 0, 0); // draw input image onto canvas
+    const imageData = inputCtx.getImageData(0, 0, inputCanvas.width, inputCanvas.height);
+
+    // run detection
+    const result = await human.detect(imageData);
+    // run segmentation
+    // const seg = await human.segmentation(inputCanvas);
+    // log.data('Segmentation:', { data: seg.data.length, alpha: typeof seg.alpha, canvas: typeof seg.canvas });
+
+    // print results summary
+    const persons = result.persons; // invoke persons getter, only used to print summary on console
+    for (let i = 0; i < persons.length; i++) {
+      const face = persons[i].face;
+      const faceTxt = face ? `score:${face.score} age:${face.age} gender:${face.gender} iris:${face.iris}` : null;
+      const body = persons[i].body;
+      const bodyTxt = body ? `score:${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}`);
+    }
+
+    // draw detected results onto canvas and save it to a file
+    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));
+    outFile.on('error', (err) => log.error('Output error:', output, err));
+    const stream = outputCanvas.createJPEGStream({ quality: 0.5, progressive: true, chromaSubsampling: true });
+    stream.pipe(outFile);
+  }
+}
+
+main();

demo/nodejs/node-event.js

@@ -0,0 +1,97 @@
+/**
+ * Human demo for NodeJS
+ */
+
+const log = require('@vladmandic/pilogger');
+const fs = require('fs');
+const process = require('process');
+
+let fetch; // fetch is dynamically imported later
+
+// 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 faceapi = require('@vladmandic/face-api'); // 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 human = null;
+
+const myConfig = {
+  modelBasePath: 'file://models/',
+  debug: false,
+  async: true,
+  filter: { enabled: false },
+  face: {
+    enabled: true,
+    detector: { enabled: true },
+    mesh: { enabled: true },
+    iris: { enabled: true },
+    description: { enabled: true },
+    emotion: { enabled: true },
+  },
+  hand: { enabled: true },
+  body: { enabled: true },
+  object: { enabled: true },
+};
+
+async function detect(input) {
+  // read input image from file or url into buffer
+  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 = await res.buffer();
+    else log.error('Invalid image URL:', input, res.status, res.statusText, res.headers.get('content-type'));
+  } else {
+    buffer = fs.readFileSync(input);
+  }
+
+  // decode image using tfjs-node so we don't need external depenencies
+  if (!buffer) return;
+  const tensor = human.tf.node.decodeImage(buffer, 3);
+
+  // run detection
+  await human.detect(tensor, myConfig);
+  human.tf.dispose(tensor); // dispose image tensor as we no longer need it
+}
+
+async function main() {
+  log.header();
+
+  human = new Human.Human(myConfig);
+
+  if (human.events) {
+    human.events.addEventListener('warmup', () => {
+      log.info('Event Warmup');
+    });
+
+    human.events.addEventListener('load', () => {
+      const loaded = Object.keys(human.models).filter((a) => human.models[a]);
+      log.info('Event Loaded:', loaded, human.tf.engine().memory());
+    });
+
+    human.events.addEventListener('image', () => {
+      log.info('Event Image:', human.process.tensor.shape);
+    });
+
+    human.events.addEventListener('detect', () => {
+      log.data('Event Detected:');
+      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.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}`);
+      }
+    });
+  }
+
+  await human.tf.ready(); // wait until tf is ready
+
+  const input = process.argv[2]; // process input
+  if (input) await detect(input);
+  else log.error('Missing <input>');
+}
+
+main();

demo/nodejs/node-fetch.js

@@ -0,0 +1,25 @@
+const fs = require('fs');
+
+// 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 faceapi = require('@vladmandic/face-api'); // 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 = {
+  modelBasePath: 'https://vladmandic.github.io/human/models/',
+};
+
+async function main(inputFile) {
+  // @ts-ignore
+  global.fetch = (await import('node-fetch')).default;
+  const human = new Human.Human(humanConfig); // create instance of human using default configuration
+  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
+  // eslint-disable-next-line no-console
+  console.log(result.gesture);
+}
+
+main('samples/in/ai-body.jpg');

demo/nodejs/node-similarity.js

@@ -0,0 +1,67 @@
+/**
+ * Human Person Similarity test for NodeJS
+ */
+
+const log = require('@vladmandic/pilogger');
+const fs = require('fs');
+const process = require('process');
+
+// 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 faceapi = require('@vladmandic/face-api'); // 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 human = null;
+
+const myConfig = {
+  modelBasePath: 'file://models/',
+  debug: true,
+  face: { emotion: { enabled: false } },
+  body: { enabled: false },
+  hand: { enabled: false },
+  gesture: { enabled: false },
+};
+
+async function init() {
+  human = new Human.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());
+}
+
+async function detect(input) {
+  if (!fs.existsSync(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']);
+  const result = await human.detect(tensor, myConfig);
+  human.tf.dispose(tensor);
+  log.state('Detected faces:', result.face.length);
+  return result;
+}
+
+async function main() {
+  log.configure({ inspect: { breakLength: 265 } });
+  log.header();
+  if (process.argv.length !== 4) {
+    log.error('Parameters: <first image> <second image> missing');
+    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) {
+    log.error('Could not detect face descriptors');
+    process.exit(1);
+  }
+  const similarity = human.similarity(res1.face[0].embedding, res2.face[0].embedding, { order: 2 });
+  log.data('Similarity: ', similarity);
+}
+
+main();

demo/nodejs/node-simple.js

@@ -0,0 +1,19 @@
+const fs = require('fs');
+
+// 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 faceapi = require('@vladmandic/face-api'); // 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
+
+async function main(inputFile) {
+  const human = new Human.Human(); // create instance of human using default configuration
+  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
+  // eslint-disable-next-line no-console
+  console.log(result);
+}
+
+main('samples/in/ai-body.jpg');

demo/nodejs/node-video.js

@@ -0,0 +1,89 @@
+/**
+ * Human demo for NodeJS
+ * Unsupported sample of using external utility ffmpeg to capture to decode video input and process it using Human
+ *
+ * Uses ffmpeg to process video input and output stream of motion jpeg images which are then parsed for frame start/end markers by pipe2jpeg
+ * Each frame triggers an event with jpeg buffer that then can be decoded and passed to human for processing
+ * 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 is not part of Human dependencies and should be installed manually
+ * Working version of ffmpeg must be present on the system
+*/
+
+const spawn = require('child_process').spawn;
+const log = require('@vladmandic/pilogger');
+// @ts-ignore pipe2jpeg is not installed by default
+// eslint-disable-next-line node/no-missing-require
+const Pipe2Jpeg = require('pipe2jpeg');
+
+// 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 faceapi = require('@vladmandic/face-api'); // 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
+const inputFile = './test.mp4';
+
+const humanConfig = {
+  modelBasePath: 'file://models/',
+  debug: false,
+  async: true,
+  filter: { enabled: false },
+  face: {
+    enabled: true,
+    detector: { enabled: true, rotation: false },
+    mesh: { enabled: true },
+    iris: { enabled: true },
+    description: { enabled: true },
+    emotion: { enabled: true },
+  },
+  hand: { enabled: false },
+  body: { enabled: false },
+  object: { enabled: false },
+};
+
+const human = new Human.Human(humanConfig);
+const pipe2jpeg = new Pipe2Jpeg();
+
+const ffmpegParams = [
+  '-loglevel', 'quiet',
+  // input
+  // '-re', // optional process video in real-time not as fast as possible
+  '-i', `${inputFile}`, // input file
+  // output
+  '-an', // drop audio
+  '-c:v', 'mjpeg', // use motion jpeg as output encoder
+  '-pix_fmt', 'yuvj422p', // typical for mp4, may need different settings for some videos
+  '-f', 'image2pipe', // pipe images as output
+  // '-vf', 'fps=5,scale=800:600', // optional video filter, do anything here such as process at fixed 5fps or resize to specific resulution
+  'pipe:1', // output to unix pipe that is then captured by pipe2jpeg
+];
+
+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);
+  log.data('gesture', JSON.stringify(res.gesture));
+  // do processing here
+  tf.dispose(tensor); // must dispose tensor
+  busy = false;
+}
+
+async function main() {
+  log.header();
+  await human.tf.ready();
+  // pre-load models
+  log.info('human:', human.version);
+  pipe2jpeg.on('jpeg', (jpegBuffer) => process(jpegBuffer));
+
+  const ffmpeg = spawn('ffmpeg', ffmpegParams, { stdio: ['ignore', 'pipe', 'ignore'] });
+  ffmpeg.on('error', (error) => log.error('ffmpeg error:', error));
+  ffmpeg.on('exit', (code, signal) => log.info('ffmpeg exit', code, signal));
+  ffmpeg.stdout.pipe(pipe2jpeg);
+}
+
+main();

demo/nodejs/node-webcam.js

@@ -0,0 +1,92 @@
+/**
+ * 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 is not part of Human dependencies and should be installed manually
+ * 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');
+// @ts-ignore node-webcam is not installed by default
+// eslint-disable-next-line node/no-missing-require
+const nodeWebCam = require('node-webcam');
+
+// 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 faceapi = require('@vladmandic/face-api'); // 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
+
+// options for node-webcam
+const tempFile = 'webcam-snap'; // node-webcam requires writting snapshot to a file, recommended to use tmpfs to avoid excessive disk writes
+const optionsCamera = {
+  callbackReturn: 'buffer', // this means whatever `fswebcam` writes to disk, no additional processing so it's fastest
+  saveShots: false, // don't save processed frame to disk, note that temp file is still created by fswebcam thus recommendation for tmpfs
+};
+const camera = nodeWebCam.create(optionsCamera);
+
+// options for human
+const optionsHuman = {
+  modelBasePath: 'file://models/',
+};
+const human = new Human.Human(optionsHuman);
+
+function buffer2tensor(buffer) {
+  return human.tf.tidy(() => {
+    if (!buffer) return null;
+    const decode = human.tf.node.decodeImage(buffer, 3);
+    let expand;
+    if (decode.shape[2] === 4) { // input is in rgba format, need to convert to rgb
+      const channels = human.tf.split(decode, 4, 2); // tf.split(tensor, 4, 2); // split rgba to channels
+      const rgb = human.tf.stack([channels[0], channels[1], channels[2]], 2); // stack channels back to rgb and ignore alpha
+      expand = human.tf.reshape(rgb, [1, decode.shape[0], decode.shape[1], 3]); // move extra dim from the end of tensor and use it as batch number instead
+    } else {
+      expand = human.tf.expandDims(decode, 0); // inpur ia rgb so use as-is
+    }
+    const cast = human.tf.cast(expand, 'float32');
+    return cast;
+  });
+}
+
+async function detect() {
+  // trigger next frame every 5 sec
+  // triggered here before actual capture and detection since we assume it will complete in less than 5sec
+  // so it's as close as possible to real 5sec and not 5sec + detection time
+  // if there is a chance of race scenario where detection takes longer than loop trigger, then trigger should be at the end of the function instead
+  setTimeout(() => detect(), 5000);
+
+  camera.capture(tempFile, (err, data) => { // gets the (default) jpeg data from from webcam
+    if (err) {
+      log.error('error capturing webcam:', err);
+    } else {
+      const tensor = buffer2tensor(data); // create tensor from image buffer
+      if (initial) log.data('input tensor:', tensor.shape);
+      // eslint-disable-next-line promise/no-promise-in-callback
+      human.detect(tensor).then((result) => {
+        if (result && result.face && result.face.length > 0) {
+          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(`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');
+        }
+      });
+    }
+    initial = false;
+  });
+  // alternatively to triggering every 5sec sec, simply trigger next frame as fast as possible
+  // setImmediate(() => process());
+}
+
+async function main() {
+  camera.list((list) => {
+    log.data('detected camera:', list);
+  });
+  await human.load();
+  detect();
+}
+
+log.header();
+main();

demo/nodejs/node.js

@@ -0,0 +1,217 @@
+/**
+ * Human demo for NodeJS
+ */
+
+const log = require('@vladmandic/pilogger');
+const fs = require('fs');
+const path = require('path');
+const process = require('process');
+
+let fetch; // fetch is dynamically imported later
+
+// 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 faceapi = require('@vladmandic/face-api'); // 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 human = null;
+
+const myConfig = {
+  // backend: 'tensorflow',
+  modelBasePath: 'file://models/',
+  debug: true,
+  async: false,
+  filter: {
+    enabled: true,
+    flip: true,
+  },
+  face: {
+    enabled: true,
+    detector: { enabled: true, rotation: false },
+    mesh: { enabled: true },
+    iris: { enabled: true },
+    description: { enabled: true },
+    emotion: { enabled: true },
+  },
+  hand: {
+    enabled: true,
+  },
+  // body: { modelPath: 'blazepose.json', enabled: true },
+  body: { enabled: true },
+  object: { enabled: true },
+};
+
+async function init() {
+  // create instance of human
+  human = new Human.Human(myConfig);
+  // wait until tf is ready
+  await human.tf.ready();
+  // pre-load models
+  log.info('Human:', human.version);
+  // log.info('Active Configuration', human.config);
+  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());
+  log.data(tf.backend()['binding'] ? tf.backend()['binding']['TF_Version'] : null);
+}
+
+async function detect(input) {
+  // read input image file and create tensor to be used for processing
+  let buffer;
+  log.info('Loading image:', input);
+  if (input.startsWith('http:') || input.startsWith('https:')) {
+    const res = await fetch(input);
+    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);
+  }
+
+  // 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
+  if (!buffer) return {};
+  const tensor = human.tf.tidy(() => {
+    const decode = human.tf.node.decodeImage(buffer, 3);
+    let expand;
+    if (decode.shape[2] === 4) { // input is in rgba format, need to convert to rgb
+      const channels = human.tf.split(decode, 4, 2); // tf.split(tensor, 4, 2); // split rgba to channels
+      const rgb = human.tf.stack([channels[0], channels[1], channels[2]], 2); // stack channels back to rgb and ignore alpha
+      expand = human.tf.reshape(rgb, [1, decode.shape[0], decode.shape[1], 3]); // move extra dim from the end of tensor and use it as batch number instead
+    } else {
+      expand = human.tf.expandDims(decode, 0);
+    }
+    const cast = human.tf.cast(expand, 'float32');
+    return cast;
+  });
+
+  // image shape contains image dimensions and depth
+  log.state('Processing:', tensor['shape']);
+
+  // run actual detection
+  let result;
+  try {
+    result = await human.detect(tensor, myConfig);
+  } catch (err) {
+    log.error('caught');
+  }
+
+  // dispose image tensor as we no longer need it
+  human.tf.dispose(tensor);
+
+  // print data to console
+  log.data('Results:');
+  if (result && result.face && result.face.length > 0) {
+    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} iris:${face.iris}`);
+    }
+  } else {
+    log.data('  Face: N/A');
+  }
+  if (result && result.body && result.body.length > 0) {
+    for (let i = 0; i < result.body.length; i++) {
+      const body = result.body[i];
+      log.data(`  Body: #${i} score:${body.score} keypoints:${body.keypoints?.length}`);
+    }
+  } else {
+    log.data('  Body: N/A');
+  }
+  if (result && result.hand && result.hand.length > 0) {
+    for (let i = 0; i < result.hand.length; i++) {
+      const hand = result.hand[i];
+      log.data(`  Hand: #${i} score:${hand.score} keypoints:${hand.keypoints?.length}`);
+    }
+  } else {
+    log.data('  Hand: N/A');
+  }
+  if (result && result.gesture && result.gesture.length > 0) {
+    for (let i = 0; i < result.gesture.length; i++) {
+      const [key, val] = Object.entries(result.gesture[i]);
+      log.data(`  Gesture: ${key[0]}#${key[1]} gesture:${val[1]}`);
+    }
+  } else {
+    log.data('  Gesture: N/A');
+  }
+
+  if (result && result.object && result.object.length > 0) {
+    for (let i = 0; i < result.object.length; i++) {
+      const object = result.object[i];
+      log.data(`  Object: #${i} score:${object.score} label:${object.label}`);
+    }
+  } else {
+    log.data('  Object: N/A');
+  }
+
+  // print data to console
+  if (result) {
+    // invoke persons getter
+    const persons = result.persons;
+
+    // write result objects to file
+    // fs.writeFileSync('result.json', JSON.stringify(result, null, 2));
+
+    log.data('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.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}`);
+    }
+  }
+
+  return result;
+}
+
+async function test() {
+  process.on('unhandledRejection', (err) => {
+    // @ts-ignore // no idea if exception message is compelte
+    log.error(err?.message || err || 'no error message');
+  });
+
+  // test with embedded full body image
+  let result;
+
+  log.state('Processing embedded warmup image: face');
+  myConfig.warmup = 'face';
+  result = await human.warmup(myConfig);
+
+  log.state('Processing embedded warmup image: full');
+  myConfig.warmup = 'full';
+  result = await human.warmup(myConfig);
+  // no need to print results as they are printed to console during detection from within the library due to human.config.debug set
+  return result;
+}
+
+async function main() {
+  log.configure({ inspect: { breakLength: 265 } });
+  log.header();
+  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) {
+    log.warn('Parameters: <input image | folder> missing');
+    await test();
+  } else if (!fs.existsSync(f) && !f.startsWith('http')) {
+    log.error(`File not found: ${process.argv[2]}`);
+  } else {
+    if (fs.existsSync(f)) {
+      const stat = fs.statSync(f);
+      if (stat.isDirectory()) {
+        const dir = fs.readdirSync(f);
+        for (const file of dir) {
+          await detect(path.join(f, file));
+        }
+      } else {
+        await detect(f);
+      }
+    } else {
+      await detect(f);
+    }
+  }
+}
+
+main();

demo/nodejs/process-folder.js

@@ -0,0 +1,78 @@
+const fs = require('fs');
+const path = require('path');
+const process = require('process');
+const log = require('@vladmandic/pilogger');
+const canvas = require('canvas');
+// const tf = require('@tensorflow/tfjs-node-gpu'); // for nodejs, `tfjs-node` or `tfjs-node-gpu` should be loaded before using Human
+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
+  debug: true,
+  async: false,
+  cacheSensitivity: 0,
+  face: { enabled: true, detector: { maxDetected: 20 } },
+  object: { enabled: true },
+  gesture: { enabled: true },
+  hand: { enabled: true },
+  body: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/movenet-multipose.json' },
+};
+
+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
+
+  const human = new Human.Human(config); // create instance of human
+  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:', Object.keys(human.models).filter((a) => human.models[a]));
+
+  const inDir = process.argv[2];
+  const outDir = process.argv[3];
+  if (process.argv.length !== 4) {
+    log.error('Parameters: <input-directory> <output-directory> missing');
+    return;
+  }
+  if (!fs.existsSync(inDir) || !fs.statSync(inDir).isDirectory() || !fs.existsSync(outDir) || !fs.statSync(outDir).isDirectory()) {
+    log.error('Invalid directory specified:', 'input:', fs.existsSync(inDir) ?? fs.statSync(inDir).isDirectory(), 'output:', 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);
+  for (const image of images) {
+    const inFile = path.join(inDir, image);
+    const buffer = fs.readFileSync(inFile);
+    const tensor = human.tf.tidy(() => {
+      const decode = human.tf.node.decodeImage(buffer, 3);
+      const expand = human.tf.expandDims(decode, 0);
+      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);
+  }
+}
+
+main();

demo/offline.html

@@ -0,0 +1,36 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta http-equiv="content-type" content="text/html; charset=utf-8">
+    <title>Human: Offline</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">
+    <meta name="keywords" content="Human">
+    <meta name="description" content="Human; Author: Vladimir Mandic <mandic00@live.com>">
+    <meta name="msapplication-tooltip" content="Human; Author: Vladimir Mandic <mandic00@live.com>">
+    <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="icon" sizes="256x256" href="../assets/icon.png">
+    <link rel="apple-touch-icon" href="../assets/icon.png">
+    <link rel="apple-touch-startup-image" 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') }
+      body { font-family: 'Lato', 'Segoe UI'; font-size: 16px; font-variant: small-caps; background: black; color: #ebebeb; }
+      h1 { font-size: 2rem; margin-top: 1.2rem; font-weight: bold; }
+      a { color: white; }
+      a:link { color: lightblue; text-decoration: none; }
+      a:hover { color: lightskyblue; text-decoration: none; }
+      .row { width: 90vw; margin: auto; margin-top: 100px; text-align: center; }
+    </style>
+</head>
+<body>
+  <div class="row text-center">
+    <h1>
+      <a href="/">Human: Offline</a><br>
+      <img alt="icon" src="../assets/icon.png">
+    </h1>
+  </div>
+</body>
+</html>

demo/typescript/README.md

@@ -0,0 +1,5 @@
+# Human Demo in TypeScript for Browsers
+
+Simple demo app that can be used as a quick-start guide for use of `Human` in browser environments
+
+- `index.ts` is compiled to `index.js` which is loaded from `index.html`

demo/typescript/index.html

@@ -0,0 +1,30 @@
+<!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>
+      @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>
+    <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: 1rem; width: 100%; padding: 8px; font-size: 0.8rem;"></div>
+  </body>
+</html>

demo/typescript/index.js

@@ -0,0 +1,17 @@
+/*
+  Human
+  homepage: <https://github.com/vladmandic/human>
+  author: <https://github.com/vladmandic>'
+*/
+
+import{Human as p}from"../../dist/human.esm.js";var w={modelBasePath:"../../models",filter:{enabled:!0,equalization:!1},face:{enabled:!0,detector:{rotation:!1},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0}},body:{enabled:!0},hand:{enabled:!0},object:{enabled:!1},gesture:{enabled:!0}},t=new p(w);t.env.perfadd=!1;t.draw.options.font='small-caps 18px "Lato"';t.draw.options.lineHeight=20;var e={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},i={detect:0,draw:0,tensors:0},d={detect:0,draw:0},s=(...a)=>{e.log.innerText+=a.join(" ")+`
+`,console.log(...a)},r=a=>e.fps.innerText=a,b=a=>e.perf.innerText="tensors:"+t.tf.memory().numTensors+" | performance: "+JSON.stringify(a).replace(/"|{|}/g,"").replace(/,/g," | ");async function h(){r("starting webcam...");let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth},height:{ideal:document.body.clientHeight}}},n=await navigator.mediaDevices.getUserMedia(a),m=new Promise(f=>{e.video.onloadeddata=()=>f(!0)});e.video.srcObject=n,e.video.play(),await m,e.canvas.width=e.video.videoWidth,e.canvas.height=e.video.videoHeight;let o=n.getVideoTracks()[0],g=o.getCapabilities?o.getCapabilities():"",v=o.getSettings?o.getSettings():"",u=o.getConstraints?o.getConstraints():"";s("video:",e.video.videoWidth,e.video.videoHeight,o.label,{stream:n,track:o,settings:v,constraints:u,capabilities:g}),e.canvas.onclick=()=>{e.video.paused?e.video.play():e.video.pause()}}async function c(){if(!e.video.paused){await t.detect(e.video);let n=t.tf.memory().numTensors;n-i.tensors!==0&&s("allocated tensors:",n-i.tensors),i.tensors=n}let a=t.now();d.detect=1e3/(a-i.detect),i.detect=a,requestAnimationFrame(c)}async function l(){if(!e.video.paused){let n=await t.next(t.result);await t.draw.canvas(e.video,e.canvas),await t.draw.all(e.canvas,n),console.log(e.canvas.width,e.canvas.height),b(n.performance)}let a=t.now();d.draw=1e3/(a-i.draw),i.draw=a,r(e.video.paused?"paused":`fps: ${d.detect.toFixed(1).padStart(5," ")} detect | ${d.draw.toFixed(1).padStart(5," ")} draw`),setTimeout(l,30)}async function y(){s("human version:",t.version,"| tfjs version:",t.tf.version["tfjs-core"]),s("platform:",t.env.platform,"| agent:",t.env.agent),r("loading..."),await t.load(),s("backend:",t.tf.getBackend(),"| available:",t.env.backends),s("loaded models:",Object.values(t.models).filter(a=>a!==null).length),r("initializing..."),await t.warmup(),await h(),await c(),await l()}window.onload=y;
+/**
+ * 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

demo/typescript/index.ts

@@ -0,0 +1,115 @@
+/**
+ * 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 { Human, Config } from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
+
+const humanConfig: Partial<Config> = { // user configuration for human, used to fine-tune behavior
+  // backend: 'webgpu' as const,
+  // async: true,
+  modelBasePath: '../../models',
+  filter: { enabled: true, equalization: false },
+  // cacheSensitivity: 0,
+  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },
+  body: { enabled: true },
+  hand: { enabled: true },
+  object: { enabled: false },
+  gesture: { enabled: true },
+};
+
+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.draw.options.font = 'small-caps 18px "Lato"'; // set font used to draw labels when using draw methods
+human.draw.options.lineHeight = 20;
+
+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,
+  perf: document.getElementById('performance') as HTMLDivElement,
+};
+const timestamp = { detect: 0, draw: 0, tensors: 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
+
+const log = (...msg) => { // helper method to output messages
+  dom.log.innerText += msg.join(' ') + '\n';
+  // 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 + ' | 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) {
+    // console.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;
+  }
+  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 = await human.next(human.result); // smoothen result using last-known results
+    await human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen
+    await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.
+    console.log(dom.canvas.width, dom.canvas.height);
+    perf(interpolated.performance); // write performance data
+  }
+  const now = human.now();
+  fps.draw = 1000 / (now - timestamp.draw);
+  timestamp.draw = now;
+  status(dom.video.paused ? 'paused' : `fps: ${fps.detect.toFixed(1).padStart(5, ' ')} detect | ${fps.draw.toFixed(1).padStart(5, ' ')} draw`); // 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 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('loaded models:', Object.values(human.models).filter((model) => model !== null).length);
+  status('initializing...');
+  await human.warmup(); // warmup function to initialize backend for future faster detection
+  await webCam(); // start webcam
+  await detectionLoop(); // start detection loop
+  await drawLoop(); // start draw loop
+}
+
+window.onload = main;

dist/tfjs.version.js

@@ -0,0 +1,7 @@
+/*
+  Human
+  homepage: <https://github.com/vladmandic/human>
+  author: <https://github.com/vladmandic>'
+*/
+
+var e="3.16.0";var s="3.16.0";var t="3.16.0";var r="3.16.0";var l="3.16.0";var i="3.16.0";var a="3.16.0";var V={tfjs:e,"tfjs-core":s,"tfjs-data":t,"tfjs-layers":r,"tfjs-converter":l,"tfjs-backend-webgl":i,"tfjs-backend-wasm":a};export{V as version};

human.service

@@ -0,0 +1,18 @@
+[Unit]
+Description=human
+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 development
+WorkingDirectory=<your-project-folder>
+StandardOutput=inherit
+StandardError=inherit
+Restart=always
+RestartSec=300
+User=vlado
+StandardOutput=null
+
+[Install]
+WantedBy=multi-user.target

models/README.md

@@ -0,0 +1,310 @@
+# Human Library: Models
+
+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' ]
+}
+```