implement blazepose and update demos

README.md

@@ -1,20 +1,23 @@
 # Human Library
 
-### 3D Face Detection, Face Embedding & Recognition,
+**3D Face Detection, Face Embedding & Recognition,**  
-### Body Pose Tracking, Hand & Finger Tracking,
+**Body Pose Tracking, Hand & Finger Tracking,**  
-### Iris Analysis, Age & Gender & Emotion Prediction
+**Iris Analysis, Age & Gender & Emotion Prediction**  
-### & Gesture Recognition
+**& Gesture Recognition**
 
 <br>
 
 Native JavaScript module using TensorFlow/JS Machine Learning library  
-Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows and Linux    
+Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows and Linux
+
 - Browser/WebWorker: Compatible with *CPU*, *WebGL*, *WASM* and *WebGPU* backends  
 - NodeJS: Compatible with software *tfjs-node* and CUDA accelerated backends *tfjs-node-gpu*  
 
+Check out [**Live Demo**](https://vladmandic.github.io/human/demo/index.html) for processing of live WebCam video or static images
+
 <br>
 
-### Project pages:
+## Project pages
 
 - [**Live Demo**](https://vladmandic.github.io/human/demo/index.html)
 - [**Code Repository**](https://github.com/vladmandic/human)
@@ -24,7 +27,7 @@ Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows an
 
 <br>
 
-### Wiki pages:
+## Wiki pages
 
 - [**Home**](https://github.com/vladmandic/human/wiki)
 - [**Demos**](https://github.com/vladmandic/human/wiki/Demos)
@@ -37,7 +40,7 @@ Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows an
 
 <br>
 
-### Additional notes:
+## Additional notes
 
 - [**Notes on Backends**](https://github.com/vladmandic/human/wiki/Backends)
 - [**Development Server**](https://github.com/vladmandic/human/wiki/Development-Server)
@@ -49,13 +52,35 @@ Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows an
 
 <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*  
+## Default models
+
+Default models in Human library are:
+
+- **Face Detection**: MediaPipe BlazeFace-Back
+- **Face Mesh**: MediaPipe FaceMesh
+- **Face Iris Analysis**: MediaPipe Iris
+- **Emotion Detection**: Oarriaga Emotion
+- **Gender Detection**: Oarriaga Gender
+- **Age Detection**: SSR-Net Age IMDB
+- **Body Analysis**: PoseNet
+- **Face Embedding**: Sirius-AI MobileFaceNet Embedding
+
+Note that alternative models are provided and can be enabled via configuration  
+For example, `PoseNet` model can be switched for `BlazePose` 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>
+
+*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!*  
 
 <br><hr><br>
 
-## Options ##
+## Options
+
+As presented in the demo application...
 
 ![Options visible in demo](assets/screenshot-menu.png)
 
@@ -65,12 +90,17 @@ Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows an
 
 <br>
 
+**Training image:**  
+
+![Example Training Image](assets/screenshot-sample.png)
+
 **Using static images:**  
 
-![Example Using Image](assets/screenshot1.jpg)
+![Example Using Image](assets/screenshot-images.jpg)
+
+**Live WebCam view:**  
+
+![Example Using WebCam](assets/screenshot-webcam.jpg)
+
 
 <br>
-
-**Using webcam:**  
-
-![Example Using WebCam](assets/screenshot2.jpg)

TODO.md

@@ -1,7 +1,5 @@
 # To-Do list for Human library
 
-- Explore BlazePose model
-- Explore MediaPipe TFLite models
 - Fix BlazeFace NodeJS missing ops
 - Prune pre-packaged models
 - Build Face embedding database

config.js

@@ -105,8 +105,7 @@ export default {
 
     age: {
       enabled: true,
-      modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'
+      modelPath: '../models/age-ssrnet-imdb.json',
-                                                   // which determines training set for model
       inputSize: 64,         // fixed value
       skipFrames: 31,        // how many frames to go without re-running the detector
                              // only used for video inputs
@@ -115,7 +114,7 @@ export default {
     gender: {
       enabled: true,
       minConfidence: 0.1,    // threshold for discarding a prediction
-      modelPath: '../models/gender.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'
+      modelPath: '../models/gender.json', // can be 'gender' or 'gender-ssrnet-imdb'
       inputSize: 64,         // fixed value
       skipFrames: 41,        // how many frames to go without re-running the detector
                              // only used for video inputs
@@ -126,7 +125,7 @@ export default {
       inputSize: 64,         // fixed value
       minConfidence: 0.1,    // threshold for discarding a prediction
       skipFrames: 21,        // how many frames to go without re-running the detector
-      modelPath: '../models/emotion-large.json', // can be 'mini', 'large'
+      modelPath: '../models/emotion.json',
     },
 
     embedding: {
@@ -138,16 +137,17 @@ export default {
 
   body: {
     enabled: true,
-    modelPath: '../models/posenet.json',
+    modelPath: '../models/posenet.json', // can be 'posenet' or 'blazepose'
-    inputSize: 257,          // fixed value
+    inputSize: 257,          // fixed value, 257 for posenet and 256 for blazepose
     maxDetections: 10,       // maximum number of people detected in the input
                              // should be set to the minimum number for performance
+                             // only valid for posenet as blazepose only detects single pose
     scoreThreshold: 0.5,     // threshold for deciding when to remove boxes based on score
                              // in non-maximum suppression
+                             // only valid for posenet as blazepose only detects single pose
     nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression
-    outputStride: 16,        // size of block in which to run point detection, smaller value means higher resolution
+                             // only valid for posenet as blazepose only detects single pose
-                             // defined by model itself, can be 8, 16, or 32
+    modelType: 'posenet-mobilenet',  // can be 'posenet-mobilenet', 'posenet-resnet', 'blazepose'
-    modelType: 'MobileNet',  // Human includes MobileNet version, but you can switch to ResNet
   },
 
   hand: {

demo/browser.js

@@ -14,10 +14,12 @@ const userConfig = {}; // add any user configuration overrides
 const userConfig = {
   backend: 'wasm',
   async: false,
+  warmup: 'full',
   videoOptimized: false,
-  face: { enabled: true, iris: { enabled: false }, mesh: { enabled: true }, age: { enabled: true }, gender: { enabled: true }, emotion: { enabled: true }, embedding: { enabled: true } },
+  face: { enabled: true, iris: { enabled: true }, mesh: { enabled: true }, age: { enabled: true }, gender: { enabled: true }, emotion: { enabled: true }, embedding: { enabled: true } },
-  body: { enabled: false },
+  hand: { enabled: true },
-  hand: { enabled: false },
+  gestures: { enabled: true },
+  body: { enabled: true, modelType: 'blazepose', modelPath: '../models/blazepose.json' },
 };
 */
 
@@ -31,15 +33,16 @@ const ui = {
   baseFontProto: 'small-caps {size} "Segoe UI"',
   baseLineWidth: 12,
   crop: true,
-  columns: 4,
+  columns: 2,
   busy: false,
   facing: true,
   useWorker: false,
   worker: 'worker.js',
   samples: ['../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg'],
   compare: '../assets/sample-me.jpg',
+  drawLabels: true,
   drawBoxes: true,
-  drawPoints: false,
+  drawPoints: true,
   drawPolygons: true,
   fillPolygons: false,
   useDepth: true,
@@ -52,6 +55,7 @@ const ui = {
   detectFPS: [],
   drawFPS: [],
   buffered: false,
+  drawWarmup: false,
   drawThread: null,
   detectThread: null,
   framesDraw: 0,
@@ -120,7 +124,7 @@ async function drawResults(input) {
   await menu.process.updateChart('FPS', ui.detectFPS);
 
   // get updated canvas
-  if (ui.buffered || !result.canvas) result.canvas = await human.image(input, userConfig);
+  if (ui.buffered || !result.canvas) result.canvas = await human.image(input).canvas;
 
   // draw image from video
   const ctx = canvas.getContext('2d');
@@ -436,6 +440,7 @@ function setupMenu() {
   });
   menu.display.addHTML('<hr style="border-style: inset; border-color: dimgray">');
   menu.display.addBool('use 3D depth', ui, 'useDepth');
+  menu.display.addBool('print labels', ui, 'drawLabels');
   menu.display.addBool('draw boxes', ui, 'drawBoxes');
   menu.display.addBool('draw polygons', ui, 'drawPolygons');
   menu.display.addBool('Fill Polygons', ui, 'fillPolygons');
@@ -530,6 +535,18 @@ function setupMenu() {
   document.getElementById('play').addEventListener('click', () => detectVideo());
 }
 
+async function drawWarmup(res) {
+  const canvas = document.getElementById('canvas');
+  canvas.width = res.canvas.width;
+  canvas.height = res.canvas.height;
+  const ctx = canvas.getContext('2d');
+  ctx.drawImage(res.canvas, 0, 0, res.canvas.width, res.canvas.height, 0, 0, canvas.width, canvas.height);
+  await draw.face(res.face, canvas, ui, human.facemesh.triangulation);
+  await draw.body(res.body, canvas, ui);
+  await draw.hand(res.hand, canvas, ui);
+  await draw.gesture(res.gesture, canvas, ui);
+}
+
 async function main() {
   log('Demo starting ...');
   log('Browser:', navigator?.userAgent);
@@ -543,7 +560,9 @@ async function main() {
   }
   if (!ui.useWorker) {
     status('initializing');
-    await human.warmup(userConfig); // this is not required, just pre-warms all models for faster initial inference
+    const res = await human.warmup(userConfig); // this is not required, just pre-warms all models for faster initial inference
+    ui.baseFont = ui.baseFontProto.replace(/{size}/, '16px');
+    if (res && res.canvas && ui.drawWarmup) await drawWarmup(res);
   }
   status('human: ready');
   document.getElementById('loader').style.display = 'none';

demo/draw.js

@@ -1,3 +1,12 @@
+async function drawPoint(canvas, x = 0, y = 0, radius = 0, color = 'black', label) {
+  const ctx = canvas.getContext('2d');
+  ctx.fillStyle = color;
+  ctx.beginPath();
+  ctx.arc(x, y, radius, 0, 2 * Math.PI);
+  ctx.fill();
+  if (label) ctx.fillText(label, x + 4, y + 4);
+}
+
 async function drawGesture(result, canvas, ui) {
   if (!result) return;
   const ctx = canvas.getContext('2d');
@@ -11,7 +20,9 @@ async function drawGesture(result, canvas, ui) {
       const label = `${where[0]} ${person}: ${what[1]}`;
       ctx.fillStyle = 'black';
       ctx.fillText(label, 8, 2 + (i * ui.baseLineHeight));
+      // ctx.fillText(label, 151, i * 16 + 101);
       ctx.fillStyle = ui.baseLabel;
+      // ctx.fillText(label, 150, i * 16 + 100);
       ctx.fillText(label, 6, 0 + (i * ui.baseLineHeight));
       i += 1;
     }
@@ -48,8 +59,10 @@ async function drawFace(result, canvas, ui, triangulation) {
       const x = Math.max(face.box[0], 0);
       const y = i * ui.baseLineHeight + face.box[1];
       ctx.fillText(labels[i], x + 5, y + 16);
+      // ctx.fillText(labels[i], 151, i * 16 + 28);
       ctx.fillStyle = ui.baseLabel;
       ctx.fillText(labels[i], x + 4, y + 15);
+      // ctx.fillText(labels[i], 150, i * 16 + 27);
     }
     ctx.fillStyle = ui.baseColor;
     ctx.stroke();
@@ -119,13 +132,17 @@ async function drawBody(result, canvas, ui) {
   const ctx = canvas.getContext('2d');
   ctx.lineJoin = 'round';
   for (let i = 0; i < result.length; i++) {
+    // result[i].keypoints = result[i].keypoints.filter((a) => a.score > 0.5);
     if (!lastDrawnPose[i] && ui.buffered) lastDrawnPose[i] = { ...result[i] };
-    ctx.fillStyle = ui.baseColor;
     ctx.strokeStyle = ui.baseColor;
     ctx.font = ui.baseFont;
     ctx.lineWidth = ui.baseLineWidth;
     if (ui.drawPoints) {
       for (let pt = 0; pt < result[i].keypoints.length; pt++) {
+        ctx.fillStyle = ui.useDepth && result[i].keypoints[pt].position.z ? `rgba(${127.5 + (2 * result[i].keypoints[pt].position.z)}, ${127.5 - (2 * result[i].keypoints[pt].position.z)}, 255, 0.5)` : ui.baseColor;
+        if (ui.drawLabels) {
+          ctx.fillText(`${result[i].keypoints[pt].part}`, result[i].keypoints[pt].position.x + 4, result[i].keypoints[pt].position.y + 4);
+        }
         ctx.beginPath();
         if (ui.buffered) {
           lastDrawnPose[i].keypoints[pt].position.x = (lastDrawnPose[i].keypoints[pt].position.x + result[i].keypoints[pt].position.x) / 2;
@@ -162,6 +179,10 @@ async function drawBody(result, canvas, ui) {
         if (part) path.lineTo(part.position.x, part.position.y);
         part = result[i].keypoints.find((a) => a.part === 'leftAnkle');
         if (part) path.lineTo(part.position.x, part.position.y);
+        part = result[i].keypoints.find((a) => a.part === 'leftHeel');
+        if (part) path.lineTo(part.position.x, part.position.y);
+        part = result[i].keypoints.find((a) => a.part === 'leftFoot');
+        if (part) path.lineTo(part.position.x, part.position.y);
       }
       // leg right
       root = result[i].keypoints.find((a) => a.part === 'rightHip');
@@ -171,6 +192,10 @@ async function drawBody(result, canvas, ui) {
         if (part) path.lineTo(part.position.x, part.position.y);
         part = result[i].keypoints.find((a) => a.part === 'rightAnkle');
         if (part) path.lineTo(part.position.x, part.position.y);
+        part = result[i].keypoints.find((a) => a.part === 'rightHeel');
+        if (part) path.lineTo(part.position.x, part.position.y);
+        part = result[i].keypoints.find((a) => a.part === 'rightFoot');
+        if (part) path.lineTo(part.position.x, part.position.y);
       }
       // arm left
       root = result[i].keypoints.find((a) => a.part === 'leftShoulder');
@@ -180,6 +205,8 @@ async function drawBody(result, canvas, ui) {
         if (part) path.lineTo(part.position.x, part.position.y);
         part = result[i].keypoints.find((a) => a.part === 'leftWrist');
         if (part) path.lineTo(part.position.x, part.position.y);
+        part = result[i].keypoints.find((a) => a.part === 'leftPalm');
+        if (part) path.lineTo(part.position.x, part.position.y);
       }
       // arm right
       root = result[i].keypoints.find((a) => a.part === 'rightShoulder');
@@ -189,6 +216,8 @@ async function drawBody(result, canvas, ui) {
         if (part) path.lineTo(part.position.x, part.position.y);
         part = result[i].keypoints.find((a) => a.part === 'rightWrist');
         if (part) path.lineTo(part.position.x, part.position.y);
+        part = result[i].keypoints.find((a) => a.part === 'rightPalm');
+        if (part) path.lineTo(part.position.x, part.position.y);
       }
       // draw all
       ctx.stroke(path);
@@ -253,4 +282,5 @@ export default {
   body: drawBody,
   hand: drawHand,
   gesture: drawGesture,
+  point: drawPoint,
 };

demo/node.js

@@ -14,16 +14,19 @@ const myConfig = {
   videoOptimized: false,
   async: false,
   face: {
-    // detector: { modelPath: 'file://models/faceboxes.json' },
+    enabled: true,
-    detector: { modelPath: 'file://models/blazeface-back.json' }, // cannot use blazeface in nodejs due to missing required kernel function in tfjs-node
+    detector: { modelPath: 'file://models/faceboxes.json', enabled: true, minConfidence: 0.5 },
-    mesh: { modelPath: 'file://models/facemesh.json' },
+    // detector: { modelPath: 'file://models/blazeface-back.json', enabled: false }, // cannot use blazeface in nodejs due to missing required kernel function in tfjs-node
-    iris: { modelPath: 'file://models/iris.json' },
+    mesh: { modelPath: 'file://models/facemesh.json', enabled: false }, // depends on blazeface detector
-    age: { modelPath: 'file://models/age-ssrnet-imdb.json' },
+    iris: { modelPath: 'file://models/iris.json', enabled: true },
-    gender: { modelPath: 'file://models/gender.json' },
+    age: { modelPath: 'file://models/age-ssrnet-imdb.json', enabled: true },
-    emotion: { modelPath: 'file://models/emotion-large.json' },
+    gender: { modelPath: 'file://models/gender.json', enabled: true },
+    emotion: { modelPath: 'file://models/emotion.json', enabled: true },
   },
-  body: { modelPath: 'file://models/posenet.json' },
+  // body: { modelPath: 'file://models/blazepose.json', modelType: 'blazepose', inputSize: 256, enabled: true },
+  body: { modelPath: 'file://models/posenet.json', modelType: 'posenet', inputSize: 257, enabled: true },
   hand: {
+    enabled: true,
     detector: { modelPath: 'file://models/handdetect.json' },
     skeleton: { modelPath: 'file://models/handskeleton.json' },
   },
@@ -35,7 +38,14 @@ async function init() {
   // create instance of human
   human = new Human(myConfig);
   // pre-load models
+  log.info('Human:', human.version);
+  log.info('Active Configuration', human.config);
+  log.info('TFJS Version:', human.tf.version_core, 'Backend:', tf.getBackend());
+  log.info('TFJS Flags:', human.tf.env().features);
   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) {
@@ -74,6 +84,7 @@ async function test() {
 
 async function main() {
   log.info('NodeJS:', process.version);
+  log.info('Current folder:', process.env.PWD);
   await init();
   if (process.argv.length !== 3) {
     log.warn('Parameters: <input image> missing');

dist/demo-browser-index.json

@@ -1,11 +1,11 @@
 {
   "inputs": {
     "dist/human.esm.js": {
-      "bytes": 1339605,
+      "bytes": 1341917,
       "imports": []
     },
     "demo/draw.js": {
-      "bytes": 10783,
+      "bytes": 12536,
       "imports": []
     },
     "demo/menu.js": {
@@ -17,7 +17,7 @@
       "imports": []
     },
     "demo/browser.js": {
-      "bytes": 27252,
+      "bytes": 28101,
       "imports": [
         {
           "path": "dist/human.esm.js",
@@ -43,7 +43,7 @@
       "imports": [],
       "exports": [],
       "inputs": {},
-      "bytes": 2040655
+      "bytes": 2051266
     },
     "dist/demo-browser-index.js": {
       "imports": [],
@@ -51,10 +51,10 @@
       "entryPoint": "demo/browser.js",
       "inputs": {
         "dist/human.esm.js": {
-          "bytesInOutput": 1332127
+          "bytesInOutput": 1334439
         },
         "demo/draw.js": {
-          "bytesInOutput": 6241
+          "bytesInOutput": 7178
         },
         "demo/menu.js": {
           "bytesInOutput": 10696
@@ -63,10 +63,10 @@
           "bytesInOutput": 6759
         },
         "demo/browser.js": {
-          "bytesInOutput": 17737
+          "bytesInOutput": 18268
         }
       },
-      "bytes": 1380945
+      "bytes": 1384725
     }
   }
 }

dist/human.esm.json

@@ -192,7 +192,7 @@
       ]
     },
     "src/posenet/modelBase.ts": {
-      "bytes": 1333,
+      "bytes": 1349,
       "imports": [
         {
           "path": "dist/tfjs.esm.js",
@@ -214,7 +214,7 @@
       ]
     },
     "src/posenet/keypoints.ts": {
-      "bytes": 2025,
+      "bytes": 2041,
       "imports": []
     },
     "src/posenet/vectors.ts": {
@@ -240,7 +240,7 @@
       ]
     },
     "src/posenet/decodePose.ts": {
-      "bytes": 5152,
+      "bytes": 5182,
       "imports": [
         {
           "path": "src/posenet/keypoints.ts",
@@ -257,7 +257,7 @@
       ]
     },
     "src/posenet/decodeMultiple.ts": {
-      "bytes": 2301,
+      "bytes": 2323,
       "imports": [
         {
           "path": "src/posenet/buildParts.ts",
@@ -383,6 +383,23 @@
         }
       ]
     },
+    "src/blazepose/blazepose.ts": {
+      "bytes": 3327,
+      "imports": [
+        {
+          "path": "src/log.ts",
+          "kind": "import-statement"
+        },
+        {
+          "path": "dist/tfjs.esm.js",
+          "kind": "import-statement"
+        },
+        {
+          "path": "src/profile.ts",
+          "kind": "import-statement"
+        }
+      ]
+    },
     "src/gesture/gesture.ts": {
       "bytes": 4496,
       "imports": []
@@ -409,7 +426,7 @@
       ]
     },
     "config.js": {
-      "bytes": 10438,
+      "bytes": 10403,
       "imports": []
     },
     "src/sample.ts": {
@@ -417,11 +434,11 @@
       "imports": []
     },
     "package.json": {
-      "bytes": 2560,
+      "bytes": 2594,
       "imports": []
     },
     "src/human.ts": {
-      "bytes": 20100,
+      "bytes": 21042,
       "imports": [
         {
           "path": "src/log.ts",
@@ -467,6 +484,10 @@
           "path": "src/handpose/handpose.ts",
           "kind": "import-statement"
         },
+        {
+          "path": "src/blazepose/blazepose.ts",
+          "kind": "import-statement"
+        },
         {
           "path": "src/gesture/gesture.ts",
           "kind": "import-statement"
@@ -499,7 +520,7 @@
       "imports": [],
       "exports": [],
       "inputs": {},
-      "bytes": 1941875
+      "bytes": 1948302
     },
     "dist/human.esm.js": {
       "imports": [],
@@ -518,7 +539,7 @@
           "bytesInOutput": 252
         },
         "dist/tfjs.esm.js": {
-          "bytesInOutput": 1056626
+          "bytesInOutput": 1056636
         },
         "src/tfjs/backend.ts": {
           "bytesInOutput": 1053
@@ -539,7 +560,7 @@
           "bytesInOutput": 5054
         },
         "src/human.ts": {
-          "bytesInOutput": 10612
+          "bytesInOutput": 11312
         },
         "src/faceboxes/faceboxes.ts": {
           "bytesInOutput": 1576
@@ -563,7 +584,7 @@
           "bytesInOutput": 1016
         },
         "src/posenet/modelBase.ts": {
-          "bytesInOutput": 646
+          "bytesInOutput": 662
         },
         "src/posenet/heapSort.ts": {
           "bytesInOutput": 1017
@@ -572,7 +593,7 @@
           "bytesInOutput": 456
         },
         "src/posenet/decodePose.ts": {
-          "bytesInOutput": 1292
+          "bytesInOutput": 1283
         },
         "src/posenet/vectors.ts": {
           "bytesInOutput": 346
@@ -581,7 +602,7 @@
           "bytesInOutput": 768
         },
         "src/posenet/decodeMultiple.ts": {
-          "bytesInOutput": 557
+          "bytesInOutput": 529
         },
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dist/human.iife.json

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dist/human.node.json

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models/emotion-mini.json

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@@ -1,6 +1,6 @@
 {
   "name": "@vladmandic/human",
-  "version": "0.30.6",
+  "version": "0.40.0",
   "description": "Human: AI-powered 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition",
   "sideEffects": false,
   "main": "dist/human.node.js",
@@ -71,6 +71,8 @@
     "age-estimation",
     "emotion-detection",
     "gender-prediction",
-    "gesture-recognition"
+    "gesture-recognition",
+    "blazeface",
+    "blazepose"
   ]
 }

src/blazepose/blazepose.ts

@@ -0,0 +1,104 @@
+import { log } from '../log';
+import * as tf from '../../dist/tfjs.esm.js';
+import * as profile from '../profile';
+
+let model;
+const labels = [
+  'nose',
+  'leftEyeInside',
+  'leftEye',
+  'leftEyeOutside',
+  'rightEyeInside',
+  'rightEye',
+  'rightEyeOutside',
+  'leftEar',
+  'rightEar',
+  'leftMouth',
+  'rightMouth',
+  'leftShoulder',
+  'rightShoulder',
+  'leftElbow',
+  'rightElbow',
+  'leftWrist',
+  'rightWrist',
+  'leftPalm',
+  'rightPalm',
+  'leftIndex',
+  'rightIndex',
+  'leftPinky',
+  'rightPinky',
+  'leftHip',
+  'rightHip',
+  'leftKnee',
+  'rightKnee',
+  'leftAnkle',
+  'rightAnkle',
+  'leftHeel',
+  'rightHeel',
+  'leftFoot',
+  'rightFoot',
+  'midHip',
+  'forehead',
+  'leftThumb',
+  'leftHand',
+  'rightThumb',
+  'rightHand',
+];
+
+export async function load(config) {
+  if (!model) {
+    model = await tf.loadGraphModel(config.body.modelPath);
+    model.width = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[2].size);
+    model.height = parseInt(model.signature.inputs['input_1:0'].tensorShape.dim[1].size);
+    if (config.debug) log(`load model: ${config.body.modelPath.match(/\/(.*)\./)[1]}`);
+  }
+  return model;
+}
+
+export async function predict(image, config) {
+  if (!model) return null;
+  if (!config.body.enabled) return null;
+  const imgSize = { width: image.shape[2], height: image.shape[1] };
+  const resize = tf.image.resizeBilinear(image, [model.width || config.body.inputSize, model.height || config.body.inputSize], false);
+  const normalize = tf.div(resize, [255.0]);
+  resize.dispose();
+  // let segmentation; // not used right now since we have keypoints and don't need to go through matrix using strides
+  // let poseflag; // irrelevant
+  let points;
+  if (!config.profile) {
+    const resT = await model.predict(normalize);
+    // segmentation = resT[0].dataSync();
+    // poseflag = resT[1].dataSync();
+    points = resT.find((t) => (t.size === 195 || t.size === 155)).dataSync();
+    resT.forEach((t) => t.dispose());
+  } else {
+    const profileData = await tf.profile(() => model.predict(normalize));
+    // segmentation = profileData.result[0].dataSync();
+    // poseflag = profileData.result[1].dataSync();
+    points = profileData.result.find((t) => t.size === 195).dataSync(); // find a tensor with 195 items which is 39 points with 5 properties
+    profileData.result.forEach((t) => t.dispose());
+    profile.run('blazepose', profileData);
+  }
+  normalize.dispose();
+  const keypoints: Array<{ id, part, position: { x, y, z }, score, presence }> = [];
+  for (let i = 0; i < points.length / 5; i++) {
+    keypoints.push({
+      id: i,
+      part: labels[i],
+      position: {
+        x: Math.trunc(imgSize.width * points[5 * i + 0] / 255),
+        y: Math.trunc(imgSize.height * points[5 * i + 1] / 255),
+        z: Math.trunc(points[5 * i + 2]) + 0, // fix negative zero
+      },
+      score: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 3])))) / 100, // reverse sigmoid value
+      presence: (100 - Math.trunc(100 / (1 + Math.exp(points[5 * i + 4])))) / 100, // reverse sigmoid value
+    });
+  }
+  // console.log('POINTS', imgSize, pts.length, pts);
+  return [{ keypoints }];
+}
+
+/*
+Model card: https://drive.google.com/file/d/10IU-DRP2ioSNjKFdiGbmmQX81xAYj88s/view
+Download: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/058_BlazePose_Full_Keypoints
+*/

src/human.ts

@@ -9,6 +9,7 @@ import * as emotion from './emotion/emotion';
 import * as embedding from './embedding/embedding';
 import * as posenet from './posenet/posenet';
 import * as handpose from './handpose/handpose';
+import * as blazepose from './blazepose/blazepose';
 import * as gesture from './gesture/gesture';
 import * as image from './image';
 import * as profile from './profile';
@@ -49,6 +50,7 @@ class Human {
   checkSanity: boolean;
   firstRun: boolean;
   perf: any;
+  image: any;
   models: any;
   // models
   facemesh: any;
@@ -74,18 +76,21 @@ class Human {
     this.models = {
       facemesh: null,
       posenet: null,
+      blazepose: null,
       handpose: null,
       iris: null,
       age: null,
       gender: null,
       emotion: null,
     };
+    // export access to image processing
+    this.image = (input) => image.process(input, this.config);
     // export raw access to underlying models
     this.facemesh = facemesh;
     this.age = age;
     this.gender = gender;
     this.emotion = emotion;
-    this.body = posenet;
+    this.body = this.config.body.modelType.startsWith('posenet') ? posenet : blazepose;
     this.hand = handpose;
   }
 
@@ -146,16 +151,18 @@ class Human {
         this.models.gender,
         this.models.emotion,
         this.models.embedding,
-        this.models.posenet,
         this.models.handpose,
+        this.models.posenet,
+        this.models.blazepose,
       ] = await Promise.all([
         this.models.face || (this.config.face.enabled ? face.load(this.config) : null),
         this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),
         this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),
         this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),
         this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),
-        this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),
         this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),
+        this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('posenet') ? posenet.load(this.config) : null),
+        this.models.posenet || (this.config.body.enabled && this.config.body.modelType.startsWith('blazepose') ? blazepose.load(this.config) : null),
       ]);
     } else {
       if (this.config.face.enabled && !this.models.face) this.models.face = await face.load(this.config);
@@ -163,8 +170,9 @@ class Human {
       if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);
       if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);
       if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);
-      if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);
       if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);
+      if (this.config.body.enabled && !this.models.posenet && this.config.body.modelType.startsWith('posenet')) this.models.posenet = await posenet.load(this.config);
+      if (this.config.body.enabled && !this.models.blazepose && this.config.body.modelType.startsWith('blazepose')) this.models.blazepose = await blazepose.load(this.config);
     }
 
     if (this.firstRun) {
@@ -346,16 +354,6 @@ class Human {
     return faceRes;
   }
 
-  /*
-  async processImage(input, userConfig = {}) {
-    this.state = 'image';
-    this.config = mergeDeep(this.config, userConfig);
-    const process = image.process(input, this.config);
-    process?.tensor?.dispose();
-    return process?.canvas;
-  }
-  */
-
   // main detect function
   async detect(input, userConfig = {}) {
     // detection happens inside a promise
@@ -374,7 +372,7 @@ class Human {
         resolve({ error });
       }
 
-      let poseRes;
+      let bodyRes;
       let handRes;
       let faceRes;
 
@@ -410,15 +408,17 @@ class Human {
         this.perf.face = Math.trunc(now() - timeStamp);
       }
 
-      // run posenet
+      // run body: can be posenet or blazepose
       this.analyze('Start Body:');
       if (this.config.async) {
-        poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];
+        if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];
+        else bodyRes = this.config.body.enabled ? blazepose.predict(process.tensor, this.config) : [];
         if (this.perf.body) delete this.perf.body;
       } else {
         this.state = 'run:body';
         timeStamp = now();
-        poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];
+        if (this.config.body.modelType.startsWith('posenet')) bodyRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];
+        else bodyRes = this.config.body.enabled ? await blazepose.predict(process.tensor, this.config) : [];
         this.perf.body = Math.trunc(now() - timeStamp);
       }
       this.analyze('End Body:');
@@ -438,7 +438,7 @@ class Human {
 
       // if async wait for results
       if (this.config.async) {
-        [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);
+        [faceRes, bodyRes, handRes] = await Promise.all([faceRes, bodyRes, handRes]);
       }
       process.tensor.dispose();
 
@@ -449,14 +449,14 @@ class Human {
       if (this.config.gesture.enabled) {
         timeStamp = now();
         // @ts-ignore
-        gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];
+        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];
         if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);
         else if (this.perf.gesture) delete this.perf.gesture;
       }
 
       this.perf.total = Math.trunc(now() - timeStart);
       this.state = 'idle';
-      resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });
+      resolve({ face: faceRes, body: bodyRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });
     });
   }
 
@@ -487,21 +487,24 @@ class Human {
           src = 'data:image/jpeg;base64,' + sample.face;
           break;
         case 'full':
+        case 'body':
           size = 1200;
           src = 'data:image/jpeg;base64,' + sample.body;
           break;
         default:
           src = null;
       }
-      const img = new Image(size, size);
+      // src = encodeURI('../assets/human-sample-face.jpg');
-      img.onload = () => {
+      const img = new Image();
+      img.onload = async () => {
         const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');
-        canvas.width = size;
+        canvas.width = img.naturalWidth;
-        canvas.height = size;
+        canvas.height = img.naturalHeight;
         const ctx = canvas.getContext('2d');
         ctx?.drawImage(img, 0, 0);
-        const data = ctx?.getImageData(0, 0, size, size);
+        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);
-        this.detect(data, this.config).then((res) => resolve(res));
+        const res = await this.detect(canvas, this.config);
+        resolve(res);
       };
       if (src) img.src = src;
       else resolve(null);

src/node.js

@@ -32,7 +32,7 @@ const config = {
     iris: { modelPath: 'file://models/iris.json' },
     age: { modelPath: 'file://models/age-ssrnet-imdb.json' },
     gender: { modelPath: 'file://models/gender.json' },
-    emotion: { modelPath: 'file://models/emotion-large.json' },
+    emotion: { modelPath: 'file://models/emotion.json' },
     embedding: { modelPath: 'file://models/mobilefacenet.json' },
   },
   body: { modelPath: 'file://models/posenet.json' },

src/posenet/decodeMultiple.ts

@@ -3,6 +3,7 @@ import * as decodePose from './decodePose';
 import * as vectors from './vectors';
 
 const kLocalMaximumRadius = 1;
+const defaultOutputStride = 16;
 
 function withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {
   return poses.some(({ keypoints }) => {
@@ -28,10 +29,10 @@ export function decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFw
     // The top element in the queue is the next root candidate.
     const root = queue.dequeue();
     // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.
-    const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);
+    const rootImageCoords = vectors.getImageCoords(root.part, defaultOutputStride, offsetsBuffer);
     if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;
     // Else start a new detection instance at the position of the root.
-    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);
+    const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, defaultOutputStride, displacementsFwdBuffer, displacementsBwdBuffer);
     const score = getInstanceScore(poses, squaredNmsRadius, keypoints);
     if (score > config.body.scoreThreshold) poses.push({ keypoints, score });
   }

src/posenet/decodePose.ts

@@ -5,6 +5,9 @@ import * as decoders from './decoders';
 const parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));
 const parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);
 const childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);
+
+const defaultOutputStride = 16;
+
 function getDisplacement(edgeId, point, displacements) {
   const numEdges = displacements.shape[2] / 2;
   return {
@@ -12,6 +15,7 @@ function getDisplacement(edgeId, point, displacements) {
     x: displacements.get(point.y, point.x, numEdges + edgeId),
   };
 }
+
 function getStridedIndexNearPoint(point, outputStride, height, width) {
   return {
     y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),
@@ -77,7 +81,7 @@ export async function decodeSinglePose(heatmapScores, offsets, config) {
   const scoresBuffer = allTensorBuffers[0];
   const offsetsBuffer = allTensorBuffers[1];
   const heatmapValuesBuffer = allTensorBuffers[2];
-  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);
+  const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, defaultOutputStride, offsetsBuffer);
   const offsetPointsBuffer = await offsetPoints.buffer();
   const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));
   const instanceKeypoints = keypointConfidence.map((score, i) => {

src/posenet/keypoints.ts

@@ -4,7 +4,7 @@ export const partNames = [
   'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',
 ];
 
-export const NUM_KEYPOINTS = exports.partNames.length;
+export const NUM_KEYPOINTS = exports.partNames.length; // 17 keypoints
 
 export const partIds = exports.partNames.reduce((result, jointName, i) => {
   result[jointName] = i;

src/posenet/modelBase.ts

@@ -20,11 +20,11 @@ export class BaseModel {
 
   predict(input, config) {
     return tf.tidy(() => {
-      const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);
+      const asFloat = (config.body.modelType === 'posenet-resnet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);
       const asBatch = asFloat.expandDims(0);
       const results = this.model.predict(asBatch);
       const results3d = results.map((y) => y.squeeze([0]));
-      const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);
+      const namedResults = (config.body.modelType === 'posenet-resnet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);
       return {
         heatmapScores: namedResults.heatmap.sigmoid(),
         offsets: namedResults.offsets,

types/blazepose/blazepose.d.ts

@@ -0,0 +1,14 @@
+export declare function load(config: any): Promise<any>;
+export declare function predict(image: any, config: any): Promise<{
+    keypoints: {
+        id: any;
+        part: any;
+        position: {
+            x;
+            y;
+            z;
+        };
+        score: any;
+        presence: any;
+    }[];
+}[] | null>;

types/human.d.ts

@@ -10,6 +10,7 @@ declare class Human {
     checkSanity: boolean;
     firstRun: boolean;
     perf: any;
+    image: any;
     models: any;
     facemesh: any;
     age: any;

wiki

@@ -1 +1 @@
-Subproject commit 55e854ea9263ca0eae7ffbb7d60b87e1ca3a7065
+Subproject commit c60f442714b1b5887ae25feb35fa413bc9996402