normalize all scores

CHANGELOG.md

@@ -1,6 +1,6 @@
 # @vladmandic/human  
 
-Version: **1.3.0**  
+Version: **1.3.1**  
 Description: **Human: AI-powered 3D Face Detection, Face Description & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition**  
 
 Author: **Vladimir Mandic <mandic00@live.com>**  
@@ -9,8 +9,9 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
 
 ## Changelog
 
-### **HEAD -> main** 2021/03/30 mandic00@live.com
+### **1.3.1** 2021/03/30 mandic00@live.com
 
+- added face3d demo
 - initial work on face3d three.js demo
 - enable buffering
 - new icons

demo/index.js

@@ -12,7 +12,7 @@ const userConfig = {
   profile: false,
   warmup: 'full',
   videoOptimized: false,
-  filter: { enabled: true },
+  filter: { enabled: false },
   face: { enabled: false,
     mesh: { enabled: true },
     iris: { enabled: true },

demo/node.js

@@ -11,8 +11,8 @@ const Human = require('../dist/human.node.js').default; // or const Human = requ
 let human = null;
 
 const myConfig = {
-  // backend: 'tensorflow',
+  backend: 'tensorflow',
-  console: true,
+  debug: true,
   videoOptimized: false,
   async: false,
   face: {
@@ -22,17 +22,15 @@ const myConfig = {
     iris: { modelPath: 'file://models/iris.json', enabled: true },
     description: { modelPath: 'file://models/faceres.json', enabled: true },
     emotion: { modelPath: 'file://models/emotion.json', enabled: true },
-    age: { modelPath: 'file://models/age.json', enabled: false },
-    gender: { modelPath: 'file://models/gender.json', enabled: false },
-    embedding: { modelPath: 'file://models/mobileface.json', enabled: false },
   },
-  // body: { modelPath: 'file://models/blazepose.json', enabled: true },
-  body: { modelPath: 'file://models/posenet.json', enabled: true },
   hand: {
     enabled: true,
     detector: { modelPath: 'file://models/handdetect.json' },
     skeleton: { modelPath: 'file://models/handskeleton.json' },
   },
+  // body: { modelPath: 'file://models/efficientpose.json', enabled: true },
+  // body: { modelPath: 'file://models/blazepose.json', enabled: true },
+  body: { modelPath: 'file://models/posenet.json', enabled: true },
   object: { modelPath: 'file://models/nanodet.json', enabled: true },
 };
 
@@ -66,6 +64,29 @@ async function detect(input) {
   // dispose image tensor as we no longer need it
   image.dispose();
   // print data to console
+  log.data('Results:');
+  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: #${i} boxConfidence:${face.boxConfidence} faceConfidence:${face.boxConfidence} age:${face.age} genderConfidence:${face.genderConfidence} gender:${face.gender} emotionScore:${emotion.score} emotion:${emotion.emotion} iris:${face.iris}`);
+  }
+  for (let i = 0; i < result.body.length; i++) {
+    const body = result.body[i];
+    log.data(`  Body: #${i} score:${body.score}`);
+  }
+  for (let i = 0; i < result.hand.length; i++) {
+    const hand = result.hand[i];
+    log.data(`  Hand: #${i} confidence:${hand.confidence}`);
+  }
+  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:${val[1]}`);
+  }
+  for (let i = 0; i < result.object.length; i++) {
+    const object = result.object[i];
+    log.data(`  Object: #${i} score:${object.score} label:${object.label}`);
+  }
+  result.face.length = 0;
   return result;
 }
 

src/efficientpose/efficientpose.ts

@@ -3,7 +3,7 @@ import * as tf from '../../dist/tfjs.esm.js';
 import * as profile from '../profile';
 
 let model;
-let keypoints = { };
+let keypoints: Array<any> = [];
 let skipped = Number.MAX_SAFE_INTEGER;
 
 const bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];
@@ -56,9 +56,9 @@ export async function predict(image, config) {
     let resT;
 
     if (!config.profile) {
-      if (config.body.enabled) resT = await model.executeAsync(tensor);
+      if (config.body.enabled) resT = await model.predict(tensor);
     } else {
-      const profileT = config.body.enabled ? await tf.profile(() => model.executeAsync(tensor)) : {};
+      const profileT = config.body.enabled ? await tf.profile(() => model.predict(tensor)) : {};
       resT = profileT.result.clone();
       profileT.result.dispose();
       profile.run('body', profileT);
@@ -79,7 +79,7 @@ export async function predict(image, config) {
         if (score > config.body.scoreThreshold) {
           parts.push({
             id,
-            score,
+            score: Math.round(100 * score) / 100,
             part: bodyParts[id],
             positionRaw: {
               xRaw: x / model.inputs[0].shape[2], // x normalized to 0..1
@@ -95,6 +95,7 @@ export async function predict(image, config) {
       stack.forEach((s) => tf.dispose(s));
       keypoints = parts;
     }
-    resolve([{ keypoints }]);
+    const score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);
+    resolve([{ score, keypoints }]);
   });
 }

src/handpose/handpose.ts

@@ -49,7 +49,7 @@ export class HandPose {
         (prediction.box.bottomRight[0] - prediction.box.topLeft[0]) / input.shape[2],
         (prediction.box.bottomRight[1] - prediction.box.topLeft[1]) / input.shape[1],
       ];
-      hands.push({ confidence: prediction.confidence, box, boxRaw, landmarks: prediction.landmarks, annotations });
+      hands.push({ confidence: Math.round(100 * prediction.confidence) / 100, box, boxRaw, landmarks: prediction.landmarks, annotations });
     }
     return hands;
   }

src/nanodet/nanodet.ts

@@ -58,7 +58,7 @@ async function process(res, inputSize, outputShape, config) {
             const result = {
               id: id++,
               strideSize,
-              score,
+              score: Math.round(100 * score) / 100,
               class: j + 1,
               label: labels[j].label,
               center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],
@@ -113,9 +113,9 @@ export async function predict(image, config) {
 
     let objectT;
     if (!config.profile) {
-      if (config.object.enabled) objectT = await model.executeAsync(transpose);
+      if (config.object.enabled) objectT = await model.predict(transpose);
     } else {
-      const profileObject = config.object.enabled ? await tf.profile(() => model.executeAsync(transpose)) : {};
+      const profileObject = config.object.enabled ? await tf.profile(() => model.predict(transpose)) : {};
       objectT = profileObject.result;
       profile.run('object', profileObject);
     }