fine tuning age and face models

.markdownlint.json

@@ -0,0 +1,6 @@
+{
+  "MD012": false,
+  "MD013": false,
+  "MD033": false,
+  "MD036": false
+}

config.js

@@ -82,10 +82,10 @@ export default {
                              // in short time (10 * 1/25 = 0.25 sec)
       skipInitial: false,    // if previous detection resulted in no faces detected,
                              // should skipFrames be reset immediately
-      minConfidence: 0.2,    // threshold for discarding a prediction
-      iouThreshold: 0.2,     // threshold for deciding whether boxes overlap too much in
+      minConfidence: 0.1,    // threshold for discarding a prediction
+      iouThreshold: 0.1,     // threshold for deciding whether boxes overlap too much in
                              // non-maximum suppression (0.1 means drop if overlap 10%)
-      scoreThreshold: 0.2,   // threshold for deciding when to remove boxes based on score
+      scoreThreshold: 0.1,   // threshold for deciding when to remove boxes based on score
                              // in non-maximum suppression,
                              // this is applied on detection objects only and before minConfidence
     },
@@ -114,7 +114,7 @@ export default {
 
     gender: {
       enabled: true,
-      minConfidence: 0.4,    // threshold for discarding a prediction
+      minConfidence: 0.1,    // threshold for discarding a prediction
       modelPath: '../models/gender.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'
       inputSize: 64,         // fixed value
       skipFrames: 41,        // how many frames to go without re-running the detector
@@ -124,7 +124,7 @@ export default {
     emotion: {
       enabled: true,
       inputSize: 64,         // fixed value
-      minConfidence: 0.2,    // threshold for discarding a prediction
+      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'
     },

demo/browser.js

@@ -31,7 +31,7 @@ const ui = {
   baseFontProto: 'small-caps {size} "Segoe UI"',
   baseLineWidth: 12,
   crop: true,
-  columns: 2,
+  columns: 4,
   busy: false,
   facing: true,
   useWorker: false,
@@ -339,7 +339,7 @@ async function processImage(input) {
   return new Promise((resolve) => {
     const image = new Image();
     image.onload = async () => {
-      log('Processing image:', image.src);
+      log('Processing image:', encodeURI(image.src));
       const canvas = document.getElementById('canvas');
       image.width = image.naturalWidth;
       image.height = image.naturalHeight;
@@ -351,7 +351,12 @@ async function processImage(input) {
       const thumb = document.createElement('canvas');
       thumb.className = 'thumbnail';
       thumb.width = window.innerWidth / (ui.columns + 0.1);
-      thumb.height = canvas.height / (window.innerWidth / thumb.width);
+      thumb.height = thumb.width * canvas.height / canvas.width;
+      if (result.face && result.face.length > 0) {
+        thumb.title = result.face.map((a, i) => `#${i} face: ${Math.trunc(100 * a.faceConfidence)}% box: ${Math.trunc(100 * a.boxConfidence)}% age: ${Math.trunc(a.age)} gender: ${Math.trunc(100 * a.genderConfidence)}% ${a.gender}`).join(' | ');
+      } else {
+        thumb.title = 'no face detected';
+      }
       const ctx = thumb.getContext('2d');
       ctx.drawImage(canvas, 0, 0, canvas.width, canvas.height, 0, 0, thumb.width, thumb.height);
       document.getElementById('samples-container').appendChild(thumb);
@@ -403,6 +408,7 @@ async function detectSampleImages() {
   log('Running detection of sample images');
   status('processing images');
   document.getElementById('samples-container').innerHTML = '';
+  for (const m of Object.values(menu)) m.hide();
   for (const image of ui.samples) await processImage(image);
   status('');
 }

src/human.ts

@@ -235,7 +235,7 @@ class Human {
     let genderRes;
     let emotionRes;
     let embeddingRes;
-    const faceRes: Array<{ confidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];
+    const faceRes: Array<{ confidence: number, boxConfidence: number, faceConfidence: number, box: any, mesh: any, meshRaw: any, boxRaw: any, annotations: any, age: number, gender: string, genderConfidence: number, emotion: string, embedding: any, iris: number }> = [];
     this.state = 'run:face';
     timeStamp = now();
     const faces = await this.models.face?.estimateFaces(input, this.config);
@@ -316,6 +316,8 @@ class Human {
       // combine results
       faceRes.push({
         confidence: face.confidence,
+        faceConfidence: face.faceConfidence,
+        boxConfidence: face.boxConfidence,
         box: face.box,
         mesh: face.mesh,
         boxRaw: face.boxRaw,