minor compatibility fixes

demo/browser.js

@@ -134,11 +134,11 @@ async function drawResults(input) {
   const avgDetect = Math.trunc(10 * ui.detectFPS.reduce((a, b) => a + b, 0) / ui.detectFPS.length) / 10;
   const avgDraw = Math.trunc(10 * ui.drawFPS.reduce((a, b) => a + b, 0) / ui.drawFPS.length) / 10;
   const warning = (ui.detectFPS.length > 5) && (avgDetect < 5) ? '<font color="lightcoral">warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models</font>' : '';
-  document.getElementById('log').innerText = `
-    video: ${ui.camera.name} | facing: ${ui.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ui.camera.width} x ${ui.camera.height} ${processing}
-    backend: ${human.tf.getBackend()} | ${memory}
-    performance: ${str(result.performance)}ms FPS process:${avgDetect} refresh:${avgDraw}
-    ${warning}
+  document.getElementById('log').innerHTML = `
+    video: ${ui.camera.name} | facing: ${ui.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ui.camera.width} x ${ui.camera.height} ${processing}<br>
+    backend: ${human.tf.getBackend()} | ${memory}<br>
+    performance: ${str(result.performance)}ms FPS process:${avgDetect} refresh:${avgDraw}<br>
+    ${warning}<br>
   `;
 
   ui.framesDraw++;

demo/menu.js

@@ -300,7 +300,7 @@ class Menu {
     const width = canvas.width / values.length;
     const max = 1 + Math.max(...values);
     const height = canvas.height / max;
-    for (const i in values) {
+    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);

src/gesture/gesture.js

@@ -1,7 +1,7 @@
 exports.body = (res) => {
   if (!res) return [];
   const gestures = [];
-  for (const i in res) {
+  for (let i = 0; i < res.length; i++) {
     // raising hands
     const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));
     const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));
@@ -21,7 +21,7 @@ exports.body = (res) => {
 exports.face = (res) => {
   if (!res) return [];
   const gestures = [];
-  for (const i in res) {
+  for (let i = 0; i < res.length; i++) {
     if (res[i].mesh && res[i].mesh.length > 0) {
       const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];
       if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });
@@ -42,7 +42,7 @@ exports.face = (res) => {
 exports.hand = (res) => {
   if (!res) return [];
   const gestures = [];
-  for (const i in res) {
+  for (let i = 0; i < res.length; i++) {
     const fingers = [];
     for (const [finger, pos] of Object.entries(res[i]['annotations'])) {
       if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger

src/hand/handdetector.js

@@ -50,24 +50,24 @@ class HandDetector {
     const batched = this.model.predict(input);
     const predictions = batched.squeeze();
     batched.dispose();
-    const scores = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());
-    const scoresVal = scores.dataSync();
+    const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());
+    const scores = scoresT.dataSync();
     const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);
     const boxes = this.normalizeBoxes(rawBoxes);
     rawBoxes.dispose();
     const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);
     const filtered = filteredT.arraySync();
 
-    scores.dispose();
+    scoresT.dispose();
     filteredT.dispose();
     const hands = [];
-    for (const boxIndex of filtered) {
-      if (scoresVal[boxIndex] >= config.hand.minConfidence) {
-        const matchingBox = tf.slice(boxes, [boxIndex, 0], [1, -1]);
-        const rawPalmLandmarks = tf.slice(predictions, [boxIndex, 5], [1, 14]);
-        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, boxIndex).reshape([-1, 2]));
+    for (const index of filtered) {
+      if (scores[index] >= config.hand.minConfidence) {
+        const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);
+        const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);
+        const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));
         rawPalmLandmarks.dispose();
-        hands.push({ box: matchingBox, palmLandmarks, confidence: scoresVal[boxIndex] });
+        hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });
       }
     }
     predictions.dispose();

src/hand/handpipeline.js

@@ -28,9 +28,9 @@ const PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;
 const PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;
 
 class HandPipeline {
-  constructor(boundingBoxDetector, meshDetector, inputSize) {
-    this.boxDetector = boundingBoxDetector;
-    this.meshDetector = meshDetector;
+  constructor(handDetector, landmarkDetector, inputSize) {
+    this.handDetector = handDetector;
+    this.landmarkDetector = landmarkDetector;
     this.inputSize = inputSize;
     this.storedBoxes = [];
     this.skipped = 1000;
@@ -90,23 +90,23 @@ class HandPipeline {
     // run new detector every skipFrames unless we only want box to start with
     let boxes;
     if ((this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {
-      boxes = await this.boxDetector.estimateHandBounds(image, config);
+      boxes = await this.handDetector.estimateHandBounds(image, config);
       // don't reset on test image
       if ((image.shape[1] !== 255) && (image.shape[2] !== 255)) this.skipped = 0;
     }
 
     // if detector result count doesn't match current working set, use it to reset current working set
     if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {
-      this.storedBoxes = [];
       this.detectedHands = 0;
-      for (const possible of boxes) this.storedBoxes.push(possible);
+      this.storedBoxes = [...boxes];
+      // for (const possible of boxes) this.storedBoxes.push(possible);
       if (this.storedBoxes.length > 0) useFreshBox = true;
     }
     const hands = [];
     // console.log(`skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);
 
     // go through working set of boxes
-    for (const i in this.storedBoxes) {
+    for (let i = 0; i < this.storedBoxes.length; i++) {
       const currentBox = this.storedBoxes[i];
       if (!currentBox) continue;
       if (config.hand.landmarks) {
@@ -120,11 +120,11 @@ class HandPipeline {
         const handImage = croppedInput.div(255);
         croppedInput.dispose();
         rotatedImage.dispose();
-        const [confidence, keypoints] = await this.meshDetector.predict(handImage);
+        const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);
         handImage.dispose();
-        const confidenceValue = confidence.dataSync()[0];
-        confidence.dispose();
-        if (confidenceValue >= config.hand.minConfidence) {
+        const confidence = confidenceT.dataSync()[0];
+        confidenceT.dispose();
+        if (confidence >= config.hand.minConfidence) {
           const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);
           const rawCoords = keypointsReshaped.arraySync();
           keypoints.dispose();
@@ -134,7 +134,7 @@ class HandPipeline {
           this.storedBoxes[i] = nextBoundingBox;
           const result = {
             landmarks: coords,
-            confidence: confidenceValue,
+            confidence,
             box: {
               topLeft: nextBoundingBox.startPoint,
               bottomRight: nextBoundingBox.endPoint,

wiki

@@ -1 +1 @@
-Subproject commit bcac4981f7df29e367259caf6b3b73e5ecde6519
+Subproject commit 9595a995f7bcf2c6b0d70fed98260c8ab4a6f0d7