fix backend conflict in webworker

.eslintrc.json

@@ -4,11 +4,11 @@
       "browser": true,
       "commonjs": true,
       "node": true,
-      "es2020": true
+      "es2021": true
   },
   "parser": "@typescript-eslint/parser",
   "parserOptions": {
-    "ecmaVersion": 2020
+    "ecmaVersion": 2021
   },
   "plugins": [
     "@typescript-eslint"

CHANGELOG.md

@@ -9,7 +9,10 @@
   
 ## Changelog
   
-### **HEAD -> main** 2021/10/03 mandic00@live.com
+### **HEAD -> main** 2021/10/04 mandic00@live.com
+
+
+### **origin/main** 2021/10/03 mandic00@live.com
 
 - added docker notes
 - breaking change: new similarity and match methods

demo/index.js

@@ -31,13 +31,6 @@ import jsonView from './helpers/jsonview.js';
 let human;
 
 let userConfig = {
-  face: { enabled: false },
-  object: { enabled: false },
-  gesture: { enabled: true },
-  hand: { enabled: false },
-  body: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/blazepose-lite.json' },
-  segmentation: { enabled: false },
-
   /*
   warmup: 'none',
   backend: 'humangl',
@@ -98,6 +91,7 @@ const ui = {
   autoPlay: false, // start webcam & detection on load
 
   // internal variables
+  exceptionHandler: false, // should capture all unhandled exceptions
   busy: false, // internal camera busy flag
   menuWidth: 0, // internal
   menuHeight: 0, // internal
@@ -115,7 +109,6 @@ const ui = {
   lastFrame: 0, // time of last frame processing
   viewportSet: false, // internal, has custom viewport been set
   background: null, // holds instance of segmentation background image
-  exceptionHandler: false, // should capture all unhandled exceptions
 
   // webrtc
   useWebRTC: false, // use webrtc as camera source instead of local webcam

package.json

@@ -67,8 +67,8 @@
     "@tensorflow/tfjs-node": "^3.9.0",
     "@tensorflow/tfjs-node-gpu": "^3.9.0",
     "@types/node": "^16.10.2",
-    "@typescript-eslint/eslint-plugin": "^4.32.0",
-    "@typescript-eslint/parser": "^4.32.0",
+    "@typescript-eslint/eslint-plugin": "^4.33.0",
+    "@typescript-eslint/parser": "^4.33.0",
     "@vladmandic/build": "^0.5.3",
     "@vladmandic/pilogger": "^0.3.3",
     "canvas": "^2.8.0",
@@ -84,7 +84,7 @@
     "rimraf": "^3.0.2",
     "seedrandom": "^3.0.5",
     "tslib": "^2.3.1",
-    "typedoc": "0.22.4",
+    "typedoc": "0.22.5",
     "typescript": "4.4.3"
   }
 }

src/body/blazepose.ts

@@ -2,7 +2,7 @@
  * BlazePose model implementation
  */
 
-import * as tf from '@tensorflow/tfjs';
+import * as tf from '../../dist/tfjs.esm.js';
 import { log, join } from '../util/util';
 import type { BodyKeypoint, BodyResult, Box, Point } from '../result';
 import type { GraphModel, Tensor } from '../tfjs/types';
@@ -87,8 +87,8 @@ async function prepareImage(input: Tensor): Promise<Tensor> {
     [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after
     [0, 0], // dont touch rbg
   ];
-  t.pad = tf.pad(input as tf.Tensor4D, padding);
-  t.resize = tf.image.resizeBilinear(t.pad as tf.Tensor4D, [inputSize[1][0], inputSize[1][1]]);
+  t.pad = tf.pad(input, padding);
+  t.resize = tf.image.resizeBilinear(t.pad, [inputSize[1][0], inputSize[1][1]]);
   const final = tf.div(t.resize, 255);
   Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));
   return final;

src/config.ts

@@ -411,7 +411,7 @@ const config: Config = {
                              // only valid for posenet and movenet-multipose as other models detects single pose
                              // set to -1 to autodetect based on number of detected faces
     minConfidence: 0.2,      // threshold for discarding a prediction
-    skipFrames: 5,           // how many max frames to go without re-running the detector
+    skipFrames: 1,           // how many max frames to go without re-running the detector
                              // only used when cacheSensitivity is not zero
 },
 

src/image/image.ts

@@ -183,8 +183,6 @@ export function process(input: Input, config: Config): { tensor: Tensor | null,
         tempCanvas.height = targetHeight;
         const tempCtx = tempCanvas.getContext('2d');
         tempCtx?.drawImage(outCanvas, 0, 0);
-        console.log('PIXELS', tempCanvas);
-        pixels = (tf.browser && env.browser) ? tf.browser.fromPixels(tempCanvas) : null;
         try {
           pixels = (tf.browser && env.browser) ? tf.browser.fromPixels(tempCanvas) : null;
         } catch (err) {

src/tfjs/humangl.ts

@@ -65,9 +65,9 @@ export async function register(instance): Promise<void> {
         config.canvas.addEventListener('webglcontextlost', async (e) => {
           log('error: humangl:', e.type);
           // log('gpu memory usage:', instance.tf.engine().backendInstance.numBytesInGPU);
-          log('possible browser memory leak using webgl');
+          log('possible browser memory leak using webgl or conflict with multiple backend registrations');
           instance.emit('error');
-          // throw new Error('browser webgl error');
+          throw new Error('browser webgl error');
           /*
           log('resetting humangl backend');
           env.initial = true;