add optional autodetected custom wasm path

CHANGELOG.md

@@ -9,8 +9,12 @@
   
 ## Changelog
   
-### **HEAD -> main** 2021/10/21 mandic00@live.com
+### **2.3.6** 2021/10/21 mandic00@live.com
 
+
+### **origin/main** 2021/10/21 mandic00@live.com
+
+- refactor human.env to a class type
 - add human.custom.esm using custom tfjs build
 
 ### **2.3.5** 2021/10/19 mandic00@live.com

demo/index.js

@@ -90,7 +90,7 @@ const ui = {
   autoPlay: false, // start webcam & detection on load
 
   // internal variables
-  exceptionHandler: false, // should capture all unhandled exceptions
+  exceptionHandler: true, // should capture all unhandled exceptions
   busy: false, // internal camera busy flag
   menuWidth: 0, // internal
   menuHeight: 0, // internal

src/hand/fingerpose.ts

@@ -226,7 +226,6 @@ export function analyze(keypoints) { // get estimations of curl / direction for
       direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),
     };
   }
-  // console.log('finger landmarks', landmarks);
   return landmarks;
 }
 
@@ -238,6 +237,5 @@ export function match(keypoints) { // compare gesture description to each known
     const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);
     if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });
   }
-  // console.log('finger poses', poses);
   return poses;
 }

src/hand/handposedetector.ts

@@ -60,7 +60,6 @@ export class HandDetector {
       const palmBox = tf.slice(t.norm, [index, 0], [1, -1]);
       const palmLandmarks = tf.tidy(() => tf.reshape(this.normalizeLandmarks(tf.slice(t.predictions, [index, 5], [1, 14]), index), [-1, 2]));
       hands.push({ box: palmBox, palmLandmarks, confidence: scores[index] });
-      // console.log('handdetector:getBoxes', nms.length, index, scores[index], config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence, palmBox.dataSync());
     }
     for (const tensor of Object.keys(t)) tf.dispose(t[tensor]); // dispose all
     return hands;

src/hand/handposepipeline.ts

@@ -141,7 +141,6 @@ export class HandPipeline {
           };
           hands.push(result);
         } else {
-          // console.log('handpipeline:estimateHands low', confidence);
           this.storedBoxes[i] = null;
         }
         tf.dispose(keypoints);

src/human.ts

@@ -175,7 +175,9 @@ export class Human {
    */
   constructor(userConfig?: Partial<Config>) {
     this.env = env;
-    defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;
+    defaults.wasmPath = tf.version_core.includes('-') // custom build or official build
+      ? 'https://vladmandic.github.io/tfjs/dist/'
+      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;
     defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';
     defaults.backend = env.browser ? 'humangl' : 'tensorflow';
     this.version = app.version; // expose version property on instance of class

test/browser.html

@@ -26,7 +26,7 @@
     <div id="state" class="state"></div>
     <canvas id="canvas" class="canvas" width=256 height=256></canvas>
     <script type="module">
-      import Human from '../dist/human.esm.js';
+      import Human from '../dist/human.custom.esm.js';
 
       const config = {
         async: true,
@@ -36,9 +36,7 @@
         object: { enabled: true },
       }
 
-      const backends = ['wasm', 'webgl', 'humangl'];
+      const backends = ['wasm', 'webgl', 'humangl', 'webgpu'];
-      // const backends = ['wasm', 'wasm'];
-      // const backends = ['humangl'];
 
       const start = performance.now();
 
@@ -91,14 +89,16 @@
         log('human tests');
         let res;
         let human = new Human(config);
+        await human.init();
+        human.env.offscreen = false;
         human.events.addEventListener('warmup', () => events('warmup'));
         human.events.addEventListener('image', () => events('image'));
         human.events.addEventListener('detect', () => events('detect'));
         const timer = setInterval(() => { document.getElementById('state').innerText = `State: ${human.state}`; }, 10);
         log({ version: human.version });
-        log({ env: human.env });
+        log({ tfjs: human.tf.version.tfjs });
+        log({ environment: human.env });
         log({ config: human.config });
-        log({ tfjs: human.tf.version.tfjs, backend: human.config.backend });
         await human.load();
         const models = Object.keys(human.models).map((model) => ({ name: model, loaded: (human.models[model] !== null) }));
         log({ models });