update dependencies and refresh release

CHANGELOG.md

@@ -1,4 +1,4 @@
-# packageJson  
+#   
 
   Version: **undefined**  
   Description: **undefined**  
@@ -11,11 +11,10 @@
   
 ### **HEAD -> main** 2021/10/15 mandic00@live.com
 
+- compress samples
 - remove handdetect from default package
 - remove posenet from default package
-
+- enhanced movenet postprocessing
-### **origin/main** 2021/10/14 mandic00@live.com
-
 - use transferrable buffer for worker messages
 - add optional anti-spoofing module
 - add node-match advanced example using worker thread pool

TODO.md

@@ -2,10 +2,14 @@
 
 ## Work in Progress
 
+- Switch to custom `tfjs` for main `human` ESM bundle
+
 <br>
 
 ### Models
 
+- Body segmentation: `robust-video-matting`
+
 <br>
 
 ### Backends
@@ -66,3 +70,12 @@ Object detection using CenterNet or NanoDet models is not working when using WAS
   <https://github.com/tensorflow/tfjs/issues/5110>  
 - Backend WASM missing kernel op `SparseToDense`  
   <https://github.com/tensorflow/tfjs/issues/4824>  
+
+<br><hr><br>
+
+## Pending Release
+
+- Enhanced **MoveNet** post-processing
+- Add optional **Anti-Spoof** module
+- Remove old **HandDetect** and **PoseNet** models from default installation
+- Refactor **ImageFX** module

demo/multithread/index.js

@@ -155,8 +155,6 @@ async function runDetection() {
     bench.begin();
   }
   const ctx = canvas.getContext('2d');
-  // const image = await human.image(video);
-  // ctx.drawImage(image.canvas, 0, 0, canvas.width, canvas.height);
   ctx.drawImage(video, 0, 0, canvas.width, canvas.height);
   const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
   if (!busy.face) {
@@ -197,7 +195,6 @@ async function setupCamera() {
       facingMode: 'user',
       resizeMode: 'crop-and-scale',
       width: { ideal: document.body.clientWidth },
-      // height: { ideal: document.body.clientHeight }, // not set as we're using aspectRation to get height instead
       aspectRatio: document.body.clientWidth / document.body.clientHeight,
     },
   };

package.json

@@ -66,14 +66,14 @@
     "@tensorflow/tfjs-layers": "^3.9.0",
     "@tensorflow/tfjs-node": "^3.9.0",
     "@tensorflow/tfjs-node-gpu": "^3.9.0",
-    "@types/node": "^16.10.9",
+    "@types/node": "^16.11.1",
-    "@typescript-eslint/eslint-plugin": "^5.0.0",
+    "@typescript-eslint/eslint-plugin": "^5.1.0",
-    "@typescript-eslint/parser": "^5.0.0",
+    "@typescript-eslint/parser": "^5.1.0",
-    "@vladmandic/build": "^0.6.0",
+    "@vladmandic/build": "^0.6.1",
     "@vladmandic/pilogger": "^0.3.3",
     "canvas": "^2.8.0",
     "dayjs": "^1.10.7",
-    "esbuild": "^0.13.6",
+    "esbuild": "^0.13.8",
     "eslint": "8.0.1",
     "eslint-config-airbnb-base": "^14.2.1",
     "eslint-plugin-import": "^2.25.2",
@@ -84,7 +84,7 @@
     "rimraf": "^3.0.2",
     "seedrandom": "^3.0.5",
     "tslib": "^2.3.1",
-    "typedoc": "0.22.5",
+    "typedoc": "0.22.6",
     "typescript": "4.4.4"
   }
 }

test/test-main.js

@@ -303,28 +303,6 @@ async function test(Human, inputConfig) {
   if (!res || res?.hand?.length !== 1 || res?.hand[0]?.landmarks?.length > 0) log('error', 'failed: detectors result hand mismatch', res?.hand?.length);
   else log('state', 'passed: detector result hand match');
 
-  // test posenet and movenet
-  log('info', 'test body variants');
-  config.body = { modelPath: 'posenet.json' };
-  res = await testDetect(human, 'samples/in/ai-body.jpg', 'posenet');
-  if (!res || res?.body?.length !== 1) log('error', 'failed: body posenet');
-  else log('state', 'passed: body posenet');
-  config.body = { modelPath: 'movenet-lightning.json' };
-  res = await testDetect(human, 'samples/in/ai-body.jpg', 'movenet');
-  if (!res || res?.body?.length !== 1) log('error', 'failed: body movenet');
-  else log('state', 'passed: body movenet');
-
-  // test handdetect and handtrack
-  log('info', 'test hand variants');
-  config.hand = { enabled: true, maxDetected: 2, minConfidence: 0.1, detector: { modelPath: 'handdetect.json' } };
-  res = await testDetect(human, 'samples/in/ai-body.jpg', 'handdetect');
-  if (!res || res?.hand?.length !== 2) log('error', 'failed: hand handdetect');
-  else log('state', 'passed: hand handdetect');
-  config.hand = { enabled: true, maxDetected: 2, minConfidence: 0.1, detector: { modelPath: 'handtrack.json' } };
-  res = await testDetect(human, 'samples/in/ai-body.jpg', 'handtrack');
-  if (!res || res?.hand?.length !== 2) log('error', 'failed: hand handdetect');
-  else log('state', 'passed: hand handdetect');
-
   // test multiple instances
   const first = new Human(config);
   const second = new Human(config);