minor blazepose optimizations

CHANGELOG.md

@@ -9,7 +9,12 @@
   
 ## Changelog
   
-### **HEAD -> main** 2021/10/13 mandic00@live.com
+### **HEAD -> main** 2021/10/15 mandic00@live.com
+
+- remove handdetect from default package
+- remove posenet from default package
+
+### **origin/main** 2021/10/14 mandic00@live.com
 
 - use transferrable buffer for worker messages
 - add optional anti-spoofing module

TODO.md

@@ -6,9 +6,6 @@
 
 ### Models
 
-- Optimize BlazePose
-- Add BlazePose heatmaps
-
 <br>
 
 ### Backends

demo/index.js

@@ -31,9 +31,9 @@ import jsonView from './helpers/jsonview.js';
 let human;
 
 let userConfig = {
-  face: { enabled: false },
-  body: { enabled: true, modelPath: '../../human-models/models/blazepose-lite.json' },
-  hand: { enabled: false },
+  // face: { enabled: false },
+  // body: { enabled: false },
+  // hand: { enabled: false },
   /*
   warmup: 'none',
   backend: 'humangl',

dist/human.esm-nobundle.js

@@ -4945,22 +4945,26 @@ function rescaleKeypoints(keypoints, outputSize2) {
   }
   return keypoints;
 }
+var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
 async function detectParts(input, config3, outputSize2) {
   var _a;
   const t = {};
   t.input = await prepareImage(input);
   [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = await ((_a = models[1]) == null ? void 0 : _a.execute(t.input, outputNodes));
+  const poseScoreRaw = (await t.poseflag.data())[0];
+  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
   const points = await t.ld.data();
   const keypointsRelative = [];
   const depth = 5;
   for (let i = 0; i < points.length / depth; i++) {
-    const score = (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100;
+    const score = sigmoid2(points[depth * i + 3]);
+    const presence = sigmoid2(points[depth * i + 4]);
+    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
     const positionRaw = [points[depth * i + 0] / inputSize2[1][0], points[depth * i + 1] / inputSize2[1][1], points[depth * i + 2] + 0];
     const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
-    keypointsRelative.push({ part: kpt[i], positionRaw, position, score });
+    keypointsRelative.push({ part: kpt[i], positionRaw, position, score: adjScore });
   }
-  const avgScore = Math.round(100 * keypointsRelative.reduce((prev, curr) => prev += curr.score, 0) / keypointsRelative.length) / 100;
-  if (avgScore < (config3.body.minConfidence || 0))
+  if (poseScore < (config3.body.minConfidence || 0))
     return null;
   const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
   const boxes = calculateBoxes(keypoints, [outputSize2[0], outputSize2[1]]);
@@ -4976,11 +4980,12 @@ async function detectParts(input, config3, outputSize2) {
     }
     annotations2[name] = pt;
   }
-  return { id: 0, score: avgScore, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations: annotations2 };
+  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations: annotations2 };
+  return body4;
 }
 async function predict2(input, config3) {
   const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
-  if (skipped3 < (config3.body.skipFrames || 0) && config3.skipFrame) {
+  if (skipped3 < (config3.body.skipFrames || 0) && config3.skipFrame && cache !== null) {
     skipped3++;
   } else {
     cache = await detectParts(input, config3, outputSize2);

dist/human.esm.js

@@ -72434,22 +72434,26 @@ function rescaleKeypoints(keypoints, outputSize2) {
   }
   return keypoints;
 }
+var sigmoid6 = (x) => 1 - 1 / (1 + Math.exp(x));
 async function detectParts(input2, config3, outputSize2) {
   var _a;
   const t = {};
   t.input = await prepareImage(input2);
   [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = await ((_a = models[1]) == null ? void 0 : _a.execute(t.input, outputNodes));
+  const poseScoreRaw = (await t.poseflag.data())[0];
+  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
   const points = await t.ld.data();
   const keypointsRelative = [];
   const depth = 5;
   for (let i = 0; i < points.length / depth; i++) {
-    const score = (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100;
+    const score = sigmoid6(points[depth * i + 3]);
+    const presence = sigmoid6(points[depth * i + 4]);
+    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
     const positionRaw = [points[depth * i + 0] / inputSize2[1][0], points[depth * i + 1] / inputSize2[1][1], points[depth * i + 2] + 0];
     const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
-    keypointsRelative.push({ part: kpt[i], positionRaw, position, score });
+    keypointsRelative.push({ part: kpt[i], positionRaw, position, score: adjScore });
   }
-  const avgScore = Math.round(100 * keypointsRelative.reduce((prev, curr) => prev += curr.score, 0) / keypointsRelative.length) / 100;
-  if (avgScore < (config3.body.minConfidence || 0))
+  if (poseScore < (config3.body.minConfidence || 0))
     return null;
   const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
   const boxes = calculateBoxes(keypoints, [outputSize2[0], outputSize2[1]]);
@@ -72465,11 +72469,12 @@ async function detectParts(input2, config3, outputSize2) {
     }
     annotations2[name] = pt;
   }
-  return { id: 0, score: avgScore, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations: annotations2 };
+  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations: annotations2 };
+  return body4;
 }
 async function predict2(input2, config3) {
   const outputSize2 = [input2.shape[2] || 0, input2.shape[1] || 0];
-  if (skipped3 < (config3.body.skipFrames || 0) && config3.skipFrame) {
+  if (skipped3 < (config3.body.skipFrames || 0) && config3.skipFrame && cache !== null) {
     skipped3++;
   } else {
     cache = await detectParts(input2, config3, outputSize2);

dist/human.node-gpu.js

@@ -4977,22 +4977,26 @@ function rescaleKeypoints(keypoints, outputSize2) {
   }
   return keypoints;
 }
+var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
 async function detectParts(input, config3, outputSize2) {
   var _a;
   const t = {};
   t.input = await prepareImage(input);
   [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = await ((_a = models[1]) == null ? void 0 : _a.execute(t.input, outputNodes));
+  const poseScoreRaw = (await t.poseflag.data())[0];
+  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
   const points = await t.ld.data();
   const keypointsRelative = [];
   const depth = 5;
   for (let i = 0; i < points.length / depth; i++) {
-    const score = (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100;
+    const score = sigmoid2(points[depth * i + 3]);
+    const presence = sigmoid2(points[depth * i + 4]);
+    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
     const positionRaw = [points[depth * i + 0] / inputSize2[1][0], points[depth * i + 1] / inputSize2[1][1], points[depth * i + 2] + 0];
     const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
-    keypointsRelative.push({ part: kpt[i], positionRaw, position, score });
+    keypointsRelative.push({ part: kpt[i], positionRaw, position, score: adjScore });
   }
-  const avgScore = Math.round(100 * keypointsRelative.reduce((prev, curr) => prev += curr.score, 0) / keypointsRelative.length) / 100;
-  if (avgScore < (config3.body.minConfidence || 0))
+  if (poseScore < (config3.body.minConfidence || 0))
     return null;
   const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
   const boxes = calculateBoxes(keypoints, [outputSize2[0], outputSize2[1]]);
@@ -5008,11 +5012,12 @@ async function detectParts(input, config3, outputSize2) {
     }
     annotations2[name] = pt;
   }
-  return { id: 0, score: avgScore, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations: annotations2 };
+  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations: annotations2 };
+  return body4;
 }
 async function predict2(input, config3) {
   const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
-  if (skipped3 < (config3.body.skipFrames || 0) && config3.skipFrame) {
+  if (skipped3 < (config3.body.skipFrames || 0) && config3.skipFrame && cache !== null) {
     skipped3++;
   } else {
     cache = await detectParts(input, config3, outputSize2);

dist/human.node-wasm.js

@@ -4978,22 +4978,26 @@ function rescaleKeypoints(keypoints, outputSize2) {
   }
   return keypoints;
 }
+var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
 async function detectParts(input, config3, outputSize2) {
   var _a;
   const t = {};
   t.input = await prepareImage(input);
   [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = await ((_a = models[1]) == null ? void 0 : _a.execute(t.input, outputNodes));
+  const poseScoreRaw = (await t.poseflag.data())[0];
+  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
   const points = await t.ld.data();
   const keypointsRelative = [];
   const depth = 5;
   for (let i = 0; i < points.length / depth; i++) {
-    const score = (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100;
+    const score = sigmoid2(points[depth * i + 3]);
+    const presence = sigmoid2(points[depth * i + 4]);
+    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
     const positionRaw = [points[depth * i + 0] / inputSize2[1][0], points[depth * i + 1] / inputSize2[1][1], points[depth * i + 2] + 0];
     const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
-    keypointsRelative.push({ part: kpt[i], positionRaw, position, score });
+    keypointsRelative.push({ part: kpt[i], positionRaw, position, score: adjScore });
   }
-  const avgScore = Math.round(100 * keypointsRelative.reduce((prev, curr) => prev += curr.score, 0) / keypointsRelative.length) / 100;
-  if (avgScore < (config3.body.minConfidence || 0))
+  if (poseScore < (config3.body.minConfidence || 0))
     return null;
   const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
   const boxes = calculateBoxes(keypoints, [outputSize2[0], outputSize2[1]]);
@@ -5009,11 +5013,12 @@ async function detectParts(input, config3, outputSize2) {
     }
     annotations2[name] = pt;
   }
-  return { id: 0, score: avgScore, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations: annotations2 };
+  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations: annotations2 };
+  return body4;
 }
 async function predict2(input, config3) {
   const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
-  if (skipped3 < (config3.body.skipFrames || 0) && config3.skipFrame) {
+  if (skipped3 < (config3.body.skipFrames || 0) && config3.skipFrame && cache !== null) {
     skipped3++;
   } else {
     cache = await detectParts(input, config3, outputSize2);

dist/human.node.js

@@ -4977,22 +4977,26 @@ function rescaleKeypoints(keypoints, outputSize2) {
   }
   return keypoints;
 }
+var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
 async function detectParts(input, config3, outputSize2) {
   var _a;
   const t = {};
   t.input = await prepareImage(input);
   [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = await ((_a = models[1]) == null ? void 0 : _a.execute(t.input, outputNodes));
+  const poseScoreRaw = (await t.poseflag.data())[0];
+  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
   const points = await t.ld.data();
   const keypointsRelative = [];
   const depth = 5;
   for (let i = 0; i < points.length / depth; i++) {
-    const score = (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100;
+    const score = sigmoid2(points[depth * i + 3]);
+    const presence = sigmoid2(points[depth * i + 4]);
+    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
     const positionRaw = [points[depth * i + 0] / inputSize2[1][0], points[depth * i + 1] / inputSize2[1][1], points[depth * i + 2] + 0];
     const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
-    keypointsRelative.push({ part: kpt[i], positionRaw, position, score });
+    keypointsRelative.push({ part: kpt[i], positionRaw, position, score: adjScore });
   }
-  const avgScore = Math.round(100 * keypointsRelative.reduce((prev, curr) => prev += curr.score, 0) / keypointsRelative.length) / 100;
-  if (avgScore < (config3.body.minConfidence || 0))
+  if (poseScore < (config3.body.minConfidence || 0))
     return null;
   const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
   const boxes = calculateBoxes(keypoints, [outputSize2[0], outputSize2[1]]);
@@ -5008,11 +5012,12 @@ async function detectParts(input, config3, outputSize2) {
     }
     annotations2[name] = pt;
   }
-  return { id: 0, score: avgScore, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations: annotations2 };
+  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations: annotations2 };
+  return body4;
 }
 async function predict2(input, config3) {
   const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
-  if (skipped3 < (config3.body.skipFrames || 0) && config3.skipFrame) {
+  if (skipped3 < (config3.body.skipFrames || 0) && config3.skipFrame && cache !== null) {
     skipped3++;
   } else {
     cache = await detectParts(input, config3, outputSize2);

src/body/blazepose.ts

@@ -56,25 +56,6 @@ function calculateBoxes(keypoints: Array<BodyKeypoint>, outputSize: [number, num
   const y = keypoints.map((a) => a.position[1]);
   const keypointsBox: Box = [Math.min(...x), Math.min(...y), Math.max(...x) - Math.min(...x), Math.max(...y) - Math.min(...y)];
   const keypointsBoxRaw: Box = [keypointsBox[0] / outputSize[0], keypointsBox[1] / outputSize[1], keypointsBox[2] / outputSize[0], keypointsBox[3] / outputSize[1]];
-  /*
-   const leftShoulder = keypoints.find((kpt) => kpt.part === 'leftShoulder');
-   const rightShoulder = keypoints.find((kpt) => kpt.part === 'rightShoulder');
-   if (!leftShoulder || !rightShoulder || !config.skipFrame) { // reset cache box coords
-     cache.box = [0, 0, 1, 1];
-     cache.boxRaw = cache.box;
-   } else { // recalculate cache box coords
-     const size = [leftShoulder.position[0] - rightShoulder.position[0], leftShoulder.position[1] - rightShoulder.position[1]];
-     const shoulderWidth = Math.sqrt((size[0] * size[0]) + (size[1] * size[1])); // distance between left and right shoulder
-     const shoulderCenter: Point = [(leftShoulder.position[0] + rightShoulder.position[0]) / 2, (leftShoulder.position[1] + rightShoulder.position[1]) / 2]; // center point between left and right shoulder
-     const bodyCenter: Point = [shoulderCenter[0], shoulderCenter[0] + (shoulderWidth), 0]; // approximate center of the body
-     const bodyCenterRaw: Point = [bodyCenter[0] / outputSize[0], bodyCenter[1] / outputSize[1], 0];
-     const bodyCenterKpt: Keypoint = { part: 'bodyCenter', positionRaw: bodyCenterRaw, position: bodyCenter, score: 1 }; // add virtual keypoint
-     keypoints.push(bodyCenterKpt);
-     const scaleFact = 2.5;
-     cache.box = [Math.trunc(bodyCenter[0] - (scaleFact * shoulderWidth)), Math.trunc(bodyCenter[1] - (scaleFact * shoulderWidth)), Math.trunc(2 * scaleFact * shoulderWidth), Math.trunc(2 * scaleFact * shoulderWidth)];
-     cache.boxRaw = [cache.box[0] / outputSize[0], cache.box[1] / outputSize[1], cache.box[2] / outputSize[0], cache.box[3] / outputSize[1]];
-   }
-   */
   return { keypointsBox, keypointsBoxRaw };
 }
 
@@ -108,23 +89,33 @@ function rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, n
   return keypoints;
 }
 
+const sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));
+
 async function detectParts(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {
   const t: Record<string, Tensor> = {};
   t.input = await prepareImage(input);
-  [t.ld/* 1,195 */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117 */, t.poseflag/* 1,1 */] = await models[1]?.execute(t.input, outputNodes) as Tensor[]; // run model
+  /**
+   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size
+   * t.segmentation:
+   * t.heatmap:
+   * t.world: 39 keypoints [x,y,z] normalized to -1..1
+   * t.poseflag: body score
+   */
+  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = await models[1]?.execute(t.input, outputNodes) as Tensor[]; // run model
+  const poseScoreRaw = (await t.poseflag.data())[0];
+  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8)); // blow up score variance 5x
   const points = await t.ld.data();
   const keypointsRelative: Array<BodyKeypoint> = [];
   const depth = 5; // each points has x,y,z,visibility,presence
   for (let i = 0; i < points.length / depth; i++) {
-    const score = (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 3])))) / 100; // normally this is from tf.sigmoid but no point of running sigmoid on full array which has coords as well
-    // const presence = (100 - Math.trunc(100 / (1 + Math.exp(points[depth * i + 4])))) / 100; // reverse sigmoid value
+    const score = sigmoid(points[depth * i + 3]);
+    const presence = sigmoid(points[depth * i + 4]);
+    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
     const positionRaw: Point = [points[depth * i + 0] / inputSize[1][0], points[depth * i + 1] / inputSize[1][1], points[depth * i + 2] + 0];
     const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];
-    // if (positionRaw[0] < 0 || positionRaw[1] < 0 || positionRaw[0] > 1 || positionRaw[1] > 1) score = 0;
-    keypointsRelative.push({ part: coords.kpt[i], positionRaw, position, score });
+    keypointsRelative.push({ part: coords.kpt[i], positionRaw, position, score: adjScore });
   }
-  const avgScore = Math.round(100 * keypointsRelative.reduce((prev, curr) => prev += curr.score, 0) / keypointsRelative.length) / 100; // average score of keypoints
-  if (avgScore < (config.body.minConfidence || 0)) return null;
+  if (poseScore < (config.body.minConfidence || 0)) return null;
   const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is cropped
   const boxes = calculateBoxes(keypoints, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints
   Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));
@@ -138,17 +129,13 @@ async function detectParts(input: Tensor, config: Config, outputSize: [number, n
     }
     annotations[name] = pt;
   }
-  return { id: 0, score: avgScore, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations };
+  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.keypointsBox, boxRaw: boxes.keypointsBoxRaw, keypoints, annotations };
+  return body;
 }
 
 export async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {
-  /** blazepose caching
-   * not fully implemented
-   * 1. if skipFrame returned cached
-   * 2. run detection based on squared full frame
-   */
   const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];
-  if ((skipped < (config.body.skipFrames || 0)) && config.skipFrame) {
+  if ((skipped < (config.body.skipFrames || 0)) && config.skipFrame && cache !== null) {
     skipped++;
   } else {
     cache = await detectParts(input, config, outputSize);

test/build.log

@@ -1,46 +1,24 @@
-2021-10-14 12:23:28 INFO:  @vladmandic/human version 2.3.3 
-2021-10-14 12:23:28 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.10.0 
-2021-10-14 12:23:28 INFO:  Application: {"name":"@vladmandic/human","version":"2.3.3"} 
-2021-10-14 12:23:28 INFO:  Environment: {"profile":"production","config":"build.json","tsconfig":true,"eslintrc":true,"git":true} 
-2021-10-14 12:23:28 INFO:  Toolchain: {"build":"0.6.0","esbuild":"0.13.6","typescript":"4.4.4","typedoc":"0.22.5","eslint":"8.0.1"} 
-2021-10-14 12:23:28 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
-2021-10-14 12:23:28 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} 
-2021-10-14 12:23:28 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1275} 
-2021-10-14 12:23:28 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":55,"inputBytes":525473,"outputBytes":434511} 
-2021-10-14 12:23:28 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1283} 
-2021-10-14 12:23:28 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":55,"inputBytes":525481,"outputBytes":434515} 
-2021-10-14 12:23:28 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1350} 
-2021-10-14 12:23:28 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":55,"inputBytes":525548,"outputBytes":434587} 
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-2021-10-14 12:24:25 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
-2021-10-14 12:24:25 INFO:  Done... 
-2021-10-15 06:46:56 INFO:  @vladmandic/human version 2.3.3 
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-2021-10-15 06:46:56 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":55,"inputBytes":525054,"outputBytes":436070} 
-2021-10-15 06:46:57 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":8,"inputBytes":3085,"outputBytes":2691961} 
-2021-10-15 06:46:57 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":55,"inputBytes":3216159,"outputBytes":1611238} 
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-2021-10-15 06:46:58 INFO:  Listening... 
+2021-10-15 09:30:57 INFO:  @vladmandic/human version 2.3.3 
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+2021-10-15 09:30:57 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":55,"inputBytes":523864,"outputBytes":434789} 
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+2021-10-15 09:30:57 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":3085,"outputBytes":856} 
+2021-10-15 09:30:58 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":55,"inputBytes":523370,"outputBytes":436272} 
+2021-10-15 09:30:58 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":8,"inputBytes":3085,"outputBytes":2691961} 
+2021-10-15 09:30:58 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":55,"inputBytes":3214475,"outputBytes":1611302} 
+2021-10-15 09:30:59 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":55,"inputBytes":3214475,"outputBytes":2928137} 
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+2021-10-15 09:31:54 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
+2021-10-15 09:31:54 INFO:  Done... 

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