update build with automatic linter

CHANGELOG.md

@@ -11,11 +11,11 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
 
 ### **HEAD -> main** 2021/06/02 mandic00@live.com
 
+- switch worker from module to iife importscripts
+- release candidate
 - added samples to git
 - implemented drag & drop for image processing
-
+- release candidate
-### **origin/main** 2021/06/01 mandic00@live.com
-
 - breaking changes to results.face output properties
 - breaking changes to results.object output properties
 - breaking changes to results.hand output properties

dist/human.node-gpu.js

@@ -128,6 +128,7 @@ var config = {
   async: true,
   warmup: "full",
   cacheSensitivity: 0.75,
+  skipFrame: false,
   filter: {
     enabled: true,
     width: 0,
@@ -187,12 +188,13 @@ var config = {
     enabled: true,
     modelPath: "movenet-lightning.json",
     maxDetected: 1,
-    minConfidence: 0.2
+    minConfidence: 0.2,
+    skipFrames: 16
   },
   hand: {
     enabled: true,
     rotation: true,
-    skipFrames: 18,
+    skipFrames: 19,
     minConfidence: 0.1,
     iouThreshold: 0.1,
     maxDetected: 2,
@@ -210,7 +212,7 @@ var config = {
     minConfidence: 0.2,
     iouThreshold: 0.4,
     maxDetected: 10,
-    skipFrames: 19
+    skipFrames: 20
   }
 };
 
@@ -4084,8 +4086,8 @@ async function predict(input, config3) {
     if (!prediction || prediction.isDisposedInternal)
       continue;
     const meshRaw = prediction.mesh.map((pt) => [
-      pt[0] / input.shape[2],
+      pt[0] / (input.shape[2] || 0),
-      pt[1] / input.shape[1],
+      pt[1] / (input.shape[1] || 0),
       pt[2] / facePipeline.meshSize
     ]);
     const annotations3 = {};
@@ -4096,14 +4098,14 @@ async function predict(input, config3) {
     const clampedBox = prediction.box ? [
       Math.trunc(Math.max(0, prediction.box.startPoint[0])),
       Math.trunc(Math.max(0, prediction.box.startPoint[1])),
-      Math.trunc(Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])),
+      Math.trunc(Math.min(input.shape[2] || 0, prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])),
-      Math.trunc(Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]))
+      Math.trunc(Math.min(input.shape[1] || 0, prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]))
     ] : [0, 0, 0, 0];
     const boxRaw3 = prediction.box ? [
-      prediction.box.startPoint[0] / input.shape[2],
+      prediction.box.startPoint[0] / (input.shape[2] || 0),
-      prediction.box.startPoint[1] / input.shape[1],
+      prediction.box.startPoint[1] / (input.shape[1] || 0),
-      (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],
+      (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / (input.shape[2] || 0),
-      (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1]
+      (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / (input.shape[1] || 0)
     ] : [0, 0, 0, 0];
     results.push({
       id: id++,
@@ -4274,13 +4276,13 @@ function match(embedding, db, threshold = 0) {
 }
 function enhance(input) {
   const image15 = tf7.tidy(() => {
-    const tensor = input.image || input.tensor || input;
+    const tensor2 = input.image || input.tensor || input;
-    if (!(tensor instanceof tf7.Tensor))
+    if (!(tensor2 instanceof tf7.Tensor))
       return null;
     const box6 = [[0.05, 0.15, 0.85, 0.85]];
     if (!model2.inputs[0].shape)
       return null;
-    const crop = tensor.shape.length === 3 ? tf7.image.cropAndResize(tf7.expandDims(tensor, 0), box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]) : tf7.image.cropAndResize(tensor, box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]);
+    const crop = tensor2.shape.length === 3 ? tf7.image.cropAndResize(tf7.expandDims(tensor2, 0), box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]) : tf7.image.cropAndResize(tensor2, box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]);
     const norm = crop.mul(255);
     return norm;
   });
@@ -4452,21 +4454,21 @@ var detectFace = async (parent, input) => {
     const rotation = calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]);
     parent.analyze("Start Emotion:");
     if (parent.config.async) {
-      emotionRes = parent.config.face.emotion.enabled ? predict2(faces[i].image, parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? predict2(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : {};
     } else {
       parent.state = "run:emotion";
       timeStamp = now();
-      emotionRes = parent.config.face.emotion.enabled ? await predict2(faces[i].image, parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? await predict2(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : {};
       parent.performance.emotion = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Emotion:");
     parent.analyze("Start Description:");
     if (parent.config.async) {
-      descRes = parent.config.face.description.enabled ? predict3(faces[i], parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? predict3(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : [];
     } else {
       parent.state = "run:description";
       timeStamp = now();
-      descRes = parent.config.face.description.enabled ? await predict3(faces[i].image, parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? await predict3(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : [];
       parent.performance.embedding = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Description:");
@@ -4838,7 +4840,7 @@ async function predict4(input, config3) {
     results3d[1] = results3d[1].sigmoid();
     return results3d;
   });
-  const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));
+  const buffers = await Promise.all(res.map((tensor2) => tensor2.buffer()));
   for (const t of res)
     t.dispose();
   const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
@@ -8183,19 +8185,19 @@ async function predict5(input, config3) {
       }
       box6[2] -= box6[0];
       box6[3] -= box6[1];
-      boxRaw3 = [box6[0] / input.shape[2], box6[1] / input.shape[1], box6[2] / input.shape[2], box6[3] / input.shape[1]];
+      boxRaw3 = [box6[0] / (input.shape[2] || 0), box6[1] / (input.shape[1] || 0), box6[2] / (input.shape[2] || 0), box6[3] / (input.shape[1] || 0)];
     } else {
       box6 = predictions[i].box ? [
         Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
         Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
-        Math.trunc(Math.min(input.shape[2], predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
-        Math.trunc(Math.min(input.shape[1], predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
+        Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
       ] : [0, 0, 0, 0];
       boxRaw3 = [
-        predictions[i].box.topLeft[0] / input.shape[2],
+        predictions[i].box.topLeft[0] / (input.shape[2] || 0),
-        predictions[i].box.topLeft[1] / input.shape[1],
+        predictions[i].box.topLeft[1] / (input.shape[1] || 0),
-        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / input.shape[2],
+        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
-        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input.shape[1]
+        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0)
       ];
     }
     hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
@@ -8334,7 +8336,7 @@ async function predict6(image15, config3) {
     return [];
   if (!config3.body.enabled)
     return [];
-  const imgSize = { width: image15.shape[2], height: image15.shape[1] };
+  const imgSize = { width: image15.shape[2] || 0, height: image15.shape[1] || 0 };
   const resize = tf14.image.resizeBilinear(image15, [model4["width"], model4["height"]], false);
   const normalize = tf14.div(resize, [255]);
   resize.dispose();
@@ -8418,7 +8420,7 @@ async function predict7(image15, config3) {
   }
   skipped3 = 0;
   return new Promise(async (resolve) => {
-    const tensor = tf15.tidy(() => {
+    const tensor2 = tf15.tidy(() => {
       if (!model5.inputs[0].shape)
         return null;
       const resize = tf15.image.resizeBilinear(image15, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
@@ -8428,8 +8430,8 @@ async function predict7(image15, config3) {
     });
     let resT;
     if (config3.body.enabled)
-      resT = await model5.predict(tensor);
+      resT = await model5.predict(tensor2);
-    tensor.dispose();
+    tensor2.dispose();
     if (resT) {
       keypoints.length = 0;
       const squeeze3 = resT.squeeze();
@@ -8503,7 +8505,7 @@ async function predict8(image15, config3) {
   }
   skipped4 = 0;
   return new Promise(async (resolve) => {
-    const tensor = tf16.tidy(() => {
+    const tensor2 = tf16.tidy(() => {
       if (!model6.inputs[0].shape)
         return null;
       const resize = tf16.image.resizeBilinear(image15, [model6.inputs[0].shape[2], model6.inputs[0].shape[1]], false);
@@ -8512,8 +8514,8 @@ async function predict8(image15, config3) {
     });
     let resT;
     if (config3.body.enabled)
-      resT = await model6.predict(tensor);
+      resT = await model6.predict(tensor2);
-    tensor.dispose();
+    tensor2.dispose();
     if (resT) {
       keypoints2.length = 0;
       const res = resT.arraySync();
@@ -8530,8 +8532,8 @@ async function predict8(image15, config3) {
               kpt3[id][0]
             ],
             position: [
-              Math.round(image15.shape[2] * kpt3[id][1]),
+              Math.round((image15.shape[2] || 0) * kpt3[id][1]),
-              Math.round(image15.shape[1] * kpt3[id][0])
+              Math.round((image15.shape[1] || 0) * kpt3[id][0])
             ]
           });
         }
@@ -9660,7 +9662,7 @@ var inCanvas;
 var outCanvas;
 var fx;
 function process4(input, config3) {
-  let tensor;
+  let tensor2;
   if (!input)
     throw new Error("Human: Input is missing");
   if (!(input instanceof tf19.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
@@ -9668,7 +9670,7 @@ function process4(input, config3) {
   }
   if (input instanceof tf19.Tensor) {
     if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3)
-      tensor = tf19.clone(input);
+      tensor2 = tf19.clone(input);
     else
       throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);
   } else {
@@ -9784,12 +9786,12 @@ function process4(input, config3) {
       pixels = tf19.browser.fromPixels(data);
     }
     const casted = pixels.toFloat();
-    tensor = casted.expandDims(0);
+    tensor2 = casted.expandDims(0);
     pixels.dispose();
     casted.dispose();
   }
   const canvas2 = config3.filter.return ? outCanvas : null;
-  return { tensor, canvas: canvas2 };
+  return { tensor: tensor2, canvas: canvas2 };
 }
 
 // src/draw/draw.ts

dist/human.node-wasm.js

@@ -129,6 +129,7 @@ var config = {
   async: true,
   warmup: "full",
   cacheSensitivity: 0.75,
+  skipFrame: false,
   filter: {
     enabled: true,
     width: 0,
@@ -188,12 +189,13 @@ var config = {
     enabled: true,
     modelPath: "movenet-lightning.json",
     maxDetected: 1,
-    minConfidence: 0.2
+    minConfidence: 0.2,
+    skipFrames: 16
   },
   hand: {
     enabled: true,
     rotation: true,
-    skipFrames: 18,
+    skipFrames: 19,
     minConfidence: 0.1,
     iouThreshold: 0.1,
     maxDetected: 2,
@@ -211,7 +213,7 @@ var config = {
     minConfidence: 0.2,
     iouThreshold: 0.4,
     maxDetected: 10,
-    skipFrames: 19
+    skipFrames: 20
   }
 };
 
@@ -4085,8 +4087,8 @@ async function predict(input, config3) {
     if (!prediction || prediction.isDisposedInternal)
       continue;
     const meshRaw = prediction.mesh.map((pt) => [
-      pt[0] / input.shape[2],
+      pt[0] / (input.shape[2] || 0),
-      pt[1] / input.shape[1],
+      pt[1] / (input.shape[1] || 0),
       pt[2] / facePipeline.meshSize
     ]);
     const annotations3 = {};
@@ -4097,14 +4099,14 @@ async function predict(input, config3) {
     const clampedBox = prediction.box ? [
       Math.trunc(Math.max(0, prediction.box.startPoint[0])),
       Math.trunc(Math.max(0, prediction.box.startPoint[1])),
-      Math.trunc(Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])),
+      Math.trunc(Math.min(input.shape[2] || 0, prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])),
-      Math.trunc(Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]))
+      Math.trunc(Math.min(input.shape[1] || 0, prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]))
     ] : [0, 0, 0, 0];
     const boxRaw3 = prediction.box ? [
-      prediction.box.startPoint[0] / input.shape[2],
+      prediction.box.startPoint[0] / (input.shape[2] || 0),
-      prediction.box.startPoint[1] / input.shape[1],
+      prediction.box.startPoint[1] / (input.shape[1] || 0),
-      (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],
+      (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / (input.shape[2] || 0),
-      (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1]
+      (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / (input.shape[1] || 0)
     ] : [0, 0, 0, 0];
     results.push({
       id: id++,
@@ -4275,13 +4277,13 @@ function match(embedding, db, threshold = 0) {
 }
 function enhance(input) {
   const image15 = tf7.tidy(() => {
-    const tensor = input.image || input.tensor || input;
+    const tensor2 = input.image || input.tensor || input;
-    if (!(tensor instanceof tf7.Tensor))
+    if (!(tensor2 instanceof tf7.Tensor))
       return null;
     const box6 = [[0.05, 0.15, 0.85, 0.85]];
     if (!model2.inputs[0].shape)
       return null;
-    const crop = tensor.shape.length === 3 ? tf7.image.cropAndResize(tf7.expandDims(tensor, 0), box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]) : tf7.image.cropAndResize(tensor, box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]);
+    const crop = tensor2.shape.length === 3 ? tf7.image.cropAndResize(tf7.expandDims(tensor2, 0), box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]) : tf7.image.cropAndResize(tensor2, box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]);
     const norm = crop.mul(255);
     return norm;
   });
@@ -4453,21 +4455,21 @@ var detectFace = async (parent, input) => {
     const rotation = calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]);
     parent.analyze("Start Emotion:");
     if (parent.config.async) {
-      emotionRes = parent.config.face.emotion.enabled ? predict2(faces[i].image, parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? predict2(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : {};
     } else {
       parent.state = "run:emotion";
       timeStamp = now();
-      emotionRes = parent.config.face.emotion.enabled ? await predict2(faces[i].image, parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? await predict2(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : {};
       parent.performance.emotion = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Emotion:");
     parent.analyze("Start Description:");
     if (parent.config.async) {
-      descRes = parent.config.face.description.enabled ? predict3(faces[i], parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? predict3(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : [];
     } else {
       parent.state = "run:description";
       timeStamp = now();
-      descRes = parent.config.face.description.enabled ? await predict3(faces[i].image, parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? await predict3(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : [];
       parent.performance.embedding = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Description:");
@@ -4839,7 +4841,7 @@ async function predict4(input, config3) {
     results3d[1] = results3d[1].sigmoid();
     return results3d;
   });
-  const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));
+  const buffers = await Promise.all(res.map((tensor2) => tensor2.buffer()));
   for (const t of res)
     t.dispose();
   const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
@@ -8184,19 +8186,19 @@ async function predict5(input, config3) {
       }
       box6[2] -= box6[0];
       box6[3] -= box6[1];
-      boxRaw3 = [box6[0] / input.shape[2], box6[1] / input.shape[1], box6[2] / input.shape[2], box6[3] / input.shape[1]];
+      boxRaw3 = [box6[0] / (input.shape[2] || 0), box6[1] / (input.shape[1] || 0), box6[2] / (input.shape[2] || 0), box6[3] / (input.shape[1] || 0)];
     } else {
       box6 = predictions[i].box ? [
         Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
         Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
-        Math.trunc(Math.min(input.shape[2], predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
-        Math.trunc(Math.min(input.shape[1], predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
+        Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
       ] : [0, 0, 0, 0];
       boxRaw3 = [
-        predictions[i].box.topLeft[0] / input.shape[2],
+        predictions[i].box.topLeft[0] / (input.shape[2] || 0),
-        predictions[i].box.topLeft[1] / input.shape[1],
+        predictions[i].box.topLeft[1] / (input.shape[1] || 0),
-        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / input.shape[2],
+        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
-        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input.shape[1]
+        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0)
       ];
     }
     hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
@@ -8335,7 +8337,7 @@ async function predict6(image15, config3) {
     return [];
   if (!config3.body.enabled)
     return [];
-  const imgSize = { width: image15.shape[2], height: image15.shape[1] };
+  const imgSize = { width: image15.shape[2] || 0, height: image15.shape[1] || 0 };
   const resize = tf14.image.resizeBilinear(image15, [model4["width"], model4["height"]], false);
   const normalize = tf14.div(resize, [255]);
   resize.dispose();
@@ -8419,7 +8421,7 @@ async function predict7(image15, config3) {
   }
   skipped3 = 0;
   return new Promise(async (resolve) => {
-    const tensor = tf15.tidy(() => {
+    const tensor2 = tf15.tidy(() => {
       if (!model5.inputs[0].shape)
         return null;
       const resize = tf15.image.resizeBilinear(image15, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
@@ -8429,8 +8431,8 @@ async function predict7(image15, config3) {
     });
     let resT;
     if (config3.body.enabled)
-      resT = await model5.predict(tensor);
+      resT = await model5.predict(tensor2);
-    tensor.dispose();
+    tensor2.dispose();
     if (resT) {
       keypoints.length = 0;
       const squeeze3 = resT.squeeze();
@@ -8504,7 +8506,7 @@ async function predict8(image15, config3) {
   }
   skipped4 = 0;
   return new Promise(async (resolve) => {
-    const tensor = tf16.tidy(() => {
+    const tensor2 = tf16.tidy(() => {
       if (!model6.inputs[0].shape)
         return null;
       const resize = tf16.image.resizeBilinear(image15, [model6.inputs[0].shape[2], model6.inputs[0].shape[1]], false);
@@ -8513,8 +8515,8 @@ async function predict8(image15, config3) {
     });
     let resT;
     if (config3.body.enabled)
-      resT = await model6.predict(tensor);
+      resT = await model6.predict(tensor2);
-    tensor.dispose();
+    tensor2.dispose();
     if (resT) {
       keypoints2.length = 0;
       const res = resT.arraySync();
@@ -8531,8 +8533,8 @@ async function predict8(image15, config3) {
               kpt3[id][0]
             ],
             position: [
-              Math.round(image15.shape[2] * kpt3[id][1]),
+              Math.round((image15.shape[2] || 0) * kpt3[id][1]),
-              Math.round(image15.shape[1] * kpt3[id][0])
+              Math.round((image15.shape[1] || 0) * kpt3[id][0])
             ]
           });
         }
@@ -9661,7 +9663,7 @@ var inCanvas;
 var outCanvas;
 var fx;
 function process4(input, config3) {
-  let tensor;
+  let tensor2;
   if (!input)
     throw new Error("Human: Input is missing");
   if (!(input instanceof tf19.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
@@ -9669,7 +9671,7 @@ function process4(input, config3) {
   }
   if (input instanceof tf19.Tensor) {
     if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3)
-      tensor = tf19.clone(input);
+      tensor2 = tf19.clone(input);
     else
       throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);
   } else {
@@ -9785,12 +9787,12 @@ function process4(input, config3) {
       pixels = tf19.browser.fromPixels(data);
     }
     const casted = pixels.toFloat();
-    tensor = casted.expandDims(0);
+    tensor2 = casted.expandDims(0);
     pixels.dispose();
     casted.dispose();
   }
   const canvas2 = config3.filter.return ? outCanvas : null;
-  return { tensor, canvas: canvas2 };
+  return { tensor: tensor2, canvas: canvas2 };
 }
 
 // src/draw/draw.ts

dist/human.node.js

@@ -128,6 +128,7 @@ var config = {
   async: true,
   warmup: "full",
   cacheSensitivity: 0.75,
+  skipFrame: false,
   filter: {
     enabled: true,
     width: 0,
@@ -187,12 +188,13 @@ var config = {
     enabled: true,
     modelPath: "movenet-lightning.json",
     maxDetected: 1,
-    minConfidence: 0.2
+    minConfidence: 0.2,
+    skipFrames: 16
   },
   hand: {
     enabled: true,
     rotation: true,
-    skipFrames: 18,
+    skipFrames: 19,
     minConfidence: 0.1,
     iouThreshold: 0.1,
     maxDetected: 2,
@@ -210,7 +212,7 @@ var config = {
     minConfidence: 0.2,
     iouThreshold: 0.4,
     maxDetected: 10,
-    skipFrames: 19
+    skipFrames: 20
   }
 };
 
@@ -4084,8 +4086,8 @@ async function predict(input, config3) {
     if (!prediction || prediction.isDisposedInternal)
       continue;
     const meshRaw = prediction.mesh.map((pt) => [
-      pt[0] / input.shape[2],
+      pt[0] / (input.shape[2] || 0),
-      pt[1] / input.shape[1],
+      pt[1] / (input.shape[1] || 0),
       pt[2] / facePipeline.meshSize
     ]);
     const annotations3 = {};
@@ -4096,14 +4098,14 @@ async function predict(input, config3) {
     const clampedBox = prediction.box ? [
       Math.trunc(Math.max(0, prediction.box.startPoint[0])),
       Math.trunc(Math.max(0, prediction.box.startPoint[1])),
-      Math.trunc(Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])),
+      Math.trunc(Math.min(input.shape[2] || 0, prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])),
-      Math.trunc(Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]))
+      Math.trunc(Math.min(input.shape[1] || 0, prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1]))
     ] : [0, 0, 0, 0];
     const boxRaw3 = prediction.box ? [
-      prediction.box.startPoint[0] / input.shape[2],
+      prediction.box.startPoint[0] / (input.shape[2] || 0),
-      prediction.box.startPoint[1] / input.shape[1],
+      prediction.box.startPoint[1] / (input.shape[1] || 0),
-      (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],
+      (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / (input.shape[2] || 0),
-      (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1]
+      (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / (input.shape[1] || 0)
     ] : [0, 0, 0, 0];
     results.push({
       id: id++,
@@ -4274,13 +4276,13 @@ function match(embedding, db, threshold = 0) {
 }
 function enhance(input) {
   const image15 = tf7.tidy(() => {
-    const tensor = input.image || input.tensor || input;
+    const tensor2 = input.image || input.tensor || input;
-    if (!(tensor instanceof tf7.Tensor))
+    if (!(tensor2 instanceof tf7.Tensor))
       return null;
     const box6 = [[0.05, 0.15, 0.85, 0.85]];
     if (!model2.inputs[0].shape)
       return null;
-    const crop = tensor.shape.length === 3 ? tf7.image.cropAndResize(tf7.expandDims(tensor, 0), box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]) : tf7.image.cropAndResize(tensor, box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]);
+    const crop = tensor2.shape.length === 3 ? tf7.image.cropAndResize(tf7.expandDims(tensor2, 0), box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]) : tf7.image.cropAndResize(tensor2, box6, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]);
     const norm = crop.mul(255);
     return norm;
   });
@@ -4452,21 +4454,21 @@ var detectFace = async (parent, input) => {
     const rotation = calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]);
     parent.analyze("Start Emotion:");
     if (parent.config.async) {
-      emotionRes = parent.config.face.emotion.enabled ? predict2(faces[i].image, parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? predict2(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : {};
     } else {
       parent.state = "run:emotion";
       timeStamp = now();
-      emotionRes = parent.config.face.emotion.enabled ? await predict2(faces[i].image, parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? await predict2(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : {};
       parent.performance.emotion = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Emotion:");
     parent.analyze("Start Description:");
     if (parent.config.async) {
-      descRes = parent.config.face.description.enabled ? predict3(faces[i], parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? predict3(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : [];
     } else {
       parent.state = "run:description";
       timeStamp = now();
-      descRes = parent.config.face.description.enabled ? await predict3(faces[i].image, parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? await predict3(faces[i].image || tf8.tensor([]), parent.config, i, faces.length) : [];
       parent.performance.embedding = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Description:");
@@ -4838,7 +4840,7 @@ async function predict4(input, config3) {
     results3d[1] = results3d[1].sigmoid();
     return results3d;
   });
-  const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));
+  const buffers = await Promise.all(res.map((tensor2) => tensor2.buffer()));
   for (const t of res)
     t.dispose();
   const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
@@ -8183,19 +8185,19 @@ async function predict5(input, config3) {
       }
       box6[2] -= box6[0];
       box6[3] -= box6[1];
-      boxRaw3 = [box6[0] / input.shape[2], box6[1] / input.shape[1], box6[2] / input.shape[2], box6[3] / input.shape[1]];
+      boxRaw3 = [box6[0] / (input.shape[2] || 0), box6[1] / (input.shape[1] || 0), box6[2] / (input.shape[2] || 0), box6[3] / (input.shape[1] || 0)];
     } else {
       box6 = predictions[i].box ? [
         Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
         Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
-        Math.trunc(Math.min(input.shape[2], predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
-        Math.trunc(Math.min(input.shape[1], predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
+        Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
       ] : [0, 0, 0, 0];
       boxRaw3 = [
-        predictions[i].box.topLeft[0] / input.shape[2],
+        predictions[i].box.topLeft[0] / (input.shape[2] || 0),
-        predictions[i].box.topLeft[1] / input.shape[1],
+        predictions[i].box.topLeft[1] / (input.shape[1] || 0),
-        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / input.shape[2],
+        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
-        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input.shape[1]
+        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0)
       ];
     }
     hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box: box6, boxRaw: boxRaw3, keypoints: keypoints3, annotations: annotations3 });
@@ -8334,7 +8336,7 @@ async function predict6(image15, config3) {
     return [];
   if (!config3.body.enabled)
     return [];
-  const imgSize = { width: image15.shape[2], height: image15.shape[1] };
+  const imgSize = { width: image15.shape[2] || 0, height: image15.shape[1] || 0 };
   const resize = tf14.image.resizeBilinear(image15, [model4["width"], model4["height"]], false);
   const normalize = tf14.div(resize, [255]);
   resize.dispose();
@@ -8418,7 +8420,7 @@ async function predict7(image15, config3) {
   }
   skipped3 = 0;
   return new Promise(async (resolve) => {
-    const tensor = tf15.tidy(() => {
+    const tensor2 = tf15.tidy(() => {
       if (!model5.inputs[0].shape)
         return null;
       const resize = tf15.image.resizeBilinear(image15, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
@@ -8428,8 +8430,8 @@ async function predict7(image15, config3) {
     });
     let resT;
     if (config3.body.enabled)
-      resT = await model5.predict(tensor);
+      resT = await model5.predict(tensor2);
-    tensor.dispose();
+    tensor2.dispose();
     if (resT) {
       keypoints.length = 0;
       const squeeze3 = resT.squeeze();
@@ -8503,7 +8505,7 @@ async function predict8(image15, config3) {
   }
   skipped4 = 0;
   return new Promise(async (resolve) => {
-    const tensor = tf16.tidy(() => {
+    const tensor2 = tf16.tidy(() => {
       if (!model6.inputs[0].shape)
         return null;
       const resize = tf16.image.resizeBilinear(image15, [model6.inputs[0].shape[2], model6.inputs[0].shape[1]], false);
@@ -8512,8 +8514,8 @@ async function predict8(image15, config3) {
     });
     let resT;
     if (config3.body.enabled)
-      resT = await model6.predict(tensor);
+      resT = await model6.predict(tensor2);
-    tensor.dispose();
+    tensor2.dispose();
     if (resT) {
       keypoints2.length = 0;
       const res = resT.arraySync();
@@ -8530,8 +8532,8 @@ async function predict8(image15, config3) {
               kpt3[id][0]
             ],
             position: [
-              Math.round(image15.shape[2] * kpt3[id][1]),
+              Math.round((image15.shape[2] || 0) * kpt3[id][1]),
-              Math.round(image15.shape[1] * kpt3[id][0])
+              Math.round((image15.shape[1] || 0) * kpt3[id][0])
             ]
           });
         }
@@ -9660,7 +9662,7 @@ var inCanvas;
 var outCanvas;
 var fx;
 function process4(input, config3) {
-  let tensor;
+  let tensor2;
   if (!input)
     throw new Error("Human: Input is missing");
   if (!(input instanceof tf19.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
@@ -9668,7 +9670,7 @@ function process4(input, config3) {
   }
   if (input instanceof tf19.Tensor) {
     if (input.shape && input.shape.length === 4 && input.shape[0] === 1 && input.shape[3] === 3)
-      tensor = tf19.clone(input);
+      tensor2 = tf19.clone(input);
     else
       throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${input.shape}`);
   } else {
@@ -9784,12 +9786,12 @@ function process4(input, config3) {
       pixels = tf19.browser.fromPixels(data);
     }
     const casted = pixels.toFloat();
-    tensor = casted.expandDims(0);
+    tensor2 = casted.expandDims(0);
     pixels.dispose();
     casted.dispose();
   }
   const canvas2 = config3.filter.return ? outCanvas : null;
-  return { tensor, canvas: canvas2 };
+  return { tensor: tensor2, canvas: canvas2 };
 }
 
 // src/draw/draw.ts

package.json

@@ -25,7 +25,8 @@
     "dev": "node --trace-warnings --unhandled-rejections=strict --trace-uncaught server/serve.js",
     "build": "rimraf dist/* typedoc/* types/* && node --trace-warnings --unhandled-rejections=strict --trace-uncaught server/build.js",
     "lint": "eslint src server demo test",
-    "test": "node --trace-warnings --unhandled-rejections=strict --trace-uncaught test/test.js"
+    "test": "node --trace-warnings --unhandled-rejections=strict --trace-uncaught test/test.js",
+    "scan": "npx auditjs@latest ossi --dev --quiet"
   },
   "keywords": [
     "human",
@@ -62,7 +63,7 @@
     "@tensorflow/tfjs-layers": "^3.6.0",
     "@tensorflow/tfjs-node": "^3.6.1",
     "@tensorflow/tfjs-node-gpu": "^3.6.1",
-    "@types/node": "^15.6.1",
+    "@types/node": "^15.9.0",
     "@typescript-eslint/eslint-plugin": "^4.26.0",
     "@typescript-eslint/parser": "^4.26.0",
     "@vladmandic/pilogger": "^0.2.17",

server/build.js

@@ -9,12 +9,15 @@ const path = require('path');
 const log = require('@vladmandic/pilogger');
 const esbuild = require('esbuild');
 const TypeDoc = require('typedoc');
+const { ESLint } = require('eslint');
+const tfjs = require('@tensorflow/tfjs/package.json');
 const changelog = require('./changelog');
 
 let logFile = 'build.log';
 
 let busy = false;
 let td = null;
+let eslint = null;
 const banner = { js: `
   /*
   Human library
@@ -49,6 +52,8 @@ const tsconfig = {
 };
 
 // common configuration
+const lintLocations = ['server/', 'demo/', 'src/', 'test/'];
+
 const config = {
   common: {
     banner,
@@ -225,7 +230,7 @@ async function getStats(json) {
 }
 
 // rebuild typings
-async function compile(entryPoint, options) {
+async function typings(entryPoint, options) {
   log.info('Generate types:', entryPoint);
   const program = ts.createProgram(entryPoint, options);
   const emit = program.emit();
@@ -250,13 +255,34 @@ async function typedoc(entryPoint) {
     td = new TypeDoc.Application();
     td.options.addReader(new TypeDoc.TSConfigReader());
     td.bootstrap({ entryPoints: [entryPoint], theme: 'wiki/theme/' });
+    td.logger.warn = log.warn;
+    td.logger.error = log.error;
+    td.logger.verbose = () => { /***/ };
+    td.logger.log = log.info;
   }
   const project = td.convert();
   if (!project) log.warn('TypeDoc: convert returned empty project');
+  if (td.logger.hasErrors() || td.logger.hasWarnings()) log.warn('TypeDoc:', 'errors:', td.logger.errorCount, 'warnings:', td.logger.warningCount);
   const result = project ? await td.generateDocs(project, 'typedoc') : null;
   if (result) log.warn('TypeDoc:', result);
 }
 
+async function lint() {
+  log.info('Running Linter:', lintLocations);
+  if (!eslint) {
+    eslint = new ESLint();
+  }
+  const results = await eslint.lintFiles(lintLocations);
+  const errors = results.reduce((prev, curr) => prev += curr.errorCount, 0);
+  const warnings = results.reduce((prev, curr) => prev += curr.warningCount, 0);
+  log.info('Linter complete: files:', results.length, 'errors:', errors, 'warnings:', warnings);
+  if (errors > 0 || warnings > 0) {
+    const formatter = await eslint.loadFormatter('stylish');
+    const text = formatter.format(results);
+    log.warn(text);
+  }
+}
+
 // rebuild on file change
 async function build(f, msg, dev = false) {
   if (busy) {
@@ -282,11 +308,11 @@ async function build(f, msg, dev = false) {
         log.state(`Build for: ${targetGroupName} type: ${targetName}:`, stats);
       }
     }
-    if (!dev) {
+    if (!dev) { // only for prod builds, skipped for dev build
-      // generate typings & typedoc only when run as explict build
+      await lint(); // run linter
-      await compile(targets.browserBundle.esm.entryPoints, tsconfig);
+      await typings(targets.browserBundle.esm.entryPoints, tsconfig); // generate typings
-      await changelog.update('../CHANGELOG.md');
+      await changelog.update('../CHANGELOG.md'); // generate changelog
-      await typedoc(targets.browserBundle.esm.entryPoints);
+      await typedoc(targets.browserBundle.esm.entryPoints); // generate typedoc
     }
     if (require.main === module) process.exit(0);
   } catch (err) {
@@ -302,6 +328,7 @@ if (require.main === module) {
   if (fs.existsSync(logFile)) fs.unlinkSync(logFile);
   log.logFile(logFile);
   log.header();
+  log.info(`Toolchain: tfjs: ${tfjs.version} esbuild ${esbuild.version}; typescript ${ts.version}; typedoc: ${TypeDoc.Application.VERSION} eslint: ${ESLint.version}`);
   build('all', 'startup');
 } else {
   exports.build = build;

server/build.log

@@ -1,17 +1,21 @@
-2021-06-02 13:37:11 INFO:  @vladmandic/human version 2.0.0 
+2021-06-03 09:38:51 INFO:  @vladmandic/human version 2.0.0 
-2021-06-02 13:37:11 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.0.0 
+2021-06-03 09:38:51 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.0.0 
-2021-06-02 13:37:11 INFO:  Build: file startup all type: production config: {"minifyWhitespace":true,"minifyIdentifiers":true,"minifySyntax":true} 
+2021-06-03 09:38:51 INFO:  Toolchain: tfjs: 3.6.0 esbuild 0.12.5; typescript 4.2.4; typedoc: 0.20.36 eslint: 7.27.0 
-2021-06-02 13:37:11 STATE: Build for: node type: tfjs: {"imports":1,"importBytes":102,"outputBytes":1292,"outputFiles":"dist/tfjs.esm.js"} 
+2021-06-03 09:38:51 INFO:  Build: file startup all type: production config: {"minifyWhitespace":true,"minifyIdentifiers":true,"minifySyntax":true} 
-2021-06-02 13:37:11 STATE: Build for: node type: node: {"imports":40,"importBytes":418593,"outputBytes":371337,"outputFiles":"dist/human.node.js"} 
+2021-06-03 09:38:51 STATE: Build for: node type: tfjs: {"imports":1,"importBytes":102,"outputBytes":1292,"outputFiles":"dist/tfjs.esm.js"} 
-2021-06-02 13:37:11 STATE: Build for: nodeGPU type: tfjs: {"imports":1,"importBytes":110,"outputBytes":1300,"outputFiles":"dist/tfjs.esm.js"} 
+2021-06-03 09:38:51 STATE: Build for: node type: node: {"imports":40,"importBytes":420707,"outputBytes":371671,"outputFiles":"dist/human.node.js"} 
-2021-06-02 13:37:11 STATE: Build for: nodeGPU type: node: {"imports":40,"importBytes":418601,"outputBytes":371341,"outputFiles":"dist/human.node-gpu.js"} 
+2021-06-03 09:38:51 STATE: Build for: nodeGPU type: tfjs: {"imports":1,"importBytes":110,"outputBytes":1300,"outputFiles":"dist/tfjs.esm.js"} 
-2021-06-02 13:37:11 STATE: Build for: nodeWASM type: tfjs: {"imports":1,"importBytes":149,"outputBytes":1367,"outputFiles":"dist/tfjs.esm.js"} 
+2021-06-03 09:38:51 STATE: Build for: nodeGPU type: node: {"imports":40,"importBytes":420715,"outputBytes":371675,"outputFiles":"dist/human.node-gpu.js"} 
-2021-06-02 13:37:11 STATE: Build for: nodeWASM type: node: {"imports":40,"importBytes":418668,"outputBytes":371413,"outputFiles":"dist/human.node-wasm.js"} 
+2021-06-03 09:38:51 STATE: Build for: nodeWASM type: tfjs: {"imports":1,"importBytes":149,"outputBytes":1367,"outputFiles":"dist/tfjs.esm.js"} 
-2021-06-02 13:37:11 STATE: Build for: browserNoBundle type: tfjs: {"imports":1,"importBytes":2478,"outputBytes":1394,"outputFiles":"dist/tfjs.esm.js"} 
+2021-06-03 09:38:51 STATE: Build for: nodeWASM type: node: {"imports":40,"importBytes":420782,"outputBytes":371747,"outputFiles":"dist/human.node-wasm.js"} 
-2021-06-02 13:37:11 STATE: Build for: browserNoBundle type: esm: {"imports":40,"importBytes":418695,"outputBytes":244745,"outputFiles":"dist/human.esm-nobundle.js"} 
+2021-06-03 09:38:51 STATE: Build for: browserNoBundle type: tfjs: {"imports":1,"importBytes":2478,"outputBytes":1394,"outputFiles":"dist/tfjs.esm.js"} 
-2021-06-02 13:37:12 STATE: Build for: browserBundle type: tfjs: {"modules":1274,"moduleBytes":4114813,"imports":7,"importBytes":2478,"outputBytes":1111418,"outputFiles":"dist/tfjs.esm.js"} 
+2021-06-03 09:38:51 STATE: Build for: browserNoBundle type: esm: {"imports":40,"importBytes":420809,"outputBytes":244956,"outputFiles":"dist/human.esm-nobundle.js"} 
-2021-06-02 13:37:12 STATE: Build for: browserBundle type: iife: {"imports":40,"importBytes":1528719,"outputBytes":1352544,"outputFiles":"dist/human.js"} 
+2021-06-03 09:38:52 STATE: Build for: browserBundle type: tfjs: {"modules":1274,"moduleBytes":4114813,"imports":7,"importBytes":2478,"outputBytes":1111418,"outputFiles":"dist/tfjs.esm.js"} 
-2021-06-02 13:37:13 STATE: Build for: browserBundle type: esm: {"imports":40,"importBytes":1528719,"outputBytes":1352536,"outputFiles":"dist/human.esm.js"} 
+2021-06-03 09:38:52 STATE: Build for: browserBundle type: iife: {"imports":40,"importBytes":1530833,"outputBytes":1352725,"outputFiles":"dist/human.js"} 
-2021-06-02 13:37:13 INFO:  Generate types: ["src/human.ts"] 
+2021-06-03 09:38:53 STATE: Build for: browserBundle type: esm: {"imports":40,"importBytes":1530833,"outputBytes":1352717,"outputFiles":"dist/human.esm.js"} 
-2021-06-02 13:37:18 INFO:  Update Change log: ["/home/vlado/dev/human/CHANGELOG.md"] 
+2021-06-03 09:38:53 INFO:  Running Linter: ["server/","demo/","src/","test/"] 
-2021-06-02 13:37:18 INFO:  Generate TypeDocs: ["src/human.ts"] 
+2021-06-03 09:39:19 INFO:  Linter complete: files: 67 errors: 0 warnings: 0 
+2021-06-03 09:39:19 INFO:  Generate types: ["src/human.ts"] 
+2021-06-03 09:39:23 INFO:  Update Change log: ["/home/vlado/dev/human/CHANGELOG.md"] 
+2021-06-03 09:39:23 INFO:  Generate TypeDocs: ["src/human.ts"] 
+2021-06-03 09:39:42 INFO:  Documentation generated at /home/vlado/dev/human/typedoc 1 

src/blazeface/blazeface.ts

@@ -29,7 +29,7 @@ export class BlazeFaceModel {
   inputSize: number;
   config: Config;
 
-  constructor(model, config) {
+  constructor(model, config: Config) {
     this.model = model;
     this.anchorsData = util.generateAnchors(model.inputs[0].shape[1]);
     this.anchors = tf.tensor2d(this.anchorsData);
@@ -37,8 +37,9 @@ export class BlazeFaceModel {
     this.config = config;
   }
 
-  async getBoundingBoxes(inputImage) {
+  async getBoundingBoxes(inputImage: Tensor) {
     // sanity check on input
+    // @ts-ignore isDisposed is internal property
     if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;
     const [batch, boxes, scores] = tf.tidy(() => {
       const resizedImage = inputImage.resizeBilinear([this.inputSize, this.inputSize]);
@@ -85,7 +86,7 @@ export class BlazeFaceModel {
   }
 }
 
-export async function load(config) {
+export async function load(config: Config) {
   const model = await tf.loadGraphModel(join(config.modelBasePath, config.face.detector.modelPath), { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });
   const blazeFace = new BlazeFaceModel(model, config);
   if (!model || !model.modelUrl) log('load model failed:', config.face.detector.modelPath);

src/blazeface/facemesh.ts

@@ -7,21 +7,22 @@ import * as tf from '../../dist/tfjs.esm.js';
 import * as blazeface from './blazeface';
 import * as facepipeline from './facepipeline';
 import * as coords from './coords';
-import { GraphModel } from '../tfjs/types';
+import { GraphModel, Tensor } from '../tfjs/types';
 import { Face } from '../result';
+import { Config } from '../config';
 
 let faceModels: [blazeface.BlazeFaceModel | null, GraphModel | null, GraphModel | null] = [null, null, null];
 let facePipeline;
 
-export async function predict(input, config): Promise<Face[]> {
+export async function predict(input: Tensor, config: Config): Promise<Face[]> {
   const predictions = await facePipeline.predict(input, config);
   const results: Array<Face> = [];
   let id = 0;
   for (const prediction of (predictions || [])) {
     if (!prediction || prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing
     const meshRaw = prediction.mesh.map((pt) => [
-      pt[0] / input.shape[2],
+      pt[0] / (input.shape[2] || 0),
-      pt[1] / input.shape[1],
+      pt[1] / (input.shape[1] || 0),
       pt[2] / facePipeline.meshSize,
     ]);
     const annotations = {};
@@ -31,14 +32,14 @@ export async function predict(input, config): Promise<Face[]> {
     const clampedBox: [number, number, number, number] = prediction.box ? [
       Math.trunc(Math.max(0, prediction.box.startPoint[0])),
       Math.trunc(Math.max(0, prediction.box.startPoint[1])),
-      Math.trunc(Math.min(input.shape[2], prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])),
+      Math.trunc(Math.min((input.shape[2] || 0), prediction.box.endPoint[0]) - Math.max(0, prediction.box.startPoint[0])),
-      Math.trunc(Math.min(input.shape[1], prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1])),
+      Math.trunc(Math.min((input.shape[1] || 0), prediction.box.endPoint[1]) - Math.max(0, prediction.box.startPoint[1])),
     ] : [0, 0, 0, 0];
     const boxRaw: [number, number, number, number] = prediction.box ? [
-      prediction.box.startPoint[0] / input.shape[2],
+      prediction.box.startPoint[0] / (input.shape[2] || 0),
-      prediction.box.startPoint[1] / input.shape[1],
+      prediction.box.startPoint[1] / (input.shape[1] || 0),
-      (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / input.shape[2],
+      (prediction.box.endPoint[0] - prediction.box.startPoint[0]) / (input.shape[2] || 0),
-      (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / input.shape[1],
+      (prediction.box.endPoint[1] - prediction.box.startPoint[1]) / (input.shape[1] || 0),
     ] : [0, 0, 0, 0];
     results.push({
       id: id++,

src/blazepose/blazepose.ts

@@ -9,10 +9,11 @@ import * as tf from '../../dist/tfjs.esm.js';
 import * as annotations from './annotations';
 import { Tensor, GraphModel } from '../tfjs/types';
 import { Body } from '../result';
+import { Config } from '../config';
 
 let model: GraphModel;
 
-export async function load(config) {
+export async function load(config: Config): Promise<GraphModel> {
   if (!model) {
     // @ts-ignore type mismatch for Graphmodel
     model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));
@@ -24,10 +25,10 @@ export async function load(config) {
   return model;
 }
 
-export async function predict(image, config): Promise<Body[]> {
+export async function predict(image: Tensor, config: Config): Promise<Body[]> {
   if (!model) return [];
   if (!config.body.enabled) return [];
-  const imgSize = { width: image.shape[2], height: image.shape[1] };
+  const imgSize = { width: (image.shape[2] || 0), height: (image.shape[1] || 0) };
   const resize = tf.image.resizeBilinear(image, [model['width'], model['height']], false);
   const normalize = tf.div(resize, [255.0]);
   resize.dispose();

src/config.ts

@@ -37,6 +37,12 @@ export interface Config {
   */
   cacheSensitivity: number;
 
+  /** Cache sensitivity
+   * - values 0..1 where 0.01 means reset cache if input changed more than 1%
+   * - set to 0 to disable caching
+  */
+  skipFrame: boolean;
+
   /** Run input through image filters before inference
    * - image filters run with near-zero latency as they are executed on the GPU
   */
@@ -147,6 +153,7 @@ export interface Config {
     modelPath: string,
     maxDetected: number,
     minConfidence: number,
+    skipFrames: number,
   },
 
   /** Controlls and configures all hand detection specific options
@@ -205,6 +212,7 @@ const config: Config = {
   cacheSensitivity: 0.75,    // cache sensitivity
                              // values 0..1 where 0.01 means reset cache if input changed more than 1%
                              // set to 0 to disable caching
+  skipFrame: false,          // internal & dynamic
   filter: {                  // run input through image filters before inference
                              // image filters run with near-zero latency as they are executed on the GPU
     enabled: true,           // enable image pre-processing filters
@@ -294,13 +302,15 @@ const config: Config = {
                              // should be set to the minimum number for performance
                              // only valid for posenet as other models detects single pose
     minConfidence: 0.2,      // threshold for discarding a prediction
+    skipFrames: 16,          // how many max frames to go without re-running the detector
+                             // only used when cacheSensitivity is not zero
 },
 
   hand: {
     enabled: true,
     rotation: true,          // use best-guess rotated hand image or just box with rotation as-is
                              // false means higher performance, but incorrect finger mapping if hand is inverted
-    skipFrames: 18,          // how many max frames to go without re-running the hand bounding box detector
+    skipFrames: 19,          // how many max frames to go without re-running the hand bounding box detector
                              // only used when cacheSensitivity is not zero
                              // e.g., if model is running st 25 FPS, we can re-use existing bounding
                              // box for updated hand skeleton analysis as the hand probably
@@ -325,7 +335,7 @@ const config: Config = {
     minConfidence: 0.2,      // threshold for discarding a prediction
     iouThreshold: 0.4,       // ammount of overlap between two detected objects before one object is removed
     maxDetected: 10,         // maximum number of objects detected in the input
-    skipFrames: 19,          // how many max frames to go without re-running the detector
+    skipFrames: 20,          // how many max frames to go without re-running the detector
                              // only used when cacheSensitivity is not zero
   },
 };

src/efficientpose/efficientpose.ts

@@ -5,7 +5,8 @@
 import { log, join } from '../helpers';
 import * as tf from '../../dist/tfjs.esm.js';
 import { Body } from '../result';
-import { GraphModel } from '../tfjs/types';
+import { GraphModel, Tensor } from '../tfjs/types';
+import { Config } from '../config';
 
 let model: GraphModel;
 
@@ -19,7 +20,7 @@ let skipped = Number.MAX_SAFE_INTEGER;
 
 const bodyParts = ['head', 'neck', 'rightShoulder', 'rightElbow', 'rightWrist', 'chest', 'leftShoulder', 'leftElbow', 'leftWrist', 'pelvis', 'rightHip', 'rightKnee', 'rightAnkle', 'leftHip', 'leftKnee', 'leftAnkle'];
 
-export async function load(config) {
+export async function load(config: Config): Promise<GraphModel> {
   if (!model) {
     // @ts-ignore type mismatch on GraphModel
     model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));
@@ -50,7 +51,7 @@ function max2d(inputs, minScore) {
   });
 }
 
-export async function predict(image, config): Promise<Body[]> {
+export async function predict(image: Tensor, config: Config): Promise<Body[]> {
   if ((skipped < config.body.skipFrames) && config.skipFrame && Object.keys(keypoints).length > 0) {
     skipped++;
     return [{ id: 0, score, box, boxRaw, keypoints }];

src/emotion/emotion.ts

@@ -3,6 +3,8 @@
  */
 
 import { log, join } from '../helpers';
+import { Config } from '../config';
+import { Tensor, GraphModel } from '../tfjs/types';
 import * as tf from '../../dist/tfjs.esm.js';
 
 const annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];
@@ -15,7 +17,7 @@ let skipped = Number.MAX_SAFE_INTEGER;
 // tuning values
 const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale
 
-export async function load(config) {
+export async function load(config: Config): Promise<GraphModel> {
   if (!model) {
     model = await tf.loadGraphModel(join(config.modelBasePath, config.face.emotion.modelPath));
     if (!model || !model.modelUrl) log('load model failed:', config.face.emotion.modelPath);
@@ -24,7 +26,7 @@ export async function load(config) {
   return model;
 }
 
-export async function predict(image, config, idx, count) {
+export async function predict(image: Tensor, config: Config, idx, count) {
   if (!model) return null;
   if ((skipped < config.face.emotion.skipFrames) && config.skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {
     skipped++;

src/face.ts

@@ -171,11 +171,11 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
     // run emotion, inherits face from blazeface
     parent.analyze('Start Emotion:');
     if (parent.config.async) {
-      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(faces[i].image, parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(faces[i].image || tf.tensor([]), parent.config, i, faces.length) : {};
     } else {
       parent.state = 'run:emotion';
       timeStamp = now();
-      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(faces[i].image, parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(faces[i].image || tf.tensor([]), parent.config, i, faces.length) : {};
       parent.performance.emotion = Math.trunc(now() - timeStamp);
     }
     parent.analyze('End Emotion:');
@@ -183,11 +183,11 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
     // run emotion, inherits face from blazeface
     parent.analyze('Start Description:');
     if (parent.config.async) {
-      descRes = parent.config.face.description.enabled ? faceres.predict(faces[i], parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? faceres.predict(faces[i].image || tf.tensor([]), parent.config, i, faces.length) : [];
     } else {
       parent.state = 'run:description';
       timeStamp = now();
-      descRes = parent.config.face.description.enabled ? await faceres.predict(faces[i].image, parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? await faceres.predict(faces[i].image || tf.tensor([]), parent.config, i, faces.length) : [];
       parent.performance.embedding = Math.trunc(now() - timeStamp);
     }
     parent.analyze('End Description:');

src/faceres/faceres.ts

@@ -7,6 +7,7 @@
 import { log, join } from '../helpers';
 import * as tf from '../../dist/tfjs.esm.js';
 import { Tensor, GraphModel } from '../tfjs/types';
+import { Config } from '../config';
 
 let model: GraphModel;
 const last: Array<{
@@ -21,7 +22,7 @@ let skipped = Number.MAX_SAFE_INTEGER;
 
 type DB = Array<{ name: string, source: string, embedding: number[] }>;
 
-export async function load(config) {
+export async function load(config: Config): Promise<GraphModel> {
   const modelUrl = join(config.modelBasePath, config.face.description.modelPath);
   if (!model) {
     // @ts-ignore type mismatch for GraphModel
@@ -32,7 +33,7 @@ export async function load(config) {
   return model;
 }
 
-export function similarity(embedding1, embedding2, order = 2): number {
+export function similarity(embedding1: Array<number>, embedding2: Array<number>, order = 2): number {
   if (!embedding1 || !embedding2) return 0;
   if (embedding1?.length === 0 || embedding2?.length === 0) return 0;
   if (embedding1?.length !== embedding2?.length) return 0;
@@ -110,7 +111,7 @@ export function enhance(input): Tensor {
   return image;
 }
 
-export async function predict(image, config, idx, count) {
+export async function predict(image: Tensor, config: Config, idx, count) {
   if (!model) return null;
   if ((skipped < config.face.description.skipFrames) && config.skipFrame && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {
     skipped++;

src/handpose/handpose.ts

@@ -7,7 +7,8 @@ import * as tf from '../../dist/tfjs.esm.js';
 import * as handdetector from './handdetector';
 import * as handpipeline from './handpipeline';
 import { Hand } from '../result';
-import { GraphModel } from '../tfjs/types';
+import { Tensor, GraphModel } from '../tfjs/types';
+import { Config } from '../config';
 
 const meshAnnotations = {
   thumb: [1, 2, 3, 4],
@@ -22,7 +23,7 @@ let handDetectorModel: GraphModel | null;
 let handPoseModel: GraphModel | null;
 let handPipeline: handpipeline.HandPipeline;
 
-export async function predict(input, config): Promise<Hand[]> {
+export async function predict(input: Tensor, config: Config): Promise<Hand[]> {
   const predictions = await handPipeline.estimateHands(input, config);
   if (!predictions) return [];
   const hands: Array<Hand> = [];
@@ -48,19 +49,19 @@ export async function predict(input, config): Promise<Hand[]> {
       }
       box[2] -= box[0];
       box[3] -= box[1];
-      boxRaw = [box[0] / input.shape[2], box[1] / input.shape[1], box[2] / input.shape[2], box[3] / input.shape[1]];
+      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];
     } else { // otherwise use box from prediction
       box = predictions[i].box ? [
         Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
         Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
-        Math.trunc(Math.min(input.shape[2], predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
-        Math.trunc(Math.min(input.shape[1], predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),
+        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),
       ] : [0, 0, 0, 0];
       boxRaw = [
-        (predictions[i].box.topLeft[0]) / input.shape[2],
+        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
-        (predictions[i].box.topLeft[1]) / input.shape[1],
+        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),
-        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / input.shape[2],
+        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
-        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / input.shape[1],
+        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),
       ];
     }
     hands.push({ id: i, score: Math.round(100 * predictions[i].confidence) / 100, box, boxRaw, keypoints, annotations });
@@ -68,7 +69,7 @@ export async function predict(input, config): Promise<Hand[]> {
   return hands;
 }
 
-export async function load(config): Promise<[unknown, unknown]> {
+export async function load(config: Config): Promise<[unknown, unknown]> {
   if (!handDetectorModel || !handPoseModel) {
     // @ts-ignore type mismatch on GraphModel
     [handDetectorModel, handPoseModel] = await Promise.all([

src/helpers.ts

@@ -12,7 +12,7 @@ export function join(folder: string, file: string): string {
 }
 
 // helper function: wrapper around console output
-export function log(...msg) {
+export function log(...msg): void {
   const dt = new Date();
   const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;
   // eslint-disable-next-line no-console

src/human.ts

@@ -132,7 +132,7 @@ export class Human {
   /** Platform and agent information detected by Human */
   sysinfo: { platform: string, agent: string };
   /** Performance object that contains values for all recently performed operations */
-  performance: Record<string, unknown>; // perf members are dynamically defined as needed
+  performance: Record<string, number>; // perf members are dynamically defined as needed
   #numTensors: number;
   #analyzeMemoryLeaks: boolean;
   #checkSanity: boolean;
@@ -258,7 +258,7 @@ export class Human {
   async load(userConfig?: Config | Record<string, unknown>) {
     this.state = 'load';
     const timeStamp = now();
-    if (userConfig) this.config = mergeDeep(this.config, userConfig);
+    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;
 
     if (this.#firstRun) { // print version info on first run and check for correct backend setup
       if (this.config.debug) log(`version: ${this.version}`);
@@ -432,7 +432,7 @@ export class Human {
       let timeStamp;
 
       // update configuration
-      this.config = mergeDeep(this.config, userConfig);
+      this.config = mergeDeep(this.config, userConfig) as Config;
 
       // sanity checks
       this.state = 'check';
@@ -478,12 +478,10 @@ export class Human {
       this.analyze('Get Image:');
 
       timeStamp = now();
-      // @ts-ignore hidden dynamic property that is not part of definitions
       this.config.skipFrame = await this.#skipFrame(process.tensor);
       if (!this.performance.frames) this.performance.frames = 0;
       if (!this.performance.cached) this.performance.cached = 0;
       (this.performance.frames as number)++;
-      // @ts-ignore hidden dynamic property that is not part of definitions
       if (this.config.skipFrame) this.performance.cached++;
       this.performance.changed = Math.trunc(now() - timeStamp);
       this.analyze('Check Changed:');
@@ -678,7 +676,7 @@ export class Human {
   */
   async warmup(userConfig?: Config | Record<string, unknown>): Promise<Result | { error }> {
     const t0 = now();
-    if (userConfig) this.config = mergeDeep(this.config, userConfig);
+    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;
     if (!this.config.warmup || this.config.warmup === 'none') return { error: 'null' };
     let res;
     if (typeof createImageBitmap === 'function') res = await this.#warmupBitmap();

src/movenet/movenet.ts

@@ -5,7 +5,8 @@
 import { log, join } from '../helpers';
 import * as tf from '../../dist/tfjs.esm.js';
 import { Body } from '../result';
-import { GraphModel } from '../tfjs/types';
+import { GraphModel, Tensor } from '../tfjs/types';
+import { Config } from '../config';
 
 let model: GraphModel;
 
@@ -19,7 +20,7 @@ let skipped = Number.MAX_SAFE_INTEGER;
 
 const bodyParts = ['nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder', 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist', 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle'];
 
-export async function load(config) {
+export async function load(config: Config): Promise<GraphModel> {
   if (!model) {
     // @ts-ignore type mismatch on GraphModel
     model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));
@@ -29,7 +30,7 @@ export async function load(config) {
   return model;
 }
 
-export async function predict(image, config): Promise<Body[]> {
+export async function predict(image: Tensor, config: Config): Promise<Body[]> {
   if ((skipped < config.body.skipFrames) && config.skipFrame && Object.keys(keypoints).length > 0) {
     skipped++;
     return [{ id: 0, score, box, boxRaw, keypoints }];
@@ -63,8 +64,8 @@ export async function predict(image, config): Promise<Body[]> {
               kpt[id][0],
             ],
             position: [ // normalized to input image size
-              Math.round(image.shape[2] * kpt[id][1]),
+              Math.round((image.shape[2] || 0) * kpt[id][1]),
-              Math.round(image.shape[1] * kpt[id][0]),
+              Math.round((image.shape[1] || 0) * kpt[id][0]),
             ],
           });
         }

src/object/centernet.ts

@@ -6,12 +6,14 @@ import { log, join } from '../helpers';
 import * as tf from '../../dist/tfjs.esm.js';
 import { labels } from './labels';
 import { Item } from '../result';
+import { GraphModel, Tensor } from '../tfjs/types';
+import { Config } from '../config';
 
 let model;
 let last: Item[] = [];
 let skipped = Number.MAX_SAFE_INTEGER;
 
-export async function load(config) {
+export async function load(config: Config): Promise<GraphModel> {
   if (!model) {
     model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));
     const inputs = Object.values(model.modelSignature['inputs']);
@@ -23,7 +25,7 @@ export async function load(config) {
   return model;
 }
 
-async function process(res, inputSize, outputShape, config) {
+async function process(res: Tensor, inputSize, outputShape, config: Config) {
   if (!res) return [];
   const results: Array<Item> = [];
   const detections = res.arraySync();
@@ -64,7 +66,7 @@ async function process(res, inputSize, outputShape, config) {
   return results;
 }
 
-export async function predict(input, config): Promise<Item[]> {
+export async function predict(input: Tensor, config: Config): Promise<Item[]> {
   if ((skipped < config.object.skipFrames) && config.skipFrame && (last.length > 0)) {
     skipped++;
     return last;

src/object/nanodet.ts

@@ -6,6 +6,8 @@ import { log, join } from '../helpers';
 import * as tf from '../../dist/tfjs.esm.js';
 import { labels } from './labels';
 import { Item } from '../result';
+import { GraphModel, Tensor } from '../tfjs/types';
+import { Config } from '../config';
 
 let model;
 let last: Array<Item> = [];
@@ -13,7 +15,7 @@ let skipped = Number.MAX_SAFE_INTEGER;
 
 const scaleBox = 2.5; // increase box size
 
-export async function load(config) {
+export async function load(config: Config): Promise<GraphModel> {
   if (!model) {
     model = await tf.loadGraphModel(join(config.modelBasePath, config.object.modelPath));
     const inputs = Object.values(model.modelSignature['inputs']);
@@ -100,7 +102,7 @@ async function process(res, inputSize, outputShape, config) {
   return results;
 }
 
-export async function predict(image, config): Promise<Item[]> {
+export async function predict(image: Tensor, config: Config): Promise<Item[]> {
   if ((skipped < config.object.skipFrames) && config.skipFrame && (last.length > 0)) {
     skipped++;
     return last;

src/posenet/posenet.ts

@@ -8,11 +8,12 @@ import * as poses from './poses';
 import * as util from './utils';
 import { Body } from '../result';
 import { Tensor, GraphModel } from '../tfjs/types';
+import { Config } from '../config';
 
 let model: GraphModel;
 const poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];
 
-export async function predict(input, config): Promise<Body[]> {
+export async function predict(input: Tensor, config: Config): Promise<Body[]> {
   const res = tf.tidy(() => {
     if (!model.inputs[0].shape) return [];
     const resized = input.resizeBilinear([model.inputs[0].shape[2], model.inputs[0].shape[1]]);
@@ -32,7 +33,7 @@ export async function predict(input, config): Promise<Body[]> {
   return scaled;
 }
 
-export async function load(config) {
+export async function load(config: Config): Promise<GraphModel> {
   if (!model) {
     // @ts-ignore type mismatch for GraphModel
     model = await tf.loadGraphModel(join(config.modelBasePath, config.body.modelPath));

src/result.ts

@@ -2,7 +2,7 @@
  * Type definitions for Human result object
  */
 
-import { Tensor } from '../dist/tfjs.esm.js';
+import { Tensor } from './tfjs/types';
 
 /** Face results
  * Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
@@ -52,8 +52,8 @@ export interface Face {
     matrix: [number, number, number, number, number, number, number, number, number],
     gaze: { bearing: number, strength: number },
   }
-  image?: typeof Tensor;
+  image?: Tensor;
-  tensor: typeof Tensor,
+  tensor: Tensor,
 }
 
 /** Body results

tsconfig.json

@@ -35,6 +35,6 @@
     "entryPoints": "src/human.ts",
     "logLevel": "Info",
     "logger": "none",
-    "theme": "wiki/theme/"
+    "theme": "wiki/theme/",
   }
 }

typedoc/classes/human.html

@@ -514,7 +514,7 @@
 				<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class">
 					<a name="performance" class="tsd-anchor"></a>
 					<h3>performance</h3>
-					<div class="tsd-signature tsd-kind-icon">performance<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">unknown</span><span class="tsd-signature-symbol">&gt;</span></div>
+					<div class="tsd-signature tsd-kind-icon">performance<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span></div>
 					<aside class="tsd-sources">
 					</aside>
 					<div class="tsd-comment tsd-typography">

typedoc/interfaces/config.html

@@ -97,6 +97,7 @@
 								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="config.html#hand" class="tsd-kind-icon">hand</a></li>
 								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="config.html#modelbasepath" class="tsd-kind-icon">model<wbr>Base<wbr>Path</a></li>
 								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="config.html#object" class="tsd-kind-icon">object</a></li>
+								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="config.html#skipframe" class="tsd-kind-icon">skip<wbr>Frame</a></li>
 								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="config.html#warmup" class="tsd-kind-icon">warmup</a></li>
 								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="config.html#wasmpath" class="tsd-kind-icon">wasm<wbr>Path</a></li>
 							</ul>
@@ -133,7 +134,7 @@
 				<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface">
 					<a name="body" class="tsd-anchor"></a>
 					<h3>body</h3>
-					<div class="tsd-signature tsd-kind-icon">body<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>enabled<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>maxDetected<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>minConfidence<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>modelPath<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> }</span></div>
+					<div class="tsd-signature tsd-kind-icon">body<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>enabled<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>maxDetected<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>minConfidence<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>modelPath<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>skipFrames<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span></div>
 					<aside class="tsd-sources">
 					</aside>
 					<div class="tsd-comment tsd-typography">
@@ -162,6 +163,9 @@
 							<li class="tsd-parameter">
 								<h5>model<wbr>Path<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span></h5>
 							</li>
+							<li class="tsd-parameter">
+								<h5>skip<wbr>Frames<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5>
+							</li>
 						</ul>
 					</div>
 				</section>
@@ -615,6 +619,22 @@
 						</ul>
 					</div>
 				</section>
+				<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface">
+					<a name="skipframe" class="tsd-anchor"></a>
+					<h3>skip<wbr>Frame</h3>
+					<div class="tsd-signature tsd-kind-icon">skip<wbr>Frame<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+					<aside class="tsd-sources">
+					</aside>
+					<div class="tsd-comment tsd-typography">
+						<div class="lead">
+							<p>Cache sensitivity</p>
+							<ul>
+								<li>values 0..1 where 0.01 means reset cache if input changed more than 1%</li>
+								<li>set to 0 to disable caching</li>
+							</ul>
+						</div>
+					</div>
+				</section>
 				<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface">
 					<a name="warmup" class="tsd-anchor"></a>
 					<h3>warmup</h3>
@@ -702,6 +722,9 @@
 							<li class=" tsd-kind-property tsd-parent-kind-interface">
 								<a href="config.html#object" class="tsd-kind-icon">object</a>
 							</li>
+							<li class=" tsd-kind-property tsd-parent-kind-interface">
+								<a href="config.html#skipframe" class="tsd-kind-icon">skip<wbr>Frame</a>
+							</li>
 							<li class=" tsd-kind-property tsd-parent-kind-interface">
 								<a href="config.html#warmup" class="tsd-kind-icon">warmup</a>
 							</li>

typedoc/interfaces/face.html

@@ -216,7 +216,7 @@
 				<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface">
 					<a name="image" class="tsd-anchor"></a>
 					<h3><span class="tsd-flag ts-flagOptional">Optional</span> image</h3>
-					<div class="tsd-signature tsd-kind-icon">image<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">typeof </span><span class="tsd-signature-type">__class</span></div>
+					<div class="tsd-signature tsd-kind-icon">image<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Tensor</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">&gt;</span></div>
 					<aside class="tsd-sources">
 					</aside>
 				</section>
@@ -291,7 +291,7 @@
 				<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface">
 					<a name="tensor" class="tsd-anchor"></a>
 					<h3>tensor</h3>
-					<div class="tsd-signature tsd-kind-icon">tensor<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">typeof </span><span class="tsd-signature-type">__class</span></div>
+					<div class="tsd-signature tsd-kind-icon">tensor<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Tensor</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">&gt;</span></div>
 					<aside class="tsd-sources">
 					</aside>
 				</section>

types/blazeface/blazeface.d.ts

@@ -6,8 +6,8 @@ export declare class BlazeFaceModel {
     anchors: Tensor;
     inputSize: number;
     config: Config;
-    constructor(model: any, config: any);
+    constructor(model: any, config: Config);
-    getBoundingBoxes(inputImage: any): Promise<{
+    getBoundingBoxes(inputImage: Tensor): Promise<{
         boxes: {
             box: {
                 startPoint: Tensor;
@@ -20,4 +20,4 @@ export declare class BlazeFaceModel {
         scaleFactor: number[];
     } | null>;
 }
-export declare function load(config: any): Promise<BlazeFaceModel>;
+export declare function load(config: Config): Promise<BlazeFaceModel>;

types/blazeface/facemesh.d.ts

@@ -1,8 +1,10 @@
 /**
  * FaceMesh & BlazeFace Module entry point
  */
+import { Tensor } from '../tfjs/types';
 import { Face } from '../result';
-export declare function predict(input: any, config: any): Promise<Face[]>;
+import { Config } from '../config';
+export declare function predict(input: Tensor, config: Config): Promise<Face[]>;
 export declare function load(config: any): Promise<[unknown, unknown, unknown]>;
 export declare const triangulation: number[];
 export declare const uvmap: number[][];

types/blazepose/blazepose.d.ts

@@ -1,6 +1,8 @@
 /**
  * BlazePose Module
  */
+import { Tensor, GraphModel } from '../tfjs/types';
 import { Body } from '../result';
-export declare function load(config: any): Promise<any>;
+import { Config } from '../config';
-export declare function predict(image: any, config: any): Promise<Body[]>;
+export declare function load(config: Config): Promise<GraphModel>;
+export declare function predict(image: Tensor, config: Config): Promise<Body[]>;

types/config.d.ts

@@ -27,6 +27,11 @@ export interface Config {
      * - set to 0 to disable caching
     */
     cacheSensitivity: number;
+    /** Cache sensitivity
+     * - values 0..1 where 0.01 means reset cache if input changed more than 1%
+     * - set to 0 to disable caching
+    */
+    skipFrame: boolean;
     /** Run input through image filters before inference
      * - image filters run with near-zero latency as they are executed on the GPU
     */
@@ -133,6 +138,7 @@ export interface Config {
         modelPath: string;
         maxDetected: number;
         minConfidence: number;
+        skipFrames: number;
     };
     /** Controlls and configures all hand detection specific options
      * - enabled: true/false

types/efficientpose/efficientpose.d.ts

@@ -2,5 +2,7 @@
  * EfficientPose Module
  */
 import { Body } from '../result';
-export declare function load(config: any): Promise<any>;
+import { GraphModel, Tensor } from '../tfjs/types';
-export declare function predict(image: any, config: any): Promise<Body[]>;
+import { Config } from '../config';
+export declare function load(config: Config): Promise<GraphModel>;
+export declare function predict(image: Tensor, config: Config): Promise<Body[]>;

types/emotion/emotion.d.ts

@@ -1,5 +1,7 @@
 /**
  * Emotion Module
  */
-export declare function load(config: any): Promise<any>;
+import { Config } from '../config';
-export declare function predict(image: any, config: any, idx: any, count: any): Promise<unknown>;
+import { Tensor, GraphModel } from '../tfjs/types';
+export declare function load(config: Config): Promise<GraphModel>;
+export declare function predict(image: Tensor, config: Config, idx: any, count: any): Promise<unknown>;

types/faceres/faceres.d.ts

@@ -3,14 +3,15 @@
  * Returns Age, Gender, Descriptor
  * Implements Face simmilarity function
  */
-import { Tensor } from '../tfjs/types';
+import { Tensor, GraphModel } from '../tfjs/types';
+import { Config } from '../config';
 declare type DB = Array<{
     name: string;
     source: string;
     embedding: number[];
 }>;
-export declare function load(config: any): Promise<any>;
+export declare function load(config: Config): Promise<GraphModel>;
-export declare function similarity(embedding1: any, embedding2: any, order?: number): number;
+export declare function similarity(embedding1: Array<number>, embedding2: Array<number>, order?: number): number;
 export declare function match(embedding: Array<number>, db: DB, threshold?: number): {
     similarity: number;
     name: string;
@@ -18,5 +19,5 @@ export declare function match(embedding: Array<number>, db: DB, threshold?: numb
     embedding: number[];
 };
 export declare function enhance(input: any): Tensor;
-export declare function predict(image: any, config: any, idx: any, count: any): Promise<unknown>;
+export declare function predict(image: Tensor, config: Config, idx: any, count: any): Promise<unknown>;
 export {};

types/handpose/handpose.d.ts

@@ -2,5 +2,7 @@
  * HandPose module entry point
  */
 import { Hand } from '../result';
-export declare function predict(input: any, config: any): Promise<Hand[]>;
+import { Tensor } from '../tfjs/types';
-export declare function load(config: any): Promise<[unknown, unknown]>;
+import { Config } from '../config';
+export declare function predict(input: Tensor, config: Config): Promise<Hand[]>;
+export declare function load(config: Config): Promise<[unknown, unknown]>;

types/human.d.ts

@@ -122,7 +122,7 @@ export declare class Human {
         agent: string;
     };
     /** Performance object that contains values for all recently performed operations */
-    performance: Record<string, unknown>;
+    performance: Record<string, number>;
     /**
      * Creates instance of Human library that is futher used for all operations
      * @param userConfig: {@link Config}

types/movenet/movenet.d.ts

@@ -2,5 +2,7 @@
  * EfficientPose Module
  */
 import { Body } from '../result';
-export declare function load(config: any): Promise<any>;
+import { GraphModel, Tensor } from '../tfjs/types';
-export declare function predict(image: any, config: any): Promise<Body[]>;
+import { Config } from '../config';
+export declare function load(config: Config): Promise<GraphModel>;
+export declare function predict(image: Tensor, config: Config): Promise<Body[]>;

types/object/centernet.d.ts

@@ -2,5 +2,7 @@
  * CenterNet object detection module
  */
 import { Item } from '../result';
-export declare function load(config: any): Promise<any>;
+import { GraphModel, Tensor } from '../tfjs/types';
-export declare function predict(input: any, config: any): Promise<Item[]>;
+import { Config } from '../config';
+export declare function load(config: Config): Promise<GraphModel>;
+export declare function predict(input: Tensor, config: Config): Promise<Item[]>;

types/object/nanodet.d.ts

@@ -2,5 +2,7 @@
  * NanoDet object detection module
  */
 import { Item } from '../result';
-export declare function load(config: any): Promise<any>;
+import { GraphModel, Tensor } from '../tfjs/types';
-export declare function predict(image: any, config: any): Promise<Item[]>;
+import { Config } from '../config';
+export declare function load(config: Config): Promise<GraphModel>;
+export declare function predict(image: Tensor, config: Config): Promise<Item[]>;

types/posenet/posenet.d.ts

@@ -2,5 +2,7 @@
  * PoseNet module entry point
  */
 import { Body } from '../result';
-export declare function predict(input: any, config: any): Promise<Body[]>;
+import { Tensor, GraphModel } from '../tfjs/types';
-export declare function load(config: any): Promise<any>;
+import { Config } from '../config';
+export declare function predict(input: Tensor, config: Config): Promise<Body[]>;
+export declare function load(config: Config): Promise<GraphModel>;

types/result.d.ts

@@ -1,7 +1,7 @@
 /**
  * Type definitions for Human result object
  */
-import { Tensor } from '../dist/tfjs.esm.js';
+import { Tensor } from './tfjs/types';
 /** Face results
  * Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
  * Some values may be null if specific model is not enabled
@@ -60,8 +60,8 @@ export interface Face {
             strength: number;
         };
     };
-    image?: typeof Tensor;
+    image?: Tensor;
-    tensor: typeof Tensor;
+    tensor: Tensor;
 }
 /** Body results
  *

wiki

@@ -1 +1 @@
-Subproject commit 7910d0735849c1fef131ca71969b8a6a080772da
+Subproject commit 0087af5684c5722b2cf7ffd3db57b8117b7ac8c5