add optional crop to multiple models

CHANGELOG.md

@@ -9,7 +9,7 @@
   
 ## Changelog
   
-### **HEAD -> main** 2023/02/25 mandic00@live.com
+### **HEAD -> main** 2023/02/28 mandic00@live.com
 
 - add electron detection
 - fix gender-ssrnet-imdb

TODO.md

@@ -46,4 +46,5 @@ No support for running in **web workers** as Safari still does not support `Offs
 
 - add `electron` detection
 - fix `gender-ssrnet-imdb`
-- add `movenet-multipose` workaround
+- fix `movenet-multipose`
+- add optional `crop` values for *emotion*, *description*, *ssrnet* and *gear* models

demo/typescript/index.js

@@ -4,6 +4,6 @@
   author: <https://github.com/vladmandic>'
 */
 
-import*as m from"../../dist/human.esm.js";var v=1920,b={modelBasePath:"../../models",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!0},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!0},hand:{enabled:!0},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new m.Human(b);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;e.draw.options.drawPoints=!0;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},s={detectFPS:0,drawFPS:0,frames:0,averageMs:0},o=(...t)=>{a.log.innerText+=t.join(" ")+`
+import*as m from"../../dist/human.esm.js";var v=1920,b={debug:!0,backend:"webgl",modelBasePath:"../../models",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!1},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1,modelPath:"https://vladmandic.github.io/human-models/models/movenet-multipose.json",minConfidence:-1},hand:{enabled:!1},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new m.Human(b);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;e.draw.options.drawPoints=!0;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},s={detectFPS:0,drawFPS:0,frames:0,averageMs:0},o=(...t)=>{a.log.innerText+=t.join(" ")+`
-`,console.log(...t)},r=t=>a.fps.innerText=t,g=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function f(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&o("allocated tensors:",t-n.tensors),n.tensors=t,s.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,s.frames++,s.averageMs=Math.round(1e3*(e.now()-n.start)/s.frames)/1e3,s.frames%100===0&&!a.video.paused&&o("performance",{...s,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(f)}async function u(){var d,i,c;if(!a.video.paused){let l=e.next(e.result),p=await e.image(a.video);e.draw.canvas(p.canvas,a.canvas);let w={bodyLabels:`person confidence [score] and ${(c=(i=(d=e.result)==null?void 0:d.body)==null?void 0:i[0])==null?void 0:c.keypoints.length} keypoints`};await e.draw.all(a.canvas,l,w),g(l.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,r(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(u,30)}async function y(){let d=(await e.webcam.enumerate())[0].deviceId;await e.webcam.start({element:a.video,crop:!1,width:v,id:d}),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function h(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),r("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.models.stats()),o("models loaded:",e.models.loaded()),o("environment",e.env),r("initializing..."),await e.warmup(),await y(),await f(),await u()}window.onload=h;
+`,console.log(...t)},i=t=>a.fps.innerText=t,g=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function f(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&o("allocated tensors:",t-n.tensors),n.tensors=t,s.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,s.frames++,s.averageMs=Math.round(1e3*(e.now()-n.start)/s.frames)/1e3,s.frames%100===0&&!a.video.paused&&o("performance",{...s,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(f)}async function u(){var d,r,l;if(!a.video.paused){let c=e.next(e.result),p=await e.image(a.video);e.draw.canvas(p.canvas,a.canvas);let w={bodyLabels:`person confidence [score] and ${(l=(r=(d=e.result)==null?void 0:d.body)==null?void 0:r[0])==null?void 0:l.keypoints.length} keypoints`};await e.draw.all(a.canvas,c,w),g(c.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,i(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(u,30)}async function h(){let d=(await e.webcam.enumerate())[0].deviceId;await e.webcam.start({element:a.video,crop:!1,width:v,id:d}),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function y(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),i("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.models.stats()),o("models loaded:",e.models.loaded()),o("environment",e.env),i("initializing..."),await e.warmup(),await h(),await f(),await u()}window.onload=y;
 //# sourceMappingURL=index.js.map

demo/typescript/index.ts

@@ -12,14 +12,16 @@ import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
 const width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations
 
 const humanConfig: Partial<H.Config> = { // user configuration for human, used to fine-tune behavior
-  // backend: 'webgpu',
+  debug: true,
+  backend: 'webgl',
   // cacheSensitivity: 0,
-  // debug: false,
+  // cacheModels: false,
+  // warmup: 'none',
   modelBasePath: '../../models',
   filter: { enabled: true, equalization: false, flip: false },
-  face: { enabled: true, detector: { rotation: true }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },
+  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },
-  body: { enabled: true },
+  body: { enabled: false, modelPath: 'https://vladmandic.github.io/human-models/models/movenet-multipose.json', minConfidence: -1 },
-  hand: { enabled: true },
+  hand: { enabled: false },
   object: { enabled: false },
   segmentation: { enabled: false },
   gesture: { enabled: true },

dist/human.esm.js

@@ -1802,7 +1802,7 @@ var C3 = Kt((wg, aI) => {
           return ly(Dr, sp), sp;
         } };
         function fe(Dr) {
-          return B === "string" ? Re(Dr) : B === "boolean" ? Boolean(Dr) : Dr;
+          return B === "string" ? Re(Dr) : B === "boolean" ? !!Dr : Dr;
         }
         var ve = cm(F), Ft = [], Qr = 0;
         if (_e)
@@ -2470,7 +2470,7 @@ var I3 = Kt((Ig, uI) => {
           return qi(hr, Za), Za;
         } };
         function qe(hr) {
-          return se === "string" ? K(hr) : se === "boolean" ? Boolean(hr) : hr;
+          return se === "string" ? K(hr) : se === "boolean" ? !!hr : hr;
         }
         var Ue = Jl(G), Wt = [], Yr = 0;
         if (nt)
@@ -14600,7 +14600,7 @@ function k5(r) {
   let { inputs: e, backend: t10, attrs: o } = r, { sparseIndices: n, sparseValues: s, defaultValue: a } = e, { outputShape: i } = o, { sliceRank: p, numUpdates: u, sliceSize: c, strides: l, outputSize: m } = S.calculateShapes(s, n, i), d = false, f = t10.bufferSync(n), h;
   switch (s.dtype) {
     case "bool": {
-      let g = t10.bufferSync(s), x = Boolean(t10.data.get(a.dataId).values[0]);
+      let g = t10.bufferSync(s), x = !!t10.data.get(a.dataId).values[0];
       h = Va(f, g, i, m, c, u, p, l, x, d);
       break;
     }
@@ -38915,13 +38915,13 @@ async function predict5(image, config3, idx, count2) {
   }
   skipped4 = 0;
   return new Promise(async (resolve) => {
-    var _a3;
+    var _a3, _b3, _c2;
     const obj = [];
     if ((_a3 = config3.face.emotion) == null ? void 0 : _a3.enabled) {
       const t10 = {};
       const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0;
-      if (config3.face.emotion["crop"] > 0) {
+      if (((_b3 = config3.face.emotion) == null ? void 0 : _b3["crop"]) > 0) {
-        const crop = config3.face.emotion["crop"];
+        const crop = (_c2 = config3.face.emotion) == null ? void 0 : _c2["crop"];
         const box = [[crop, crop, 1 - crop, 1 - crop]];
         t10.resize = eK.cropAndResize(image, box, [0], [inputSize10, inputSize10]);
       } else {
@@ -38969,11 +38969,19 @@ async function load7(config3) {
     log("cached model:", model9["modelUrl"]);
   return model9;
 }
-function enhance(input) {
+function enhance(input, config3) {
+  var _a2, _b2;
   const tensor = input.image || input.tensor || input;
   if (!(model9 == null ? void 0 : model9.inputs[0].shape))
     return tensor;
-  const crop = eK.resizeBilinear(tensor, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
+  let crop;
+  if (((_a2 = config3.face.description) == null ? void 0 : _a2["crop"]) > 0) {
+    const cropval = (_b2 = config3.face.description) == null ? void 0 : _b2["crop"];
+    const box = [[cropval, cropval, 1 - cropval, 1 - cropval]];
+    crop = eK.cropAndResize(tensor, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]);
+  } else {
+    crop = eK.resizeBilinear(tensor, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
+  }
   const norm = ne(crop, constants.tf255);
   Ot(crop);
   return norm;
@@ -38998,7 +39006,7 @@ async function predict6(image, config3, idx, count2) {
   return new Promise(async (resolve) => {
     var _a3;
     if ((_a3 = config3.face.description) == null ? void 0 : _a3.enabled) {
-      const enhanced = enhance(image);
+      const enhanced = enhance(image, config3);
       const resT = model9 == null ? void 0 : model9.execute(enhanced);
       lastTime5 = now();
       Ot(enhanced);
@@ -39175,21 +39183,25 @@ async function predict9(image, config3, idx, count2) {
   }
   skipped8 = 0;
   return new Promise(async (resolve) => {
-    var _a3, _b3;
+    var _a3, _b3, _c2, _d2;
     if (!(model12 == null ? void 0 : model12.inputs[0].shape))
       return;
     const t10 = {};
-    const box = [[0, 0.1, 0.9, 0.9]];
+    let box = [[0, 0.1, 0.9, 0.9]];
+    if (((_a3 = config3.face.gear) == null ? void 0 : _a3["crop"]) > 0) {
+      const crop = (_b3 = config3.face.gear) == null ? void 0 : _b3["crop"];
+      box = [[crop, crop, 1 - crop, 1 - crop]];
+    }
     t10.resize = eK.cropAndResize(image, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]);
     const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
-    if ((_a3 = config3.face.gear) == null ? void 0 : _a3.enabled)
+    if ((_c2 = config3.face.gear) == null ? void 0 : _c2.enabled)
       [t10.age, t10.gender, t10.race] = model12.execute(t10.resize, ["age_output", "gender_output", "race_output"]);
     const gender2 = await t10.gender.data();
     obj.gender = gender2[0] > gender2[1] ? "male" : "female";
     obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100;
     const race = await t10.race.data();
     for (let i = 0; i < race.length; i++) {
-      if (race[i] > (((_b3 = config3.face.gear) == null ? void 0 : _b3.minConfidence) || 0.2))
+      if (race[i] > (((_d2 = config3.face.gear) == null ? void 0 : _d2.minConfidence) || 0.2))
         obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] });
     }
     obj.race.sort((a, b) => b.score - a.score);
@@ -39234,14 +39246,20 @@ async function predict10(image, config3, idx, count2) {
   }
   skipped9 = 0;
   return new Promise(async (resolve) => {
-    var _a3;
+    var _a3, _b3, _c3;
     if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape)
       return;
     const t10 = {};
-    t10.resize = eK.resizeBilinear(image, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
+    if (((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3["crop"]) > 0) {
+      const crop = (_b3 = config3.face["ssrnet"]) == null ? void 0 : _b3["crop"];
+      const box = [[crop, crop, 1 - crop, 1 - crop]];
+      t10.resize = eK.cropAndResize(image, box, [0], [model13.inputs[0].shape[2], model13.inputs[0].shape[1]]);
+    } else {
+      t10.resize = eK.resizeBilinear(image, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
+    }
     t10.enhance = ne(t10.resize, constants.tf255);
     const obj = { age: 0 };
-    if ((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3.enabled)
+    if ((_c3 = config3.face["ssrnet"]) == null ? void 0 : _c3.enabled)
       t10.age = model13.execute(t10.enhance);
     if (t10.age) {
       const data = await t10.age.data();
@@ -39284,15 +39302,21 @@ async function predict11(image, config3, idx, count2) {
   }
   skipped10 = 0;
   return new Promise(async (resolve) => {
-    var _a3;
+    var _a3, _b3, _c3;
     if (!(model14 == null ? void 0 : model14.inputs[0].shape))
       return;
     const t10 = {};
-    t10.resize = eK.resizeBilinear(image, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false);
+    if (((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3["crop"]) > 0) {
+      const crop = (_b3 = config3.face["ssrnet"]) == null ? void 0 : _b3["crop"];
+      const box = [[crop, crop, 1 - crop, 1 - crop]];
+      t10.resize = eK.cropAndResize(image, box, [0], [model14.inputs[0].shape[2], model14.inputs[0].shape[1]]);
+    } else {
+      t10.resize = eK.resizeBilinear(image, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false);
+    }
     t10.enhance = Ee(() => {
-      var _a4, _b3;
+      var _a4, _b4;
       let normalize2;
-      if (((_b3 = (_a4 = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a4[0].shape) == null ? void 0 : _b3[3]) === 1) {
+      if (((_b4 = (_a4 = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a4[0].shape) == null ? void 0 : _b4[3]) === 1) {
         const [red, green, blue] = Oa(t10.resize, 3, 3);
         const redNorm = ne(red, rgb2[0]);
         const greenNorm = ne(green, rgb2[1]);
@@ -39305,7 +39329,7 @@ async function predict11(image, config3, idx, count2) {
       return normalize2;
     });
     const obj = { gender: "unknown", genderScore: 0 };
-    if ((_a3 = config3.face["ssrnet"]) == null ? void 0 : _a3.enabled)
+    if ((_c3 = config3.face["ssrnet"]) == null ? void 0 : _c3.enabled)
       t10.gender = model14.execute(t10.enhance);
     const data = await t10.gender.data();
     obj.gender = data[0] > data[1] ? "female" : "male";

package.json

@@ -78,8 +78,8 @@
     "tensorflow"
   ],
   "devDependencies": {
-    "@html-eslint/eslint-plugin": "^0.16.0",
+    "@html-eslint/eslint-plugin": "^0.17.1",
-    "@html-eslint/parser": "^0.16.0",
+    "@html-eslint/parser": "^0.17.1",
     "@microsoft/api-extractor": "^7.34.4",
     "@tensorflow/tfjs-backend-cpu": "^4.2.0",
     "@tensorflow/tfjs-backend-wasm": "^4.2.0",
@@ -91,15 +91,15 @@
     "@tensorflow/tfjs-layers": "^4.2.0",
     "@tensorflow/tfjs-node": "^4.2.0",
     "@tensorflow/tfjs-node-gpu": "^4.2.0",
-    "@types/node": "^18.14.2",
+    "@types/node": "^18.14.6",
     "@types/offscreencanvas": "^2019.7.0",
-    "@typescript-eslint/eslint-plugin": "^5.54.0",
+    "@typescript-eslint/eslint-plugin": "^5.54.1",
-    "@typescript-eslint/parser": "^5.54.0",
+    "@typescript-eslint/parser": "^5.54.1",
     "@vladmandic/build": "0.8.2",
     "@vladmandic/pilogger": "^0.4.7",
     "@vladmandic/tfjs": "github:vladmandic/tfjs",
     "canvas": "^2.11.0",
-    "esbuild": "^0.17.10",
+    "esbuild": "^0.17.11",
     "eslint": "8.35.0",
     "eslint-config-airbnb-base": "^15.0.0",
     "eslint-plugin-html": "^7.1.0",
@@ -108,9 +108,9 @@
     "eslint-plugin-markdown": "^3.0.0",
     "eslint-plugin-node": "^11.1.0",
     "eslint-plugin-promise": "^6.1.1",
-    "rimraf": "^4.1.2",
+    "rimraf": "^4.3.1",
     "tslib": "^2.5.0",
-    "typedoc": "0.23.26",
+    "typedoc": "0.24.0-beta.2",
     "typescript": "4.9.5"
   }
 }

src/face/faceres.ts

@@ -32,10 +32,17 @@ export async function load(config: Config): Promise<GraphModel> {
   return model;
 }
 
-export function enhance(input): Tensor {
+export function enhance(input, config: Config): Tensor {
   const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened
   if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing
-  const crop: Tensor = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
+  let crop: Tensor;
+  if (config.face.description?.['crop'] > 0) { // optional crop
+    const cropval = config.face.description?.['crop'];
+    const box = [[cropval, cropval, 1 - cropval, 1 - cropval]];
+    crop = tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
+  } else {
+    crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
+  }
   const norm: Tensor = tf.mul(crop, constants.tf255);
   tf.dispose(crop);
   return norm;
@@ -75,7 +82,7 @@ export async function predict(image: Tensor4D, config: Config, idx: number, coun
   skipped = 0;
   return new Promise(async (resolve) => {
     if (config.face.description?.enabled) {
-      const enhanced = enhance(image);
+      const enhanced = enhance(image, config);
       const resT = model?.execute(enhanced) as Tensor[];
       lastTime = now();
       tf.dispose(enhanced);

src/gear/emotion.ts

@@ -48,9 +48,9 @@ export async function predict(image: Tensor4D, config: Config, idx: number, coun
     if (config.face.emotion?.enabled) {
       const t: Record<string, Tensor> = {};
       const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;
-      if (config.face.emotion['crop'] > 0) { // optional crop
+      if (config.face.emotion?.['crop'] > 0) { // optional crop
-        const crop = config.face.emotion['crop'];
+        const crop = config.face.emotion?.['crop'];
-        const box = [[crop, crop, 1 - crop, 1 - crop]]; // empyrical values for top, left, bottom, right
+        const box = [[crop, crop, 1 - crop, 1 - crop]];
         t.resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);
       } else {
         t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);

src/gear/gear.ts

@@ -41,7 +41,11 @@ export async function predict(image: Tensor4D, config: Config, idx: number, coun
     if (!model?.inputs[0].shape) return;
     const t: Record<string, Tensor> = {};
     // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);
-    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right
+    let box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right
+    if (config.face.gear?.['crop'] > 0) { // optional crop config value
+      const crop = config.face.gear?.['crop'];
+      box = [[crop, crop, 1 - crop, 1 - crop]];
+    }
     t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
     const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };
     if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];

src/gear/ssrnet-age.ts

@@ -37,7 +37,13 @@ export async function predict(image: Tensor4D, config: Config, idx: number, coun
   return new Promise(async (resolve) => {
     if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;
     const t: Record<string, Tensor> = {};
-    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
+    if (config.face['ssrnet']?.['crop'] > 0) { // optional crop
+      const crop = config.face['ssrnet']?.['crop'];
+      const box = [[crop, crop, 1 - crop, 1 - crop]];
+      t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
+    } else {
+      t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
+    }
     t.enhance = tf.mul(t.resize, constants.tf255);
     const obj = { age: 0 };
     if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;

src/gear/ssrnet-gender.ts

@@ -41,7 +41,13 @@ export async function predict(image: Tensor4D, config: Config, idx, count): Prom
   return new Promise(async (resolve) => {
     if (!model?.inputs[0].shape) return;
     const t: Record<string, Tensor> = {};
-    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
+    if (config.face['ssrnet']?.['crop'] > 0) { // optional crop
+      const crop = config.face['ssrnet']?.['crop'];
+      const box = [[crop, crop, 1 - crop, 1 - crop]];
+      t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
+    } else {
+      t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);
+    }
     t.enhance = tf.tidy(() => {
       let normalize: Tensor;
       if (model?.inputs?.[0].shape?.[3] === 1) {

test/build.log

@@ -1,50 +1,50 @@
-2023-02-28 14:59:24 DATA:  Build {"name":"@vladmandic/human","version":"3.0.5"} 
+2023-03-06 17:26:10 DATA:  Build {"name":"@vladmandic/human","version":"3.0.5"} 
-2023-02-28 14:59:24 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.5"} 
+2023-03-06 17:26:10 INFO:  Application: {"name":"@vladmandic/human","version":"3.0.5"} 
-2023-02-28 14:59:24 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
+2023-03-06 17:26:10 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
-2023-02-28 14:59:24 INFO:  Toolchain: {"build":"0.8.2","esbuild":"0.17.10","typescript":"4.9.5","typedoc":"0.23.26","eslint":"8.35.0"} 
+2023-03-06 17:26:10 INFO:  Toolchain: {"build":"0.8.2","esbuild":"0.17.11","typescript":"4.9.5","typedoc":"0.23.26","eslint":"8.35.0"} 
-2023-02-28 14:59:24 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
+2023-03-06 17:26:10 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
-2023-02-28 14:59:24 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} 
+2023-03-06 17:26:10 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":361} 
+2023-03-06 17:26:10 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":361} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":569,"outputBytes":924} 
+2023-03-06 17:26:10 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":569,"outputBytes":924} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":673124,"outputBytes":319556} 
+2023-03-06 17:26:10 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":674199,"outputBytes":320338} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":577,"outputBytes":928} 
+2023-03-06 17:26:10 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":577,"outputBytes":928} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":673128,"outputBytes":319560} 
+2023-03-06 17:26:10 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":674203,"outputBytes":320342} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":665,"outputBytes":1876} 
+2023-03-06 17:26:10 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":665,"outputBytes":1876} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":674076,"outputBytes":319671} 
+2023-03-06 17:26:10 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":675151,"outputBytes":320453} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1375,"outputBytes":670} 
+2023-03-06 17:26:10 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1375,"outputBytes":670} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":672870,"outputBytes":318124} 
+2023-03-06 17:26:10 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":673945,"outputBytes":318902} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1375,"outputBytes":1151306} 
+2023-03-06 17:26:11 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1375,"outputBytes":1151285} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1823506,"outputBytes":1465356} 
+2023-03-06 17:26:11 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1824560,"outputBytes":1466098} 
-2023-02-28 14:59:24 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1823506,"outputBytes":1932610} 
+2023-03-06 17:26:11 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1824560,"outputBytes":1933980} 
-2023-02-28 14:59:28 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} 
+2023-03-06 17:26:15 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":15} 
-2023-02-28 14:59:31 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":81,"generated":true} 
+2023-03-06 17:26:17 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":81,"generated":true} 
-2023-02-28 14:59:31 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6162,"outputBytes":2901} 
+2023-03-06 17:26:17 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6308,"outputBytes":3027} 
-2023-02-28 14:59:31 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17503,"outputBytes":9403} 
+2023-03-06 17:26:17 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17503,"outputBytes":9403} 
-2023-02-28 14:59:41 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":170,"errors":0,"warnings":0} 
+2023-03-06 17:26:26 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":170,"errors":0,"warnings":0} 
-2023-02-28 14:59:42 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
+2023-03-06 17:26:26 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
-2023-02-28 14:59:42 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} 
+2023-03-06 17:26:26 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} 
-2023-02-28 14:59:42 INFO:  Done... 
+2023-03-06 17:26:26 INFO:  Done... 
-2023-02-28 14:59:42 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} 
+2023-03-06 17:26:26 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} 
-2023-02-28 14:59:42 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} 
+2023-03-06 17:26:26 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} 
-2023-02-28 14:59:42 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} 
+2023-03-06 17:26:26 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} 
-2023-02-28 14:59:42 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} 
+2023-03-06 17:26:26 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} 
-2023-02-28 14:59:42 STATE: Filter: {"input":"types/tfjs-core.d.ts"} 
+2023-03-06 17:26:26 STATE: Filter: {"input":"types/tfjs-core.d.ts"} 
-2023-02-28 14:59:43 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":210} 
+2023-03-06 17:26:27 STATE: API-Extractor: {"succeeeded":true,"errors":0,"warnings":210} 
-2023-02-28 14:59:43 STATE: Filter: {"input":"types/human.d.ts"} 
+2023-03-06 17:26:27 STATE: Filter: {"input":"types/human.d.ts"} 
-2023-02-28 14:59:43 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} 
+2023-03-06 17:26:27 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} 
-2023-02-28 14:59:43 STATE: Write: {"output":"dist/human.esm.d.ts"} 
+2023-03-06 17:26:27 STATE: Write: {"output":"dist/human.esm.d.ts"} 
-2023-02-28 14:59:43 STATE: Write: {"output":"dist/human.d.ts"} 
+2023-03-06 17:26:27 STATE: Write: {"output":"dist/human.d.ts"} 
-2023-02-28 14:59:43 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} 
+2023-03-06 17:26:27 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} 
-2023-02-28 14:59:43 STATE: Write: {"output":"dist/human.node.d.ts"} 
+2023-03-06 17:26:27 STATE: Write: {"output":"dist/human.node.d.ts"} 
-2023-02-28 14:59:43 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} 
+2023-03-06 17:26:27 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} 
-2023-02-28 14:59:43 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} 
+2023-03-06 17:26:27 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} 
-2023-02-28 14:59:43 STATE: Models {"folder":"./models","models":12} 
+2023-03-06 17:26:27 STATE: Models {"folder":"./models","models":12} 
-2023-02-28 14:59:43 STATE: Models {"folder":"../human-models/models","models":44} 
+2023-03-06 17:26:27 STATE: Models {"folder":"../human-models/models","models":44} 
-2023-02-28 14:59:43 STATE: Models {"folder":"../blazepose/model/","models":4} 
+2023-03-06 17:26:27 STATE: Models {"folder":"../blazepose/model/","models":4} 
-2023-02-28 14:59:43 STATE: Models {"folder":"../anti-spoofing/model","models":1} 
+2023-03-06 17:26:27 STATE: Models {"folder":"../anti-spoofing/model","models":1} 
-2023-02-28 14:59:43 STATE: Models {"folder":"../efficientpose/models","models":3} 
+2023-03-06 17:26:27 STATE: Models {"folder":"../efficientpose/models","models":3} 
-2023-02-28 14:59:43 STATE: Models {"folder":"../insightface/models","models":5} 
+2023-03-06 17:26:27 STATE: Models {"folder":"../insightface/models","models":5} 
-2023-02-28 14:59:43 STATE: Models {"folder":"../movenet/models","models":3} 
+2023-03-06 17:26:27 STATE: Models {"folder":"../movenet/models","models":3} 
-2023-02-28 14:59:43 STATE: Models {"folder":"../nanodet/models","models":4} 
+2023-03-06 17:26:27 STATE: Models {"folder":"../nanodet/models","models":4} 
-2023-02-28 14:59:43 STATE: Models: {"count":58,"totalSize":380063249} 
+2023-03-06 17:26:28 STATE: Models: {"count":58,"totalSize":380063249} 
-2023-02-28 14:59:43 INFO:  Human Build complete... {"logFile":"test/build.log"} 
+2023-03-06 17:26:28 INFO:  Human Build complete... {"logFile":"test/build.log"} 

tsconfig.json

@@ -20,7 +20,6 @@
     "experimentalDecorators": true,
     "forceConsistentCasingInFileNames": true,
     "importHelpers": true,
-    "importsNotUsedAsValues": "error",
     "isolatedModules": false,
     "noEmitHelpers": true,
     "noEmitOnError": false,