update typescript

CHANGELOG.md

@@ -9,8 +9,9 @@
   
 ## Changelog
   
-### **HEAD -> main** 2021/11/16 mandic00@live.com
+### **HEAD -> main** 2021/11/17 mandic00@live.com
 
+- optimize centernet
 - cache frequent tf constants
 - add extra face rotation prior to mesh
 - release 2.5.2

README.md

@@ -331,7 +331,7 @@ For more info, see [**Configuration Details**](https://github.com/vladmandic/hum
 
 <br><hr><br>
 
-`Human` library is written in `TypeScript` [4.4](https://www.typescriptlang.org/docs/handbook/intro.html)  
+`Human` library is written in `TypeScript` [4.5](https://www.typescriptlang.org/docs/handbook/intro.html)  
 Conforming to latest `JavaScript` [ECMAScript version 2021](https://262.ecma-international.org/) standard  
 Build target is `JavaScript` [EMCAScript version 2018](https://262.ecma-international.org/11.0/)  
 

TODO.md

@@ -43,13 +43,14 @@ MoveNet MultiPose model does not work with WASM backend due to missing F32 broad
 ## Pending Release Notes
 
 New:
-- new demo `demos/faceid` that utilizes multiple algorithm  to validate input before triggering face recognition
+- New type definitions rollup
-- new optional model `liveness`  
+- New demo `demos/faceid` that utilizes multiple algorithm  to validate input before triggering face recognition
+- New optional model `liveness`  
   checks if input appears to be a real-world live image or a recording  
   best used together with `antispoofing` that checks if input appears to have a realistic face
-- new face masking option in `face.config.detector.mask`  
+- New face masking option in `face.config.detector.mask`  
   result is shading of face image outside of face area which is useful for increased sensitivity of other modules that rely on detected face as input 
-- new face crop option in `face.config.detector.cropFactor`
+- New face crop option in `face.config.detector.cropFactor`
   result is user-definable fine-tuning for other modules that rely on detected face as input 
 
 Other:
@@ -60,14 +61,3 @@ Other:
 - Documentation overhaul
 - Fix for optional `gear`, `ssrnet`, `mobilefacenet` modules
 - Fix for Firefox WebGPU compatibility issue
-
-```
-cp tfjs/tfjs.esm.d.ts types/lib/dist/
-node_modules/.bin/api-extractor run --local --verbose
-cp types/human.d.ts dist/human.esm-nobundle.d.ts
-cp types/human.d.ts dist/human.esm.d.ts
-cp types/human.d.ts dist/human.d.ts
-cp types/human.d.ts dist/human.node-gpu.d.ts
-cp types/human.d.ts dist/human.node.d.ts
-cp types/human.d.ts dist/human.node-wasm.d.ts
-```

dist/human.d.ts

@@ -1,3 +1,5 @@
+/// <reference types="offscreencanvas" />
+
 /** meta-function that performs draw for: canvas, face, body, hand */
 declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
 
@@ -1208,17 +1210,7 @@ declare class Human {
      * @param userConfig - {@link Config}
      * @returns result - {@link Result}
      */
-    warmup(userConfig?: Partial<Config>): Promise<Result | {
+    warmup(userConfig?: Partial<Config>): Promise<Result>;
-        error: any; /** Container for events dispatched by Human
-        * Possible events:
-        * - `create`: triggered when Human object is instantiated
-        * - `load`: triggered when models are loaded (explicitly or on-demand)
-        * - `image`: triggered when input image is processed
-        * - `result`: triggered when detection is complete
-        * - `warmup`: triggered when warmup is complete
-        * - `error`: triggered on some errors
-        */
-    }>;
     /** Run detect with tensorflow profiling
      * - result object will contain total exeuction time information for top-20 kernels
      * - actual detection object can be accessed via `human.result`

dist/human.esm-nobundle.d.ts

@@ -1,3 +1,5 @@
+/// <reference types="offscreencanvas" />
+
 /** meta-function that performs draw for: canvas, face, body, hand */
 declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
 
@@ -1208,17 +1210,7 @@ declare class Human {
      * @param userConfig - {@link Config}
      * @returns result - {@link Result}
      */
-    warmup(userConfig?: Partial<Config>): Promise<Result | {
+    warmup(userConfig?: Partial<Config>): Promise<Result>;
-        error: any; /** Container for events dispatched by Human
-        * Possible events:
-        * - `create`: triggered when Human object is instantiated
-        * - `load`: triggered when models are loaded (explicitly or on-demand)
-        * - `image`: triggered when input image is processed
-        * - `result`: triggered when detection is complete
-        * - `warmup`: triggered when warmup is complete
-        * - `error`: triggered on some errors
-        */
-    }>;
     /** Run detect with tensorflow profiling
      * - result object will contain total exeuction time information for top-20 kernels
      * - actual detection object can be accessed via `human.result`

dist/human.esm-nobundle.js

@@ -10,13 +10,6 @@ var __getOwnPropNames = Object.getOwnPropertyNames;
 var __hasOwnProp = Object.prototype.hasOwnProperty;
 var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value;
 var __markAsModule = (target) => __defProp(target, "__esModule", { value: true });
-var __require = /* @__PURE__ */ ((x) => typeof require !== "undefined" ? require : typeof Proxy !== "undefined" ? new Proxy(x, {
-  get: (a, b) => (typeof require !== "undefined" ? require : a)[b]
-}) : x)(function(x) {
-  if (typeof require !== "undefined")
-    return require.apply(this, arguments);
-  throw new Error('Dynamic require of "' + x + '" is not supported');
-});
 var __export = (target, all2) => {
   __markAsModule(target);
   for (var name in all2)
@@ -1348,19 +1341,6 @@ var Env = class {
   async updateCPU() {
     const cpu = { model: "", flags: [] };
     if (this.node && this.platform.startsWith("linux")) {
-      const fs = __require("fs");
-      try {
-        const data = fs.readFileSync("/proc/cpuinfo").toString();
-        for (const line of data.split("\n")) {
-          if (line.startsWith("model name")) {
-            cpu.model = line.match(/:(.*)/g)[0].replace(":", "").trim();
-          }
-          if (line.startsWith("flags")) {
-            cpu.flags = line.match(/:(.*)/g)[0].replace(":", "").trim().split(" ").sort();
-          }
-        }
-      } catch (e) {
-      }
     }
     if (!this["cpu"])
       Object.defineProperty(this, "cpu", { value: cpu });
@@ -12131,7 +12111,7 @@ var face2 = (res) => {
   const gestures = [];
   for (let i = 0; i < res.length; i++) {
     if (res[i].mesh && res[i].mesh.length > 450) {
-      const zDiff = res[i].mesh[33][2] - res[i].mesh[263][2];
+      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
       const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
       if (Math.abs(zDiff / xDiff) <= 0.15)
         gestures.push({ face: i, gesture: "facing center" });
@@ -12146,7 +12126,7 @@ var face2 = (res) => {
       const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
       if (mouthOpen > 10)
         gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
-      const chinDepth = res[i].mesh[152][2];
+      const chinDepth = res[i].mesh[152][2] || 0;
       if (Math.abs(chinDepth) > 10)
         gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
     }
@@ -12209,7 +12189,7 @@ var hand2 = (res) => {
       }
     }
     if (fingers && fingers.length > 0) {
-      const closest = fingers.reduce((best, a) => best.position[2] < a.position[2] ? best : a);
+      const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
       gestures.push({ hand: i, gesture: `${closest.name} forward` });
       const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
       gestures.push({ hand: i, gesture: `${highest.name} up` });
@@ -13211,6 +13191,8 @@ async function warmupCanvas(instance) {
       img = new Image();
     else if (env.Image)
       img = new env.Image();
+    else
+      return;
     img.onload = async () => {
       const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
       if (!canvas3) {
@@ -13258,8 +13240,9 @@ async function warmup(instance, userConfig) {
   instance.state = "warmup";
   if (userConfig)
     instance.config = mergeDeep(instance.config, userConfig);
-  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none")
+  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") {
-    return { error: "null" };
+    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };
+  }
   let res;
   return new Promise(async (resolve) => {
     if (typeof createImageBitmap === "function")

dist/human.esm.d.ts

@@ -1,3 +1,5 @@
+/// <reference types="offscreencanvas" />
+
 /** meta-function that performs draw for: canvas, face, body, hand */
 declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
 
@@ -1208,17 +1210,7 @@ declare class Human {
      * @param userConfig - {@link Config}
      * @returns result - {@link Result}
      */
-    warmup(userConfig?: Partial<Config>): Promise<Result | {
+    warmup(userConfig?: Partial<Config>): Promise<Result>;
-        error: any; /** Container for events dispatched by Human
-        * Possible events:
-        * - `create`: triggered when Human object is instantiated
-        * - `load`: triggered when models are loaded (explicitly or on-demand)
-        * - `image`: triggered when input image is processed
-        * - `result`: triggered when detection is complete
-        * - `warmup`: triggered when warmup is complete
-        * - `error`: triggered on some errors
-        */
-    }>;
     /** Run detect with tensorflow profiling
      * - result object will contain total exeuction time information for top-20 kernels
      * - actual detection object can be accessed via `human.result`

dist/human.esm.js

@@ -71732,19 +71732,6 @@ var Env = class {
   async updateCPU() {
     const cpu = { model: "", flags: [] };
     if (this.node && this.platform.startsWith("linux")) {
-      const fs = __require("fs");
-      try {
-        const data = fs.readFileSync("/proc/cpuinfo").toString();
-        for (const line of data.split("\n")) {
-          if (line.startsWith("model name")) {
-            cpu.model = line.match(/:(.*)/g)[0].replace(":", "").trim();
-          }
-          if (line.startsWith("flags")) {
-            cpu.flags = line.match(/:(.*)/g)[0].replace(":", "").trim().split(" ").sort();
-          }
-        }
-      } catch (e) {
-      }
     }
     if (!this["cpu"])
       Object.defineProperty(this, "cpu", { value: cpu });
@@ -82515,7 +82502,7 @@ var face2 = (res) => {
   const gestures = [];
   for (let i = 0; i < res.length; i++) {
     if (res[i].mesh && res[i].mesh.length > 450) {
-      const zDiff = res[i].mesh[33][2] - res[i].mesh[263][2];
+      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
       const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
       if (Math.abs(zDiff / xDiff) <= 0.15)
         gestures.push({ face: i, gesture: "facing center" });
@@ -82530,7 +82517,7 @@ var face2 = (res) => {
       const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
       if (mouthOpen > 10)
         gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
-      const chinDepth = res[i].mesh[152][2];
+      const chinDepth = res[i].mesh[152][2] || 0;
       if (Math.abs(chinDepth) > 10)
         gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
     }
@@ -82593,7 +82580,7 @@ var hand2 = (res) => {
       }
     }
     if (fingers && fingers.length > 0) {
-      const closest = fingers.reduce((best, a) => best.position[2] < a.position[2] ? best : a);
+      const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
       gestures.push({ hand: i, gesture: `${closest.name} forward` });
       const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
       gestures.push({ hand: i, gesture: `${highest.name} up` });
@@ -83595,6 +83582,8 @@ async function warmupCanvas(instance) {
       img = new Image();
     else if (env2.Image)
       img = new env2.Image();
+    else
+      return;
     img.onload = async () => {
       const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
       if (!canvas3) {
@@ -83642,8 +83631,9 @@ async function warmup(instance, userConfig) {
   instance.state = "warmup";
   if (userConfig)
     instance.config = mergeDeep(instance.config, userConfig);
-  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none")
+  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") {
-    return { error: "null" };
+    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };
+  }
   let res;
   return new Promise(async (resolve) => {
     if (typeof createImageBitmap === "function")

dist/human.node-gpu.d.ts

@@ -1,3 +1,5 @@
+/// <reference types="offscreencanvas" />
+
 /** meta-function that performs draw for: canvas, face, body, hand */
 declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
 
@@ -1208,17 +1210,7 @@ declare class Human {
      * @param userConfig - {@link Config}
      * @returns result - {@link Result}
      */
-    warmup(userConfig?: Partial<Config>): Promise<Result | {
+    warmup(userConfig?: Partial<Config>): Promise<Result>;
-        error: any; /** Container for events dispatched by Human
-        * Possible events:
-        * - `create`: triggered when Human object is instantiated
-        * - `load`: triggered when models are loaded (explicitly or on-demand)
-        * - `image`: triggered when input image is processed
-        * - `result`: triggered when detection is complete
-        * - `warmup`: triggered when warmup is complete
-        * - `error`: triggered on some errors
-        */
-    }>;
     /** Run detect with tensorflow profiling
      * - result object will contain total exeuction time information for top-20 kernels
      * - actual detection object can be accessed via `human.result`

dist/human.node-gpu.js

@@ -1358,19 +1358,6 @@ var Env = class {
   async updateCPU() {
     const cpu = { model: "", flags: [] };
     if (this.node && this.platform.startsWith("linux")) {
-      const fs = require("fs");
-      try {
-        const data = fs.readFileSync("/proc/cpuinfo").toString();
-        for (const line of data.split("\n")) {
-          if (line.startsWith("model name")) {
-            cpu.model = line.match(/:(.*)/g)[0].replace(":", "").trim();
-          }
-          if (line.startsWith("flags")) {
-            cpu.flags = line.match(/:(.*)/g)[0].replace(":", "").trim().split(" ").sort();
-          }
-        }
-      } catch (e) {
-      }
     }
     if (!this["cpu"])
       Object.defineProperty(this, "cpu", { value: cpu });
@@ -12200,7 +12187,7 @@ var face2 = (res) => {
   const gestures = [];
   for (let i = 0; i < res.length; i++) {
     if (res[i].mesh && res[i].mesh.length > 450) {
-      const zDiff = res[i].mesh[33][2] - res[i].mesh[263][2];
+      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
       const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
       if (Math.abs(zDiff / xDiff) <= 0.15)
         gestures.push({ face: i, gesture: "facing center" });
@@ -12215,7 +12202,7 @@ var face2 = (res) => {
       const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
       if (mouthOpen > 10)
         gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
-      const chinDepth = res[i].mesh[152][2];
+      const chinDepth = res[i].mesh[152][2] || 0;
       if (Math.abs(chinDepth) > 10)
         gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
     }
@@ -12278,7 +12265,7 @@ var hand2 = (res) => {
       }
     }
     if (fingers && fingers.length > 0) {
-      const closest = fingers.reduce((best, a) => best.position[2] < a.position[2] ? best : a);
+      const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
       gestures.push({ hand: i, gesture: `${closest.name} forward` });
       const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
       gestures.push({ hand: i, gesture: `${highest.name} up` });
@@ -13281,6 +13268,8 @@ async function warmupCanvas(instance) {
       img = new Image();
     else if (env.Image)
       img = new env.Image();
+    else
+      return;
     img.onload = async () => {
       const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
       if (!canvas3) {
@@ -13328,8 +13317,9 @@ async function warmup(instance, userConfig) {
   instance.state = "warmup";
   if (userConfig)
     instance.config = mergeDeep(instance.config, userConfig);
-  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none")
+  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") {
-    return { error: "null" };
+    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };
+  }
   let res;
   return new Promise(async (resolve) => {
     if (typeof createImageBitmap === "function")

dist/human.node-wasm.d.ts

@@ -1,3 +1,5 @@
+/// <reference types="offscreencanvas" />
+
 /** meta-function that performs draw for: canvas, face, body, hand */
 declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
 
@@ -1208,17 +1210,7 @@ declare class Human {
      * @param userConfig - {@link Config}
      * @returns result - {@link Result}
      */
-    warmup(userConfig?: Partial<Config>): Promise<Result | {
+    warmup(userConfig?: Partial<Config>): Promise<Result>;
-        error: any; /** Container for events dispatched by Human
-        * Possible events:
-        * - `create`: triggered when Human object is instantiated
-        * - `load`: triggered when models are loaded (explicitly or on-demand)
-        * - `image`: triggered when input image is processed
-        * - `result`: triggered when detection is complete
-        * - `warmup`: triggered when warmup is complete
-        * - `error`: triggered on some errors
-        */
-    }>;
     /** Run detect with tensorflow profiling
      * - result object will contain total exeuction time information for top-20 kernels
      * - actual detection object can be accessed via `human.result`

dist/human.node-wasm.js

@@ -1359,19 +1359,6 @@ var Env = class {
   async updateCPU() {
     const cpu = { model: "", flags: [] };
     if (this.node && this.platform.startsWith("linux")) {
-      const fs = require("fs");
-      try {
-        const data = fs.readFileSync("/proc/cpuinfo").toString();
-        for (const line of data.split("\n")) {
-          if (line.startsWith("model name")) {
-            cpu.model = line.match(/:(.*)/g)[0].replace(":", "").trim();
-          }
-          if (line.startsWith("flags")) {
-            cpu.flags = line.match(/:(.*)/g)[0].replace(":", "").trim().split(" ").sort();
-          }
-        }
-      } catch (e) {
-      }
     }
     if (!this["cpu"])
       Object.defineProperty(this, "cpu", { value: cpu });
@@ -12201,7 +12188,7 @@ var face2 = (res) => {
   const gestures = [];
   for (let i = 0; i < res.length; i++) {
     if (res[i].mesh && res[i].mesh.length > 450) {
-      const zDiff = res[i].mesh[33][2] - res[i].mesh[263][2];
+      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
       const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
       if (Math.abs(zDiff / xDiff) <= 0.15)
         gestures.push({ face: i, gesture: "facing center" });
@@ -12216,7 +12203,7 @@ var face2 = (res) => {
       const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
       if (mouthOpen > 10)
         gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
-      const chinDepth = res[i].mesh[152][2];
+      const chinDepth = res[i].mesh[152][2] || 0;
       if (Math.abs(chinDepth) > 10)
         gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
     }
@@ -12279,7 +12266,7 @@ var hand2 = (res) => {
       }
     }
     if (fingers && fingers.length > 0) {
-      const closest = fingers.reduce((best, a) => best.position[2] < a.position[2] ? best : a);
+      const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
       gestures.push({ hand: i, gesture: `${closest.name} forward` });
       const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
       gestures.push({ hand: i, gesture: `${highest.name} up` });
@@ -13282,6 +13269,8 @@ async function warmupCanvas(instance) {
       img = new Image();
     else if (env.Image)
       img = new env.Image();
+    else
+      return;
     img.onload = async () => {
       const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
       if (!canvas3) {
@@ -13329,8 +13318,9 @@ async function warmup(instance, userConfig) {
   instance.state = "warmup";
   if (userConfig)
     instance.config = mergeDeep(instance.config, userConfig);
-  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none")
+  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") {
-    return { error: "null" };
+    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };
+  }
   let res;
   return new Promise(async (resolve) => {
     if (typeof createImageBitmap === "function")

dist/human.node.d.ts

@@ -1,3 +1,5 @@
+/// <reference types="offscreencanvas" />
+
 /** meta-function that performs draw for: canvas, face, body, hand */
 declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
 
@@ -1208,17 +1210,7 @@ declare class Human {
      * @param userConfig - {@link Config}
      * @returns result - {@link Result}
      */
-    warmup(userConfig?: Partial<Config>): Promise<Result | {
+    warmup(userConfig?: Partial<Config>): Promise<Result>;
-        error: any; /** Container for events dispatched by Human
-        * Possible events:
-        * - `create`: triggered when Human object is instantiated
-        * - `load`: triggered when models are loaded (explicitly or on-demand)
-        * - `image`: triggered when input image is processed
-        * - `result`: triggered when detection is complete
-        * - `warmup`: triggered when warmup is complete
-        * - `error`: triggered on some errors
-        */
-    }>;
     /** Run detect with tensorflow profiling
      * - result object will contain total exeuction time information for top-20 kernels
      * - actual detection object can be accessed via `human.result`

dist/human.node.js

@@ -1358,19 +1358,6 @@ var Env = class {
   async updateCPU() {
     const cpu = { model: "", flags: [] };
     if (this.node && this.platform.startsWith("linux")) {
-      const fs = require("fs");
-      try {
-        const data = fs.readFileSync("/proc/cpuinfo").toString();
-        for (const line of data.split("\n")) {
-          if (line.startsWith("model name")) {
-            cpu.model = line.match(/:(.*)/g)[0].replace(":", "").trim();
-          }
-          if (line.startsWith("flags")) {
-            cpu.flags = line.match(/:(.*)/g)[0].replace(":", "").trim().split(" ").sort();
-          }
-        }
-      } catch (e) {
-      }
     }
     if (!this["cpu"])
       Object.defineProperty(this, "cpu", { value: cpu });
@@ -12200,7 +12187,7 @@ var face2 = (res) => {
   const gestures = [];
   for (let i = 0; i < res.length; i++) {
     if (res[i].mesh && res[i].mesh.length > 450) {
-      const zDiff = res[i].mesh[33][2] - res[i].mesh[263][2];
+      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
       const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
       if (Math.abs(zDiff / xDiff) <= 0.15)
         gestures.push({ face: i, gesture: "facing center" });
@@ -12215,7 +12202,7 @@ var face2 = (res) => {
       const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
       if (mouthOpen > 10)
         gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
-      const chinDepth = res[i].mesh[152][2];
+      const chinDepth = res[i].mesh[152][2] || 0;
       if (Math.abs(chinDepth) > 10)
         gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
     }
@@ -12278,7 +12265,7 @@ var hand2 = (res) => {
       }
     }
     if (fingers && fingers.length > 0) {
-      const closest = fingers.reduce((best, a) => best.position[2] < a.position[2] ? best : a);
+      const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
       gestures.push({ hand: i, gesture: `${closest.name} forward` });
       const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
       gestures.push({ hand: i, gesture: `${highest.name} up` });
@@ -13281,6 +13268,8 @@ async function warmupCanvas(instance) {
       img = new Image();
     else if (env.Image)
       img = new env.Image();
+    else
+      return;
     img.onload = async () => {
       const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
       if (!canvas3) {
@@ -13328,8 +13317,9 @@ async function warmup(instance, userConfig) {
   instance.state = "warmup";
   if (userConfig)
     instance.config = mergeDeep(instance.config, userConfig);
-  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none")
+  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") {
-    return { error: "null" };
+    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };
+  }
   let res;
   return new Promise(async (resolve) => {
     if (typeof createImageBitmap === "function")

package.json

@@ -54,6 +54,7 @@
   ],
   "devDependencies": {
     "@microsoft/api-extractor": "^7.18.19",
+    "@tensorflow/tfjs": "^3.11.0",
     "@tensorflow/tfjs-backend-cpu": "^3.11.0",
     "@tensorflow/tfjs-backend-wasm": "^3.11.0",
     "@tensorflow/tfjs-backend-webgl": "^3.11.0",
@@ -62,9 +63,8 @@
     "@tensorflow/tfjs-core": "^3.11.0",
     "@tensorflow/tfjs-data": "^3.11.0",
     "@tensorflow/tfjs-layers": "^3.11.0",
-    "@tensorflow/tfjs-node-gpu": "^3.11.0",
     "@tensorflow/tfjs-node": "^3.11.0",
-    "@tensorflow/tfjs": "^3.11.0",
+    "@tensorflow/tfjs-node-gpu": "^3.11.0",
     "@types/node": "^16.11.7",
     "@typescript-eslint/eslint-plugin": "^5.4.0",
     "@typescript-eslint/parser": "^5.4.0",
@@ -74,18 +74,18 @@
     "canvas": "^2.8.0",
     "dayjs": "^1.10.7",
     "esbuild": "^0.13.14",
+    "eslint": "8.2.0",
     "eslint-config-airbnb-base": "^15.0.0",
     "eslint-plugin-html": "^6.2.0",
     "eslint-plugin-import": "^2.25.3",
     "eslint-plugin-json": "^3.1.0",
     "eslint-plugin-node": "^11.1.0",
     "eslint-plugin-promise": "^5.1.1",
-    "eslint": "8.2.0",
     "node-fetch": "^3.1.0",
     "rimraf": "^3.0.2",
     "seedrandom": "^3.0.5",
     "tslib": "^2.3.1",
     "typedoc": "0.22.9",
-    "typescript": "4.4.4"
+    "typescript": "4.5.2"
   }
 }

src/face/antispoof.ts

@@ -24,7 +24,7 @@ export async function load(config: Config): Promise<GraphModel> {
   return model;
 }
 
-export async function predict(image: Tensor, config: Config, idx, count): Promise<number> {
+export async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {
   if (!model) return 0;
   const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);
   const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);

src/face/liveness.ts

@@ -24,7 +24,7 @@ export async function load(config: Config): Promise<GraphModel> {
   return model;
 }
 
-export async function predict(image: Tensor, config: Config, idx, count): Promise<number> {
+export async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {
   if (!model) return 0;
   const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);
   const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);

src/gear/emotion.ts

@@ -28,7 +28,7 @@ export async function load(config: Config): Promise<GraphModel> {
   return model;
 }
 
-export async function predict(image: Tensor, config: Config, idx, count): Promise<Array<{ score: number, emotion: string }>> {
+export async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: string }>> {
   if (!model) return [];
   const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);
   const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);

src/gear/ssrnet-age.ts

@@ -31,7 +31,7 @@ export async function load(config: Config) {
 }
 
 // eslint-disable-next-line @typescript-eslint/no-explicit-any
-export async function predict(image: Tensor, config: Config, idx, count): Promise<{ age: number }> {
+export async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {
   if (!model) return { age: 0 };
   const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);
   const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);

src/gesture/gesture.ts

@@ -2,7 +2,7 @@
  * Gesture detection algorithm
  */
 
-import type { GestureResult } from '../result';
+import type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';
 import * as fingerPose from '../hand/fingerpose';
 
 /** face gesture type */
@@ -31,7 +31,7 @@ export type HandGesture =
   | 'victory'
   | 'thumbs up';
 
-export const body = (res): GestureResult[] => {
+export const body = (res: BodyResult[]): GestureResult[] => {
   if (!res) return [];
   const gestures: Array<{ body: number, gesture: BodyGesture }> = [];
   for (let i = 0; i < res.length; i++) {
@@ -53,12 +53,12 @@ export const body = (res): GestureResult[] => {
   return gestures;
 };
 
-export const face = (res): GestureResult[] => {
+export const face = (res: FaceResult[]): GestureResult[] => {
   if (!res) return [];
   const gestures: Array<{ face: number, gesture: FaceGesture }> = [];
   for (let i = 0; i < res.length; i++) {
     if (res[i].mesh && res[i].mesh.length > 450) {
-      const zDiff = res[i].mesh[33][2] - res[i].mesh[263][2];
+      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
       const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
       if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });
       else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });
@@ -68,14 +68,14 @@ export const face = (res): GestureResult[] => {
       if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });
       const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
       if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
-      const chinDepth = res[i].mesh[152][2];
+      const chinDepth = res[i].mesh[152][2] || 0;
       if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });
     }
   }
   return gestures;
 };
 
-export const iris = (res): GestureResult[] => {
+export const iris = (res: FaceResult[]): GestureResult[] => {
   if (!res) return [];
   const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];
   for (let i = 0; i < res.length; i++) {
@@ -116,18 +116,18 @@ export const iris = (res): GestureResult[] => {
   return gestures;
 };
 
-export const hand = (res): GestureResult[] => {
+export const hand = (res: HandResult[]): GestureResult[] => {
   if (!res) return [];
   const gestures: Array<{ hand: number, gesture: HandGesture }> = [];
   for (let i = 0; i < res.length; i++) {
-    const fingers: Array<{ name: string, position: number }> = [];
+    const fingers: Array<{ name: string, position: Point }> = [];
     if (res[i]['annotations']) {
       for (const [finger, pos] of Object.entries(res[i]['annotations'])) {
         if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger
       }
     }
     if (fingers && fingers.length > 0) {
-      const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));
+      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));
       gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });
       const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));
       gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });

src/human.ts

@@ -347,8 +347,8 @@ export class Human {
       if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;
       else kernels[kernel.name] = kernel.kernelTimeMs;
     }
-    const kernelArr: Array<{ name, ms }> = [];
+    const kernelArr: Array<{ name: string, ms: number }> = [];
-    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] })); // convert to array
+    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array
     kernelArr.sort((a, b) => b.ms - a.ms); // sort
     kernelArr.length = 20; // crop
     const res: Record<string, number> = {};
@@ -497,7 +497,7 @@ export class Human {
       let gestureRes: GestureResult[] = [];
       if (this.config.gesture.enabled) {
         timeStamp = now();
-        gestureRes = [...gesture.face(faceRes), ...gesture.body(bodyRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];
+        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];
         if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
         else if (this.performance.gesture) delete this.performance.gesture;
       }

src/tfjs/types.ts

@@ -4,16 +4,16 @@
  * TensorFlow Tensor type
  * @external
  */
-export { Tensor, TensorLike, Rank } from '@tensorflow/tfjs-core/dist/index';
+export type { Tensor, TensorLike, Rank } from '@tensorflow/tfjs-core/dist/index';
 
 /**
  * TensorFlow GraphModel type
  * @external
  */
-export { GraphModel } from '@tensorflow/tfjs-converter/dist/index';
+export type { GraphModel } from '@tensorflow/tfjs-converter/dist/index';
 
 /** Tensorflow Long type
  * @external long
  */
 // eslint-disable-next-line node/no-missing-import
-export { Long } from 'long';
+export type { Long } from 'long';

src/util/env.ts

@@ -149,6 +149,7 @@ export class Env {
     const cpu = { model: '', flags: [] };
     if (this.node && this.platform.startsWith('linux')) {
       // eslint-disable-next-line global-require
+      /*
       const fs = require('fs');
       try {
         const data = fs.readFileSync('/proc/cpuinfo').toString();
@@ -160,7 +161,8 @@ export class Env {
             cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();
           }
         }
-      } catch { /**/ }
+      } catch { }
+      */
     }
     if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });
     else this['cpu'] = cpu;

src/util/util.ts

@@ -65,7 +65,7 @@ export const minmax = (data: Array<number>) => data.reduce((acc: Array<number>,
 }, []);
 
 // helper function: async wait
-export async function wait(time) {
+export async function wait(time: number) {
   const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });
   await waiting;
 }

src/warmup.ts

@@ -48,10 +48,11 @@ async function warmupCanvas(instance: Human) {
         src = null;
     }
     // src = encodeURI('../assets/human-sample-upper.jpg');
-    let img;
+    let img: HTMLImageElement;
     if (typeof Image !== 'undefined') img = new Image();
     // @ts-ignore env.image is an external monkey-patch
     else if (env.Image) img = new env.Image();
+    else return;
     img.onload = async () => {
       const canvas = image.canvas(img.naturalWidth, img.naturalHeight);
       if (!canvas) {
@@ -103,11 +104,13 @@ async function warmupNode(instance: Human) {
  * - only used for `webgl` and `humangl` backends
  * @param userConfig?: Config
 */
-export async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | { error }> {
+export async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result> {
   const t0 = now();
   instance.state = 'warmup';
   if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;
-  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') return { error: 'null' };
+  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {
+    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };
+  }
   let res;
   return new Promise(async (resolve) => {
     if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);

test/build.log

@@ -0,0 +1,25 @@
+2021-11-17 16:48:30 INFO:  Application: {"name":"@vladmandic/human","version":"2.5.2"} 
+2021-11-17 16:48:30 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
+2021-11-17 16:48:30 INFO:  Toolchain: {"build":"0.6.4","esbuild":"0.13.14","typescript":"4.5.2","typedoc":"0.22.9","eslint":"8.2.0"} 
+2021-11-17 16:48:30 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
+2021-11-17 16:48:30 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
+2021-11-17 16:48:30 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1275} 
+2021-11-17 16:48:30 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":62,"inputBytes":546864,"outputBytes":462510} 
+2021-11-17 16:48:30 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1283} 
+2021-11-17 16:48:31 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":62,"inputBytes":546872,"outputBytes":462514} 
+2021-11-17 16:48:31 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1350} 
+2021-11-17 16:48:31 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":62,"inputBytes":546939,"outputBytes":462586} 
+2021-11-17 16:48:31 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":1652} 
+2021-11-17 16:48:31 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2326,"outputBytes":912} 
+2021-11-17 16:48:31 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":62,"inputBytes":546501,"outputBytes":464266} 
+2021-11-17 16:48:31 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":307,"outputBytes":2500732} 
+2021-11-17 16:48:32 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":62,"inputBytes":3046321,"outputBytes":1625475} 
+2021-11-17 16:48:32 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":62,"inputBytes":3046321,"outputBytes":2970799} 
+2021-11-17 16:48:54 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":108} 
+2021-11-17 16:48:54 WARN:  You are running with an unsupported TypeScript version! TypeDoc supports 4.0, 4.1, 4.2, 4.3, 4.4 
+2021-11-17 16:49:03 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":52,"generated":true} 
+2021-11-17 16:49:03 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5811,"outputBytes":3821} 
+2021-11-17 16:49:03 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":15166,"outputBytes":11786} 
+2021-11-17 16:49:43 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":93,"errors":0,"warnings":0} 
+2021-11-17 16:49:44 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
+2021-11-17 16:49:44 INFO:  Done... 

tsconfig.json

@@ -6,7 +6,7 @@
     "outDir": "types",
     "baseUrl": "./",
     "paths": { "tslib": ["./node_modules/tslib/tslib.d.ts"] },
-    "lib": ["esnext", "dom", "webworker"],
+    "lib": ["esnext", "dom"],
     "allowJs": true,
     "allowSyntheticDefaultImports": false,
     "allowUnreachableCode": false,

types/human.d.ts

@@ -1,3 +1,5 @@
+/// <reference types="offscreencanvas" />
+
 /** meta-function that performs draw for: canvas, face, body, hand */
 declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
 
@@ -1208,17 +1210,7 @@ declare class Human {
      * @param userConfig - {@link Config}
      * @returns result - {@link Result}
      */
-    warmup(userConfig?: Partial<Config>): Promise<Result | {
+    warmup(userConfig?: Partial<Config>): Promise<Result>;
-        error: any; /** Container for events dispatched by Human
-        * Possible events:
-        * - `create`: triggered when Human object is instantiated
-        * - `load`: triggered when models are loaded (explicitly or on-demand)
-        * - `image`: triggered when input image is processed
-        * - `result`: triggered when detection is complete
-        * - `warmup`: triggered when warmup is complete
-        * - `error`: triggered on some errors
-        */
-    }>;
     /** Run detect with tensorflow profiling
      * - result object will contain total exeuction time information for top-20 kernels
      * - actual detection object can be accessed via `human.result`

wiki

@@ -1 +1 @@
-Subproject commit 799273cb94e64d08280479435cc11daedabd5a60
+Subproject commit 7b35db4dca9ba3a41b62ec11257a474b2d12c132