parallelized agegender operations

README.md

@@ -43,7 +43,7 @@ There are multiple ways to use `Human` library, pick one that suits you:
 - `dist/human.esm-nobundle.js`: ESM format non-minified bundle without TFJS for Browsers
 - `dist/human.cjs`: CommonJS format non-minified bundle without TFJS for NodeJS
 
-All versions include `sourcemap`
+All versions include `sourcemap` and build `manifest`
 
 Defaults:
 ```json
@@ -348,12 +348,15 @@ result = {
     }
   ],
   performance = {  // performance data of last execution for each module measuredin miliseconds
-    body,
-    hand,
-    face,
-    agegender,
-    emotion,
-    total,
+    config,        // time to parse configuration
+    load,          // time to load models
+    sanity,        // time for input verification
+    body,          // model time
+    hand,          // model time
+    face,          // model time
+    agegender,     // model time
+    emotion,       // model time
+    total,         // end to end time
   }
 }
 ```

dist/human.cjs

@@ -3915,15 +3915,21 @@ var require_ssrnet = __commonJS((exports2) => {
     } else {
       enhance = await getImage(image, config2.face.age.inputSize);
     }
+    const promises = [];
+    let ageT;
+    let genderT;
+    if (config2.face.age.enabled)
+      promises.push(ageT = models2.age.predict(enhance));
+    if (config2.face.gender.enabled)
+      promises.push(genderT = models2.gender.predict(enhance));
+    await Promise.all(promises);
     const obj = {};
-    if (config2.face.age.enabled) {
-      const ageT = await models2.age.predict(enhance);
+    if (ageT) {
       const data = await ageT.data();
       obj.age = Math.trunc(10 * data[0]) / 10;
       tf2.dispose(ageT);
     }
-    if (config2.face.gender.enabled) {
-      const genderT = await models2.gender.predict(enhance);
+    if (genderT) {
       const data = await genderT.data();
       const confidence = Math.trunc(Math.abs(1.9 * 100 * (data[0] - 0.5))) / 100;
       if (confidence > config2.face.gender.minConfidence) {
@@ -5132,7 +5138,7 @@ var require_config = __commonJS((exports2) => {
 var require_package = __commonJS((exports2, module2) => {
   module2.exports = {
     name: "@vladmandic/human",
-    version: "0.3.5",
+    version: "0.3.6",
     description: "human: 3D Face Detection, Iris Tracking and Age & Gender Prediction",
     sideEffects: false,
     main: "dist/human.cjs",
@@ -5285,27 +5291,36 @@ async function load(userConfig) {
 }
 async function detect(input, userConfig = {}) {
   state = "config";
+  const perf = {};
+  let timeStamp;
+  timeStamp = now();
   config = mergeDeep(defaults, userConfig);
+  perf.config = Math.trunc(now() - timeStamp);
+  timeStamp = now();
   state = "check";
   const error = sanity(input);
   if (error) {
     log(error, input);
     return {error};
   }
+  perf.sanity = Math.trunc(now() - timeStamp);
   return new Promise(async (resolve) => {
+    const timeStart = now();
     const loadedModels = Object.values(models).filter((a) => a).length;
     if (loadedModels === 0)
       log("Human library starting");
+    timeStamp = now();
     if (tf.getBackend() !== config.backend) {
       state = "backend";
       log("Human library setting backend:", config.backend);
       await tf.setBackend(config.backend);
       await tf.ready();
     }
+    perf.body = Math.trunc(now() - timeStamp);
+    timeStamp = now();
     state = "load";
     await load();
-    const perf = {};
-    let timeStamp;
+    perf.load = Math.trunc(now() - timeStamp);
     if (config.scoped)
       tf.engine().startScope();
     analyze("Start Detect:");
@@ -5342,7 +5357,6 @@ async function detect(input, userConfig = {}) {
         const emotionData = config.face.emotion.enabled ? await emotion.predict(face.image, config) : {};
         perf.emotion = Math.trunc(now() - timeStamp);
         face.image.dispose();
-        delete face.image;
         const iris = face.annotations.leftEyeIris && face.annotations.rightEyeIris ? Math.max(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0], face.annotations.rightEyeIris[3][0] - face.annotations.rightEyeIris[1][0]) : 0;
         faceRes.push({
           confidence: face.confidence,
@@ -5362,7 +5376,7 @@ async function detect(input, userConfig = {}) {
     if (config.scoped)
       tf.engine().endScope();
     analyze("End Scope:");
-    perf.total = Object.values(perf).reduce((a, b) => a + b);
+    perf.total = Math.trunc(now() - timeStart);
     resolve({face: faceRes, body: poseRes, hand: handRes, performance: perf});
   });
 }

dist/human.cjs.json

@@ -116,7 +116,7 @@
       "imports": []
     },
     "src/index.js": {
-      "bytes": 7175,
+      "bytes": 7437,
       "imports": [
         {
           "path": "src/facemesh/facemesh.js"
@@ -245,7 +245,7 @@
       ]
     },
     "src/ssrnet/ssrnet.js": {
-      "bytes": 1856,
+      "bytes": 1965,
       "imports": []
     }
   },
@@ -253,7 +253,7 @@
     "dist/human.cjs.map": {
       "imports": [],
       "inputs": {},
-      "bytes": 219147
+      "bytes": 219752
     },
     "dist/human.cjs": {
       "imports": [],
@@ -283,7 +283,7 @@
           "bytesInOutput": 2950
         },
         "src/ssrnet/ssrnet.js": {
-          "bytesInOutput": 2068
+          "bytesInOutput": 2194
         },
         "src/emotion/emotion.js": {
           "bytesInOutput": 2134
@@ -346,10 +346,10 @@
           "bytesInOutput": 2748
         },
         "src/index.js": {
-          "bytesInOutput": 6171
+          "bytesInOutput": 6431
         }
       },
-      "bytes": 133638
+      "bytes": 134024
     }
   }
 }

dist/human.esm-nobundle.json

@@ -116,7 +116,7 @@
       "imports": []
     },
     "src/index.js": {
-      "bytes": 7175,
+      "bytes": 7437,
       "imports": [
         {
           "path": "src/facemesh/facemesh.js"
@@ -245,7 +245,7 @@
       ]
     },
     "src/ssrnet/ssrnet.js": {
-      "bytes": 1856,
+      "bytes": 1965,
       "imports": []
     }
   },
@@ -253,7 +253,7 @@
     "dist/human.esm-nobundle.js.map": {
       "imports": [],
       "inputs": {},
-      "bytes": 197443
+      "bytes": 198045
     },
     "dist/human.esm-nobundle.js": {
       "imports": [],
@@ -283,7 +283,7 @@
           "bytesInOutput": 1391
         },
         "src/ssrnet/ssrnet.js": {
-          "bytesInOutput": 1099
+          "bytesInOutput": 1149
         },
         "src/emotion/emotion.js": {
           "bytesInOutput": 1148
@@ -346,10 +346,10 @@
           "bytesInOutput": 2275
         },
         "src/index.js": {
-          "bytesInOutput": 3410
+          "bytesInOutput": 3513
         }
       },
-      "bytes": 69193
+      "bytes": 69346
     }
   }
 }

dist/human.esm.json

@@ -291,7 +291,7 @@
       "imports": []
     },
     "src/index.js": {
-      "bytes": 7175,
+      "bytes": 7437,
       "imports": [
         {
           "path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
@@ -436,7 +436,7 @@
       ]
     },
     "src/ssrnet/ssrnet.js": {
-      "bytes": 1856,
+      "bytes": 1965,
       "imports": [
         {
           "path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
@@ -464,7 +464,7 @@
     "dist/human.esm.js.map": {
       "imports": [],
       "inputs": {},
-      "bytes": 4958494
+      "bytes": 4959096
     },
     "dist/human.esm.js": {
       "imports": [],
@@ -551,7 +551,7 @@
           "bytesInOutput": 1376
         },
         "src/ssrnet/ssrnet.js": {
-          "bytesInOutput": 1100
+          "bytesInOutput": 1150
         },
         "src/emotion/emotion.js": {
           "bytesInOutput": 1143
@@ -614,10 +614,10 @@
           "bytesInOutput": 2276
         },
         "src/index.js": {
-          "bytesInOutput": 3495
+          "bytesInOutput": 3608
         }
       },
-      "bytes": 1106100
+      "bytes": 1106263
     }
   }
 }

dist/human.json

@@ -291,7 +291,7 @@
       "imports": []
     },
     "src/index.js": {
-      "bytes": 7175,
+      "bytes": 7437,
       "imports": [
         {
           "path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
@@ -436,7 +436,7 @@
       ]
     },
     "src/ssrnet/ssrnet.js": {
-      "bytes": 1856,
+      "bytes": 1965,
       "imports": [
         {
           "path": "node_modules/@tensorflow/tfjs/dist/tf.node.js"
@@ -464,7 +464,7 @@
     "dist/human.js.map": {
       "imports": [],
       "inputs": {},
-      "bytes": 4958494
+      "bytes": 4959096
     },
     "dist/human.js": {
       "imports": [],
@@ -551,7 +551,7 @@
           "bytesInOutput": 1376
         },
         "src/ssrnet/ssrnet.js": {
-          "bytesInOutput": 1100
+          "bytesInOutput": 1150
         },
         "src/emotion/emotion.js": {
           "bytesInOutput": 1143
@@ -614,10 +614,10 @@
           "bytesInOutput": 2276
         },
         "src/index.js": {
-          "bytesInOutput": 3495
+          "bytesInOutput": 3608
         }
       },
-      "bytes": 1106109
+      "bytes": 1106272
     }
   }
 }

src/index.js

@@ -89,36 +89,46 @@ async function load(userConfig) {
 
 async function detect(input, userConfig = {}) {
   state = 'config';
+  const perf = {};
+  let timeStamp;
+
+  timeStamp = now();
   config = mergeDeep(defaults, userConfig);
+  perf.config = Math.trunc(now() - timeStamp);
 
   // sanity checks
+  timeStamp = now();
   state = 'check';
   const error = sanity(input);
   if (error) {
     log(error, input);
     return { error };
   }
+  perf.sanity = Math.trunc(now() - timeStamp);
 
   // eslint-disable-next-line no-async-promise-executor
   return new Promise(async (resolve) => {
+    const timeStart = now();
+
     // check number of loaded models
     const loadedModels = Object.values(models).filter((a) => a).length;
     if (loadedModels === 0) log('Human library starting');
 
     // configure backend
+    timeStamp = now();
     if (tf.getBackend() !== config.backend) {
       state = 'backend';
       log('Human library setting backend:', config.backend);
       await tf.setBackend(config.backend);
       await tf.ready();
     }
+    perf.body = Math.trunc(now() - timeStamp);
 
     // load models if enabled
+    timeStamp = now();
     state = 'load';
     await load();
-
-    const perf = {};
-    let timeStamp;
+    perf.load = Math.trunc(now() - timeStamp);
 
     if (config.scoped) tf.engine().startScope();
 
@@ -164,8 +174,9 @@ async function detect(input, userConfig = {}) {
         timeStamp = now();
         const emotionData = config.face.emotion.enabled ? await emotion.predict(face.image, config) : {};
         perf.emotion = Math.trunc(now() - timeStamp);
+
+        // dont need face anymore
         face.image.dispose();
-        delete face.image;
         // calculate iris distance
         // iris: array[ bottom, left, top, right, center ]
         const iris = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)
@@ -191,8 +202,7 @@ async function detect(input, userConfig = {}) {
     if (config.scoped) tf.engine().endScope();
     analyze('End Scope:');
 
-    // combine and return results
-    perf.total = Object.values(perf).reduce((a, b) => a + b);
+    perf.total = Math.trunc(now() - timeStart);
     resolve({ face: faceRes, body: poseRes, hand: handRes, performance: perf });
   });
 }

src/ssrnet/ssrnet.js

@@ -36,15 +36,21 @@ async function predict(image, config) {
   } else {
     enhance = await getImage(image, config.face.age.inputSize);
   }
+
+  const promises = [];
+  let ageT;
+  let genderT;
+  if (config.face.age.enabled) promises.push(ageT = models.age.predict(enhance));
+  if (config.face.gender.enabled) promises.push(genderT = models.gender.predict(enhance));
+  await Promise.all(promises);
+
   const obj = {};
-  if (config.face.age.enabled) {
-    const ageT = await models.age.predict(enhance);
+  if (ageT) {
     const data = await ageT.data();
     obj.age = Math.trunc(10 * data[0]) / 10;
     tf.dispose(ageT);
   }
-  if (config.face.gender.enabled) {
-    const genderT = await models.gender.predict(enhance);
+  if (genderT) {
     const data = await genderT.data();
     const confidence = Math.trunc(Math.abs(1.9 * 100 * (data[0] - 0.5))) / 100;
     if (confidence > config.face.gender.minConfidence) {
@@ -53,6 +59,7 @@ async function predict(image, config) {
     }
     tf.dispose(genderT);
   }
+
   tf.dispose(enhance);
   last = obj;
   return obj;