check in latest build

build/allFacesFactory.d.ts

@@ -3,4 +3,4 @@ import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
 import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
 import { FullFaceDescription } from './FullFaceDescription';
 import { TNetInput } from './types';
-export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence: number) => Promise<FullFaceDescription[]>;
+export declare function allFacesFactory(detectionNet: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;

build/allFacesFactory.js

@@ -4,34 +4,45 @@ var tslib_1 = require("tslib");
 var extractFaceTensors_1 = require("./extractFaceTensors");
 var FullFaceDescription_1 = require("./FullFaceDescription");
 function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
-    return function (input, minConfidence) {
+    return function (input, minConfidence, useBatchProcessing) {
+        if (useBatchProcessing === void 0) { useBatchProcessing = false; }
         return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var detections, faceTensors, faceLandmarksByFace, alignedFaceBoxes, alignedFaceTensors, descriptors;
-            return tslib_1.__generator(this, function (_a) {
-                switch (_a.label) {
+            var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, alignedFaceTensors, descriptors, _b;
+            return tslib_1.__generator(this, function (_c) {
+                switch (_c.label) {
                     case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
                     case 1:
-                        detections = _a.sent();
-                        return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)
-                            /**
-                            const faceLandmarksByFace = await Promise.all(faceTensors.map(
-                              faceTensor => landmarkNet.detectLandmarks(faceTensor)
-                            )) as FaceLandmarks[]
-                             */
-                        ];
+                        detections = _c.sent();
+                        return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, detections)];
                     case 2:
-                        faceTensors = _a.sent();
+                        faceTensors = _c.sent();
+                        if (!useBatchProcessing) return [3 /*break*/, 4];
                         return [4 /*yield*/, landmarkNet.detectLandmarks(faceTensors)];
                     case 3:
-                        faceLandmarksByFace = _a.sent();
+                        _a = _c.sent();
+                        return [3 /*break*/, 6];
+                    case 4: return [4 /*yield*/, Promise.all(faceTensors.map(function (faceTensor) { return landmarkNet.detectLandmarks(faceTensor); }))];
+                    case 5:
+                        _a = _c.sent();
+                        _c.label = 6;
+                    case 6:
+                        faceLandmarksByFace = _a;
                         faceTensors.forEach(function (t) { return t.dispose(); });
                         alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
                         return [4 /*yield*/, extractFaceTensors_1.extractFaceTensors(input, alignedFaceBoxes)];
-                    case 4:
-                        alignedFaceTensors = _a.sent();
-                        return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
-                    case 5:
-                        descriptors = _a.sent();
+                    case 7:
+                        alignedFaceTensors = _c.sent();
+                        if (!useBatchProcessing) return [3 /*break*/, 9];
+                        return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceTensors)];
+                    case 8:
+                        _b = _c.sent();
+                        return [3 /*break*/, 11];
+                    case 9: return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
+                    case 10:
+                        _b = _c.sent();
+                        _c.label = 11;
+                    case 11:
+                        descriptors = _b;
                         alignedFaceTensors.forEach(function (t) { return t.dispose(); });
                         return [2 /*return*/, detections.map(function (detection, i) {
                                 return new FullFaceDescription_1.FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(detection.getBox()), descriptors[i]);

build/allFacesFactory.js.map

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\ No newline at end of file

build/faceRecognitionNet/FaceRecognitionNet.d.ts

@@ -5,7 +5,7 @@ export declare class FaceRecognitionNet {
     private _params;
     load(weightsOrUrl: Float32Array | string | undefined): Promise<void>;
     extractWeights(weights: Float32Array): void;
-    forwardInput(input: NetInput): Promise<tf.Tensor2D>;
+    forwardInput(input: NetInput): tf.Tensor2D;
     forward(input: TNetInput): Promise<tf.Tensor2D>;
-    computeFaceDescriptor(input: TNetInput): Promise<Float32Array>;
+    computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
 }

build/faceRecognitionNet/FaceRecognitionNet.js

@@ -37,13 +37,11 @@ var FaceRecognitionNet = /** @class */ (function () {
         this._params = extractParams_1.extractParams(weights);
     };
     FaceRecognitionNet.prototype.forwardInput = function (input) {
-        return tslib_1.__awaiter(this, void 0, void 0, function () {
         var _this = this;
-            return tslib_1.__generator(this, function (_a) {
         if (!this._params) {
             throw new Error('FaceRecognitionNet - load model before inference');
         }
-                return [2 /*return*/, tf.tidy(function () {
+        return tf.tidy(function () {
             var batchTensor = input.toBatchTensor(150, true);
             var normalized = normalize_1.normalize(batchTensor);
             var out = convLayer_1.convDown(normalized, _this._params.conv32_down);
@@ -65,8 +63,6 @@ var FaceRecognitionNet = /** @class */ (function () {
             var globalAvg = out.mean([1, 2]);
             var fullyConnected = tf.matMul(globalAvg, _this._params.fc);
             return fullyConnected;
-                    })];
-            });
         });
     };
     FaceRecognitionNet.prototype.forward = function (input) {
@@ -84,20 +80,21 @@ var FaceRecognitionNet = /** @class */ (function () {
     };
     FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
         return tslib_1.__awaiter(this, void 0, void 0, function () {
-            var result, _a, data;
-            return tslib_1.__generator(this, function (_b) {
-                switch (_b.label) {
-                    case 0:
-                        _a = this.forward;
-                        return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
-                    case 1: return [4 /*yield*/, _a.apply(this, [_b.sent()])];
+            var _this = this;
+            var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
+            return tslib_1.__generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, toNetInput_1.toNetInput(input, true)];
+                    case 1:
+                        netInput = _a.sent();
+                        faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
+                        return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];
                     case 2:
-                        result = _b.sent();
-                        return [4 /*yield*/, result.data()];
-                    case 3:
-                        data = _b.sent();
-                        result.dispose();
-                        return [2 /*return*/, data];
+                        faceDescriptorsForBatch = _a.sent();
+                        faceDescriptorTensors.forEach(function (t) { return t.dispose(); });
+                        return [2 /*return*/, netInput.isBatchInput
+                                ? faceDescriptorsForBatch
+                                : faceDescriptorsForBatch[0]];
                 }
             });
         });

build/faceRecognitionNet/FaceRecognitionNet.js.map

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\ No newline at end of file

build/faceRecognitionNet/normalize.js

@@ -3,9 +3,9 @@ Object.defineProperty(exports, "__esModule", { value: true });
 var tf = require("@tensorflow/tfjs-core");
 function normalize(x) {
     return tf.tidy(function () {
-        var avg_r = tf.fill([1, 150, 150, 1], 122.782);
-        var avg_g = tf.fill([1, 150, 150, 1], 117.001);
-        var avg_b = tf.fill([1, 150, 150, 1], 104.298);
+        var avg_r = tf.fill(x.shape.slice(0, 3).concat([1]), 122.782);
+        var avg_g = tf.fill(x.shape.slice(0, 3).concat([1]), 117.001);
+        var avg_b = tf.fill(x.shape.slice(0, 3).concat([1]), 104.298);
         var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);
         return tf.div(tf.sub(x, avg_rgb), tf.scalar(256));
     });

build/faceRecognitionNet/normalize.js.map

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+{"version":3,"file":"normalize.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/normalize.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAE5C,mBAA0B,CAAc;IACtC,OAAO,EAAE,CAAC,IAAI,CAAC;QACb,IAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAK,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,SAAE,CAAC,IAAG,OAAO,CAAC,CAAC;QAC5D,IAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAK,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,SAAE,CAAC,IAAG,OAAO,CAAC,CAAC;QAC5D,IAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CAAK,CAAC,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,SAAE,CAAC,IAAG,OAAO,CAAC,CAAC;QAC5D,IAAM,OAAO,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,EAAE,CAAC,CAAC,CAAA;QAEnD,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,OAAO,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,CAAA;IACnD,CAAC,CAAC,CAAA;AACJ,CAAC;AATD,8BASC"}
\ No newline at end of file

build/globalApi.d.ts

@@ -16,5 +16,5 @@ export declare function loadFaceRecognitionModel(url: string): Promise<void>;
 export declare function loadModels(url: string): Promise<[void, void, void]>;
 export declare function locateFaces(input: TNetInput, minConfidence?: number, maxResults?: number): Promise<FaceDetection[]>;
 export declare function detectLandmarks(input: TNetInput): Promise<FaceLandmarks | FaceLandmarks[]>;
-export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array>;
-export declare const allFaces: (input: tf.Tensor | NetInput | TNetInput, minConfidence: number) => Promise<FullFaceDescription[]>;
+export declare function computeFaceDescriptor(input: TNetInput): Promise<Float32Array | Float32Array[]>;
+export declare const allFaces: (input: tf.Tensor | NetInput | TNetInput, minConfidence: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;

build/globalApi.js.map

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+{"version":3,"file":"globalApi.js","sourceRoot":"","sources":["../src/globalApi.ts"],"names":[],"mappings":";;AAEA,qDAAoD;AAEpD,wEAAuE;AACvE,qEAAoE;AAEpE,8EAA6E;AAKhE,QAAA,YAAY,GAAG,IAAI,mCAAgB,EAAE,CAAA;AACrC,QAAA,WAAW,GAAG,IAAI,iCAAe,EAAE,CAAA;AACnC,QAAA,cAAc,GAAG,IAAI,uCAAkB,EAAE,CAAA;AAEtD,gCAAuC,GAAW;IAChD,OAAO,oBAAY,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AAC/B,CAAC;AAFD,wDAEC;AAED,+BAAsC,GAAW;IAC/C,OAAO,mBAAW,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AAC9B,CAAC;AAFD,sDAEC;AAED,kCAAyC,GAAW;IAClD,OAAO,sBAAc,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AACjC,CAAC;AAFD,4DAEC;AAED,oBAA2B,GAAW;IACpC,OAAO,OAAO,CAAC,GAAG,CAAC;QACjB,sBAAsB,CAAC,GAAG,CAAC;QAC3B,qBAAqB,CAAC,GAAG,CAAC;QAC1B,wBAAwB,CAAC,GAAG,CAAC;KAC9B,CAAC,CAAA;AACJ,CAAC;AAND,gCAMC;AAED,qBACE,KAAgB,EAChB,aAAsB,EACtB,UAAmB;IAEnB,OAAO,oBAAY,CAAC,WAAW,CAAC,KAAK,EAAE,aAAa,EAAE,UAAU,CAAC,CAAA;AACnE,CAAC;AAND,kCAMC;AAED,yBACE,KAAgB;IAEhB,OAAO,mBAAW,CAAC,eAAe,CAAC,KAAK,CAAC,CAAA;AAC3C,CAAC;AAJD,0CAIC;AAED,+BACE,KAAgB;IAEhB,OAAO,sBAAc,CAAC,qBAAqB,CAAC,KAAK,CAAC,CAAA;AACpD,CAAC;AAJD,sDAIC;AAEY,QAAA,QAAQ,GAIiB,iCAAe,CACnD,oBAAY,EACZ,mBAAW,EACX,sBAAc,CACf,CAAA"}
\ No newline at end of file

dist/face-api.js

@@ -2128,9 +2128,9 @@
 
   function normalize(x) {
       return tidy(function () {
-          var avg_r = fill([1, 150, 150, 1], 122.782);
-          var avg_g = fill([1, 150, 150, 1], 117.001);
-          var avg_b = fill([1, 150, 150, 1], 104.298);
+          var avg_r = fill(x.shape.slice(0, 3).concat([1]), 122.782);
+          var avg_g = fill(x.shape.slice(0, 3).concat([1]), 117.001);
+          var avg_b = fill(x.shape.slice(0, 3).concat([1]), 104.298);
           var avg_rgb = concat([avg_r, avg_g, avg_b], 3);
           return div(sub(x, avg_rgb), scalar(256));
       });
@@ -2195,13 +2195,11 @@
           this._params = extractParams$2(weights);
       };
       FaceRecognitionNet.prototype.forwardInput = function (input) {
-          return __awaiter$1(this, void 0, void 0, function () {
           var _this = this;
-              return __generator$1(this, function (_a) {
           if (!this._params) {
               throw new Error('FaceRecognitionNet - load model before inference');
           }
-                  return [2 /*return*/, tidy(function () {
+          return tidy(function () {
               var batchTensor = input.toBatchTensor(150, true);
               var normalized = normalize(batchTensor);
               var out = convDown(normalized, _this._params.conv32_down);
@@ -2223,8 +2221,6 @@
               var globalAvg = out.mean([1, 2]);
               var fullyConnected = matMul(globalAvg, _this._params.fc);
               return fullyConnected;
-                      })];
-              });
           });
       };
       FaceRecognitionNet.prototype.forward = function (input) {
@@ -2242,20 +2238,21 @@
       };
       FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
           return __awaiter$1(this, void 0, void 0, function () {
-              var result, _a, data;
-              return __generator$1(this, function (_b) {
-                  switch (_b.label) {
-                      case 0:
-                          _a = this.forward;
-                          return [4 /*yield*/, toNetInput(input, true)];
-                      case 1: return [4 /*yield*/, _a.apply(this, [_b.sent()])];
+              var _this = this;
+              var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
+              return __generator$1(this, function (_a) {
+                  switch (_a.label) {
+                      case 0: return [4 /*yield*/, toNetInput(input, true)];
+                      case 1:
+                          netInput = _a.sent();
+                          faceDescriptorTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
+                          return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];
                       case 2:
-                          result = _b.sent();
-                          return [4 /*yield*/, result.data()];
-                      case 3:
-                          data = _b.sent();
-                          result.dispose();
-                          return [2 /*return*/, data];
+                          faceDescriptorsForBatch = _a.sent();
+                          faceDescriptorTensors.forEach(function (t) { return t.dispose(); });
+                          return [2 /*return*/, netInput.isBatchInput
+                                  ? faceDescriptorsForBatch
+                                  : faceDescriptorsForBatch[0]];
                   }
               });
           });
@@ -2270,34 +2267,45 @@
   }
 
   function allFacesFactory(detectionNet, landmarkNet, recognitionNet) {
-      return function (input, minConfidence) {
+      return function (input, minConfidence, useBatchProcessing) {
+          if (useBatchProcessing === void 0) { useBatchProcessing = false; }
           return __awaiter$1(this, void 0, void 0, function () {
-              var detections, faceTensors, faceLandmarksByFace, alignedFaceBoxes, alignedFaceTensors, descriptors;
-              return __generator$1(this, function (_a) {
-                  switch (_a.label) {
+              var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, alignedFaceTensors, descriptors, _b;
+              return __generator$1(this, function (_c) {
+                  switch (_c.label) {
                       case 0: return [4 /*yield*/, detectionNet.locateFaces(input, minConfidence)];
                       case 1:
-                          detections = _a.sent();
-                          return [4 /*yield*/, extractFaceTensors(input, detections)
-                              /**
-                              const faceLandmarksByFace = await Promise.all(faceTensors.map(
-                                faceTensor => landmarkNet.detectLandmarks(faceTensor)
-                              )) as FaceLandmarks[]
-                               */
-                          ];
+                          detections = _c.sent();
+                          return [4 /*yield*/, extractFaceTensors(input, detections)];
                       case 2:
-                          faceTensors = _a.sent();
+                          faceTensors = _c.sent();
+                          if (!useBatchProcessing) return [3 /*break*/, 4];
                           return [4 /*yield*/, landmarkNet.detectLandmarks(faceTensors)];
                       case 3:
-                          faceLandmarksByFace = _a.sent();
+                          _a = _c.sent();
+                          return [3 /*break*/, 6];
+                      case 4: return [4 /*yield*/, Promise.all(faceTensors.map(function (faceTensor) { return landmarkNet.detectLandmarks(faceTensor); }))];
+                      case 5:
+                          _a = _c.sent();
+                          _c.label = 6;
+                      case 6:
+                          faceLandmarksByFace = _a;
                           faceTensors.forEach(function (t) { return t.dispose(); });
                           alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
                           return [4 /*yield*/, extractFaceTensors(input, alignedFaceBoxes)];
-                      case 4:
-                          alignedFaceTensors = _a.sent();
-                          return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
-                      case 5:
-                          descriptors = _a.sent();
+                      case 7:
+                          alignedFaceTensors = _c.sent();
+                          if (!useBatchProcessing) return [3 /*break*/, 9];
+                          return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceTensors)];
+                      case 8:
+                          _b = _c.sent();
+                          return [3 /*break*/, 11];
+                      case 9: return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
+                      case 10:
+                          _b = _c.sent();
+                          _c.label = 11;
+                      case 11:
+                          descriptors = _b;
                           alignedFaceTensors.forEach(function (t) { return t.dispose(); });
                           return [2 /*return*/, detections.map(function (detection, i) {
                                   return new FullFaceDescription(detection, faceLandmarksByFace[i].shiftByPoint(detection.getBox()), descriptors[i]);