Update to tensorflowjs 1.0.1

build/commonjs/mtcnn/Mtcnn.js

@@ -39,7 +39,7 @@ var Mtcnn = /** @class */ (function (_super) {
                         stats = {};
                         tsTotal = Date.now();
                         imgTensor = tf.tidy(function () {
-                            return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(tf.fromPixels(inputCanvas)).toFloat());
+                            return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(tf.browser.fromPixels(inputCanvas)).toFloat());
                         });
                         onReturn = function (results) {
                             // dispose tensors on return

build/commonjs/mtcnn/Mtcnn.js.map

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\ No newline at end of file
+{"version":3,"file":"Mtcnn.js","sourceRoot":"","sources":["../../../src/mtcnn/Mtcnn.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAA0G;AAE1G,0DAAyD;AACzD,4DAA2D;AAC3D,0CAAgF;AAChF,mDAAkD;AAClD,mCAAqC;AACrC,iDAAgD;AAChD,2EAA0E;AAC1E,uDAAsD;AACtD,+CAA6D;AAC7D,6CAA4C;AAC5C,mCAAkC;AAClC,mCAAkC;AAClC,mCAAkC;AAGlC;IAA2B,iCAAwB;IAEjD;eACE,kBAAM,OAAO,CAAC;IAChB,CAAC;IAEY,4BAAY,GAAzB,UACE,KAAe,EACf,aAAiC;QAAjC,8BAAA,EAAA,kBAAiC;;;;;;wBAGzB,MAAM,GAAK,IAAI,OAAT,CAAS;wBAEvB,IAAI,CAAC,MAAM,EAAE;4BACX,MAAM,IAAI,KAAK,CAAC,qCAAqC,CAAC,CAAA;yBACvD;wBAEK,WAAW,GAAG,KAAK,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA;wBAErC,IAAI,CAAC,WAAW,EAAE;4BAChB,MAAM,IAAI,KAAK,CAAC,6GAA6G,CAAC,CAAA;yBAC/H;wBAEK,KAAK,GAAQ,EAAE,CAAA;wBAEf,OAAO,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBAEpB,SAAS,GAAG,EAAE,CAAC,IAAI,CAAC;4BACxB,OAAA,+BAAc,CACZ,EAAE,CAAC,UAAU,CAAC,EAAE,CAAC,OAAO,CAAC,UAAU,CAAC,WAAW,CAAC,CAAC,CAAC,OAAO,EAAiB,CAC3E;wBAFD,CAEC,CACF,CAAA;wBAEK,QAAQ,GAAG,UAAC,OAAY;4BAC5B,4BAA4B;4BAC5B,SAAS,CAAC,OAAO,EAAE,CAAA;4BACnB,KAAK,CAAC,KAAK,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,OAAO,CAAA;4BAClC,OAAO,OAAO,CAAA;wBAChB,CAAC,CAAA;wBAEK,KAAkB,SAAS,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,EAAzC,MAAM,QAAA,EAAE,KAAK,QAAA,CAA4B;wBAE1C,KAMF,IAAI,2BAAY,CAAC,aAAa,CAAC,EALjC,WAAW,iBAAA,EACX,WAAW,iBAAA,EACX,YAAY,kBAAA,EACZ,eAAe,qBAAA,EACf,UAAU,gBAAA,CACuB;wBAE7B,MAAM,GAAG,CAAC,UAAU,IAAI,yBAAW,CAAC,WAAW,EAAE,WAAW,EAAE,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAC;6BAClF,MAAM,CAAC,UAAA,KAAK;4BACX,IAAM,KAAK,GAAG,mCAAgB,CAAC,KAAK,EAAE,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAA;4BACtD,OAAO,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,KAAK,EAAE,KAAK,CAAC,MAAM,CAAC,GAAG,kBAAS,CAAA;wBACxD,CAAC,CAAC;6BACD,KAAK,CAAC,CAAC,EAAE,YAAY,CAAC,CAAA;wBAEzB,KAAK,CAAC,MAAM,GAAG,MAAM,CAAA;wBACrB,KAAK,CAAC,OAAO,GAAG,MAAM,CAAC,GAAG,CAAC,UAAA,KAAK,IAAI,OAAA,mCAAgB,CAAC,KAAK,EAAE,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,EAAxC,CAAwC,CAAC,CAAA;wBAEzE,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACN,qBAAM,eAAM,CAAC,SAAS,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,IAAI,EAAE,KAAK,CAAC,EAAA;;wBAA9E,IAAI,GAAG,SAAuE;wBACpF,KAAK,CAAC,YAAY,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAEpC,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,MAAM,EAAE;4BACtB,sBAAO,QAAQ,CAAC,EAAE,OAAO,EAAE,EAAE,EAAE,KAAK,OAAA,EAAE,CAAC,EAAA;yBACxC;wBAED,KAAK,CAAC,oBAAoB,GAAG,IAAI,CAAC,KAAK,CAAC,MAAM,CAAA;wBAC9C,oFAAoF;wBACpF,6BAA6B;wBAC7B,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACF,qBAAM,eAAM,CAAC,WAAW,EAAE,IAAI,CAAC,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,IAAI,EAAE,KAAK,CAAC,EAAA;;wBAApF,IAAI,GAAG,SAA6E;wBAC1F,KAAK,CAAC,YAAY,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAEpC,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,MAAM,EAAE;4BACtB,sBAAO,QAAQ,CAAC,EAAE,OAAO,EAAE,EAAE,EAAE,KAAK,OAAA,EAAE,CAAC,EAAA;yBACxC;wBAED,KAAK,CAAC,oBAAoB,GAAG,IAAI,CAAC,KAAK,CAAC,MAAM,CAAA;wBAE9C,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACF,qBAAM,eAAM,CAAC,WAAW,EAAE,IAAI,CAAC,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,IAAI,EAAE,KAAK,CAAC,EAAA;;wBAApF,IAAI,GAAG,SAA6E;wBAC1F,KAAK,CAAC,YAAY,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAE9B,OAAO,GAAG,IAAI,CAAC,KAAK,CAAC,GAAG,CAAC,UAAC,GAAG,EAAE,GAAG,IAAK,OAAA,mCAAuB,CAClE,mCAAuB,CACrB,EAAE,EACF,IAAI,6BAAa,CACf,IAAI,CAAC,MAAM,CAAC,GAAG,CAAC,EAChB,IAAI,kCAAI,CACN,GAAG,CAAC,IAAI,GAAG,KAAK,EAChB,GAAG,CAAC,GAAG,GAAG,MAAM,EAChB,GAAG,CAAC,KAAK,GAAG,KAAK,EACjB,GAAG,CAAC,MAAM,GAAG,MAAM,CACpB,EACD;4BACE,MAAM,QAAA;4BACN,KAAK,OAAA;yBACN,CACF,CACF,EACD,IAAI,+BAAc,CAChB,IAAI,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,UAAA,EAAE,IAAI,OAAA,EAAE,CAAC,GAAG,CAAC,IAAI,mCAAK,CAAC,GAAG,CAAC,IAAI,EAAE,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,CAAC,IAAI,mCAAK,CAAC,GAAG,CAAC,KAAK,EAAE,GAAG,CAAC,MAAM,CAAC,CAAC,EAA1E,CAA0E,CAAC,EACtG,EAAE,KAAK,EAAE,GAAG,CAAC,KAAK,EAAE,MAAM,EAAE,GAAG,CAAC,MAAM,EAAE,CACzC,CACF,EArB4C,CAqB5C,CAAC,CAAA;wBAEF,sBAAO,QAAQ,CAAC,EAAE,OAAO,SAAA,EAAE,KAAK,OAAA,EAAE,CAAC,EAAA;;;;KACpC;IAEY,uBAAO,GAApB,UACE,KAAgB,EAChB,aAAiC;QAAjC,8BAAA,EAAA,kBAAiC;;;;;;wBAGzB,KAAA,IAAI,CAAC,YAAY,CAAA;wBACrB,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;4BADzB,qBAAM,SAAA,IAAI,GACR,SAAuB;4BACvB,aAAa,EACd,EAAA;4BAJH,sBAAO,CACL,SAGC,CACF,CAAC,OAAO,EAAA;;;;KACV;IAEY,gCAAgB,GAA7B,UACE,KAAgB,EAChB,aAAiC;QAAjC,8BAAA,EAAA,kBAAiC;;;;;;wBAE1B,KAAA,IAAI,CAAC,YAAY,CAAA;wBACtB,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;4BADzB,sBAAO,SAAA,IAAI,GACT,SAAuB;4BACvB,aAAa,EACd,EAAA;;;;KACF;IAES,mCAAmB,GAA7B;QACE,OAAO,aAAa,CAAA;IACtB,CAAC;IAES,0CAA0B,GAApC,UAAqC,SAA4B;QAC/D,OAAO,uDAA0B,CAAC,SAAS,CAAC,CAAA;IAC9C,CAAC;IAES,6BAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,YAAC;AAAD,CAAC,AAhJD,CAA2B,2CAAa,GAgJvC;AAhJY,sBAAK"}
\ No newline at end of file

build/commonjs/mtcnn/stage1.js

@@ -20,15 +20,15 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold
     var indices = [];
     for (var y = 0; y < scoresTensor.shape[0]; y++) {
         for (var x = 0; x < scoresTensor.shape[1]; x++) {
-            if (scoresTensor.get(y, x) >= scoreThreshold) {
+            if (scoresTensor.arraySync()[y][x] >= scoreThreshold) {
                 indices.push(new tfjs_image_recognition_base_1.Point(x, y));
             }
         }
     }
     var boundingBoxes = indices.map(function (idx) {
         var cell = new tfjs_image_recognition_base_1.BoundingBox(Math.round((idx.y * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.x * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.y * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale), Math.round((idx.x * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale));
-        var score = scoresTensor.get(idx.y, idx.x);
-        var region = new MtcnnBox_1.MtcnnBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
+        var score = scoresTensor.arraySync()[idx.y][idx.x];
+        var region = new MtcnnBox_1.MtcnnBox(regionsTensor.arraySync()[idx.y][idx.x][0], regionsTensor.arraySync()[idx.y][idx.x][1], regionsTensor.arraySync()[idx.y][idx.x][2], regionsTensor.arraySync()[idx.y][idx.x][3]);
         return {
             cell: cell,
             score: score,

build/commonjs/mtcnn/stage1.js.map

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\ No newline at end of file

build/commonjs/mtcnn/stage2.js

@@ -48,7 +48,7 @@ function stage2(img, inputBoxes, scoreThreshold, params, stats) {
                         indicesNms = tfjs_image_recognition_base_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
                         stats.stage2_nms = Date.now() - ts;
                         regions_1 = indicesNms.map(function (idx) {
-                            return new MtcnnBox_1.MtcnnBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
+                            return new MtcnnBox_1.MtcnnBox(rnetOuts[indices[idx]].regions.arraySync()[0][0], rnetOuts[indices[idx]].regions.arraySync()[0][1], rnetOuts[indices[idx]].regions.arraySync()[0][2], rnetOuts[indices[idx]].regions.arraySync()[0][3]);
                         });
                         finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
                         finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });

build/commonjs/mtcnn/stage2.js.map

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\ No newline at end of file
+{"version":3,"file":"stage2.js","sourceRoot":"","sources":["../../../src/mtcnn/stage2.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAqE;AAErE,6DAA4D;AAC5D,uCAAsC;AACtC,+BAA8B;AAG9B,SAAsB,MAAM,CAC1B,GAAsB,EACtB,UAAiB,EACjB,cAAsB,EACtB,MAAkB,EAClB,KAAU;;;;;;oBAGN,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACA,qBAAM,yCAAmB,CAAC,GAAG,EAAE,UAAU,EAAE,EAAE,KAAK,EAAE,EAAE,EAAE,MAAM,EAAE,EAAE,EAAE,CAAC,EAAA;;oBAAlF,UAAU,GAAG,SAAqE;oBACxF,KAAK,CAAC,0BAA0B,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAElD,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACT,QAAQ,GAAG,UAAU,CAAC,GAAG,CAC7B,UAAA,SAAS;wBACP,IAAM,GAAG,GAAG,WAAI,CAAC,SAAS,EAAE,MAAM,CAAC,CAAA;wBACnC,SAAS,CAAC,OAAO,EAAE,CAAA;wBACnB,OAAO,GAAG,CAAA;oBACZ,CAAC,CACF,CAAA;oBACD,KAAK,CAAC,WAAW,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAE7B,YAAY,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC;wBACtC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,QAAQ,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,MAAM,EAAV,CAAU,CAAC,CAAC;wBAC5C,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,MAAM,CAAA;oBACP,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;oBAAC,qBAAM,YAAY,CAAC,IAAI,EAAE,EAAA;;oBAA7C,MAAM,GAAG,cAAW,SAAyB,EAAC;oBACpD,YAAY,CAAC,OAAO,EAAE,CAAA;oBAEhB,OAAO,GAAG,MAAM;yBACnB,GAAG,CAAC,UAAC,KAAK,EAAE,GAAG,IAAK,OAAA,CAAC,EAAE,KAAK,OAAA,EAAE,GAAG,KAAA,EAAE,CAAC,EAAhB,CAAgB,CAAC;yBACrC,MAAM,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,KAAK,GAAG,cAAc,EAAxB,CAAwB,CAAC;yBACrC,GAAG,CAAC,UAAC,EAAO;4BAAL,YAAG;wBAAO,OAAA,GAAG;oBAAH,CAAG,CAAC,CAAA;oBAElB,aAAa,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,UAAU,CAAC,GAAG,CAAC,EAAf,CAAe,CAAC,CAAA;oBACnD,cAAc,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,MAAM,CAAC,GAAG,CAAC,EAAX,CAAW,CAAC,CAAA;oBAElD,UAAU,GAAU,EAAE,CAAA;oBACtB,WAAW,GAAa,EAAE,CAAA;oBAE9B,IAAI,aAAa,CAAC,MAAM,GAAG,CAAC,EAAE;wBAC5B,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACT,UAAU,GAAG,+CAAiB,CAClC,aAAa,EACb,cAAc,EACd,GAAG,CACJ,CAAA;wBACD,KAAK,CAAC,UAAU,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAE5B,YAAU,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG;4BAChC,OAAA,IAAI,mBAAQ,CACV,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,SAAS,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAChD,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,SAAS,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAChD,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,SAAS,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAChD,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,SAAS,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CACjD;wBALD,CAKC,CACF,CAAA;wBAED,WAAW,GAAG,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,cAAc,CAAC,GAAG,CAAC,EAAnB,CAAmB,CAAC,CAAA;wBACxD,UAAU,GAAG,UAAU,CAAC,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC,IAAK,OAAA,aAAa,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,SAAO,CAAC,CAAC,CAAC,CAAC,EAAxC,CAAwC,CAAC,CAAA;qBAClF;oBAED,QAAQ,CAAC,OAAO,CAAC,UAAA,CAAC;wBAChB,CAAC,CAAC,OAAO,CAAC,OAAO,EAAE,CAAA;wBACnB,CAAC,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;oBACpB,CAAC,CAAC,CAAA;oBAEF,sBAAO;4BACL,KAAK,EAAE,UAAU;4BACjB,MAAM,EAAE,WAAW;yBACpB,EAAA;;;;CACF;AAtED,wBAsEC"}
\ No newline at end of file

build/commonjs/mtcnn/stage3.js

@@ -39,7 +39,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
                         var idx = _a.idx;
                         return idx;
                     });
-                    filteredRegions = indices.map(function (idx) { return new MtcnnBox_1.MtcnnBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
+                    filteredRegions = indices.map(function (idx) { return new MtcnnBox_1.MtcnnBox(onetOuts[idx].regions.arraySync()[0][0], onetOuts[idx].regions.arraySync()[0][1], onetOuts[idx].regions.arraySync()[0][2], onetOuts[idx].regions.arraySync()[0][3]); });
                     filteredBoxes = indices
                         .map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
                     filteredScores = indices.map(function (idx) { return scores[idx]; });
@@ -54,7 +54,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
                         finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
                         points = indicesNms.map(function (idx, i) {
                             return Array(5).fill(0).map(function (_, ptIdx) {
-                                return new tfjs_image_recognition_base_1.Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
+                                return new tfjs_image_recognition_base_1.Point(((onetOuts[idx].points.arraySync()[0][ptIdx] * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.arraySync()[0][ptIdx + 5] * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
                             });
                         });
                     }

build/commonjs/mtcnn/stage3.js.map

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\ No newline at end of file
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build/commonjs/ssdMobilenetv1/SsdMobilenetv1.js

@@ -73,12 +73,12 @@ var SsdMobilenetv1 = /** @class */ (function (_super) {
                         results = indices
                             .map(function (idx) {
                             var _a = [
-                                Math.max(0, boxes.get(idx, 0)),
-                                Math.min(1.0, boxes.get(idx, 2))
+                                Math.max(0, boxes.arraySync()[idx][0]),
+                                Math.min(1.0, boxes.arraySync()[idx][2])
                             ].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
                             var _b = [
-                                Math.max(0, boxes.get(idx, 1)),
-                                Math.min(1.0, boxes.get(idx, 3))
+                                Math.max(0, boxes.arraySync()[idx][1]),
+                                Math.min(1.0, boxes.arraySync()[idx][3])
                             ].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
                             return new FaceDetection_1.FaceDetection(scoresData[idx], new tfjs_image_recognition_base_1.Rect(left, top, right - left, bottom - top), {
                                 height: netInput.getInputHeight(0),

build/commonjs/ssdMobilenetv1/nonMaxSuppression.js

@@ -32,14 +32,14 @@ function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThre
 }
 exports.nonMaxSuppression = nonMaxSuppression;
 function IOU(boxes, i, j) {
-    var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2));
-    var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3));
-    var ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2));
-    var xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3));
-    var yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2));
-    var xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3));
-    var ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2));
-    var xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3));
+    var yminI = Math.min(boxes.arraySync()[i][0], boxes.arraySync()[i][2]);
+    var xminI = Math.min(boxes.arraySync()[i][1], boxes.arraySync()[i][3]);
+    var ymaxI = Math.max(boxes.arraySync()[i][0], boxes.arraySync()[i][2]);
+    var xmaxI = Math.max(boxes.arraySync()[i][1], boxes.arraySync()[i][3]);
+    var yminJ = Math.min(boxes.arraySync()[j][0], boxes.arraySync()[j][2]);
+    var xminJ = Math.min(boxes.arraySync()[j][1], boxes.arraySync()[j][3]);
+    var ymaxJ = Math.max(boxes.arraySync()[j][0], boxes.arraySync()[j][2]);
+    var xmaxJ = Math.max(boxes.arraySync()[j][1], boxes.arraySync()[j][3]);
     var areaI = (ymaxI - yminI) * (xmaxI - xminI);
     var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
     if (areaI <= 0 || areaJ <= 0) {

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\ No newline at end of file

build/es6/mtcnn/Mtcnn.js

@@ -37,7 +37,7 @@ var Mtcnn = /** @class */ (function (_super) {
                         stats = {};
                         tsTotal = Date.now();
                         imgTensor = tf.tidy(function () {
-                            return bgrToRgbTensor(tf.expandDims(tf.fromPixels(inputCanvas)).toFloat());
+                            return bgrToRgbTensor(tf.expandDims(tf.browser.fromPixels(inputCanvas)).toFloat());
                         });
                         onReturn = function (results) {
                             // dispose tensors on return

build/es6/mtcnn/Mtcnn.js.map

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\ No newline at end of file

build/es6/mtcnn/stage1.js

@@ -18,15 +18,15 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold
     var indices = [];
     for (var y = 0; y < scoresTensor.shape[0]; y++) {
         for (var x = 0; x < scoresTensor.shape[1]; x++) {
-            if (scoresTensor.get(y, x) >= scoreThreshold) {
+            if (scoresTensor.arraySync()[y][x] >= scoreThreshold) {
                 indices.push(new Point(x, y));
             }
         }
     }
     var boundingBoxes = indices.map(function (idx) {
         var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale));
-        var score = scoresTensor.get(idx.y, idx.x);
-        var region = new MtcnnBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
+        var score = scoresTensor.arraySync()[idx.y][idx.x];
+        var region = new MtcnnBox(regionsTensor.arraySync()[idx.y][idx.x][0], regionsTensor.arraySync()[idx.y][idx.x][1], regionsTensor.arraySync()[idx.y][idx.x][2], regionsTensor.arraySync()[idx.y][idx.x][3]);
         return {
             cell: cell,
             score: score,

build/es6/mtcnn/stage1.js.map

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\ No newline at end of file

build/es6/mtcnn/stage2.js

@@ -46,7 +46,7 @@ export function stage2(img, inputBoxes, scoreThreshold, params, stats) {
                         indicesNms = nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
                         stats.stage2_nms = Date.now() - ts;
                         regions_1 = indicesNms.map(function (idx) {
-                            return new MtcnnBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
+                            return new MtcnnBox(rnetOuts[indices[idx]].regions.arraySync()[0][0], rnetOuts[indices[idx]].regions.arraySync()[0][1], rnetOuts[indices[idx]].regions.arraySync()[0][2], rnetOuts[indices[idx]].regions.arraySync()[0][3]);
                         });
                         finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
                         finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });

build/es6/mtcnn/stage2.js.map

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\ No newline at end of file
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\ No newline at end of file

build/es6/mtcnn/stage3.js

@@ -37,7 +37,7 @@ export function stage3(img, inputBoxes, scoreThreshold, params, stats) {
                         var idx = _a.idx;
                         return idx;
                     });
-                    filteredRegions = indices.map(function (idx) { return new MtcnnBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
+                    filteredRegions = indices.map(function (idx) { return new MtcnnBox(onetOuts[idx].regions.arraySync()[0][0], onetOuts[idx].regions.arraySync()[0][1], onetOuts[idx].regions.arraySync()[0][2], onetOuts[idx].regions.arraySync()[0][3]); });
                     filteredBoxes = indices
                         .map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
                     filteredScores = indices.map(function (idx) { return scores[idx]; });
@@ -52,7 +52,7 @@ export function stage3(img, inputBoxes, scoreThreshold, params, stats) {
                         finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
                         points = indicesNms.map(function (idx, i) {
                             return Array(5).fill(0).map(function (_, ptIdx) {
-                                return new Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
+                                return new Point(((onetOuts[idx].points.arraySync()[0][ptIdx] * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.arraySync()[0][ptIdx + 5] * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
                             });
                         });
                     }

build/es6/mtcnn/stage3.js.map

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\ No newline at end of file
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\ No newline at end of file

build/es6/ssdMobilenetv1/SsdMobilenetv1.js

@@ -71,12 +71,12 @@ var SsdMobilenetv1 = /** @class */ (function (_super) {
                         results = indices
                             .map(function (idx) {
                             var _a = [
-                                Math.max(0, boxes.get(idx, 0)),
-                                Math.min(1.0, boxes.get(idx, 2))
+                                Math.max(0, boxes.arraySync()[idx][0]),
+                                Math.min(1.0, boxes.arraySync()[idx][2])
                             ].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
                             var _b = [
-                                Math.max(0, boxes.get(idx, 1)),
-                                Math.min(1.0, boxes.get(idx, 3))
+                                Math.max(0, boxes.arraySync()[idx][1]),
+                                Math.min(1.0, boxes.arraySync()[idx][3])
                             ].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
                             return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
                                 height: netInput.getInputHeight(0),

build/es6/ssdMobilenetv1/nonMaxSuppression.js

@@ -29,14 +29,14 @@ export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, sc
     return selected;
 }
 function IOU(boxes, i, j) {
-    var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2));
-    var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3));
-    var ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2));
-    var xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3));
-    var yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2));
-    var xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3));
-    var ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2));
-    var xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3));
+    var yminI = Math.min(boxes.arraySync()[i][0], boxes.arraySync()[i][2]);
+    var xminI = Math.min(boxes.arraySync()[i][1], boxes.arraySync()[i][3]);
+    var ymaxI = Math.max(boxes.arraySync()[i][0], boxes.arraySync()[i][2]);
+    var xmaxI = Math.max(boxes.arraySync()[i][1], boxes.arraySync()[i][3]);
+    var yminJ = Math.min(boxes.arraySync()[j][0], boxes.arraySync()[j][2]);
+    var xminJ = Math.min(boxes.arraySync()[j][1], boxes.arraySync()[j][3]);
+    var ymaxJ = Math.max(boxes.arraySync()[j][0], boxes.arraySync()[j][2]);
+    var xmaxJ = Math.max(boxes.arraySync()[j][1], boxes.arraySync()[j][3]);
     var areaI = (ymaxI - yminI) * (xmaxI - xminI);
     var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
     if (areaI <= 0 || areaJ <= 0) {

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+      "version": "1.0.1",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/@tensorflow/tfjs-node/-/tfjs-node-1.0.1.tgz",
+      "integrity": "sha512-lGRfG5LgHqXLvVof8Xj3PYVqpyjl/vP282G6ezvE7ikh48orcpFs1P/f+70Bf95cT1LrK0ecmg9ZWE/jftXgRA==",
       "dev": true,
       "requires": {
-        "@tensorflow/tfjs": "~0.14.2",
+        "@tensorflow/tfjs": "~1.0.1",
         "adm-zip": "^0.4.11",
         "bindings": "~1.3.0",
         "https-proxy-agent": "^2.2.1",
@@ -188,12 +106,6 @@
       "integrity": "sha512-BaOFpaddRVV8qykJoWHrHtamml880oh0+DIZWbtJgx0pu+KhDF1gER5hSfCIfzyMrbjMuYFnLUfyo1l0JUVU3Q==",
       "dev": true
     },
-    "@types/long": {
-      "version": "3.0.32",
-      "resolved": "https://registry.npmjs.org/@types/long/-/long-3.0.32.tgz",
-      "integrity": "sha512-ZXyOOm83p7X8p3s0IYM3VeueNmHpkk/yMlP8CLeOnEcu6hIwPH7YjZBvhQkR0ZFS2DqZAxKtJ/M5fcuv3OU5BA==",
-      "dev": true
-    },
     "@types/node": {
       "version": "10.12.18",
       "resolved": "https://registry.npmjs.org/@types/node/-/node-10.12.18.tgz",
@@ -201,9 +113,9 @@
       "dev": true
     },
     "@types/node-fetch": {
-      "version": "2.1.4",
-      "resolved": "https://registry.npmjs.org/@types/node-fetch/-/node-fetch-2.1.4.tgz",
-      "integrity": "sha512-tR1ekaXUGpmzOcDXWU9BW73YfA2/VW1DF1FH+wlJ82BbCSnWTbdX+JkqWQXWKIGsFPnPsYadbXfNgz28g+ccWg==",
+      "version": "2.1.6",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/@types/node-fetch/-/node-fetch-2.1.6.tgz",
+      "integrity": "sha512-Hv1jgh3pfpUEl2F2mqUd1AfLSk1YbUCeBJFaP36t7esAO617dErqdxWb5cdG2NfJGOofkmBW36fdx0dVewxDRg==",
       "dev": true,
       "requires": {
         "@types/node": "*"
@@ -211,17 +123,17 @@
     },
     "@types/seedrandom": {
       "version": "2.4.27",
-      "resolved": "http://registry.npmjs.org/@types/seedrandom/-/seedrandom-2.4.27.tgz",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/@types/seedrandom/-/seedrandom-2.4.27.tgz",
       "integrity": "sha1-nbVjk33YaRX2kJK8QyWdL0hXjkE="
     },
     "@types/webgl-ext": {
       "version": "0.0.30",
-      "resolved": "https://registry.npmjs.org/@types/webgl-ext/-/webgl-ext-0.0.30.tgz",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/@types/webgl-ext/-/webgl-ext-0.0.30.tgz",
       "integrity": "sha512-LKVgNmBxN0BbljJrVUwkxwRYqzsAEPcZOe6S2T6ZaBDIrFp0qu4FNlpc5sM1tGbXUYFgdVQIoeLk1Y1UoblyEg=="
     },
     "@types/webgl2": {
       "version": "0.0.4",
-      "resolved": "https://registry.npmjs.org/@types/webgl2/-/webgl2-0.0.4.tgz",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/@types/webgl2/-/webgl2-0.0.4.tgz",
       "integrity": "sha512-PACt1xdErJbMUOUweSrbVM7gSIYm1vTncW2hF6Os/EeWi6TXYAYMPp+8v6rzHmypE5gHrxaxZNXgMkJVIdZpHw=="
     },
     "abbrev": {
@@ -248,7 +160,7 @@
     },
     "adm-zip": {
       "version": "0.4.13",
-      "resolved": "https://registry.npmjs.org/adm-zip/-/adm-zip-0.4.13.tgz",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/adm-zip/-/adm-zip-0.4.13.tgz",
       "integrity": "sha512-fERNJX8sOXfel6qCBCMPvZLzENBEhZTzKqg6vrOW5pvoEaQuJhRU4ndTAh6lHOxn1I6jnz2NHra56ZODM751uw==",
       "dev": true
     },
@@ -260,7 +172,7 @@
     },
     "agent-base": {
       "version": "4.2.1",
-      "resolved": "https://registry.npmjs.org/agent-base/-/agent-base-4.2.1.tgz",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/agent-base/-/agent-base-4.2.1.tgz",
       "integrity": "sha512-JVwXMr9nHYTUXsBFKUqhJwvlcYU/blreOEUkhNR2eXZIvwd+c+o5V4MgDPKWnMS/56awN3TRzIP+KoPn+roQtg==",
       "dev": true,
       "requires": {
@@ -567,7 +479,7 @@
     },
     "bindings": {
       "version": "1.3.1",
-      "resolved": "https://registry.npmjs.org/bindings/-/bindings-1.3.1.tgz",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/bindings/-/bindings-1.3.1.tgz",
       "integrity": "sha512-i47mqjF9UbjxJhxGf+pZ6kSxrnI3wBLlnGI2ArWJ4r0VrvDS7ZYXkprq/pLaBWYq4GM0r4zdHY+NNRqEMU7uew==",
       "dev": true
     },
@@ -1473,14 +1385,14 @@
       }
     },
     "es6-promise": {
-      "version": "4.2.5",
-      "resolved": "https://registry.npmjs.org/es6-promise/-/es6-promise-4.2.5.tgz",
-      "integrity": "sha512-n6wvpdE43VFtJq+lUDYDBFUwV8TZbuGXLV4D6wKafg13ldznKsyEvatubnmUe31zcvelSzOHF+XbaT+Bl9ObDg==",
+      "version": "4.2.6",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/es6-promise/-/es6-promise-4.2.6.tgz",
+      "integrity": "sha512-aRVgGdnmW2OiySVPUC9e6m+plolMAJKjZnQlCwNSuK5yQ0JN61DZSO1X1Ufd1foqWRAlig0rhduTCHe7sVtK5Q==",
       "dev": true
     },
     "es6-promisify": {
       "version": "5.0.0",
-      "resolved": "https://registry.npmjs.org/es6-promisify/-/es6-promisify-5.0.0.tgz",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/es6-promisify/-/es6-promisify-5.0.0.tgz",
       "integrity": "sha1-UQnWLz5W6pZ8S2NQWu8IKRyKUgM=",
       "dev": true,
       "requires": {
@@ -2683,7 +2595,7 @@
     },
     "https-proxy-agent": {
       "version": "2.2.1",
-      "resolved": "https://registry.npmjs.org/https-proxy-agent/-/https-proxy-agent-2.2.1.tgz",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/https-proxy-agent/-/https-proxy-agent-2.2.1.tgz",
       "integrity": "sha512-HPCTS1LW51bcyMYbxUIOO4HEOlQ1/1qRaFWcyxvwaqUS9TY88aoEuHUY33kuAh1YhVVaDQhLZsnPd+XNARWZlQ==",
       "dev": true,
       "requires": {
@@ -2693,7 +2605,7 @@
       "dependencies": {
         "debug": {
           "version": "3.2.6",
-          "resolved": "https://registry.npmjs.org/debug/-/debug-3.2.6.tgz",
+          "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/debug/-/debug-3.2.6.tgz",
           "integrity": "sha512-mel+jf7nrtEl5Pn1Qx46zARXKDpBbvzezse7p7LqINmdoIk8PYP5SySaxEmYv6TZ0JyEKA1hsCId6DIhgITtWQ==",
           "dev": true,
           "requires": {
@@ -2702,7 +2614,7 @@
         },
         "ms": {
           "version": "2.1.1",
-          "resolved": "https://registry.npmjs.org/ms/-/ms-2.1.1.tgz",
+          "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/ms/-/ms-2.1.1.tgz",
           "integrity": "sha512-tgp+dl5cGk28utYktBsrFqA7HKgrhgPsg6Z/EfhWI4gl1Hwq8B/GmY/0oXZ6nF8hDVesS/FpnYaD/kOWhYQvyg==",
           "dev": true
         }
@@ -3429,12 +3341,6 @@
         }
       }
     },
-    "long": {
-      "version": "4.0.0",
-      "resolved": "https://registry.npmjs.org/long/-/long-4.0.0.tgz",
-      "integrity": "sha512-XsP+KhQif4bjX1kbuSiySJFNAehNxgLb6hPRGJ9QsUr8ajHkuXGdrHmFUTUUXhDwVX2R5bY4JNZEwbUiMhV+MA==",
-      "dev": true
-    },
     "loud-rejection": {
       "version": "1.6.0",
       "resolved": "https://registry.npmjs.org/loud-rejection/-/loud-rejection-1.6.0.tgz",
@@ -3735,7 +3641,7 @@
     },
     "node-fetch": {
       "version": "2.3.0",
-      "resolved": "https://registry.npmjs.org/node-fetch/-/node-fetch-2.3.0.tgz",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/node-fetch/-/node-fetch-2.3.0.tgz",
       "integrity": "sha512-MOd8pV3fxENbryESLgVIeaGKrdl+uaYhCSSVkjeOb/31/njTpcis5aWfdqgNlHIrKOLRbMnfPINPOML2CIFeXA==",
       "dev": true
     },
@@ -4241,39 +4147,10 @@
     },
     "progress": {
       "version": "2.0.3",
-      "resolved": "https://registry.npmjs.org/progress/-/progress-2.0.3.tgz",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/progress/-/progress-2.0.3.tgz",
       "integrity": "sha512-7PiHtLll5LdnKIMw100I+8xJXR5gW2QwWYkT6iJva0bXitZKa/XMrSbdmg3r2Xnaidz9Qumd0VPaMrZlF9V9sA==",
       "dev": true
     },
-    "protobufjs": {
-      "version": "6.8.8",
-      "resolved": "https://registry.npmjs.org/protobufjs/-/protobufjs-6.8.8.tgz",
-      "integrity": "sha512-AAmHtD5pXgZfi7GMpllpO3q1Xw1OYldr+dMUlAnffGTAhqkg72WdmSY71uKBF/JuyiKs8psYbtKrhi0ASCD8qw==",
-      "dev": true,
-      "requires": {
-        "@protobufjs/aspromise": "^1.1.2",
-        "@protobufjs/base64": "^1.1.2",
-        "@protobufjs/codegen": "^2.0.4",
-        "@protobufjs/eventemitter": "^1.1.0",
-        "@protobufjs/fetch": "^1.1.0",
-        "@protobufjs/float": "^1.0.2",
-        "@protobufjs/inquire": "^1.1.0",
-        "@protobufjs/path": "^1.1.2",
-        "@protobufjs/pool": "^1.1.0",
-        "@protobufjs/utf8": "^1.1.0",
-        "@types/long": "^4.0.0",
-        "@types/node": "^10.1.0",
-        "long": "^4.0.0"
-      },
-      "dependencies": {
-        "@types/long": {
-          "version": "4.0.0",
-          "resolved": "https://registry.npmjs.org/@types/long/-/long-4.0.0.tgz",
-          "integrity": "sha512-1w52Nyx4Gq47uuu0EVcsHBxZFJgurQ+rTKS3qMHxR1GY2T8c2AJYd6vZoZ9q1rupaDjU0yT+Jc2XTyXkjeMA+Q==",
-          "dev": true
-        }
-      }
-    },
     "pseudomap": {
       "version": "1.0.2",
       "resolved": "https://registry.npmjs.org/pseudomap/-/pseudomap-1.0.2.tgz",
@@ -4841,7 +4718,7 @@
     },
     "seedrandom": {
       "version": "2.4.3",
-      "resolved": "https://registry.npmjs.org/seedrandom/-/seedrandom-2.4.3.tgz",
+      "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/seedrandom/-/seedrandom-2.4.3.tgz",
       "integrity": "sha1-JDhQTa0zkXMUv/GKxNeU8W1qrsw="
     },
     "semver": {
@@ -5369,6 +5246,19 @@
       "requires": {
         "@tensorflow/tfjs-core": "0.14.2",
         "tslib": "^1.9.3"
+      },
+      "dependencies": {
+        "@tensorflow/tfjs-core": {
+          "version": "0.14.2",
+          "resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/@tensorflow/tfjs-core/-/tfjs-core-0.14.2.tgz",
+          "integrity": "sha512-VVbcu6H3ioKCkfkep/gQASfzPnQt3C5v+4ppH9pQ6Lf0lD+l3NMuMJYxa8Wjac1TfiWhFEX58bJvhpMfTGsUlg==",
+          "requires": {
+            "@types/seedrandom": "2.4.27",
+            "@types/webgl-ext": "0.0.30",
+            "@types/webgl2": "0.0.4",
+            "seedrandom": "2.4.3"
+          }
+        }
       }
     },
     "through2": {

package.json

@@ -35,12 +35,12 @@
   "author": "justadudewhohacks",
   "license": "MIT",
   "dependencies": {
-    "@tensorflow/tfjs-core": "0.14.2",
+    "@tensorflow/tfjs-core": "1.0.1",
     "tfjs-image-recognition-base": "^0.4.1",
     "tslib": "^1.9.3"
   },
   "devDependencies": {
-    "@tensorflow/tfjs-node": "^0.2.3",
+    "@tensorflow/tfjs-node": "^1.0.1",
     "@types/jasmine": "^3.3.8",
     "@types/node": "^10.12.18",
     "canvas": "2.0.1",

src/mtcnn/Mtcnn.ts

@@ -45,7 +45,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
 
     const imgTensor = tf.tidy(() =>
       bgrToRgbTensor(
-        tf.expandDims(tf.fromPixels(inputCanvas)).toFloat() as tf.Tensor4D
+        tf.expandDims(tf.browser.fromPixels(inputCanvas)).toFloat() as tf.Tensor4D
       )
     )
 

src/mtcnn/stage1.ts

@@ -30,7 +30,7 @@ function extractBoundingBoxes(
   const indices: Point[] = []
   for (let y = 0; y < scoresTensor.shape[0]; y++) {
     for (let x = 0; x < scoresTensor.shape[1]; x++) {
-      if (scoresTensor.get(y, x) >= scoreThreshold) {
+      if (scoresTensor.arraySync()[y][x] >= scoreThreshold) {
         indices.push(new Point(x, y))
       }
     }
@@ -44,13 +44,13 @@ function extractBoundingBoxes(
       Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale)
     )
 
-    const score = scoresTensor.get(idx.y, idx.x)
+    const score = scoresTensor.arraySync()[idx.y][idx.x]
 
     const region = new MtcnnBox(
-      regionsTensor.get(idx.y, idx.x, 0),
-      regionsTensor.get(idx.y, idx.x, 1),
-      regionsTensor.get(idx.y, idx.x, 2),
-      regionsTensor.get(idx.y, idx.x, 3)
+      regionsTensor.arraySync()[idx.y][idx.x][0],
+      regionsTensor.arraySync()[idx.y][idx.x][1],
+      regionsTensor.arraySync()[idx.y][idx.x][2],
+      regionsTensor.arraySync()[idx.y][idx.x][3]
     )
 
     return {

src/mtcnn/stage2.ts

@@ -56,10 +56,10 @@ export async function stage2(
 
     const regions = indicesNms.map(idx =>
       new MtcnnBox(
-        rnetOuts[indices[idx]].regions.get(0, 0),
-        rnetOuts[indices[idx]].regions.get(0, 1),
-        rnetOuts[indices[idx]].regions.get(0, 2),
-        rnetOuts[indices[idx]].regions.get(0, 3)
+        rnetOuts[indices[idx]].regions.arraySync()[0][0],
+        rnetOuts[indices[idx]].regions.arraySync()[0][1],
+        rnetOuts[indices[idx]].regions.arraySync()[0][2],
+        rnetOuts[indices[idx]].regions.arraySync()[0][3]
       )
     )
 

src/mtcnn/stage3.ts

@@ -40,10 +40,10 @@ export async function stage3(
     .map(({ idx }) => idx)
 
   const filteredRegions = indices.map(idx => new MtcnnBox(
-    onetOuts[idx].regions.get(0, 0),
-    onetOuts[idx].regions.get(0, 1),
-    onetOuts[idx].regions.get(0, 2),
-    onetOuts[idx].regions.get(0, 3)
+    onetOuts[idx].regions.arraySync()[0][0],
+    onetOuts[idx].regions.arraySync()[0][1],
+    onetOuts[idx].regions.arraySync()[0][2],
+    onetOuts[idx].regions.arraySync()[0][3]
   ))
   const filteredBoxes = indices
     .map((idx, i) => inputBoxes[idx].calibrate(filteredRegions[i]))
@@ -69,8 +69,8 @@ export async function stage3(
     points = indicesNms.map((idx, i) =>
       Array(5).fill(0).map((_, ptIdx) =>
         new Point(
-          ((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left) ,
-          ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top)
+          ((onetOuts[idx].points.arraySync()[0][ptIdx] * (finalBoxes[i].width + 1)) + finalBoxes[i].left) ,
+          ((onetOuts[idx].points.arraySync()[0][ptIdx+5] * (finalBoxes[i].height + 1)) + finalBoxes[i].top)
         )
       )
     )

src/ssdMobilenetv1/SsdMobilenetv1.ts

@@ -88,12 +88,12 @@ export class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
     const results = indices
       .map(idx => {
         const [top, bottom] = [
-          Math.max(0, boxes.get(idx, 0)),
-          Math.min(1.0, boxes.get(idx, 2))
+          Math.max(0, boxes.arraySync()[idx][0]),
+          Math.min(1.0, boxes.arraySync()[idx][2])
         ].map(val => val * padY)
         const [left, right] = [
-          Math.max(0, boxes.get(idx, 1)),
-          Math.min(1.0, boxes.get(idx, 3))
+          Math.max(0, boxes.arraySync()[idx][1]),
+          Math.min(1.0, boxes.arraySync()[idx][3])
         ].map(val => val * padX)
         return new FaceDetection(
           scoresData[idx],

src/ssdMobilenetv1/nonMaxSuppression.ts

@@ -49,14 +49,14 @@ export function nonMaxSuppression(
 }
 
 function IOU(boxes: tf.Tensor2D, i: number, j: number) {
-  const yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2))
-  const xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3))
-  const ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2))
-  const xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3))
-  const yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2))
-  const xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3))
-  const ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2))
-  const xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3))
+  const yminI = Math.min(boxes.arraySync()[i][0], boxes.arraySync()[i][2])
+  const xminI = Math.min(boxes.arraySync()[i][1], boxes.arraySync()[i][3])
+  const ymaxI = Math.max(boxes.arraySync()[i][0], boxes.arraySync()[i][2])
+  const xmaxI = Math.max(boxes.arraySync()[i][1], boxes.arraySync()[i][3])
+  const yminJ = Math.min(boxes.arraySync()[j][0], boxes.arraySync()[j][2])
+  const xminJ = Math.min(boxes.arraySync()[j][1], boxes.arraySync()[j][3])
+  const ymaxJ = Math.max(boxes.arraySync()[j][0], boxes.arraySync()[j][2])
+  const xmaxJ = Math.max(boxes.arraySync()[j][1], boxes.arraySync()[j][3])
   const areaI = (ymaxI - yminI) * (xmaxI - xminI)
   const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)
   if (areaI <= 0 || areaJ <= 0) {

test/tests/dom/extractFaceTensors.test.ts

@@ -6,7 +6,7 @@ describe('extractFaceTensors', () => {
   let imgTensor: tf.Tensor3D
 
   beforeAll(async () => {
-    imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/face1.png')))
+    imgTensor = tf.browser.fromPixels(createCanvasFromMedia(await loadImage('test/images/face1.png')))
   })
 
   describe('extracts tensors', () => {

test/tests/faceExpressionNet/faceExpressionNet.test.ts

@@ -57,7 +57,7 @@ describe('faceExpressionNet', () => {
     })
 
     it('computes face landmarks for batch of tf.Tensor3D', async () => {
-      const inputs = [imgElAngry, imgElSurprised].map(el => tf.fromPixels(createCanvasFromMedia(el)))
+      const inputs = [imgElAngry, imgElSurprised].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
 
       const results = await faceExpressionNet.predictExpressions(inputs) as FaceExpressionPrediction[][]
       expect(Array.isArray(results)).toBe(true)
@@ -80,7 +80,7 @@ describe('faceExpressionNet', () => {
     })
 
     it('computes face landmarks for batch of mixed inputs', async () => {
-      const inputs = [imgElAngry, tf.fromPixels(createCanvasFromMedia(imgElSurprised))]
+      const inputs = [imgElAngry, tf.browser.fromPixels(createCanvasFromMedia(imgElSurprised))]
 
       const results = await faceExpressionNet.predictExpressions(inputs) as FaceExpressionPrediction[][]
       expect(Array.isArray(results)).toBe(true)
@@ -125,7 +125,7 @@ describe('faceExpressionNet', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(createCanvasFromMedia(imgElAngry))
+        const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgElAngry))
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensor))
@@ -136,7 +136,7 @@ describe('faceExpressionNet', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.fromPixels(createCanvasFromMedia(el)))
+        const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensors))
@@ -147,7 +147,7 @@ describe('faceExpressionNet', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensor))
@@ -159,7 +159,7 @@ describe('faceExpressionNet', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgElAngry, imgElAngry, imgElAngry]
-          .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensors))
@@ -186,7 +186,7 @@ describe('faceExpressionNet', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(createCanvasFromMedia(imgElAngry))
+        const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgElAngry))
 
         await expectAllTensorsReleased(async () => {
           await faceExpressionNet.predictExpressions(tensor)
@@ -196,7 +196,7 @@ describe('faceExpressionNet', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.fromPixels(createCanvasFromMedia(el)))
+        const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
 
 
         await expectAllTensorsReleased(async () => {
@@ -207,7 +207,7 @@ describe('faceExpressionNet', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           await faceExpressionNet.predictExpressions(tensor)
@@ -218,7 +218,7 @@ describe('faceExpressionNet', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgElAngry, imgElAngry, imgElAngry]
-          .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           await faceExpressionNet.predictExpressions(tensors)

test/tests/faceLandmarkNet/faceLandmark68Net.test.ts

@@ -92,7 +92,7 @@ describe('faceLandmark68Net', () => {
     })
 
     it('computes face landmarks for batch of tf.Tensor3D', async () => {
-      const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(createCanvasFromMedia(el)))
+      const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
 
       const faceLandmarkPositions = [
         faceLandmarkPositions1,
@@ -117,7 +117,7 @@ describe('faceLandmark68Net', () => {
     })
 
     it('computes face landmarks for batch of mixed inputs', async () => {
-      const inputs = [imgEl1, tf.fromPixels(createCanvasFromMedia(imgEl2)), tf.fromPixels(createCanvasFromMedia(imgElRect))]
+      const inputs = [imgEl1, tf.browser.fromPixels(createCanvasFromMedia(imgEl2)), tf.browser.fromPixels(createCanvasFromMedia(imgElRect))]
 
       const faceLandmarkPositions = [
         faceLandmarkPositions1,
@@ -164,7 +164,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
+        const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
 
         await expectAllTensorsReleased(async () => {
           const netInput = new NetInput([tensor])
@@ -176,7 +176,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
+        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
 
         await expectAllTensorsReleased(async () => {
           const netInput = new NetInput(tensors)
@@ -188,7 +188,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensor))
@@ -200,7 +200,7 @@ describe('faceLandmark68Net', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgEl1, imgEl1, imgEl1]
-          .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensors))
@@ -227,7 +227,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
+        const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68Net.detectLandmarks(tensor)
@@ -237,7 +237,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
+        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
 
 
         await expectAllTensorsReleased(async () => {
@@ -248,7 +248,7 @@ describe('faceLandmark68Net', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68Net.detectLandmarks(tensor)
@@ -259,7 +259,7 @@ describe('faceLandmark68Net', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgEl1, imgEl1, imgEl1]
-          .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68Net.detectLandmarks(tensors)

test/tests/faceLandmarkNet/faceLandmark68TinyNet.test.ts

@@ -91,7 +91,7 @@ describe('faceLandmark68TinyNet', () => {
     })
 
     it('computes face landmarks for batch of tf.Tensor3D', async () => {
-      const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(createCanvasFromMedia(el)))
+      const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
 
       const faceLandmarkPositions = [
         faceLandmarkPositions1,
@@ -116,7 +116,7 @@ describe('faceLandmark68TinyNet', () => {
     })
 
     it('computes face landmarks for batch of mixed inputs', async () => {
-      const inputs = [imgEl1, tf.fromPixels(createCanvasFromMedia(imgEl2)), tf.fromPixels(createCanvasFromMedia(imgElRect))]
+      const inputs = [imgEl1, tf.browser.fromPixels(createCanvasFromMedia(imgEl2)), tf.browser.fromPixels(createCanvasFromMedia(imgElRect))]
 
       const faceLandmarkPositions = [
         faceLandmarkPositions1,
@@ -164,7 +164,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
+        const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
 
         await expectAllTensorsReleased(async () => {
           const netInput = new NetInput([tensor])
@@ -176,7 +176,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
+        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
 
         await expectAllTensorsReleased(async () => {
           const netInput = new NetInput(tensors)
@@ -188,7 +188,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensor))
@@ -200,7 +200,7 @@ describe('faceLandmark68TinyNet', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgEl1, imgEl1, imgEl1]
-          .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensors))
@@ -227,7 +227,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
+        const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68TinyNet.detectLandmarks(tensor)
@@ -237,7 +237,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
+        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
 
 
         await expectAllTensorsReleased(async () => {
@@ -248,7 +248,7 @@ describe('faceLandmark68TinyNet', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68TinyNet.detectLandmarks(tensor)
@@ -259,7 +259,7 @@ describe('faceLandmark68TinyNet', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgEl1, imgEl1, imgEl1]
-          .map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           await faceLandmark68TinyNet.detectLandmarks(tensors)

test/tests/faceRecognitionNet/faceRecognitionNet.test.ts

@@ -60,7 +60,7 @@ describe('faceRecognitionNet', () => {
     })
 
     it('computes face descriptors for batch of tf.Tensor3D', async () => {
-      const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(el))
+      const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.browser.fromPixels(el))
 
       const faceDescriptors = [
         faceDescriptor1,
@@ -77,7 +77,7 @@ describe('faceRecognitionNet', () => {
     })
 
     it('computes face descriptors for batch of mixed inputs', async () => {
-      const inputs = [imgEl1, tf.fromPixels(imgEl2), tf.fromPixels(imgElRect)]
+      const inputs = [imgEl1, tf.browser.fromPixels(imgEl2), tf.browser.fromPixels(imgElRect)]
 
       const faceDescriptors = [
         faceDescriptor1,
@@ -116,7 +116,7 @@ describe('faceRecognitionNet', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(imgEl1)
+        const tensor = tf.browser.fromPixels(imgEl1)
 
         await expectAllTensorsReleased(async () => {
           const netInput = new NetInput([tensor])
@@ -128,7 +128,7 @@ describe('faceRecognitionNet', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
+        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(el))
 
         await expectAllTensorsReleased(async () => {
           const netInput = new NetInput(tensors)
@@ -140,7 +140,7 @@ describe('faceRecognitionNet', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.browser.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensor))
@@ -152,7 +152,7 @@ describe('faceRecognitionNet', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgEl1, imgEl1, imgEl1]
-          .map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.browser.fromPixels(el).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensors))
@@ -179,7 +179,7 @@ describe('faceRecognitionNet', () => {
       })
 
       it('single tf.Tensor3D', async () => {
-        const tensor = tf.fromPixels(imgEl1)
+        const tensor = tf.browser.fromPixels(imgEl1)
 
         await expectAllTensorsReleased(async () => {
           await faceRecognitionNet.computeFaceDescriptor(tensor)
@@ -189,7 +189,7 @@ describe('faceRecognitionNet', () => {
       })
 
       it('multiple tf.Tensor3Ds', async () => {
-        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
+        const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(el))
 
 
         await expectAllTensorsReleased(async () => {
@@ -200,7 +200,7 @@ describe('faceRecognitionNet', () => {
       })
 
       it('single batch size 1 tf.Tensor4Ds', async () => {
-        const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
+        const tensor = tf.tidy(() => tf.browser.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
 
         await expectAllTensorsReleased(async () => {
           await faceRecognitionNet.computeFaceDescriptor(tensor)
@@ -211,7 +211,7 @@ describe('faceRecognitionNet', () => {
 
       it('multiple batch size 1 tf.Tensor4Ds', async () => {
         const tensors = [imgEl1, imgEl1, imgEl1]
-          .map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
+          .map(el => tf.tidy(() => tf.browser.fromPixels(el).expandDims())) as tf.Tensor4D[]
 
         await expectAllTensorsReleased(async () => {
           await faceRecognitionNet.computeFaceDescriptor(tensors)

test/tests/ssdMobilenetv1/ssdMobilenetv1.node.test.ts

@@ -15,7 +15,7 @@ describe('ssdMobilenetv1 - node', () => {
   const expectedScores = [0.54, 0.81, 0.97, 0.88, 0.84, 0.61]
 
   beforeAll(async () => {
-    imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
+    imgTensor = tf.browser.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
     expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedSsdBoxes)
   })
 

test/tests/tinyFaceDetector/tinyFaceDetector.node.test.ts

@@ -15,7 +15,7 @@ describe('tinyFaceDetector - node', () => {
   const expectedScores = [0.7, 0.82, 0.93, 0.86, 0.79, 0.84]
 
   beforeAll(async () => {
-    imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
+    imgTensor = tf.browser.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
     expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedTinyFaceDetectorBoxes)
   })