@tensorflow-models/facemesh - npm Package Compare versions

Comparing version 0.0.3 to 0.0.4

dist/box.js

 "use strict";
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
 Object.defineProperty(exports, "__esModule", { value: true });
-const tf = require("@tensorflow/tfjs-core");
-function disposeBox(box) {
-    if (box != null && box.startPoint != null) {
-        box.startEndTensor.dispose();
-        box.startPoint.dispose();
-        box.endPoint.dispose();
-    }
-}
-exports.disposeBox = disposeBox;
-function createBox(startEndTensor, startPoint, endPoint) {
-    return {
-        startEndTensor,
-        startPoint: startPoint != null ? startPoint :
-            tf.slice(startEndTensor, [0, 0], [-1, 2]),
-        endPoint: endPoint != null ? endPoint :
-            tf.slice(startEndTensor, [0, 2], [-1, 2])
-    };
-}
-exports.createBox = createBox;
+var tf = require("@tensorflow/tfjs-core");
 function scaleBoxCoordinates(box, factor) {
-    const newStart = tf.mul(box.startPoint, factor);
-    const newEnd = tf.mul(box.endPoint, factor);
-    return createBox(tf.concat2d([newStart, newEnd], 1));
+    var startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+    var endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+    return { startPoint: startPoint, endPoint: endPoint };
 }
 exports.scaleBoxCoordinates = scaleBoxCoordinates;
 function getBoxSize(box) {
-    return tf.tidy(() => {
-        const diff = tf.sub(box.endPoint, box.startPoint);
-        return tf.abs(diff);
-    });
+    return [
+        Math.abs(box.endPoint[0] - box.startPoint[0]),
+        Math.abs(box.endPoint[1] - box.startPoint[1])
+    ];
 }
 exports.getBoxSize = getBoxSize;
 function getBoxCenter(box) {
-    return tf.tidy(() => {
-        const halfSize = tf.div(tf.sub(box.endPoint, box.startPoint), 2);
-        return tf.add(box.startPoint, halfSize);
-    });
+    return [
+        box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
+        box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
+    ];
 }
 exports.getBoxCenter = getBoxCenter;
 function cutBoxFromImageAndResize(box, image, cropSize) {
-    const height = image.shape[1];
-    const width = image.shape[2];
-    const xyxy = box.startEndTensor;
-    return tf.tidy(() => {
-        const yxyx = tf.concat2d([
-            xyxy.slice([0, 1], [-1, 1]), xyxy.slice([0, 0], [-1, 1]),
-            xyxy.slice([0, 3], [-1, 1]), xyxy.slice([0, 2], [-1, 1])
-        ], 0);
-        const roundedCoords = tf.div(yxyx.transpose(), [height, width, height, width]);
-        return tf.image.cropAndResize(image, roundedCoords, [0], cropSize);
-    });
+    var h = image.shape[1];
+    var w = image.shape[2];
+    var boxes = [[
+            box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,
+            box.endPoint[0] / w
+        ]];
+    return tf.image.cropAndResize(image, boxes, [0], cropSize);
 }
 exports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;
-function enlargeBox(box, factor = 1.5) {
-    return tf.tidy(() => {
-        const center = getBoxCenter(box);
-        const size = getBoxSize(box);
-        const newSize = tf.mul(tf.div(size, 2), factor);
-        const newStart = tf.sub(center, newSize);
-        const newEnd = tf.add(center, newSize);
-        return createBox(tf.concat2d([newStart, newEnd], 1), newStart, newEnd);
-    });
+/**
+ * Enlarges the box by the provided factor.
+ * @param box An object with startPoint and endPoint properties describing the
+ * outlines of the box to be enlarged.
+ * @param factor optional The enlargement factor. Defaults to 1.5
+ */
+function enlargeBox(box, factor) {
+    if (factor === void 0) { factor = 1.5; }
+    var center = getBoxCenter(box);
+    var size = getBoxSize(box);
+    var newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];
+    var startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
+    var endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
+    return { startPoint: startPoint, endPoint: endPoint, landmarks: box.landmarks };
 }
 exports.enlargeBox = enlargeBox;
 //# sourceMappingURL=box.js.map

dist/facemesh.esm.js

@@ -17,2 +17,2 @@ /**
     */
-import{slice,add,div,sub,mul,concat2d,Tensor,tidy,concat,tensor1d,tensor2d,sigmoid,image,browser,env,reshape,stack,abs}from"@tensorflow/tfjs-core";import{loadGraphModel}from"@tensorflow/tfjs-converter";const disposeBox=e=>{e.startEndTensor.dispose(),e.startPoint.dispose(),e.endPoint.dispose()},createBox=e=>({startEndTensor:e,startPoint:slice(e,[0,0],[-1,2]),endPoint:slice(e,[0,2],[-1,2])}),scaleBox=(e,t)=>{const s=mul(e.startPoint,t),o=mul(e.endPoint,t),n=concat2d([s,o],1);return createBox(n)},ANCHORS_CONFIG={strides:[8,16],anchors:[2,6]},NUM_LANDMARKS=6;function generateAnchors(e,t,s){const o=[];for(let n=0;n<s.strides.length;n++){const i=s.strides[n],r=Math.floor((t+i-1)/i),a=Math.floor((e+i-1)/i),c=s.anchors[n];for(let e=0;e<r;e++){const t=i*(e+.5);for(let e=0;e<a;e++){const s=i*(e+.5);for(let e=0;e<c;e++)o.push([s,t])}}}return o}function decodeBounds(e,t,s){const o=slice(e,[0,1],[-1,2]),n=add(o,t),i=slice(e,[0,3],[-1,2]),r=div(i,s),a=div(n,s),c=div(r,2),l=sub(a,c),d=add(a,c),h=mul(l,s),u=mul(d,s);return concat2d([h,u],1)}function getInputTensorDimensions(e){return e instanceof Tensor?[e.shape[0],e.shape[1]]:[e.height,e.width]}function flipFaceHorizontal(e,t){let s,o,n;if(e.topLeft instanceof Tensor&&e.bottomRight instanceof Tensor){const[i,r]=tidy(()=>[concat([sub(t-1,e.topLeft.slice(0,1)),e.topLeft.slice(1,1)]),concat([sub(t-1,e.bottomRight.slice(0,1)),e.bottomRight.slice(1,1)])]);s=i,o=r,null!=e.landmarks&&(n=tidy(()=>{const s=sub(tensor1d([t-1,0]),e.landmarks),o=tensor1d([1,-1]);return mul(s,o)}))}else{const[i,r]=e.topLeft,[a,c]=e.bottomRight;s=[t-1-i,r],o=[t-1-a,c],null!=e.landmarks&&(n=e.landmarks.map(e=>[t-1-e[0],e[1]]))}const i={topLeft:s,bottomRight:o};return null!=n&&(i.landmarks=n),null!=e.probability&&(i.probability=e.probability instanceof Tensor?e.probability.clone():e.probability),i}function scaleBoxFromPrediction(e,t){return tidy(()=>{let s;return s=e.hasOwnProperty("box")?e.box:e,scaleBox(s,t).startEndTensor.squeeze()})}class BlazeFaceModel{constructor(e,t,s,o,n,i){this.blazeFaceModel=e,this.width=t,this.height=s,this.maxFaces=o,this.anchorsData=generateAnchors(t,s,ANCHORS_CONFIG),this.anchors=tensor2d(this.anchorsData),this.inputSizeData=[t,s],this.inputSize=tensor1d([t,s]),this.iouThreshold=n,this.scoreThreshold=i}async getBoundingBoxes(e,t,s=!0){const[o,n,i]=tidy(()=>{const t=e.resizeBilinear([this.width,this.height]),s=mul(sub(t.div(255),.5),2),o=this.blazeFaceModel.predict(s).squeeze(),n=decodeBounds(o,this.anchors,this.inputSize),i=slice(o,[0,0],[-1,1]);return[o,n,sigmoid(i).squeeze()]}),r=console.warn;console.warn=(()=>{});const a=image.nonMaxSuppression(n,i,this.maxFaces,this.iouThreshold,this.scoreThreshold);console.warn=r;const c=await a.array();a.dispose();let l=c.map(e=>slice(n,[e,0],[1,-1]));t||(l=await Promise.all(l.map(async e=>{const t=await e.array();return e.dispose(),t})));const d=e.shape[1],h=e.shape[2];let u;u=t?div([h,d],this.inputSize):[h/this.inputSizeData[0],d/this.inputSizeData[1]];const p=[];for(let e=0;e<l.length;e++){const n=l[e],r=tidy(()=>{const r=createBox(n instanceof Tensor?n:tensor2d(n));if(!s)return r;const a=c[e];let l;return l=t?this.anchors.slice([a,0],[1,2]):this.anchorsData[a],{box:r,landmarks:slice(o,[a,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]),probability:slice(i,[a],[1]),anchor:l}});p.push(r)}return n.dispose(),i.dispose(),o.dispose(),{boxes:p,scaleFactor:u}}async estimateFaces(e,t=!1,s=!1,o=!0){const[,n]=getInputTensorDimensions(e),i=tidy(()=>(e instanceof Tensor||(e=browser.fromPixels(e)),e.toFloat().expandDims(0))),{boxes:r,scaleFactor:a}=await this.getBoundingBoxes(i,t,o);return i.dispose(),t?r.map(e=>{const t=scaleBoxFromPrediction(e,a);let i={topLeft:t.slice([0],[2]),bottomRight:t.slice([2],[2])};if(o){const{landmarks:t,probability:s,anchor:o}=e,n=t.add(o).mul(a);i.landmarks=n,i.probability=s}return s&&(i=flipFaceHorizontal(i,n)),i}):Promise.all(r.map(async e=>{const t=scaleBoxFromPrediction(e,a);let i;if(o){const[s,o,n]=await Promise.all([e.landmarks,t,e.probability].map(async e=>e.array())),r=e.anchor,[c,l]=a,d=s.map(e=>[(e[0]+r[0])*c,(e[1]+r[1])*l]);i={topLeft:o.slice(0,2),bottomRight:o.slice(2),landmarks:d,probability:n},disposeBox(e.box),e.landmarks.dispose(),e.probability.dispose()}else{const e=await t.array();i={topLeft:e.slice(0,2),bottomRight:e.slice(2)}}return t.dispose(),s&&(i=flipFaceHorizontal(i,n)),i}))}}const BLAZEFACE_MODEL_URL="https://tfhub.dev/tensorflow/tfjs-model/blazeface/1/default/1";async function load({maxFaces:e=10,inputWidth:t=128,inputHeight:s=128,iouThreshold:o=.3,scoreThreshold:n=.75}={}){const i=await loadGraphModel(BLAZEFACE_MODEL_URL,{fromTFHub:!0});return new BlazeFaceModel(i,t,s,e,o,n)}const MESH_ANNOTATIONS={silhouette:[10,338,297,332,284,251,389,356,454,323,361,288,397,365,379,378,400,377,152,148,176,149,150,136,172,58,132,93,234,127,162,21,54,103,67,109],lipsUpperOuter:[61,185,40,39,37,0,267,269,270,409,291],lipsLowerOuter:[146,91,181,84,17,314,405,321,375,291],lipsUpperInner:[78,191,80,81,82,13,312,311,310,415,308],lipsLowerInner:[78,95,88,178,87,14,317,402,318,324,308],rightEyeUpper0:[246,161,160,159,158,157,173],rightEyeLower0:[33,7,163,144,145,153,154,155,133],rightEyeUpper1:[247,30,29,27,28,56,190],rightEyeLower1:[130,25,110,24,23,22,26,112,243],rightEyeUpper2:[113,225,224,223,222,221,189],rightEyeLower2:[226,31,228,229,230,231,232,233,244],rightEyeLower3:[143,111,117,118,119,120,121,128,245],rightEyebrowUpper:[156,70,63,105,66,107,55,193],rightEyebrowLower:[35,124,46,53,52,65],leftEyeUpper0:[466,388,387,386,385,384,398],leftEyeLower0:[263,249,390,373,374,380,381,382,362],leftEyeUpper1:[467,260,259,257,258,286,414],leftEyeLower1:[359,255,339,254,253,252,256,341,463],leftEyeUpper2:[342,445,444,443,442,441,413],leftEyeLower2:[446,261,448,449,450,451,452,453,464],leftEyeLower3:[372,340,346,347,348,349,350,357,465],leftEyebrowUpper:[383,300,293,334,296,336,285,417],leftEyebrowLower:[265,353,276,283,282,295],midwayBetweenEyes:[168],noseTip:[1],noseBottom:[2],noseRightCorner:[98],noseLeftCorner:[327],rightCheek:[205],leftCheek:[425]};function disposeBox$1(e){null!=e&&null!=e.startPoint&&(e.startEndTensor.dispose(),e.startPoint.dispose(),e.endPoint.dispose())}function createBox$1(e,t,s){return{startEndTensor:e,startPoint:null!=t?t:slice(e,[0,0],[-1,2]),endPoint:null!=s?s:slice(e,[0,2],[-1,2])}}function scaleBoxCoordinates(e,t){const s=mul(e.startPoint,t),o=mul(e.endPoint,t);return createBox$1(concat2d([s,o],1))}function getBoxSize(e){return tidy(()=>{const t=sub(e.endPoint,e.startPoint);return abs(t)})}function getBoxCenter(e){return tidy(()=>{const t=div(sub(e.endPoint,e.startPoint),2);return add(e.startPoint,t)})}function cutBoxFromImageAndResize(e,t,s){const o=t.shape[1],n=t.shape[2],i=e.startEndTensor;return tidy(()=>{const e=concat2d([i.slice([0,1],[-1,1]),i.slice([0,0],[-1,1]),i.slice([0,3],[-1,1]),i.slice([0,2],[-1,1])],0),r=div(e.transpose(),[o,n,o,n]);return image.cropAndResize(t,r,[0],s)})}function enlargeBox(e,t=1.5){return tidy(()=>{const s=getBoxCenter(e),o=getBoxSize(e),n=mul(div(o,2),t),i=sub(s,n),r=add(s,n);return createBox$1(concat2d([i,r],1),i,r)})}const LANDMARKS_COUNT=468,UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD=.25;class Pipeline{constructor(e,t,s,o,n,i){this.regionsOfInterest=[],this.runsWithoutFaceDetector=0,this.boundingBoxDetector=e,this.meshDetector=t,this.meshWidth=s,this.meshHeight=o,this.maxContinuousChecks=n,this.maxFaces=i}async predict(e){if(this.shouldUpdateRegionsOfInterest()){const t=!0,s=!1,{boxes:o,scaleFactor:n}=await this.boundingBoxDetector.getBoundingBoxes(e,t,s);if(0===o.length)return n.dispose(),this.clearAllRegionsOfInterest(),null;const i=o.map(e=>enlargeBox(scaleBoxCoordinates(e,n)));o.forEach(disposeBox$1),this.updateRegionsOfInterest(i),this.runsWithoutFaceDetector=0}else this.runsWithoutFaceDetector++;return tidy(()=>this.regionsOfInterest.map((t,s)=>{const o=cutBoxFromImageAndResize(t,e,[this.meshHeight,this.meshWidth]).div(255),[,n,i]=this.meshDetector.predict(o),r=reshape(i,[-1,3]),a=div(getBoxSize(t),[this.meshWidth,this.meshHeight]),c=mul(r,a.concat(tensor2d([1],[1,1]),1)).add(t.startPoint.concat(tensor2d([0],[1,1]),1)),l=this.calculateLandmarksBoundingBox(c);return disposeBox$1(this.regionsOfInterest[s]),this.regionsOfInterest[s]=l,{coords:r,scaledCoords:c,box:l,flag:n.squeeze()}}))}updateRegionsOfInterest(e){for(let t=0;t<e.length;t++){const s=e[t],o=this.regionsOfInterest[t];let n=0;if(o&&o.startPoint){const[e,t,i,r]=s.startEndTensor.arraySync()[0],[a,c,l,d]=o.startEndTensor.arraySync()[0],h=Math.max(e,a),u=Math.max(t,c),p=(Math.min(i,l)-h)*(Math.min(r,d)-u);n=p/((i-e)*(r-t)+(l-a)*(d-t)-p)}n>UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD?disposeBox$1(s):(this.regionsOfInterest[t]=s,disposeBox$1(o))}for(let t=e.length;t<this.regionsOfInterest.length;t++)disposeBox$1(this.regionsOfInterest[t]);this.regionsOfInterest=this.regionsOfInterest.slice(0,e.length)}clearRegionOfInterest(e){null!=this.regionsOfInterest[e]&&(disposeBox$1(this.regionsOfInterest[e]),this.regionsOfInterest=[...this.regionsOfInterest.slice(0,e),...this.regionsOfInterest.slice(e+1)])}clearAllRegionsOfInterest(){for(let e=0;e<this.regionsOfInterest.length;e++)disposeBox$1(this.regionsOfInterest[e]);this.regionsOfInterest=[]}shouldUpdateRegionsOfInterest(){const e=this.regionsOfInterest.length,t=0===e;return 1===this.maxFaces||t?t:e!==this.maxFaces&&this.runsWithoutFaceDetector>=this.maxContinuousChecks}calculateLandmarksBoundingBox(e){const t=e.slice([0,0],[LANDMARKS_COUNT,1]),s=e.slice([0,1],[LANDMARKS_COUNT,1]);return enlargeBox(createBox$1(stack([t.min(),s.min(),t.max(),s.max()]).expandDims(0)))}}const 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function load$1({maxContinuousChecks:e=5,detectionConfidence:t=.9,maxFaces:s=10,iouThreshold:o=.3,scoreThreshold:n=.75}={}){const[i,r]=await Promise.all([loadDetectorModel(s,o,n),loadMeshModel()]);return new FaceMesh(i,r,e,t,s)}async function loadDetectorModel(e,t,s){return load({maxFaces:e,iouThreshold:t,scoreThreshold:s})}async function loadMeshModel(){return loadGraphModel(FACEMESH_GRAPHMODEL_PATH,{fromTFHub:!0})}function getInputTensorDimensions$1(e){return e instanceof Tensor?[e.shape[0],e.shape[1]]:[e.height,e.width]}function flipFaceHorizontal$1(e,t){if(e.mesh instanceof Tensor){const[s,o,n,i]=tidy(()=>{const s=tensor1d([t-1,0,0]),o=tensor1d([1,-1,1]);return tidy(()=>[concat([sub(t-1,e.boundingBox.topLeft.slice(0,1)),e.boundingBox.topLeft.slice(1,1)]),concat([sub(t-1,e.boundingBox.bottomRight.slice(0,1)),e.boundingBox.bottomRight.slice(1,1)]),sub(s,e.mesh).mul(o),sub(s,e.scaledMesh).mul(o)])});return Object.assign({},e,{boundingBox:{topLeft:s,bottomRight:o},mesh:n,scaledMesh:i})}return Object.assign({},e,{boundingBox:{topLeft:[t-1-e.boundingBox.topLeft[0],e.boundingBox.topLeft[1]],bottomRight:[t-1-e.boundingBox.bottomRight[0],e.boundingBox.bottomRight[1]]},mesh:e.mesh.map(e=>{const s=e.slice(0);return s[0]=t-1-e[0],s}),scaledMesh:e.scaledMesh.map(e=>{const s=e.slice(0);return s[0]=t-1-e[0],s})})}class FaceMesh{constructor(e,t,s,o,n){this.pipeline=new Pipeline(e,t,MESH_MODEL_INPUT_WIDTH,MESH_MODEL_INPUT_HEIGHT,s,n),this.detectionConfidence=o}static getAnnotations(){return MESH_ANNOTATIONS}static getUVCoords(){return UV_COORDS}async estimateFaces(e,t=!1,s=!1){const[,o]=getInputTensorDimensions$1(e),n=tidy(()=>(e instanceof Tensor||(e=browser.fromPixels(e)),e.toFloat().expandDims(0))),i=env().get("WEBGL_PACK_DEPTHWISECONV");env().set("WEBGL_PACK_DEPTHWISECONV",!0);const r=await this.pipeline.predict(n);return env().set("WEBGL_PACK_DEPTHWISECONV",i),n.dispose(),null!=r&&r.length>0?Promise.all(r.map(async(e,n)=>{const{coords:i,scaledCoords:r,box:a,flag:c}=e;let l=[c];t||(l=l.concat([i,r,a.startPoint,a.endPoint]));const d=await Promise.all(l.map(async e=>e.array())),h=d[0];if(c.dispose(),h<this.detectionConfidence&&this.pipeline.clearRegionOfInterest(n),t){const e={faceInViewConfidence:h,mesh:i,scaledMesh:r,boundingBox:{topLeft:a.startPoint.squeeze(),bottomRight:a.endPoint.squeeze()}};return s?flipFaceHorizontal$1(e,o):e}const[u,p,f,m]=d.slice(1);r.dispose(),i.dispose();let g={faceInViewConfidence:h,boundingBox:{topLeft:f,bottomRight:m},mesh:u,scaledMesh:p};s&&(g=flipFaceHorizontal$1(g,o));const b={};for(const e in MESH_ANNOTATIONS)b[e]=MESH_ANNOTATIONS[e].map(e=>g.scaledMesh[e]);return g.annotations=b,g})):[]}}export{load$1 as load,FaceMesh};
+import{tensor2d as t,tensor1d as e,tidy as n,mul as o,sub as r,concat2d as i,slice as s,sigmoid as a,image as c,div as u,Tensor as h,browser as l,add as f,concat as d,reshape as p,env as m,getBackend as b}from"@tensorflow/tfjs-core";import{loadGraphModel as g}from"@tensorflow/tfjs-converter";var P=function(){return(P=Object.assign||function(t){for(var e,n=1,o=arguments.length;n<o;n++)for(var r in e=arguments[n])Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r]);return t}).apply(this,arguments)};function y(t,e,n,o){return new(n||(n=Promise))((function(r,i){function s(t){try{c(o.next(t))}catch(t){i(t)}}function a(t){try{c(o.throw(t))}catch(t){i(t)}}function c(t){var e;t.done?r(t.value):(e=t.value,e instanceof n?e:new n((function(t){t(e)}))).then(s,a)}c((o=o.apply(t,e||[])).next())}))}function v(t,e){var n,o,r,i,s={label:0,sent:function(){if(1&r[0])throw r[1];return r[1]},trys:[],ops:[]};return i={next:a(0),throw:a(1),return:a(2)},"function"==typeof Symbol&&(i[Symbol.iterator]=function(){return this}),i;function a(i){return function(a){return function(i){if(n)throw new TypeError("Generator is already executing.");for(;s;)try{if(n=1,o&&(r=2&i[0]?o.return:i[0]?o.throw||((r=o.return)&&r.call(o),0):o.next)&&!(r=r.call(o,i[1])).done)return r;switch(o=0,r&&(i=[2&i[0],r.value]),i[0]){case 0:case 1:r=i;break;case 4:return s.label++,{value:i[1],done:!1};case 5:s.label++,o=i[1],i=[0];continue;case 7:i=s.ops.pop(),s.trys.pop();continue;default:if(!(r=s.trys,(r=r.length>0&&r[r.length-1])||6!==i[0]&&2!==i[0])){s=0;continue}if(3===i[0]&&(!r||i[1]>r[0]&&i[1]<r[3])){s.label=i[1];break}if(6===i[0]&&s.label<r[1]){s.label=r[1],r=i;break}if(r&&s.label<r[2]){s.label=r[2],s.ops.push(i);break}r[2]&&s.ops.pop(),s.trys.pop();continue}i=e.call(t,s)}catch(t){i=[6,t],o=0}finally{n=r=0}if(5&i[0])throw i[1];return{value:i[0]?i[1]:void 0,done:!0}}([i,a])}}}const w=t=>({startEndTensor:t,startPoint:s(t,[0,0],[-1,2]),endPoint:s(t,[0,2],[-1,2])}),x={strides:[8,16],anchors:[2,6]};function E(t,i){let s,a,c;if(t.topLeft instanceof h&&t.bottomRight instanceof h){const[u,h]=n(()=>[d([r(i-1,t.topLeft.slice(0,1)),t.topLeft.slice(1,1)]),d([r(i-1,t.bottomRight.slice(0,1)),t.bottomRight.slice(1,1)])]);s=u,a=h,null!=t.landmarks&&(c=n(()=>{const n=r(e([i-1,0]),t.landmarks),s=e([1,-1]);return o(n,s)}))}else{const[e,n]=t.topLeft,[o,r]=t.bottomRight;s=[i-1-e,n],a=[i-1-o,r],null!=t.landmarks&&(c=t.landmarks.map(t=>[i-1-t[0],t[1]]))}const u={topLeft:s,bottomRight:a};return null!=c&&(u.landmarks=c),null!=t.probability&&(u.probability=t.probability instanceof h?t.probability.clone():t.probability),u}function L(t,e){return n(()=>{let n;return n=t.hasOwnProperty("box")?t.box:t,((t,e)=>{const n=o(t.startPoint,e),r=o(t.endPoint,e),s=i([n,r],1);return w(s)})(n,e).startEndTensor.squeeze()})}class M{constructor(n,o,r,i,s,a){this.blazeFaceModel=n,this.width=o,this.height=r,this.maxFaces=i,this.anchorsData=function(t,e,n){const o=[];for(let r=0;r<n.strides.length;r++){const i=n.strides[r],s=Math.floor((e+i-1)/i),a=Math.floor((t+i-1)/i),c=n.anchors[r];for(let t=0;t<s;t++){const e=i*(t+.5);for(let t=0;t<a;t++){const n=i*(t+.5);for(let t=0;t<c;t++)o.push([n,e])}}}return o}(o,r,x),this.anchors=t(this.anchorsData),this.inputSizeData=[o,r],this.inputSize=e([o,r]),this.iouThreshold=s,this.scoreThreshold=a}async getBoundingBoxes(e,l,d=!0){const[p,m,b]=n(()=>{const t=e.resizeBilinear([this.width,this.height]),n=o(r(t.div(255),.5),2),c=this.blazeFaceModel.predict(n).squeeze(),h=function(t,e,n){const a=s(t,[0,1],[-1,2]),c=f(a,e),h=s(t,[0,3],[-1,2]),l=u(h,n),d=u(c,n),p=u(l,2),m=r(d,p),b=f(d,p),g=o(m,n),P=o(b,n);return i([g,P],1)}(c,this.anchors,this.inputSize),l=s(c,[0,0],[-1,1]);return[c,h,a(l).squeeze()]}),g=console.warn;console.warn=()=>{};const P=c.nonMaxSuppression(m,b,this.maxFaces,this.iouThreshold,this.scoreThreshold);console.warn=g;const y=await P.array();P.dispose();let v=y.map(t=>s(m,[t,0],[1,-1]));l||(v=await Promise.all(v.map(async t=>{const e=await t.array();return t.dispose(),e})));const x=e.shape[1],E=e.shape[2];let L;L=l?u([E,x],this.inputSize):[E/this.inputSizeData[0],x/this.inputSizeData[1]];const M=[];for(let e=0;e<v.length;e++){const o=v[e],r=n(()=>{const n=w(o instanceof h?o:t(o));if(!d)return n;const r=y[e];let i;return i=l?this.anchors.slice([r,0],[1,2]):this.anchorsData[r],{box:n,landmarks:s(p,[r,5],[1,-1]).squeeze().reshape([6,-1]),probability:s(b,[r],[1]),anchor:i}});M.push(r)}return m.dispose(),b.dispose(),p.dispose(),{boxes:M,scaleFactor:L}}async estimateFaces(t,e=!1,o=!1,r=!0){const[,i]=function(t){return t instanceof h?[t.shape[0],t.shape[1]]:[t.height,t.width]}(t),s=n(()=>(t instanceof h||(t=l.fromPixels(t)),t.toFloat().expandDims(0))),{boxes:a,scaleFactor:c}=await this.getBoundingBoxes(s,e,r);return s.dispose(),e?a.map(t=>{const e=L(t,c);let n={topLeft:e.slice([0],[2]),bottomRight:e.slice([2],[2])};if(r){const{landmarks:e,probability:o,anchor:r}=t,i=e.add(r).mul(c);n.landmarks=i,n.probability=o}return o&&(n=E(n,i)),n}):Promise.all(a.map(async t=>{const e=L(t,c);let n;if(r){const[o,r,i]=await Promise.all([t.landmarks,e,t.probability].map(async t=>t.array())),s=t.anchor,[a,u]=c,h=o.map(t=>[(t[0]+s[0])*a,(t[1]+s[1])*u]);n={topLeft:r.slice(0,2),bottomRight:r.slice(2),landmarks:h,probability:i},(t=>{t.startEndTensor.dispose(),t.startPoint.dispose(),t.endPoint.dispose()})(t.box),t.landmarks.dispose(),t.probability.dispose()}else{const t=await e.array();n={topLeft:t.slice(0,2),bottomRight:t.slice(2)}}return e.dispose(),o&&(n=E(n,i)),n}))}}async function k({maxFaces:t=10,inputWidth:e=128,inputHeight:n=128,iouThreshold:o=.3,scoreThreshold:r=.75}={}){const i=await g("https://tfhub.dev/tensorflow/tfjs-model/blazeface/1/default/1",{fromTFHub:!0});return new M(i,e,n,t,o,r)}var O={silhouette:[10,338,297,332,284,251,389,356,454,323,361,288,397,365,379,378,400,377,152,148,176,149,150,136,172,58,132,93,234,127,162,21,54,103,67,109],lipsUpperOuter:[61,185,40,39,37,0,267,269,270,409,291],lipsLowerOuter:[146,91,181,84,17,314,405,321,375,291],lipsUpperInner:[78,191,80,81,82,13,312,311,310,415,308],lipsLowerInner:[78,95,88,178,87,14,317,402,318,324,308],rightEyeUpper0:[246,161,160,159,158,157,173],rightEyeLower0:[33,7,163,144,145,153,154,155,133],rightEyeUpper1:[247,30,29,27,28,56,190],rightEyeLower1:[130,25,110,24,23,22,26,112,243],rightEyeUpper2:[113,225,224,223,222,221,189],rightEyeLower2:[226,31,228,229,230,231,232,233,244],rightEyeLower3:[143,111,117,118,119,120,121,128,245],rightEyebrowUpper:[156,70,63,105,66,107,55,193],rightEyebrowLower:[35,124,46,53,52,65],leftEyeUpper0:[466,388,387,386,385,384,398],leftEyeLower0:[263,249,390,373,374,380,381,382,362],leftEyeUpper1:[467,260,259,257,258,286,414],leftEyeLower1:[359,255,339,254,253,252,256,341,463],leftEyeUpper2:[342,445,444,443,442,441,413],leftEyeLower2:[446,261,448,449,450,451,452,453,464],leftEyeLower3:[372,340,346,347,348,349,350,357,465],leftEyebrowUpper:[383,300,293,334,296,336,285,417],leftEyebrowLower:[265,353,276,283,282,295],midwayBetweenEyes:[168],noseTip:[1],noseBottom:[2],noseRightCorner:[98],noseLeftCorner:[327],rightCheek:[205],leftCheek:[425]};function B(t){return[Math.abs(t.endPoint[0]-t.startPoint[0]),Math.abs(t.endPoint[1]-t.startPoint[1])]}function I(t){return[t.startPoint[0]+(t.endPoint[0]-t.startPoint[0])/2,t.startPoint[1]+(t.endPoint[1]-t.startPoint[1])/2]}function C(t,e){void 0===e&&(e=1.5);var n=I(t),o=B(t),r=[e*o[0]/2,e*o[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:t.landmarks}}function R(t,e){var n,o=Math.PI/2-Math.atan2(-(e[1]-t[1]),e[0]-t[0]);return(n=o)-2*Math.PI*Math.floor((n+Math.PI)/(2*Math.PI))}function F(t,e){return[[1,0,t],[0,1,e],[0,0,1]]}function T(t,e){for(var n=0,o=0;o<t.length;o++)n+=t[o]*e[o];return n}function D(t,e){for(var n=[],o=0;o<t.length;o++)n.push(t[o][e]);return n}function z(t,e){for(var n=[],o=t.length,r=0;r<o;r++){n.push([]);for(var i=0;i<o;i++)n[r].push(T(t[r],D(e,i)))}return n}function S(t,e){var n=Math.cos(t),o=Math.sin(t),r=[[n,-o,0],[o,n,0],[0,0,1]],i=z(F(e[0],e[1]),r);return z(i,F(-e[0],-e[1]))}var W=[1,168],U=[3,2],H=function(){function e(t,e,n,o,r,i){this.regionsOfInterest=[],this.runsWithoutFaceDetector=0,this.boundingBoxDetector=t,this.meshDetector=e,this.meshWidth=n,this.meshHeight=o,this.maxContinuousChecks=r,this.maxFaces=i}return e.prototype.transformRawCoords=function(t,e,n,o){var r,i,s,a,c=this,u=B({startPoint:e.startPoint,endPoint:e.endPoint}),h=[u[0]/this.meshWidth,u[1]/this.meshHeight],l=t.map((function(t){return[h[0]*(t[0]-c.meshWidth/2),h[1]*(t[1]-c.meshHeight/2),t[2]]})),f=S(n,[0,0]),d=l.map((function(t){return function(t,e){return[T(t,e[0]),T(t,e[1])]}(t,f).concat([t[2]])})),p=(i=[[(r=o)[0][0],r[1][0]],[r[0][1],r[1][1]]],s=[r[0][2],r[1][2]],a=[-T(i[0],s),-T(i[1],s)],[i[0].concat(a[0]),i[1].concat(a[1]),[0,0,1]]),m=I({startPoint:e.startPoint,endPoint:e.endPoint}).concat([1]),b=[T(m,p[0]),T(m,p[1])];return d.map((function(t){return[t[0]+b[0],t[1]+b[1],t[2]]}))},e.prototype.predict=function(e){return y(this,void 0,void 0,(function(){var o,r,i,s,a=this;return v(this,(function(u){switch(u.label){case 0:return this.shouldUpdateRegionsOfInterest()?(!1,!0,[4,this.boundingBoxDetector.getBoundingBoxes(e,!1,!0)]):[3,2];case 1:return o=u.sent(),r=o.boxes,i=o.scaleFactor,0===r.length?(this.regionsOfInterest=[],[2,null]):(s=r.map((function(t){var e,n,o={startPoint:t.box.startPoint.squeeze().arraySync(),endPoint:t.box.endPoint.squeeze().arraySync()},r=C((n=i,{startPoint:[(e=o).startPoint[0]*n[0],e.startPoint[1]*n[1]],endPoint:[e.endPoint[0]*n[0],e.endPoint[1]*n[1]]}));return P({},r,{landmarks:t.landmarks.arraySync()})})),r.forEach((function(t){null!=t&&null!=t.startPoint&&(t.startEndTensor.dispose(),t.startPoint.dispose(),t.endPoint.dispose())})),this.updateRegionsOfInterest(s),this.runsWithoutFaceDetector=0,[3,3]);case 2:this.runsWithoutFaceDetector++,u.label=3;case 3:return[2,n((function(){return a.regionsOfInterest.map((function(n,o){var r;if(468===n.landmarks.length){var i=W[0],s=W[1];r=R(n.landmarks[i],n.landmarks[s])}else{i=U[0],s=U[1];r=R(n.landmarks[i],n.landmarks[s])}var u=I({startPoint:n.startPoint,endPoint:n.endPoint}),h=[u[0]/e.shape[2],u[1]/e.shape[1]],l=c.rotateWithOffset(e,r,0,h),f=S(-r,u),d=function(t,e,n){var o=e.shape[1],r=e.shape[2],i=[[t.startPoint[1]/o,t.startPoint[0]/r,t.endPoint[1]/o,t.endPoint[0]/r]];return c.cropAndResize(e,i,[0],n)}({startPoint:n.startPoint,endPoint:n.endPoint},l,[a.meshHeight,a.meshWidth]).div(255),m=a.meshDetector.predict(d),b=m[1],g=m[2],y=p(g,[-1,3]),v=y.arraySync(),w=a.transformRawCoords(v,n,r,f),x=t(w),E=a.calculateLandmarksBoundingBox(w);return a.regionsOfInterest[o]=P({},E,{landmarks:x.arraySync()}),{coords:y,scaledCoords:x,box:E,flag:b.squeeze()}}))}))]}}))}))},e.prototype.updateRegionsOfInterest=function(t){for(var e=0;e<t.length;e++){var n=t[e],o=this.regionsOfInterest[e],r=0;if(o&&o.startPoint){var i=n.startPoint,s=i[0],a=i[1],c=n.endPoint,u=c[0],h=c[1],l=o.startPoint,f=l[0],d=l[1],p=o.endPoint,m=p[0],b=p[1],g=Math.max(s,f),P=Math.max(a,d),y=(Math.min(u,m)-g)*(Math.min(h,b)-P);r=y/((u-s)*(h-a)+(m-f)*(b-a)-y)}r<.25&&(this.regionsOfInterest[e]=n)}this.regionsOfInterest=this.regionsOfInterest.slice(0,t.length)},e.prototype.clearRegionOfInterest=function(t){null!=this.regionsOfInterest[t]&&(this.regionsOfInterest=this.regionsOfInterest.slice(0,t).concat(this.regionsOfInterest.slice(t+1)))},e.prototype.shouldUpdateRegionsOfInterest=function(){var t=this.regionsOfInterest.length,e=0===t;return 1===this.maxFaces||e?e:t!==this.maxFaces&&this.runsWithoutFaceDetector>=this.maxContinuousChecks},e.prototype.calculateLandmarksBoundingBox=function(t){var e=t.map((function(t){return t[0]})),n=t.map((function(t){return t[1]})),o={startPoint:[Math.min.apply(Math,e),Math.min.apply(Math,n)],endPoint:[Math.max.apply(Math,e),Math.max.apply(Math,n)]};return 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q(t){var e=void 0===t?{}:t,n=e.maxContinuousChecks,o=void 0===n?5:n,r=e.detectionConfidence,i=void 0===r?.9:r,s=e.maxFaces,a=void 0===s?10:s,c=e.iouThreshold,u=void 0===c?.3:c,h=e.scoreThreshold,l=void 0===h?.75:h;return y(this,void 0,void 0,(function(){var t,e,n;return v(this,(function(r){switch(r.label){case 0:return[4,Promise.all([V(a,u,l),_()])];case 1:return t=r.sent(),e=t[0],n=t[1],[2,new G(e,n,o,i,a)]}}))}))}function V(t,e,n){return y(this,void 0,void 0,(function(){return v(this,(function(o){return[2,k({maxFaces:t,iouThreshold:e,scoreThreshold:n})]}))}))}function _(){return y(this,void 0,void 0,(function(){return v(this,(function(t){return[2,g("https://tfhub.dev/mediapipe/tfjs-model/facemesh/1/default/1",{fromTFHub:!0})]}))}))}function A(t,o){if(t.mesh instanceof h){var i=n((function(){var i=e([o-1,0,0]),s=e([1,-1,1]);return n((function(){return[d([r(o-1,t.boundingBox.topLeft.slice(0,1)),t.boundingBox.topLeft.slice(1,1)]),d([r(o-1,t.boundingBox.bottomRight.slice(0,1)),t.boundingBox.bottomRight.slice(1,1)]),r(i,t.mesh).mul(s),r(i,t.scaledMesh).mul(s)]}))})),s=i[0],a=i[1],c=i[2],u=i[3];return Object.assign({},t,{boundingBox:{topLeft:s,bottomRight:a},mesh:c,scaledMesh:u})}return Object.assign({},t,{boundingBox:{topLeft:[o-1-t.boundingBox.topLeft[0],t.boundingBox.topLeft[1]],bottomRight:[o-1-t.boundingBox.bottomRight[0],t.boundingBox.bottomRight[1]]},mesh:t.mesh.map((function(t){var e=t.slice(0);return e[0]=o-1-t[0],e})),scaledMesh:t.scaledMesh.map((function(t){var e=t.slice(0);return e[0]=o-1-t[0],e}))})}var G=function(){function t(t,e,n,o,r){this.pipeline=new H(t,e,192,192,n,r),this.detectionConfidence=o}return t.getAnnotations=function(){return O},t.getUVCoords=function(){return j},t.prototype.estimateFaces=function(t,o,r){return void 0===o&&(o=!1),void 0===r&&(r=!1),y(this,void 0,void 0,(function(){var i,s,a,c,u,f=this;return v(this,(function(d){switch(d.label){case 0:return i=function(t){return t instanceof h?[t.shape[0],t.shape[1]]:[t.height,t.width]}(t),s=i[1],a=n((function(){return t instanceof h||(t=l.fromPixels(t)),t.toFloat().expandDims(0)})),"webgl"!==b()?[3,2]:(u=m().get("WEBGL_PACK_DEPTHWISECONV"),m().set("WEBGL_PACK_DEPTHWISECONV",!0),[4,this.pipeline.predict(a)]);case 1:return c=d.sent(),m().set("WEBGL_PACK_DEPTHWISECONV",u),[3,4];case 2:return[4,this.pipeline.predict(a)];case 3:c=d.sent(),d.label=4;case 4:return a.dispose(),null!=c&&c.length>0?[2,Promise.all(c.map((function(t,n){return y(f,void 0,void 0,(function(){var i,a,c,u,h,l,f,d,p,m,b,g,P,w,x=this;return v(this,(function(E){switch(E.label){case 0:return i=t.coords,a=t.scaledCoords,c=t.box,u=t.flag,h=[u],o||(h=h.concat([i,a])),[4,Promise.all(h.map((function(t){return y(x,void 0,void 0,(function(){return v(this,(function(e){return[2,t.array()]}))}))})))];case 1:if(l=E.sent(),f=l[0],u.dispose(),f<this.detectionConfidence&&this.pipeline.clearRegionOfInterest(n),o)return d={faceInViewConfidence:f,mesh:i,scaledMesh:a,boundingBox:{topLeft:e(c.startPoint),bottomRight:e(c.endPoint)}},r?[2,A(d,s)]:[2,d];for(w in p=l.slice(1),m=p[0],b=p[1],a.dispose(),i.dispose(),g={faceInViewConfidence:f,boundingBox:{topLeft:c.startPoint,bottomRight:c.endPoint},mesh:m,scaledMesh:b},r&&(g=A(g,s)),P={},O)P[w]=O[w].map((function(t){return g.scaledMesh[t]}));return g.annotations=P,[2,g]}}))}))})))]:[2,[]]}}))}))},t}();export{G as FaceMesh,q as load};

dist/facemesh.min.js

@@ -17,2 +17,2 @@ /**
     */
-!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?e(exports,require("@tensorflow/tfjs-core"),require("@tensorflow/tfjs-converter")):"function"==typeof define&&define.amd?define(["exports","@tensorflow/tfjs-core","@tensorflow/tfjs-converter"],e):e(t.facemesh={},t.tf,t.tf)}(this,function(t,e,s){"use strict";const n=t=>{t.startEndTensor.dispose(),t.startPoint.dispose(),t.endPoint.dispose()},o=t=>({startEndTensor:t,startPoint:e.slice(t,[0,0],[-1,2]),endPoint:e.slice(t,[0,2],[-1,2])}),i=(t,s)=>{const n=e.mul(t.startPoint,s),i=e.mul(t.endPoint,s),r=e.concat2d([n,i],1);return o(r)},r={strides:[8,16],anchors:[2,6]},a=6;function c(t,s){let n,o,i;if(t.topLeft instanceof e.Tensor&&t.bottomRight instanceof e.Tensor){const[r,a]=e.tidy(()=>[e.concat([e.sub(s-1,t.topLeft.slice(0,1)),t.topLeft.slice(1,1)]),e.concat([e.sub(s-1,t.bottomRight.slice(0,1)),t.bottomRight.slice(1,1)])]);n=r,o=a,null!=t.landmarks&&(i=e.tidy(()=>{const n=e.sub(e.tensor1d([s-1,0]),t.landmarks),o=e.tensor1d([1,-1]);return e.mul(n,o)}))}else{const[e,r]=t.topLeft,[a,c]=t.bottomRight;n=[s-1-e,r],o=[s-1-a,c],null!=t.landmarks&&(i=t.landmarks.map(t=>[s-1-t[0],t[1]]))}const r={topLeft:n,bottomRight:o};return null!=i&&(r.landmarks=i),null!=t.probability&&(r.probability=t.probability instanceof e.Tensor?t.probability.clone():t.probability),r}function l(t,s){return e.tidy(()=>{let e;return e=t.hasOwnProperty("box")?t.box:t,i(e,s).startEndTensor.squeeze()})}class h{constructor(t,s,n,o,i,a){this.blazeFaceModel=t,this.width=s,this.height=n,this.maxFaces=o,this.anchorsData=function(t,e,s){const n=[];for(let o=0;o<s.strides.length;o++){const i=s.strides[o],r=Math.floor((e+i-1)/i),a=Math.floor((t+i-1)/i),c=s.anchors[o];for(let t=0;t<r;t++){const e=i*(t+.5);for(let t=0;t<a;t++){const s=i*(t+.5);for(let t=0;t<c;t++)n.push([s,e])}}}return n}(s,n,r),this.anchors=e.tensor2d(this.anchorsData),this.inputSizeData=[s,n],this.inputSize=e.tensor1d([s,n]),this.iouThreshold=i,this.scoreThreshold=a}async getBoundingBoxes(t,s,n=!0){const[i,r,c]=e.tidy(()=>{const s=t.resizeBilinear([this.width,this.height]),n=e.mul(e.sub(s.div(255),.5),2),o=this.blazeFaceModel.predict(n).squeeze(),i=function(t,s,n){const o=e.slice(t,[0,1],[-1,2]),i=e.add(o,s),r=e.slice(t,[0,3],[-1,2]),a=e.div(r,n),c=e.div(i,n),l=e.div(a,2),h=e.sub(c,l),d=e.add(c,l),u=e.mul(h,n),f=e.mul(d,n);return e.concat2d([u,f],1)}(o,this.anchors,this.inputSize),r=e.slice(o,[0,0],[-1,1]);return[o,i,e.sigmoid(r).squeeze()]}),l=console.warn;console.warn=(()=>{});const h=e.image.nonMaxSuppression(r,c,this.maxFaces,this.iouThreshold,this.scoreThreshold);console.warn=l;const d=await h.array();h.dispose();let u=d.map(t=>e.slice(r,[t,0],[1,-1]));s||(u=await Promise.all(u.map(async t=>{const e=await t.array();return t.dispose(),e})));const f=t.shape[1],p=t.shape[2];let m;m=s?e.div([p,f],this.inputSize):[p/this.inputSizeData[0],f/this.inputSizeData[1]];const g=[];for(let t=0;t<u.length;t++){const r=u[t],l=e.tidy(()=>{const l=o(r instanceof e.Tensor?r:e.tensor2d(r));if(!n)return l;const h=d[t];let u;return u=s?this.anchors.slice([h,0],[1,2]):this.anchorsData[h],{box:l,landmarks:e.slice(i,[h,a-1],[1,-1]).squeeze().reshape([a,-1]),probability:e.slice(c,[h],[1]),anchor:u}});g.push(l)}return r.dispose(),c.dispose(),i.dispose(),{boxes:g,scaleFactor:m}}async estimateFaces(t,s=!1,o=!1,i=!0){const[,r]=function(t){return t instanceof e.Tensor?[t.shape[0],t.shape[1]]:[t.height,t.width]}(t),a=e.tidy(()=>(t instanceof e.Tensor||(t=e.browser.fromPixels(t)),t.toFloat().expandDims(0))),{boxes:h,scaleFactor:d}=await this.getBoundingBoxes(a,s,i);return a.dispose(),s?h.map(t=>{const e=l(t,d);let s={topLeft:e.slice([0],[2]),bottomRight:e.slice([2],[2])};if(i){const{landmarks:e,probability:n,anchor:o}=t,i=e.add(o).mul(d);s.landmarks=i,s.probability=n}return o&&(s=c(s,r)),s}):Promise.all(h.map(async t=>{const e=l(t,d);let s;if(i){const[o,i,r]=await Promise.all([t.landmarks,e,t.probability].map(async t=>t.array())),a=t.anchor,[c,l]=d,h=o.map(t=>[(t[0]+a[0])*c,(t[1]+a[1])*l]);s={topLeft:i.slice(0,2),bottomRight:i.slice(2),landmarks:h,probability:r},n(t.box),t.landmarks.dispose(),t.probability.dispose()}else{const t=await e.array();s={topLeft:t.slice(0,2),bottomRight:t.slice(2)}}return e.dispose(),o&&(s=c(s,r)),s}))}}const d="https://tfhub.dev/tensorflow/tfjs-model/blazeface/1/default/1";const u={silhouette:[10,338,297,332,284,251,389,356,454,323,361,288,397,365,379,378,400,377,152,148,176,149,150,136,172,58,132,93,234,127,162,21,54,103,67,109],lipsUpperOuter:[61,185,40,39,37,0,267,269,270,409,291],lipsLowerOuter:[146,91,181,84,17,314,405,321,375,291],lipsUpperInner:[78,191,80,81,82,13,312,311,310,415,308],lipsLowerInner:[78,95,88,178,87,14,317,402,318,324,308],rightEyeUpper0:[246,161,160,159,158,157,173],rightEyeLower0:[33,7,163,144,145,153,154,155,133],rightEyeUpper1:[247,30,29,27,28,56,190],rightEyeLower1:[130,25,110,24,23,22,26,112,243],rightEyeUpper2:[113,225,224,223,222,221,189],rightEyeLower2:[226,31,228,229,230,231,232,233,244],rightEyeLower3:[143,111,117,118,119,120,121,128,245],rightEyebrowUpper:[156,70,63,105,66,107,55,193],rightEyebrowLower:[35,124,46,53,52,65],leftEyeUpper0:[466,388,387,386,385,384,398],leftEyeLower0:[263,249,390,373,374,380,381,382,362],leftEyeUpper1:[467,260,259,257,258,286,414],leftEyeLower1:[359,255,339,254,253,252,256,341,463],leftEyeUpper2:[342,445,444,443,442,441,413],leftEyeLower2:[446,261,448,449,450,451,452,453,464],leftEyeLower3:[372,340,346,347,348,349,350,357,465],leftEyebrowUpper:[383,300,293,334,296,336,285,417],leftEyebrowLower:[265,353,276,283,282,295],midwayBetweenEyes:[168],noseTip:[1],noseBottom:[2],noseRightCorner:[98],noseLeftCorner:[327],rightCheek:[205],leftCheek:[425]};function f(t){null!=t&&null!=t.startPoint&&(t.startEndTensor.dispose(),t.startPoint.dispose(),t.endPoint.dispose())}function p(t,s,n){return{startEndTensor:t,startPoint:null!=s?s:e.slice(t,[0,0],[-1,2]),endPoint:null!=n?n:e.slice(t,[0,2],[-1,2])}}function m(t){return e.tidy(()=>{const s=e.sub(t.endPoint,t.startPoint);return e.abs(s)})}function g(t,s=1.5){return e.tidy(()=>{const n=function(t){return e.tidy(()=>{const s=e.div(e.sub(t.endPoint,t.startPoint),2);return e.add(t.startPoint,s)})}(t),o=m(t),i=e.mul(e.div(o,2),s),r=e.sub(n,i),a=e.add(n,i);return p(e.concat2d([r,a],1),r,a)})}const b=468,y=.25;class x{constructor(t,e,s,n,o,i){this.regionsOfInterest=[],this.runsWithoutFaceDetector=0,this.boundingBoxDetector=t,this.meshDetector=e,this.meshWidth=s,this.meshHeight=n,this.maxContinuousChecks=o,this.maxFaces=i}async predict(t){if(this.shouldUpdateRegionsOfInterest()){const s=!0,n=!1,{boxes:o,scaleFactor:i}=await this.boundingBoxDetector.getBoundingBoxes(t,s,n);if(0===o.length)return i.dispose(),this.clearAllRegionsOfInterest(),null;const r=o.map(t=>g(function(t,s){const n=e.mul(t.startPoint,s),o=e.mul(t.endPoint,s);return p(e.concat2d([n,o],1))}(t,i)));o.forEach(f),this.updateRegionsOfInterest(r),this.runsWithoutFaceDetector=0}else this.runsWithoutFaceDetector++;return e.tidy(()=>this.regionsOfInterest.map((s,n)=>{const o=function(t,s,n){const o=s.shape[1],i=s.shape[2],r=t.startEndTensor;return e.tidy(()=>{const t=e.concat2d([r.slice([0,1],[-1,1]),r.slice([0,0],[-1,1]),r.slice([0,3],[-1,1]),r.slice([0,2],[-1,1])],0),a=e.div(t.transpose(),[o,i,o,i]);return e.image.cropAndResize(s,a,[0],n)})}(s,t,[this.meshHeight,this.meshWidth]).div(255),[,i,r]=this.meshDetector.predict(o),a=e.reshape(r,[-1,3]),c=e.div(m(s),[this.meshWidth,this.meshHeight]),l=e.mul(a,c.concat(e.tensor2d([1],[1,1]),1)).add(s.startPoint.concat(e.tensor2d([0],[1,1]),1)),h=this.calculateLandmarksBoundingBox(l);return f(this.regionsOfInterest[n]),this.regionsOfInterest[n]=h,{coords:a,scaledCoords:l,box:h,flag:i.squeeze()}}))}updateRegionsOfInterest(t){for(let e=0;e<t.length;e++){const s=t[e],n=this.regionsOfInterest[e];let o=0;if(n&&n.startPoint){const[t,e,i,r]=s.startEndTensor.arraySync()[0],[a,c,l,h]=n.startEndTensor.arraySync()[0],d=Math.max(t,a),u=Math.max(e,c),f=(Math.min(i,l)-d)*(Math.min(r,h)-u);o=f/((i-t)*(r-e)+(l-a)*(h-e)-f)}o>y?f(s):(this.regionsOfInterest[e]=s,f(n))}for(let e=t.length;e<this.regionsOfInterest.length;e++)f(this.regionsOfInterest[e]);this.regionsOfInterest=this.regionsOfInterest.slice(0,t.length)}clearRegionOfInterest(t){null!=this.regionsOfInterest[t]&&(f(this.regionsOfInterest[t]),this.regionsOfInterest=[...this.regionsOfInterest.slice(0,t),...this.regionsOfInterest.slice(t+1)])}clearAllRegionsOfInterest(){for(let t=0;t<this.regionsOfInterest.length;t++)f(this.regionsOfInterest[t]);this.regionsOfInterest=[]}shouldUpdateRegionsOfInterest(){const t=this.regionsOfInterest.length,e=0===t;return 1===this.maxFaces||e?e:t!==this.maxFaces&&this.runsWithoutFaceDetector>=this.maxContinuousChecks}calculateLandmarksBoundingBox(t){const s=t.slice([0,0],[b,1]),n=t.slice([0,1],[b,1]);return g(p(e.stack([s.min(),n.min(),s.max(),n.max()]).expandDims(0)))}}const 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s=t=>({startEndTensor:t,startPoint:e.slice(t,[0,0],[-1,2]),endPoint:e.slice(t,[0,2],[-1,2])}),a={strides:[8,16],anchors:[2,6]};function c(t,n){let o,r,i;if(t.topLeft instanceof e.Tensor&&t.bottomRight instanceof e.Tensor){const[s,a]=e.tidy(()=>[e.concat([e.sub(n-1,t.topLeft.slice(0,1)),t.topLeft.slice(1,1)]),e.concat([e.sub(n-1,t.bottomRight.slice(0,1)),t.bottomRight.slice(1,1)])]);o=s,r=a,null!=t.landmarks&&(i=e.tidy(()=>{const o=e.sub(e.tensor1d([n-1,0]),t.landmarks),r=e.tensor1d([1,-1]);return e.mul(o,r)}))}else{const[e,s]=t.topLeft,[a,c]=t.bottomRight;o=[n-1-e,s],r=[n-1-a,c],null!=t.landmarks&&(i=t.landmarks.map(t=>[n-1-t[0],t[1]]))}const s={topLeft:o,bottomRight:r};return null!=i&&(s.landmarks=i),null!=t.probability&&(s.probability=t.probability instanceof e.Tensor?t.probability.clone():t.probability),s}function u(t,n){return e.tidy(()=>{let o;return o=t.hasOwnProperty("box")?t.box:t,((t,n)=>{const o=e.mul(t.startPoint,n),r=e.mul(t.endPoint,n),i=e.concat2d([o,r],1);return 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e.concat2d([d,f],1)}(r,this.anchors,this.inputSize),s=e.slice(r,[0,0],[-1,1]);return[r,i,e.sigmoid(s).squeeze()]}),c=console.warn;console.warn=()=>{};const u=e.image.nonMaxSuppression(i,a,this.maxFaces,this.iouThreshold,this.scoreThreshold);console.warn=c;const h=await u.array();u.dispose();let l=h.map(t=>e.slice(i,[t,0],[1,-1]));n||(l=await Promise.all(l.map(async t=>{const e=await t.array();return t.dispose(),e})));const d=t.shape[1],f=t.shape[2];let p;p=n?e.div([f,d],this.inputSize):[f/this.inputSizeData[0],d/this.inputSizeData[1]];const m=[];for(let t=0;t<l.length;t++){const i=l[t],c=e.tidy(()=>{const c=s(i instanceof e.Tensor?i:e.tensor2d(i));if(!o)return c;const u=h[t];let l;return l=n?this.anchors.slice([u,0],[1,2]):this.anchorsData[u],{box:c,landmarks:e.slice(r,[u,5],[1,-1]).squeeze().reshape([6,-1]),probability:e.slice(a,[u],[1]),anchor:l}});m.push(c)}return i.dispose(),a.dispose(),r.dispose(),{boxes:m,scaleFactor:p}}async estimateFaces(t,n=!1,o=!1,r=!0){const[,i]=function(t){return t instanceof e.Tensor?[t.shape[0],t.shape[1]]:[t.height,t.width]}(t),s=e.tidy(()=>(t instanceof e.Tensor||(t=e.browser.fromPixels(t)),t.toFloat().expandDims(0))),{boxes:a,scaleFactor:h}=await this.getBoundingBoxes(s,n,r);return s.dispose(),n?a.map(t=>{const e=u(t,h);let n={topLeft:e.slice([0],[2]),bottomRight:e.slice([2],[2])};if(r){const{landmarks:e,probability:o,anchor:r}=t,i=e.add(r).mul(h);n.landmarks=i,n.probability=o}return o&&(n=c(n,i)),n}):Promise.all(a.map(async t=>{const e=u(t,h);let n;if(r){const[o,r,i]=await Promise.all([t.landmarks,e,t.probability].map(async t=>t.array())),s=t.anchor,[a,c]=h,u=o.map(t=>[(t[0]+s[0])*a,(t[1]+s[1])*c]);n={topLeft:r.slice(0,2),bottomRight:r.slice(2),landmarks:u,probability:i},(t=>{t.startEndTensor.dispose(),t.startPoint.dispose(),t.endPoint.dispose()})(t.box),t.landmarks.dispose(),t.probability.dispose()}else{const t=await e.array();n={topLeft:t.slice(0,2),bottomRight:t.slice(2)}}return e.dispose(),o&&(n=c(n,i)),n}))}}async function l({maxFaces:t=10,inputWidth:e=128,inputHeight:o=128,iouThreshold:r=.3,scoreThreshold:i=.75}={}){const s=await n.loadGraphModel("https://tfhub.dev/tensorflow/tfjs-model/blazeface/1/default/1",{fromTFHub:!0});return new h(s,e,o,t,r,i)}var d={silhouette:[10,338,297,332,284,251,389,356,454,323,361,288,397,365,379,378,400,377,152,148,176,149,150,136,172,58,132,93,234,127,162,21,54,103,67,109],lipsUpperOuter:[61,185,40,39,37,0,267,269,270,409,291],lipsLowerOuter:[146,91,181,84,17,314,405,321,375,291],lipsUpperInner:[78,191,80,81,82,13,312,311,310,415,308],lipsLowerInner:[78,95,88,178,87,14,317,402,318,324,308],rightEyeUpper0:[246,161,160,159,158,157,173],rightEyeLower0:[33,7,163,144,145,153,154,155,133],rightEyeUpper1:[247,30,29,27,28,56,190],rightEyeLower1:[130,25,110,24,23,22,26,112,243],rightEyeUpper2:[113,225,224,223,222,221,189],rightEyeLower2:[226,31,228,229,230,231,232,233,244],rightEyeLower3:[143,111,117,118,119,120,121,128,245],rightEyebrowUpper:[156,70,63,105,66,107,55,193],rightEyebrowLower:[35,124,46,53,52,65],leftEyeUpper0:[466,388,387,386,385,384,398],leftEyeLower0:[263,249,390,373,374,380,381,382,362],leftEyeUpper1:[467,260,259,257,258,286,414],leftEyeLower1:[359,255,339,254,253,252,256,341,463],leftEyeUpper2:[342,445,444,443,442,441,413],leftEyeLower2:[446,261,448,449,450,451,452,453,464],leftEyeLower3:[372,340,346,347,348,349,350,357,465],leftEyebrowUpper:[383,300,293,334,296,336,285,417],leftEyebrowLower:[265,353,276,283,282,295],midwayBetweenEyes:[168],noseTip:[1],noseBottom:[2],noseRightCorner:[98],noseLeftCorner:[327],rightCheek:[205],leftCheek:[425]};function f(t){return[Math.abs(t.endPoint[0]-t.startPoint[0]),Math.abs(t.endPoint[1]-t.startPoint[1])]}function p(t){return[t.startPoint[0]+(t.endPoint[0]-t.startPoint[0])/2,t.startPoint[1]+(t.endPoint[1]-t.startPoint[1])/2]}function m(t,e){void 0===e&&(e=1.5);var n=p(t),o=f(t),r=[e*o[0]/2,e*o[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:t.landmarks}}function b(t,e){var n,o=Math.PI/2-Math.atan2(-(e[1]-t[1]),e[0]-t[0]);return(n=o)-2*Math.PI*Math.floor((n+Math.PI)/(2*Math.PI))}function g(t,e){return[[1,0,t],[0,1,e],[0,0,1]]}function y(t,e){for(var n=0,o=0;o<t.length;o++)n+=t[o]*e[o];return n}function P(t,e){for(var n=[],o=0;o<t.length;o++)n.push(t[o][e]);return n}function v(t,e){for(var n=[],o=t.length,r=0;r<o;r++){n.push([]);for(var i=0;i<o;i++)n[r].push(y(t[r],P(e,i)))}return n}function w(t,e){var n=Math.cos(t),o=Math.sin(t),r=[[n,-o,0],[o,n,0],[0,0,1]],i=v(g(e[0],e[1]),r);return v(i,g(-e[0],-e[1]))}var x=[1,168],M=[3,2],E=function(){function t(t,e,n,o,r,i){this.regionsOfInterest=[],this.runsWithoutFaceDetector=0,this.boundingBoxDetector=t,this.meshDetector=e,this.meshWidth=n,this.meshHeight=o,this.maxContinuousChecks=r,this.maxFaces=i}return t.prototype.transformRawCoords=function(t,e,n,o){var r,i,s,a,c=this,u=f({startPoint:e.startPoint,endPoint:e.endPoint}),h=[u[0]/this.meshWidth,u[1]/this.meshHeight],l=t.map((function(t){return[h[0]*(t[0]-c.meshWidth/2),h[1]*(t[1]-c.meshHeight/2),t[2]]})),d=w(n,[0,0]),m=l.map((function(t){return function(t,e){return[y(t,e[0]),y(t,e[1])]}(t,d).concat([t[2]])})),b=(i=[[(r=o)[0][0],r[1][0]],[r[0][1],r[1][1]]],s=[r[0][2],r[1][2]],a=[-y(i[0],s),-y(i[1],s)],[i[0].concat(a[0]),i[1].concat(a[1]),[0,0,1]]),g=p({startPoint:e.startPoint,endPoint:e.endPoint}).concat([1]),P=[y(g,b[0]),y(g,b[1])];return m.map((function(t){return[t[0]+P[0],t[1]+P[1],t[2]]}))},t.prototype.predict=function(t){return r(this,void 0,void 0,(function(){var n,r,s,a,c=this;return i(this,(function(i){switch(i.label){case 0:return this.shouldUpdateRegionsOfInterest()?(!1,!0,[4,this.boundingBoxDetector.getBoundingBoxes(t,!1,!0)]):[3,2];case 1:return n=i.sent(),r=n.boxes,s=n.scaleFactor,0===r.length?(this.regionsOfInterest=[],[2,null]):(a=r.map((function(t){var e,n,r={startPoint:t.box.startPoint.squeeze().arraySync(),endPoint:t.box.endPoint.squeeze().arraySync()},i=m((n=s,{startPoint:[(e=r).startPoint[0]*n[0],e.startPoint[1]*n[1]],endPoint:[e.endPoint[0]*n[0],e.endPoint[1]*n[1]]}));return o({},i,{landmarks:t.landmarks.arraySync()})})),r.forEach((function(t){null!=t&&null!=t.startPoint&&(t.startEndTensor.dispose(),t.startPoint.dispose(),t.endPoint.dispose())})),this.updateRegionsOfInterest(a),this.runsWithoutFaceDetector=0,[3,3]);case 2:this.runsWithoutFaceDetector++,i.label=3;case 3:return[2,e.tidy((function(){return c.regionsOfInterest.map((function(n,r){var i;if(468===n.landmarks.length){var s=x[0],a=x[1];i=b(n.landmarks[s],n.landmarks[a])}else{s=M[0],a=M[1];i=b(n.landmarks[s],n.landmarks[a])}var u=p({startPoint:n.startPoint,endPoint:n.endPoint}),h=[u[0]/t.shape[2],u[1]/t.shape[1]],l=e.image.rotateWithOffset(t,i,0,h),d=w(-i,u),f=function(t,n,o){var r=n.shape[1],i=n.shape[2],s=[[t.startPoint[1]/r,t.startPoint[0]/i,t.endPoint[1]/r,t.endPoint[0]/i]];return e.image.cropAndResize(n,s,[0],o)}({startPoint:n.startPoint,endPoint:n.endPoint},l,[c.meshHeight,c.meshWidth]).div(255),m=c.meshDetector.predict(f),g=m[1],y=m[2],P=e.reshape(y,[-1,3]),v=P.arraySync(),E=c.transformRawCoords(v,n,i,d),L=e.tensor2d(E),k=c.calculateLandmarksBoundingBox(E);return c.regionsOfInterest[r]=o({},k,{landmarks:L.arraySync()}),{coords:P,scaledCoords:L,box:k,flag:g.squeeze()}}))}))]}}))}))},t.prototype.updateRegionsOfInterest=function(t){for(var e=0;e<t.length;e++){var n=t[e],o=this.regionsOfInterest[e],r=0;if(o&&o.startPoint){var 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k(t,e,n){return r(this,void 0,void 0,(function(){return i(this,(function(o){return[2,l({maxFaces:t,iouThreshold:e,scoreThreshold:n})]}))}))}function O(){return r(this,void 0,void 0,(function(){return i(this,(function(t){return[2,n.loadGraphModel("https://tfhub.dev/mediapipe/tfjs-model/facemesh/1/default/1",{fromTFHub:!0})]}))}))}function B(t,n){if(t.mesh instanceof e.Tensor){var o=e.tidy((function(){var o=e.tensor1d([n-1,0,0]),r=e.tensor1d([1,-1,1]);return e.tidy((function(){return[e.concat([e.sub(n-1,t.boundingBox.topLeft.slice(0,1)),t.boundingBox.topLeft.slice(1,1)]),e.concat([e.sub(n-1,t.boundingBox.bottomRight.slice(0,1)),t.boundingBox.bottomRight.slice(1,1)]),e.sub(o,t.mesh).mul(r),e.sub(o,t.scaledMesh).mul(r)]}))})),r=o[0],i=o[1],s=o[2],a=o[3];return Object.assign({},t,{boundingBox:{topLeft:r,bottomRight:i},mesh:s,scaledMesh:a})}return Object.assign({},t,{boundingBox:{topLeft:[n-1-t.boundingBox.topLeft[0],t.boundingBox.topLeft[1]],bottomRight:[n-1-t.boundingBox.bottomRight[0],t.boundingBox.bottomRight[1]]},mesh:t.mesh.map((function(t){var e=t.slice(0);return e[0]=n-1-t[0],e})),scaledMesh:t.scaledMesh.map((function(t){var e=t.slice(0);return e[0]=n-1-t[0],e}))})}var I=function(){function t(t,e,n,o,r){this.pipeline=new E(t,e,192,192,n,r),this.detectionConfidence=o}return t.getAnnotations=function(){return d},t.getUVCoords=function(){return L},t.prototype.estimateFaces=function(t,n,o){return void 0===n&&(n=!1),void 0===o&&(o=!1),r(this,void 0,void 0,(function(){var s,a,c,u,h,l=this;return i(this,(function(f){switch(f.label){case 0:return s=function(t){return t instanceof e.Tensor?[t.shape[0],t.shape[1]]:[t.height,t.width]}(t),a=s[1],c=e.tidy((function(){return t instanceof e.Tensor||(t=e.browser.fromPixels(t)),t.toFloat().expandDims(0)})),"webgl"!==e.getBackend()?[3,2]:(h=e.env().get("WEBGL_PACK_DEPTHWISECONV"),e.env().set("WEBGL_PACK_DEPTHWISECONV",!0),[4,this.pipeline.predict(c)]);case 1:return u=f.sent(),e.env().set("WEBGL_PACK_DEPTHWISECONV",h),[3,4];case 2:return[4,this.pipeline.predict(c)];case 3:u=f.sent(),f.label=4;case 4:return c.dispose(),null!=u&&u.length>0?[2,Promise.all(u.map((function(t,s){return r(l,void 0,void 0,(function(){var c,u,h,l,f,p,m,b,g,y,P,v,w,x,M=this;return i(this,(function(E){switch(E.label){case 0:return c=t.coords,u=t.scaledCoords,h=t.box,l=t.flag,f=[l],n||(f=f.concat([c,u])),[4,Promise.all(f.map((function(t){return r(M,void 0,void 0,(function(){return i(this,(function(e){return[2,t.array()]}))}))})))];case 1:if(p=E.sent(),m=p[0],l.dispose(),m<this.detectionConfidence&&this.pipeline.clearRegionOfInterest(s),n)return b={faceInViewConfidence:m,mesh:c,scaledMesh:u,boundingBox:{topLeft:e.tensor1d(h.startPoint),bottomRight:e.tensor1d(h.endPoint)}},o?[2,B(b,a)]:[2,b];for(x in g=p.slice(1),y=g[0],P=g[1],u.dispose(),c.dispose(),v={faceInViewConfidence:m,boundingBox:{topLeft:h.startPoint,bottomRight:h.endPoint},mesh:y,scaledMesh:P},o&&(v=B(v,a)),w={},d)w[x]=d[x].map((function(t){return v.scaledMesh[t]}));return v.annotations=w,[2,v]}}))}))})))]:[2,[]]}}))}))},t}();t.FaceMesh=I,t.load=function(t){var e=void 0===t?{}:t,n=e.maxContinuousChecks,o=void 0===n?5:n,s=e.detectionConfidence,a=void 0===s?.9:s,c=e.maxFaces,u=void 0===c?10:c,h=e.iouThreshold,l=void 0===h?.3:h,d=e.scoreThreshold,f=void 0===d?.75:d;return r(this,void 0,void 0,(function(){var t,e,n;return i(this,(function(r){switch(r.label){case 0:return[4,Promise.all([k(u,l,f),O()])];case 1:return t=r.sent(),e=t[0],n=t[1],[2,new I(e,n,o,a,u)]}}))}))},Object.defineProperty(t,"__esModule",{value:!0})}));

dist/index.js

 "use strict";
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
 Object.defineProperty(exports, "__esModule", { value: true });
-const blazeface = require("@tensorflow-models/blazeface");
-const tfconv = require("@tensorflow/tfjs-converter");
-const tf = require("@tensorflow/tfjs-core");
-const keypoints_1 = require("./keypoints");
-const pipeline_1 = require("./pipeline");
-const uv_coords_1 = require("./uv_coords");
-const FACEMESH_GRAPHMODEL_PATH = 'https://tfhub.dev/mediapipe/tfjs-model/facemesh/1/default/1';
-const MESH_MODEL_INPUT_WIDTH = 192;
-const MESH_MODEL_INPUT_HEIGHT = 192;
-async function load({ maxContinuousChecks = 5, detectionConfidence = 0.9, maxFaces = 10, iouThreshold = 0.3, scoreThreshold = 0.75 } = {}) {
-    const [blazeFace, blazeMeshModel] = await Promise.all([
-        loadDetectorModel(maxFaces, iouThreshold, scoreThreshold), loadMeshModel()
-    ]);
-    const faceMesh = new FaceMesh(blazeFace, blazeMeshModel, maxContinuousChecks, detectionConfidence, maxFaces);
-    return faceMesh;
+var blazeface = require("@tensorflow-models/blazeface");
+var tfconv = require("@tensorflow/tfjs-converter");
+var tf = require("@tensorflow/tfjs-core");
+var keypoints_1 = require("./keypoints");
+var pipeline_1 = require("./pipeline");
+var uv_coords_1 = require("./uv_coords");
+var FACEMESH_GRAPHMODEL_PATH = 'https://tfhub.dev/mediapipe/tfjs-model/facemesh/1/default/1';
+var MESH_MODEL_INPUT_WIDTH = 192;
+var MESH_MODEL_INPUT_HEIGHT = 192;
+/**
+ * Load the model.
+ *
+ * @param options - a configuration object with the following properties:
+ *  - `maxContinuousChecks` How many frames to go without running the bounding
+ * box detector. Only relevant if maxFaces > 1. Defaults to 5.
+ *  - `detectionConfidence` Threshold for discarding a prediction. Defaults to
+ * 0.9.
+ *  - `maxFaces` The maximum number of faces detected in the input. Should be
+ * set to the minimum number for performance. Defaults to 10.
+ *  - `iouThreshold` A float representing the threshold for deciding whether
+ * boxes overlap too much in non-maximum suppression. Must be between [0, 1].
+ * Defaults to 0.3.
+ *  - `scoreThreshold` A threshold for deciding when to remove boxes based
+ * on score in non-maximum suppression. Defaults to 0.75.
+ */
+function load(_a) {
+    var _b = _a === void 0 ? {} : _a, _c = _b.maxContinuousChecks, maxContinuousChecks = _c === void 0 ? 5 : _c, _d = _b.detectionConfidence, detectionConfidence = _d === void 0 ? 0.9 : _d, _e = _b.maxFaces, maxFaces = _e === void 0 ? 10 : _e, _f = _b.iouThreshold, iouThreshold = _f === void 0 ? 0.3 : _f, _g = _b.scoreThreshold, scoreThreshold = _g === void 0 ? 0.75 : _g;
+    return __awaiter(this, void 0, void 0, function () {
+        var _h, blazeFace, blazeMeshModel, faceMesh;
+        return __generator(this, function (_j) {
+            switch (_j.label) {
+                case 0: return [4 /*yield*/, Promise.all([
+                        loadDetectorModel(maxFaces, iouThreshold, scoreThreshold), loadMeshModel()
+                    ])];
+                case 1:
+                    _h = _j.sent(), blazeFace = _h[0], blazeMeshModel = _h[1];
+                    faceMesh = new FaceMesh(blazeFace, blazeMeshModel, maxContinuousChecks, detectionConfidence, maxFaces);
+                    return [2 /*return*/, faceMesh];
+            }
+        });
+    });
 }
 exports.load = load;
-async function loadDetectorModel(maxFaces, iouThreshold, scoreThreshold) {
-    return blazeface.load({ maxFaces, iouThreshold, scoreThreshold });
+function loadDetectorModel(maxFaces, iouThreshold, scoreThreshold) {
+    return __awaiter(this, void 0, void 0, function () {
+        return __generator(this, function (_a) {
+            return [2 /*return*/, blazeface.load({ maxFaces: maxFaces, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold })];
+        });
+    });
 }
-async function loadMeshModel() {
-    return tfconv.loadGraphModel(FACEMESH_GRAPHMODEL_PATH, { fromTFHub: true });
+function loadMeshModel() {
+    return __awaiter(this, void 0, void 0, function () {
+        return __generator(this, function (_a) {
+            return [2 /*return*/, tfconv.loadGraphModel(FACEMESH_GRAPHMODEL_PATH, { fromTFHub: true })];
+        });
+    });
 }
@@ -32,6 +117,6 @@ function getInputTensorDimensions(input) {
     if (face.mesh instanceof tf.Tensor) {
-        const [topLeft, bottomRight, mesh, scaledMesh] = tf.tidy(() => {
-            const subtractBasis = tf.tensor1d([imageWidth - 1, 0, 0]);
-            const multiplyBasis = tf.tensor1d([1, -1, 1]);
-            return tf.tidy(() => {
+        var _a = tf.tidy(function () {
+            var subtractBasis = tf.tensor1d([imageWidth - 1, 0, 0]);
+            var multiplyBasis = tf.tensor1d([1, -1, 1]);
+            return tf.tidy(function () {
                 return [
@@ -50,4 +135,4 @@ tf.concat([
             });
-        });
-        return Object.assign({}, face, { boundingBox: { topLeft, bottomRight }, mesh, scaledMesh });
+        }), topLeft = _a[0], bottomRight = _a[1], mesh = _a[2], scaledMesh = _a[3];
+        return Object.assign({}, face, { boundingBox: { topLeft: topLeft, bottomRight: bottomRight }, mesh: mesh, scaledMesh: scaledMesh });
     }
@@ -65,9 +150,9 @@ return Object.assign({}, face, {
         },
-        mesh: (face.mesh).map(coord => {
-            const flippedCoord = coord.slice(0);
+        mesh: (face.mesh).map(function (coord) {
+            var flippedCoord = coord.slice(0);
             flippedCoord[0] = imageWidth - 1 - coord[0];
             return flippedCoord;
         }),
-        scaledMesh: face.scaledMesh.map(coord => {
-            const flippedCoord = coord.slice(0);
+        scaledMesh: face.scaledMesh.map(function (coord) {
+            var flippedCoord = coord.slice(0);
             flippedCoord[0] = imageWidth - 1 - coord[0];
@@ -78,79 +163,128 @@ return flippedCoord;
 }
-class FaceMesh {
-    constructor(blazeFace, blazeMeshModel, maxContinuousChecks, detectionConfidence, maxFaces) {
+var FaceMesh = /** @class */ (function () {
+    function FaceMesh(blazeFace, blazeMeshModel, maxContinuousChecks, detectionConfidence, maxFaces) {
         this.pipeline = new pipeline_1.Pipeline(blazeFace, blazeMeshModel, MESH_MODEL_INPUT_WIDTH, MESH_MODEL_INPUT_HEIGHT, maxContinuousChecks, maxFaces);
         this.detectionConfidence = detectionConfidence;
     }
-    static getAnnotations() {
+    FaceMesh.getAnnotations = function () {
         return keypoints_1.MESH_ANNOTATIONS;
-    }
-    static getUVCoords() {
+    };
+    /**
+     * Returns an array of UV coordinates for the 468 facial keypoint vertices in
+     * mesh_map.jpg. Can be used to map textures to the facial mesh.
+     */
+    FaceMesh.getUVCoords = function () {
         return uv_coords_1.UV_COORDS;
-    }
-    async estimateFaces(input, returnTensors = false, flipHorizontal = false) {
-        const [, width] = getInputTensorDimensions(input);
-        const image = tf.tidy(() => {
-            if (!(input instanceof tf.Tensor)) {
-                input = tf.browser.fromPixels(input);
-            }
-            return input.toFloat().expandDims(0);
-        });
-        const savedWebglPackDepthwiseConvFlag = tf.env().get('WEBGL_PACK_DEPTHWISECONV');
-        tf.env().set('WEBGL_PACK_DEPTHWISECONV', true);
-        const predictions = await this.pipeline.predict(image);
-        tf.env().set('WEBGL_PACK_DEPTHWISECONV', savedWebglPackDepthwiseConvFlag);
-        image.dispose();
-        if (predictions != null && predictions.length > 0) {
-            return Promise.all(predictions.map(async (prediction, i) => {
-                const { coords, scaledCoords, box, flag } = prediction;
-                let tensorsToRead = [flag];
-                if (!returnTensors) {
-                    tensorsToRead = tensorsToRead.concat([coords, scaledCoords, box.startPoint, box.endPoint]);
-                }
-                const tensorValues = await Promise.all(tensorsToRead.map(async (d) => d.array()));
-                const flagValue = tensorValues[0];
-                flag.dispose();
-                if (flagValue < this.detectionConfidence) {
-                    this.pipeline.clearRegionOfInterest(i);
-                }
-                if (returnTensors) {
-                    const annotatedPrediction = {
-                        faceInViewConfidence: flagValue,
-                        mesh: coords,
-                        scaledMesh: scaledCoords,
-                        boundingBox: {
-                            topLeft: box.startPoint.squeeze(),
-                            bottomRight: box.endPoint.squeeze()
+    };
+    /**
+     * Returns an array of faces in an image.
+     *
+     * @param input The image to classify. Can be a tensor, DOM element image,
+     * video, or canvas.
+     * @param returnTensors (defaults to `false`) Whether to return tensors as
+     * opposed to values.
+     * @param flipHorizontal Whether to flip/mirror the facial keypoints
+     * horizontally. Should be true for videos that are flipped by default (e.g.
+     * webcams).
+     *
+     * @return An array of AnnotatedPrediction objects.
+     */
+    FaceMesh.prototype.estimateFaces = function (input, returnTensors, flipHorizontal) {
+        if (returnTensors === void 0) { returnTensors = false; }
+        if (flipHorizontal === void 0) { flipHorizontal = false; }
+        return __awaiter(this, void 0, void 0, function () {
+            var _a, width, image, predictions, savedWebglPackDepthwiseConvFlag;
+            var _this = this;
+            return __generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = getInputTensorDimensions(input), width = _a[1];
+                        image = tf.tidy(function () {
+                            if (!(input instanceof tf.Tensor)) {
+                                input = tf.browser.fromPixels(input);
+                            }
+                            return input.toFloat().expandDims(0);
+                        });
+                        if (!(tf.getBackend() === 'webgl')) return [3 /*break*/, 2];
+                        savedWebglPackDepthwiseConvFlag = tf.env().get('WEBGL_PACK_DEPTHWISECONV');
+                        tf.env().set('WEBGL_PACK_DEPTHWISECONV', true);
+                        return [4 /*yield*/, this.pipeline.predict(image)];
+                    case 1:
+                        predictions = _b.sent();
+                        tf.env().set('WEBGL_PACK_DEPTHWISECONV', savedWebglPackDepthwiseConvFlag);
+                        return [3 /*break*/, 4];
+                    case 2: return [4 /*yield*/, this.pipeline.predict(image)];
+                    case 3:
+                        predictions = _b.sent();
+                        _b.label = 4;
+                    case 4:
+                        image.dispose();
+                        if (predictions != null && predictions.length > 0) {
+                            return [2 /*return*/, Promise.all(predictions.map(function (prediction, i) { return __awaiter(_this, void 0, void 0, function () {
+                                    var coords, scaledCoords, box, flag, tensorsToRead, tensorValues, flagValue, annotatedPrediction_1, _a, coordsArr, coordsArrScaled, annotatedPrediction, annotations, key;
+                                    var _this = this;
+                                    return __generator(this, function (_b) {
+                                        switch (_b.label) {
+                                            case 0:
+                                                coords = prediction.coords, scaledCoords = prediction.scaledCoords, box = prediction.box, flag = prediction.flag;
+                                                tensorsToRead = [flag];
+                                                if (!returnTensors) {
+                                                    tensorsToRead = tensorsToRead.concat([coords, scaledCoords]);
+                                                }
+                                                return [4 /*yield*/, Promise.all(tensorsToRead.map(function (d) { return __awaiter(_this, void 0, void 0, function () { return __generator(this, function (_a) {
+                                                        return [2 /*return*/, d.array()];
+                                                    }); }); }))];
+                                            case 1:
+                                                tensorValues = _b.sent();
+                                                flagValue = tensorValues[0];
+                                                flag.dispose();
+                                                if (flagValue < this.detectionConfidence) {
+                                                    this.pipeline.clearRegionOfInterest(i);
+                                                }
+                                                if (returnTensors) {
+                                                    annotatedPrediction_1 = {
+                                                        faceInViewConfidence: flagValue,
+                                                        mesh: coords,
+                                                        scaledMesh: scaledCoords,
+                                                        boundingBox: {
+                                                            topLeft: tf.tensor1d(box.startPoint),
+                                                            bottomRight: tf.tensor1d(box.endPoint)
+                                                        }
+                                                    };
+                                                    if (flipHorizontal) {
+                                                        return [2 /*return*/, flipFaceHorizontal(annotatedPrediction_1, width)];
+                                                    }
+                                                    return [2 /*return*/, annotatedPrediction_1];
+                                                }
+                                                _a = tensorValues.slice(1), coordsArr = _a[0], coordsArrScaled = _a[1];
+                                                scaledCoords.dispose();
+                                                coords.dispose();
+                                                annotatedPrediction = {
+                                                    faceInViewConfidence: flagValue,
+                                                    boundingBox: { topLeft: box.startPoint, bottomRight: box.endPoint },
+                                                    mesh: coordsArr,
+                                                    scaledMesh: coordsArrScaled
+                                                };
+                                                if (flipHorizontal) {
+                                                    annotatedPrediction =
+                                                        flipFaceHorizontal(annotatedPrediction, width);
+                                                }
+                                                annotations = {};
+                                                for (key in keypoints_1.MESH_ANNOTATIONS) {
+                                                    annotations[key] = keypoints_1.MESH_ANNOTATIONS[key].map(function (index) { return annotatedPrediction.scaledMesh[index]; });
+                                                }
+                                                annotatedPrediction['annotations'] = annotations;
+                                                return [2 /*return*/, annotatedPrediction];
+                                        }
+                                    });
+                                }); }))];
                         }
-                    };
-                    if (flipHorizontal) {
-                        return flipFaceHorizontal(annotatedPrediction, width);
-                    }
-                    return annotatedPrediction;
+                        return [2 /*return*/, []];
                 }
-                const [coordsArr, coordsArrScaled, topLeft, bottomRight] = tensorValues.slice(1);
-                scaledCoords.dispose();
-                coords.dispose();
-                let annotatedPrediction = {
-                    faceInViewConfidence: flagValue,
-                    boundingBox: { topLeft, bottomRight },
-                    mesh: coordsArr,
-                    scaledMesh: coordsArrScaled
-                };
-                if (flipHorizontal) {
-                    annotatedPrediction =
-                        flipFaceHorizontal(annotatedPrediction, width);
-                }
-                const annotations = {};
-                for (const key in keypoints_1.MESH_ANNOTATIONS) {
-                    annotations[key] = keypoints_1.MESH_ANNOTATIONS[key].map(index => annotatedPrediction.scaledMesh[index]);
-                }
-                annotatedPrediction['annotations'] = annotations;
-                return annotatedPrediction;
-            }));
-        }
-        return [];
-    }
-}
+            });
+        });
+    };
+    return FaceMesh;
+}());
 exports.FaceMesh = FaceMesh;
 //# sourceMappingURL=index.js.map

dist/keypoints.js

 "use strict";
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
 Object.defineProperty(exports, "__esModule", { value: true });
@@ -3,0 +19,0 @@ exports.MESH_ANNOTATIONS = {

dist/pipeline.js

 "use strict";
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+var __assign = (this && this.__assign) || function () {
+    __assign = Object.assign || function(t) {
+        for (var s, i = 1, n = arguments.length; i < n; i++) {
+            s = arguments[i];
+            for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p))
+                t[p] = s[p];
+        }
+        return t;
+    };
+    return __assign.apply(this, arguments);
+};
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
 Object.defineProperty(exports, "__esModule", { value: true });
-const tf = require("@tensorflow/tfjs-core");
-const box_1 = require("./box");
-const LANDMARKS_COUNT = 468;
-const UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD = 0.25;
-class Pipeline {
-    constructor(boundingBoxDetector, meshDetector, meshWidth, meshHeight, maxContinuousChecks, maxFaces) {
+var tf = require("@tensorflow/tfjs-core");
+var box_1 = require("./box");
+var util_1 = require("./util");
+var UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD = 0.25;
+var LANDMARKS_COUNT = 468;
+var MESH_MODEL_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [1, 168];
+var BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [3, 2];
+// The Pipeline coordinates between the bounding box and skeleton models.
+var Pipeline = /** @class */ (function () {
+    function Pipeline(boundingBoxDetector, meshDetector, meshWidth, meshHeight, maxContinuousChecks, maxFaces) {
+        // An array of facial bounding boxes.
         this.regionsOfInterest = [];
@@ -18,93 +85,155 @@ this.runsWithoutFaceDetector = 0;
     }
-    async predict(input) {
-        if (this.shouldUpdateRegionsOfInterest()) {
-            const returnTensors = true;
-            const annotateFace = false;
-            const { boxes, scaleFactor } = await this.boundingBoxDetector.getBoundingBoxes(input, returnTensors, annotateFace);
-            if (boxes.length === 0) {
-                scaleFactor.dispose();
-                this.clearAllRegionsOfInterest();
-                return null;
-            }
-            const scaledBoxes = boxes.map((prediction) => box_1.enlargeBox(box_1.scaleBoxCoordinates(prediction, scaleFactor)));
-            boxes.forEach(box_1.disposeBox);
-            this.updateRegionsOfInterest(scaledBoxes);
-            this.runsWithoutFaceDetector = 0;
-        }
-        else {
-            this.runsWithoutFaceDetector++;
-        }
-        return tf.tidy(() => {
-            return this.regionsOfInterest.map((box, i) => {
-                const face = box_1.cutBoxFromImageAndResize(box, input, [
-                    this.meshHeight, this.meshWidth
-                ]).div(255);
-                const [, flag, coords] = this.meshDetector.predict(face);
-                const coordsReshaped = tf.reshape(coords, [-1, 3]);
-                const normalizedBox = tf.div(box_1.getBoxSize(box), [this.meshWidth, this.meshHeight]);
-                const scaledCoords = tf.mul(coordsReshaped, normalizedBox.concat(tf.tensor2d([1], [1, 1]), 1))
-                    .add(box.startPoint.concat(tf.tensor2d([0], [1, 1]), 1));
-                const landmarksBox = this.calculateLandmarksBoundingBox(scaledCoords);
-                const previousBox = this.regionsOfInterest[i];
-                box_1.disposeBox(previousBox);
-                this.regionsOfInterest[i] = landmarksBox;
-                const prediction = {
-                    coords: coordsReshaped,
-                    scaledCoords,
-                    box: landmarksBox,
-                    flag: flag.squeeze()
-                };
-                return prediction;
+    Pipeline.prototype.transformRawCoords = function (rawCoords, box, angle, rotationMatrix) {
+        var _this = this;
+        var boxSize = box_1.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });
+        var scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];
+        var coordsScaled = rawCoords.map(function (coord) {
+            return [
+                scaleFactor[0] * (coord[0] - _this.meshWidth / 2),
+                scaleFactor[1] * (coord[1] - _this.meshHeight / 2), coord[2]
+            ];
+        });
+        var coordsRotationMatrix = util_1.buildRotationMatrix(angle, [0, 0]);
+        var coordsRotated = coordsScaled.map(function (coord) {
+            var rotated = util_1.rotatePoint(coord, coordsRotationMatrix);
+            return rotated.concat([coord[2]]);
+        });
+        var inverseRotationMatrix = util_1.invertTransformMatrix(rotationMatrix);
+        var boxCenter = box_1.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }).concat([
+            1
+        ]);
+        var originalBoxCenter = [
+            util_1.dot(boxCenter, inverseRotationMatrix[0]),
+            util_1.dot(boxCenter, inverseRotationMatrix[1])
+        ];
+        return coordsRotated.map(function (coord) { return ([
+            coord[0] + originalBoxCenter[0],
+            coord[1] + originalBoxCenter[1], coord[2]
+        ]); });
+    };
+    /**
+     * Returns an array of predictions for each face in the input.
+     *
+     * @param input - tensor of shape [1, H, W, 3].
+     */
+    Pipeline.prototype.predict = function (input) {
+        return __awaiter(this, void 0, void 0, function () {
+            var returnTensors, annotateFace, _a, boxes, scaleFactor_1, scaledBoxes;
+            var _this = this;
+            return __generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        if (!this.shouldUpdateRegionsOfInterest()) return [3 /*break*/, 2];
+                        returnTensors = false;
+                        annotateFace = true;
+                        return [4 /*yield*/, this.boundingBoxDetector.getBoundingBoxes(input, returnTensors, annotateFace)];
+                    case 1:
+                        _a = _b.sent(), boxes = _a.boxes, scaleFactor_1 = _a.scaleFactor;
+                        if (boxes.length === 0) {
+                            this.regionsOfInterest = [];
+                            return [2 /*return*/, null];
+                        }
+                        scaledBoxes = boxes.map(function (prediction) {
+                            var predictionBoxCPU = {
+                                startPoint: prediction.box.startPoint.squeeze().arraySync(),
+                                endPoint: prediction.box.endPoint.squeeze().arraySync()
+                            };
+                            var scaledBox = box_1.scaleBoxCoordinates(predictionBoxCPU, scaleFactor_1);
+                            var enlargedBox = box_1.enlargeBox(scaledBox);
+                            return __assign({}, enlargedBox, { landmarks: prediction.landmarks.arraySync() });
+                        });
+                        boxes.forEach(function (box) {
+                            if (box != null && box.startPoint != null) {
+                                box.startEndTensor.dispose();
+                                box.startPoint.dispose();
+                                box.endPoint.dispose();
+                            }
+                        });
+                        this.updateRegionsOfInterest(scaledBoxes);
+                        this.runsWithoutFaceDetector = 0;
+                        return [3 /*break*/, 3];
+                    case 2:
+                        this.runsWithoutFaceDetector++;
+                        _b.label = 3;
+                    case 3: return [2 /*return*/, tf.tidy(function () {
+                            return _this.regionsOfInterest.map(function (box, i) {
+                                var angle;
+                                // The facial bounding box landmarks could come either from blazeface
+                                // (if we are using a fresh box), or from the mesh model (if we are
+                                // reusing an old box).
+                                var boxLandmarksFromMeshModel = box.landmarks.length === LANDMARKS_COUNT;
+                                if (boxLandmarksFromMeshModel) {
+                                    var indexOfNose = MESH_MODEL_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[0], indexOfForehead = MESH_MODEL_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[1];
+                                    angle = util_1.computeRotation(box.landmarks[indexOfNose], box.landmarks[indexOfForehead]);
+                                }
+                                else {
+                                    var indexOfNose = BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[0], indexOfForehead = BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[1];
+                                    angle = util_1.computeRotation(box.landmarks[indexOfNose], box.landmarks[indexOfForehead]);
+                                }
+                                var faceCenter = box_1.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });
+                                var faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];
+                                var rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);
+                                var rotationMatrix = util_1.buildRotationMatrix(-angle, faceCenter);
+                                var boxCPU = { startPoint: box.startPoint, endPoint: box.endPoint };
+                                var face = box_1.cutBoxFromImageAndResize(boxCPU, rotatedImage, [
+                                    _this.meshHeight, _this.meshWidth
+                                ]).div(255);
+                                // The first returned tensor represents facial contours, which are
+                                // included in the coordinates.
+                                var _a = _this.meshDetector.predict(face), flag = _a[1], coords = _a[2];
+                                var coordsReshaped = tf.reshape(coords, [-1, 3]);
+                                var rawCoords = coordsReshaped.arraySync();
+                                var transformedCoordsData = _this.transformRawCoords(rawCoords, box, angle, rotationMatrix);
+                                var transformedCoords = tf.tensor2d(transformedCoordsData);
+                                var landmarksBox = _this.calculateLandmarksBoundingBox(transformedCoordsData);
+                                _this.regionsOfInterest[i] = __assign({}, landmarksBox, { landmarks: transformedCoords.arraySync() });
+                                var prediction = {
+                                    coords: coordsReshaped,
+                                    scaledCoords: transformedCoords,
+                                    box: landmarksBox,
+                                    flag: flag.squeeze()
+                                };
+                                return prediction;
+                            });
+                        })];
+                }
             });
         });
-    }
-    updateRegionsOfInterest(boxes) {
-        for (let i = 0; i < boxes.length; i++) {
-            const box = boxes[i];
-            const previousBox = this.regionsOfInterest[i];
-            let iou = 0;
+    };
+    // Updates regions of interest if the intersection over union between
+    // the incoming and previous regions falls below a threshold.
+    Pipeline.prototype.updateRegionsOfInterest = function (boxes) {
+        for (var i = 0; i < boxes.length; i++) {
+            var box = boxes[i];
+            var previousBox = this.regionsOfInterest[i];
+            var iou = 0;
             if (previousBox && previousBox.startPoint) {
-                const [boxStartX, boxStartY, boxEndX, boxEndY] = box.startEndTensor.arraySync()[0];
-                const [previousBoxStartX, previousBoxStartY, previousBoxEndX, previousBoxEndY] = previousBox.startEndTensor.arraySync()[0];
-                const xStartMax = Math.max(boxStartX, previousBoxStartX);
-                const yStartMax = Math.max(boxStartY, previousBoxStartY);
-                const xEndMin = Math.min(boxEndX, previousBoxEndX);
-                const yEndMin = Math.min(boxEndY, previousBoxEndY);
-                const intersection = (xEndMin - xStartMax) * (yEndMin - yStartMax);
-                const boxArea = (boxEndX - boxStartX) * (boxEndY - boxStartY);
-                const previousBoxArea = (previousBoxEndX - previousBoxStartX) *
+                var _a = box.startPoint, boxStartX = _a[0], boxStartY = _a[1];
+                var _b = box.endPoint, boxEndX = _b[0], boxEndY = _b[1];
+                var _c = previousBox.startPoint, previousBoxStartX = _c[0], previousBoxStartY = _c[1];
+                var _d = previousBox.endPoint, previousBoxEndX = _d[0], previousBoxEndY = _d[1];
+                var xStartMax = Math.max(boxStartX, previousBoxStartX);
+                var yStartMax = Math.max(boxStartY, previousBoxStartY);
+                var xEndMin = Math.min(boxEndX, previousBoxEndX);
+                var yEndMin = Math.min(boxEndY, previousBoxEndY);
+                var intersection = (xEndMin - xStartMax) * (yEndMin - yStartMax);
+                var boxArea = (boxEndX - boxStartX) * (boxEndY - boxStartY);
+                var previousBoxArea = (previousBoxEndX - previousBoxStartX) *
                     (previousBoxEndY - boxStartY);
                 iou = intersection / (boxArea + previousBoxArea - intersection);
             }
-            if (iou > UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD) {
-                box_1.disposeBox(box);
-            }
-            else {
+            if (iou < UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD) {
                 this.regionsOfInterest[i] = box;
-                box_1.disposeBox(previousBox);
             }
         }
-        for (let i = boxes.length; i < this.regionsOfInterest.length; i++) {
-            box_1.disposeBox(this.regionsOfInterest[i]);
-        }
         this.regionsOfInterest = this.regionsOfInterest.slice(0, boxes.length);
-    }
-    clearRegionOfInterest(index) {
+    };
+    Pipeline.prototype.clearRegionOfInterest = function (index) {
         if (this.regionsOfInterest[index] != null) {
-            box_1.disposeBox(this.regionsOfInterest[index]);
-            this.regionsOfInterest = [
-                ...this.regionsOfInterest.slice(0, index),
-                ...this.regionsOfInterest.slice(index + 1)
-            ];
+            this.regionsOfInterest = this.regionsOfInterest.slice(0, index).concat(this.regionsOfInterest.slice(index + 1));
         }
-    }
-    clearAllRegionsOfInterest() {
-        for (let i = 0; i < this.regionsOfInterest.length; i++) {
-            box_1.disposeBox(this.regionsOfInterest[i]);
-        }
-        this.regionsOfInterest = [];
-    }
-    shouldUpdateRegionsOfInterest() {
-        const roisCount = this.regionsOfInterest.length;
-        const noROIs = roisCount === 0;
+    };
+    Pipeline.prototype.shouldUpdateRegionsOfInterest = function () {
+        var roisCount = this.regionsOfInterest.length;
+        var noROIs = roisCount === 0;
         if (this.maxFaces === 1 || noROIs) {
@@ -115,12 +244,14 @@ return noROIs;
             this.runsWithoutFaceDetector >= this.maxContinuousChecks;
-    }
-    calculateLandmarksBoundingBox(landmarks) {
-        const xs = landmarks.slice([0, 0], [LANDMARKS_COUNT, 1]);
-        const ys = landmarks.slice([0, 1], [LANDMARKS_COUNT, 1]);
-        const boxMinMax = tf.stack([xs.min(), ys.min(), xs.max(), ys.max()]);
-        const box = box_1.createBox(boxMinMax.expandDims(0));
-        return box_1.enlargeBox(box);
-    }
-}
+    };
+    Pipeline.prototype.calculateLandmarksBoundingBox = function (landmarks) {
+        var xs = landmarks.map(function (d) { return d[0]; });
+        var ys = landmarks.map(function (d) { return d[1]; });
+        var startPoint = [Math.min.apply(Math, xs), Math.min.apply(Math, ys)];
+        var endPoint = [Math.max.apply(Math, xs), Math.max.apply(Math, ys)];
+        var box = { startPoint: startPoint, endPoint: endPoint };
+        return box_1.enlargeBox({ startPoint: box.startPoint, endPoint: box.endPoint });
+    };
+    return Pipeline;
+}());
 exports.Pipeline = Pipeline;
 //# sourceMappingURL=pipeline.js.map

dist/uv_coords.js

 "use strict";
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
 Object.defineProperty(exports, "__esModule", { value: true });
@@ -3,0 +19,0 @@ exports.UV_COORDS = [

dist/version.js

 "use strict";
+/** @license See the LICENSE file. */
 Object.defineProperty(exports, "__esModule", { value: true });
-const version = '0.0.3';
+// This code is auto-generated, do not modify this file!
+var version = '0.0.4';
 exports.version = version;
 //# sourceMappingURL=version.js.map

package.json

 {
   "name": "@tensorflow-models/facemesh",
-  "version": "0.0.3",
+  "version": "0.0.4",
   "description": "Pretrained facemesh model",
@@ -16,23 +16,27 @@ "main": "dist/index.js",
   "peerDependencies": {
-    "@tensorflow/tfjs-converter": "^1.6.1",
-    "@tensorflow/tfjs-core": "^1.6.1"
+    "@tensorflow/tfjs-converter": "^2.1.0",
+    "@tensorflow/tfjs-core": "^2.1.0"
   },
   "dependencies": {
-    "@tensorflow-models/blazeface": "0.0.5"
+    "@tensorflow-models/blazeface": "0.0.5",
+    "rimraf": "^3.0.2"
   },
   "devDependencies": {
-    "@tensorflow/tfjs-converter": "^1.6.1",
-    "@tensorflow/tfjs-core": "^1.6.1",
-    "@types/jasmine": "~2.5.53",
-    "jasmine": "~3.2.0",
-    "jasmine-core": "~3.1.0",
-    "rimraf": "~2.6.2",
-    "rollup": "~0.58.2",
-    "rollup-plugin-node-resolve": "~3.3.0",
-    "rollup-plugin-typescript2": "~0.13.0",
-    "rollup-plugin-uglify": "~3.0.0",
-    "ts-node": "~5.0.0",
-    "tslint": "~5.8.0",
-    "typescript": "3.3.3333",
-    "yalc": "^1.0.0-pre.27"
+    "@rollup/plugin-commonjs": "^11.0.2",
+    "@rollup/plugin-node-resolve": "^7.1.1",
+    "@rollup/plugin-typescript": "^3.0.0",
+    "@tensorflow/tfjs-converter": "^2.1.0",
+    "@tensorflow/tfjs-core": "^2.1.0",
+    "@tensorflow/tfjs-backend-cpu": "^2.1.0",
+    "@types/jasmine": "~2.8.8",
+    "babel-core": "~6.26.0",
+    "babel-plugin-transform-runtime": "~6.23.0",
+    "jasmine-core": "~3.5.0",
+    "rollup": "~2.3.2",
+    "rollup-plugin-terser": "~5.3.0",
+    "rollup-plugin-visualizer": "~3.3.2",
+    "ts-node": "~8.8.2",
+    "tslint": "~5.18.0",
+    "typescript": "~3.5.3",
+    "yalc": "~1.0.0-pre.21"
   },
@@ -42,3 +46,3 @@ "scripts": {
     "publish-local": "yarn build && rollup -c && yalc publish",
-    "test": "ts-node run_tests.ts",
+    "test": "yarn build && ts-node --skip-ignore run_tests.ts",
     "build-npm": "yarn build && rollup -c",
@@ -45,0 +49,0 @@ "lint": "tslint -p . -t verbose",

README.md

@@ -18,2 +18,16 @@ # MediaPipe Facemesh
 
+Via script tags:
+
+```html
+<!-- Require the peer dependencies of facemesh. -->
+<script src="https://unpkg.com/@tensorflow/tfjs-core@2.1.0/dist/tf-core.js"></script>
+<script src="https://unpkg.com/@tensorflow/tfjs-converter@2.1.0/dist/tf-converter.js"></script>
+
+<!-- You must explicitly require a TF.js backend if you're not using the tfs union bundle. -->
+<script src="https://unpkg.com/@tensorflow/tfjs-backend-wasm@2.1.0/dist/tf-backend-wasm.js"></script>
+<!-- Alternatively you can use the WebGL backend: <script src="https://unpkg.com/@tensorflow/tfjs-backend-webgl@2.1.0/dist/tf-backend-webgl.js"></script> -->
+```
+
+Via npm:
+
 Using `yarn`:
@@ -23,22 +37,17 @@
 
-Using `npm`:
+    $ yarn add @tensorflow/tfjs-core, @tensorflow/tfjs-converter
+    $ yarn add @tensorflow/tfjs-backend-wasm # or @tensorflow/tfjs-backend-webgl
 
-    $ npm install @tensorflow-models/facemesh
-
-Note that this package specifies `@tensorflow/tfjs-core` and `@tensorflow/tfjs-converter` as peer dependencies, so they will also need to be installed.
-
 ## Usage
 
-To import in npm:
+If you are using via npm, first add:
 
 ```js
 const facemesh = require('@tensorflow-models/facemesh');
-```
 
-or as a standalone script tag:
+// If you are using the WASM backend:
+require('@tensorflow/tfjs-backend-wasm'); // You need to require the backend explicitly because facemesh itself does not
 
-```html
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter"></script>
-<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/facemesh"></script>
+// If you are using the WebGL backend:
+// require('@tensorflow/tfjs-backend-webgl');
 ```
@@ -45,0 +54,0 @@

rollup.config.js

@@ -18,5 +18,5 @@ /**
 
-import node from 'rollup-plugin-node-resolve';
-import typescript from 'rollup-plugin-typescript2';
-import uglify from 'rollup-plugin-uglify';
+import resolve from '@rollup/plugin-node-resolve';
+import typescript from '@rollup/plugin-typescript';
+import {terser} from 'rollup-plugin-terser';
 
@@ -40,12 +40,14 @@ const PREAMBLE = `/**
 
-function minify() {
-  return uglify({output: {preamble: PREAMBLE}});
-}
+function config({ plugins = [], output = {}, tsCompilerOptions = {} }) {
+  const defaultTsOptions = {
+    include: ['src/**/*.ts'],
+    module: 'ES2015',
+  };
+  const tsoptions = Object.assign({}, defaultTsOptions, tsCompilerOptions);
 
-function config({plugins = [], output = {}}) {
   return {
     input: 'src/index.ts',
     plugins: [
-      typescript({tsconfigOverride: {compilerOptions: {module: 'ES2015'}}}),
-      node(), ...plugins
+      typescript(tsoptions),
+      resolve(), ...plugins
     ],
@@ -70,13 +72,9 @@ output: {
   config({
-    plugins: [
-      minify()
-    ],
+    plugins: [terser({output: {preamble: PREAMBLE, comments: false}})],
     output: {format: 'umd', name: 'facemesh', file: 'dist/facemesh.min.js'}
   }),
   config({
-    plugins: [
-      minify()
-    ],
+    plugins: [terser({output: {preamble: PREAMBLE, comments: false}})],
     output: {format: 'es', file: 'dist/facemesh.esm.js'}
   })
 ];

run_tests.ts

@@ -18,5 +18,24 @@ /**
 
+// Use the CPU backend for running tests.
+import '@tensorflow/tfjs-backend-cpu';
+// tslint:disable-next-line:no-imports-from-dist
 import * as jasmine_util from '@tensorflow/tfjs-core/dist/jasmine_util';
-import {runTests} from '../test_util';
 
-runTests(jasmine_util);
+// tslint:disable-next-line:no-require-imports
+const jasmineCtor = require('jasmine');
+// tslint:disable-next-line:no-require-imports
+
+Error.stackTraceLimit = Infinity;
+
+process.on('unhandledRejection', e => {
+  throw e;
+});
+
+jasmine_util.setTestEnvs(
+    [{name: 'test-facemesh', backendName: 'cpu', flags: {}}]);
+
+const unitTests = 'src/**/*_test.ts';
+
+const runner = new jasmineCtor();
+runner.loadConfig({spec_files: [unitTests], random: false});
+runner.execute();

tsconfig.json

 {
-  "compilerOptions": {
-    "module": "commonjs",
-    "moduleResolution": "node",
-    "noImplicitAny": true,
-    "sourceMap": true,
-    "removeComments": true,
-    "preserveConstEnums": true,
-    "declaration": true,
-    "target": "es2017",
-    "lib": [
-      "es2015",
-      "dom"
-    ],
-    "outDir": "./dist",
-    "noUnusedLocals": true,
-    "noImplicitReturns": true,
-    "noImplicitThis": true,
-    "alwaysStrict": true,
-    "noUnusedParameters": false,
-    "pretty": true,
-    "noFallthroughCasesInSwitch": true,
-    "allowUnreachableCode": false,
-    "experimentalDecorators": true
-  },
+  "extends": "../tsconfig",
   "include": [
@@ -30,5 +7,7 @@ "src/"
   "exclude": [
-    "node_modules/",
-    "dist/"
-  ]
+    "node_modules/"
+  ],
+  "compilerOptions": {
+    "outDir": "./dist"
+  }
 }

No alert changes

Improved metrics

Total package byte prevSize

10857528

increased by0.51%

Lines of code

2895

increased by31.17%

Number of lines in readme file

143

increased by6.72%

Worsened metrics

Dependency count

4

increased by33.33%

Dev dependency count

17

increased by21.43%

Number of package files

24

decreased by-17.24%

Dependency changes

• + Addedrimraf@^3.0.2

• + Added@tensorflow/tfjs-converter@2.8.6(transitive)

• + Added@tensorflow/tfjs-core@2.8.6(transitive)

• + Addedbalanced-match@1.0.2(transitive)

• + Addedbrace-expansion@1.1.11(transitive)

• + Addedconcat-map@0.0.1(transitive)

• + Addedfs.realpath@1.0.0(transitive)

• + Addedglob@7.2.3(transitive)

• + Addedinflight@1.0.6(transitive)

• + Addedinherits@2.0.4(transitive)

• + Addedminimatch@3.1.2(transitive)

• + Addednode-fetch@2.6.13(transitive)

• + Addedonce@1.4.0(transitive)

• + Addedpath-is-absolute@1.0.1(transitive)

• + Addedrimraf@3.0.2(transitive)

• + Addedtr46@0.0.3(transitive)

• + Addedwebidl-conversions@3.0.1(transitive)

• + Addedwhatwg-url@5.0.0(transitive)

• + Addedwrappy@1.0.2(transitive)