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* from \"-!../../../node_modules/mini-css-extract-plugin/dist/loader.js??ref--7-oneOf-1-0!../../../node_modules/css-loader/dist/cjs.js??ref--7-oneOf-1-1!../../../node_modules/vue-loader/lib/loaders/stylePostLoader.js!../../../node_modules/postcss-loader/src/index.js??ref--7-oneOf-1-2!../../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Facerecognition.vue?vue&type=style&index=0&id=6cc2f6f3&prod&scoped=true&lang=css\"","'use strict';\nvar defineBuiltIn = require('../internals/define-built-in');\nvar uncurryThis = require('../internals/function-uncurry-this');\nvar toString = require('../internals/to-string');\nvar validateArgumentsLength = require('../internals/validate-arguments-length');\n\nvar $URLSearchParams = URLSearchParams;\nvar URLSearchParamsPrototype = $URLSearchParams.prototype;\nvar getAll = uncurryThis(URLSearchParamsPrototype.getAll);\nvar $has = uncurryThis(URLSearchParamsPrototype.has);\nvar params = new $URLSearchParams('a=1');\n\n// `undefined` case is a Chromium 117 bug\n// https://bugs.chromium.org/p/v8/issues/detail?id=14222\nif (params.has('a', 2) || !params.has('a', undefined)) {\n  defineBuiltIn(URLSearchParamsPrototype, 'has', function has(name /* , value */) {\n    var length = arguments.length;\n    var $value = length < 2 ? undefined : arguments[1];\n    if (length && $value === undefined) return $has(this, name);\n    var values = getAll(this, name); // also validates `this`\n    validateArgumentsLength(length, 1);\n    var value = toString($value);\n    var index = 0;\n    while (index < values.length) {\n      if (values[index++] === value) return true;\n    } return false;\n  }, { enumerable: true, unsafe: true });\n}\n","module.exports = \"data:image/png;base64,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\"","'use strict';\nvar DESCRIPTORS = require('../internals/descriptors');\nvar uncurryThis = require('../internals/function-uncurry-this');\nvar defineBuiltInAccessor = require('../internals/define-built-in-accessor');\n\nvar URLSearchParamsPrototype = URLSearchParams.prototype;\nvar forEach = uncurryThis(URLSearchParamsPrototype.forEach);\n\n// `URLSearchParams.prototype.size` getter\n// https://github.com/whatwg/url/pull/734\nif (DESCRIPTORS && !('size' in URLSearchParamsPrototype)) {\n  defineBuiltInAccessor(URLSearchParamsPrototype, 'size', {\n    get: function size() {\n      var count = 0;\n      forEach(this, function () { count++; });\n      return count;\n    },\n    configurable: true,\n    enumerable: true\n  });\n}\n","import { __assign } from \"tslib\";\r\nimport { createFileSystem } from './createFileSystem';\r\nexport function createNodejsEnv() {\r\n    var Canvas = global['Canvas'] || global['HTMLCanvasElement'];\r\n    var Image = global['Image'] || global['HTMLImageElement'];\r\n    var createCanvasElement = function () {\r\n        if (Canvas) {\r\n            return new Canvas();\r\n        }\r\n        throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\r\n    };\r\n    var createImageElement = function () {\r\n        if (Image) {\r\n            return new Image();\r\n        }\r\n        throw new Error('createImageElement - missing Image implementation for nodejs environment');\r\n    };\r\n    var fetch = global['fetch'] || function () {\r\n        throw new Error('fetch - missing fetch implementation for nodejs environment');\r\n    };\r\n    var fileSystem = createFileSystem();\r\n    return __assign({ Canvas: Canvas || /** @class */ (function () {\r\n            function Canvas() {\r\n            }\r\n            return Canvas;\r\n        }()), CanvasRenderingContext2D: global['CanvasRenderingContext2D'] || /** @class */ (function () {\r\n            function class_1() {\r\n            }\r\n            return class_1;\r\n        }()), Image: Image || /** @class */ (function () {\r\n            function Image() {\r\n            }\r\n            return Image;\r\n        }()), ImageData: global['ImageData'] || /** @class */ (function () {\r\n            function class_2() {\r\n            }\r\n            return class_2;\r\n        }()), Video: global['HTMLVideoElement'] || /** @class */ (function () {\r\n            function class_3() {\r\n            }\r\n            return class_3;\r\n        }()), createCanvasElement: createCanvasElement,\r\n        createImageElement: createImageElement,\r\n        fetch: fetch }, fileSystem);\r\n}\r\n//# sourceMappingURL=createNodejsEnv.js.map","'use strict';\nvar defineBuiltIn = require('../internals/define-built-in');\nvar uncurryThis = require('../internals/function-uncurry-this');\nvar toString = require('../internals/to-string');\nvar validateArgumentsLength = require('../internals/validate-arguments-length');\n\nvar $URLSearchParams = URLSearchParams;\nvar URLSearchParamsPrototype = $URLSearchParams.prototype;\nvar append = uncurryThis(URLSearchParamsPrototype.append);\nvar $delete = uncurryThis(URLSearchParamsPrototype['delete']);\nvar forEach = uncurryThis(URLSearchParamsPrototype.forEach);\nvar push = uncurryThis([].push);\nvar params = new $URLSearchParams('a=1&a=2&b=3');\n\nparams['delete']('a', 1);\n// `undefined` case is a Chromium 117 bug\n// https://bugs.chromium.org/p/v8/issues/detail?id=14222\nparams['delete']('b', undefined);\n\nif (params + '' !== 'a=2') {\n  defineBuiltIn(URLSearchParamsPrototype, 'delete', function (name /* , value */) {\n    var length = arguments.length;\n    var $value = length < 2 ? undefined : arguments[1];\n    if (length && $value === undefined) return $delete(this, name);\n    var entries = [];\n    forEach(this, function (v, k) { // also validates `this`\n      push(entries, { key: k, value: v });\n    });\n    validateArgumentsLength(length, 1);\n    var key = toString(name);\n    var value = toString($value);\n    var index = 0;\n    var dindex = 0;\n    var found = false;\n    var entriesLength = entries.length;\n    var entry;\n    while (index < entriesLength) {\n      entry = entries[index++];\n      if (found || entry.key === key) {\n        found = true;\n        $delete(this, entry.key);\n      } else dindex++;\n    }\n    while (dindex < entriesLength) {\n      entry = entries[dindex++];\n      if (!(entry.key === key && entry.value === value)) append(this, entry.key, entry.value);\n    }\n  }, { enumerable: true, unsafe: true });\n}\n","export function isNodejs() {\r\n    return typeof global === 'object'\r\n        && typeof require === 'function'\r\n        && typeof module !== 'undefined'\r\n        // issues with gatsby.js: module.exports is undefined\r\n        // && !!module.exports\r\n        && typeof process !== 'undefined' && !!process.version;\r\n}\r\n//# sourceMappingURL=isNodejs.js.map","module.exports = \"data:image/png;base64,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\"","module.exports = \"data:image/png;base64,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\"","var render = function render(){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"face_expression_recognition\"},[_c('div',{staticClass:\"left\"},[_c('div',{staticClass:\"Imagerecognition\"},[_c('label',[_vm._v(\"更换图片\")]),_c('div',{staticClass:\"imgbg\"},[_c('div',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.imgEl!=null),expression:\"imgEl!=null\"}],staticClass:\"see imgsee\",attrs:{\"id\":\"imgsee\"}},[_c('img',{attrs:{\"id\":\"myImg\",\"src\":\"http://210.5.159.76:8095//File/TemporaryFile/Uploads/images/publicize/1.png\"}}),_c('canvas',{attrs:{\"id\":\"myCanvas\"}})]),_c('div',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.imgEl==null),expression:\"imgEl==null\"}],staticClass:\"default\"},[_c('img',{attrs:{\"src\":require(\"../../../public/img/img03.png\")}})])]),_c('input',{staticClass:\"updataimg\",attrs:{\"type\":\"file\",\"accept\":\"image/png, image/jpeg\"},on:{\"change\":function($event){return _vm.fnChange($event)}}})]),_c('div',{staticClass:\"result\"},[_vm._l((_vm.expression),function(item){return _c('div',{staticClass:\"emote\"},[_c('span',[_vm._v(_vm._s(item.Name))]),_c('span',[_vm._v(_vm._s(Math.floor(item.value*100))+\"%\")])])}),_c('div',{staticClass:\"gender item\"},[_c('span',[_vm._v(_vm._s(_vm.sex))]),_c('label',[_vm._v(\"性别\")])]),_c('div',{staticClass:\"age item\"},[_c('span',[_vm._v(_vm._s(_vm.years))]),_c('label',[_vm._v(\"年龄\")])])],2)]),_c('div',{staticClass:\"right\"},[_c('div',{staticClass:\"videorecognition\"},[_c('label',[_vm._v(\"上传视频\")]),_vm._m(0),_c('div',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.myvideoCanvas==null),expression:\"myvideoCanvas==null\"}],staticClass:\"playicon\"},[_c('img',{attrs:{\"src\":require(\"../../../public/img/play.png\")}})]),_c('input',{attrs:{\"type\":\"file\",\"accept\":\"video/mp4, video/ogg, video/webm\"},on:{\"change\":function($event){return _vm.videoChange($event)}}}),_c('div',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.myvideoCanvas!=null),expression:\"myvideoCanvas!=null\"}],staticClass:\"Identifycontent\"},[_c('p',{staticClass:\"title\"},[_vm._v(\"识别信息\")]),_c('div',{staticClass:\"other\"},[_vm._l((_vm.videoexpression),function(item){return _c('p',[_vm._v(\"表情：\"+_vm._s(item.Name)+\" \"+_vm._s(item.value*100))])}),_c('p',[_vm._v(\"性别：\"+_vm._s(_vm.videosex))]),_c('p',[_vm._v(\"年龄：\"+_vm._s(_vm.videoyears))])],2)])]),_c('div',{staticClass:\"camerarecognition\",on:{\"click\":_vm.Openlive}},[_c('label',[_vm._v(\"打开摄像头\")]),_c('div',{staticClass:\"livesee\"},[_c('video',{attrs:{\"id\":\"mylive\",\"src\":\"\",\"muted\":\"\",\"loop\":\"\",\"playsinline\":\"\"},domProps:{\"muted\":true},on:{\"loadedmetadata\":_vm.liveRun}}),_c('canvas',{attrs:{\"id\":\"myliveCanvas\"}})]),_c('div',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.myliveCanvas==null),expression:\"myliveCanvas==null\"}],staticClass:\"playicon\"},[_c('img',{attrs:{\"src\":require(\"../../../public/img/img02.png\")}})]),_c('div',{directives:[{name:\"show\",rawName:\"v-show\",value:(_vm.myliveCanvas!=null),expression:\"myliveCanvas!=null\"}],staticClass:\"Identifycontent\"},[_c('p',{staticClass:\"title\"},[_vm._v(\"识别信息\")]),_c('div',{staticClass:\"other\"},[_vm._l((_vm.cameraexpression),function(item){return _c('p',[_vm._v(\"表情：\"+_vm._s(item.Name)+\" \"+_vm._s(Math.floor(item.value*100)))])}),_c('p',[_vm._v(\"性别：\"+_vm._s(_vm.camerasex))]),_c('p',[_vm._v(\"年龄：\"+_vm._s(_vm.camerayears))])],2)])])])])\n}\nvar staticRenderFns = [function (){var _vm=this,_c=_vm._self._c;return _c('div',{staticClass:\"videosee\"},[_c('video',{attrs:{\"id\":\"myVideo\",\"src\":\"\",\"playsinline\":\"\"}}),_c('canvas',{attrs:{\"id\":\"myvideoCanvas\"}})])\n}]\n\nexport { render, staticRenderFns }","export function drawContour(ctx, points, isClosed) {\r\n    if (isClosed === void 0) { isClosed = false; }\r\n    ctx.beginPath();\r\n    points.slice(1).forEach(function (_a, prevIdx) {\r\n        var x = _a.x, y = _a.y;\r\n        var from = points[prevIdx];\r\n        ctx.moveTo(from.x, from.y);\r\n        ctx.lineTo(x, y);\r\n    });\r\n    if (isClosed) {\r\n        var from = points[points.length - 1];\r\n        var to = points[0];\r\n        if (!from || !to) {\r\n            return;\r\n        }\r\n        ctx.moveTo(from.x, from.y);\r\n        ctx.lineTo(to.x, to.y);\r\n    }\r\n    ctx.stroke();\r\n}\r\n//# sourceMappingURL=drawContour.js.map","import { isValidNumber } from '../utils';\r\nvar Dimensions = /** @class */ (function () {\r\n    function Dimensions(width, height) {\r\n        if (!isValidNumber(width) || !isValidNumber(height)) {\r\n            throw new Error(\"Dimensions.constructor - expected width and height to be valid numbers, instead have \" + JSON.stringify({ width: width, height: height }));\r\n        }\r\n        this._width = width;\r\n        this._height = height;\r\n    }\r\n    Object.defineProperty(Dimensions.prototype, \"width\", {\r\n        get: function () { return this._width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Dimensions.prototype, \"height\", {\r\n        get: function () { return this._height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Dimensions.prototype.reverse = function () {\r\n        return new Dimensions(1 / this.width, 1 / this.height);\r\n    };\r\n    return Dimensions;\r\n}());\r\nexport { Dimensions };\r\n//# sourceMappingURL=Dimensions.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { Point } from '../classes';\r\nimport { Dimensions } from '../classes/Dimensions';\r\nexport function isTensor(tensor, dim) {\r\n    return tensor instanceof tf.Tensor && tensor.shape.length === dim;\r\n}\r\nexport function isTensor1D(tensor) {\r\n    return isTensor(tensor, 1);\r\n}\r\nexport function isTensor2D(tensor) {\r\n    return isTensor(tensor, 2);\r\n}\r\nexport function isTensor3D(tensor) {\r\n    return isTensor(tensor, 3);\r\n}\r\nexport function isTensor4D(tensor) {\r\n    return isTensor(tensor, 4);\r\n}\r\nexport function isFloat(num) {\r\n    return num % 1 !== 0;\r\n}\r\nexport function isEven(num) {\r\n    return num % 2 === 0;\r\n}\r\nexport function round(num, prec) {\r\n    if (prec === void 0) { prec = 2; }\r\n    var f = Math.pow(10, prec);\r\n    return Math.floor(num * f) / f;\r\n}\r\nexport function isDimensions(obj) {\r\n    return obj && obj.width && obj.height;\r\n}\r\nexport function computeReshapedDimensions(_a, inputSize) {\r\n    var width = _a.width, height = _a.height;\r\n    var scale = inputSize / Math.max(height, width);\r\n    return new Dimensions(Math.round(width * scale), Math.round(height * scale));\r\n}\r\nexport function getCenterPoint(pts) {\r\n    return pts.reduce(function (sum, pt) { return sum.add(pt); }, new Point(0, 0))\r\n        .div(new Point(pts.length, pts.length));\r\n}\r\nexport function range(num, start, step) {\r\n    return Array(num).fill(0).map(function (_, i) { return start + (i * step); });\r\n}\r\nexport function isValidNumber(num) {\r\n    return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;\r\n}\r\nexport function isValidProbablitiy(num) {\r\n    return isValidNumber(num) && 0 <= num && num <= 1.0;\r\n}\r\n//# sourceMappingURL=index.js.map","var Point = /** @class */ (function () {\r\n    function Point(x, y) {\r\n        this._x = x;\r\n        this._y = y;\r\n    }\r\n    Object.defineProperty(Point.prototype, \"x\", {\r\n        get: function () { return this._x; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Point.prototype, \"y\", {\r\n        get: function () { return this._y; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Point.prototype.add = function (pt) {\r\n        return new Point(this.x + pt.x, this.y + pt.y);\r\n    };\r\n    Point.prototype.sub = function (pt) {\r\n        return new Point(this.x - pt.x, this.y - pt.y);\r\n    };\r\n    Point.prototype.mul = function (pt) {\r\n        return new Point(this.x * pt.x, this.y * pt.y);\r\n    };\r\n    Point.prototype.div = function (pt) {\r\n        return new Point(this.x / pt.x, this.y / pt.y);\r\n    };\r\n    Point.prototype.abs = function () {\r\n        return new Point(Math.abs(this.x), Math.abs(this.y));\r\n    };\r\n    Point.prototype.magnitude = function () {\r\n        return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));\r\n    };\r\n    Point.prototype.floor = function () {\r\n        return new Point(Math.floor(this.x), Math.floor(this.y));\r\n    };\r\n    return Point;\r\n}());\r\nexport { Point };\r\n//# sourceMappingURL=Point.js.map","import { isDimensions, isValidNumber } from '../utils';\r\nimport { Point } from './Point';\r\nvar Box = /** @class */ (function () {\r\n    function Box(_box, allowNegativeDimensions) {\r\n        if (allowNegativeDimensions === void 0) { allowNegativeDimensions = true; }\r\n        var box = (_box || {});\r\n        var isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\r\n        var isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\r\n        if (!isRect && !isBbox) {\r\n            throw new Error(\"Box.constructor - expected box to be IBoundingBox | IRect, instead have \" + JSON.stringify(box));\r\n        }\r\n        var _a = isRect\r\n            ? [box.x, box.y, box.width, box.height]\r\n            : [box.left, box.top, box.right - box.left, box.bottom - box.top], x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        Box.assertIsValidBox({ x: x, y: y, width: width, height: height }, 'Box.constructor', allowNegativeDimensions);\r\n        this._x = x;\r\n        this._y = y;\r\n        this._width = width;\r\n        this._height = height;\r\n    }\r\n    Box.isRect = function (rect) {\r\n        return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\r\n    };\r\n    Box.assertIsValidBox = function (box, callee, allowNegativeDimensions) {\r\n        if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }\r\n        if (!Box.isRect(box)) {\r\n            throw new Error(callee + \" - invalid box: \" + JSON.stringify(box) + \", expected object with properties x, y, width, height\");\r\n        }\r\n        if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\r\n            throw new Error(callee + \" - width (\" + box.width + \") and height (\" + box.height + \") must be positive numbers\");\r\n        }\r\n    };\r\n    Object.defineProperty(Box.prototype, \"x\", {\r\n        get: function () { return this._x; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"y\", {\r\n        get: function () { return this._y; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"width\", {\r\n        get: function () { return this._width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"height\", {\r\n        get: function () { return this._height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"left\", {\r\n        get: function () { return this.x; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"top\", {\r\n        get: function () { return this.y; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"right\", {\r\n        get: function () { return this.x + this.width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"bottom\", {\r\n        get: function () { return this.y + this.height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"area\", {\r\n        get: function () { return this.width * this.height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"topLeft\", {\r\n        get: function () { return new Point(this.left, this.top); },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"topRight\", {\r\n        get: function () { return new Point(this.right, this.top); },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"bottomLeft\", {\r\n        get: function () { return new Point(this.left, this.bottom); },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"bottomRight\", {\r\n        get: function () { return new Point(this.right, this.bottom); },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Box.prototype.round = function () {\r\n        var _a = [this.x, this.y, this.width, this.height]\r\n            .map(function (val) { return Math.round(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.floor = function () {\r\n        var _a = [this.x, this.y, this.width, this.height]\r\n            .map(function (val) { return Math.floor(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.toSquare = function () {\r\n        var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n        var diff = Math.abs(width - height);\r\n        if (width < height) {\r\n            x -= (diff / 2);\r\n            width += diff;\r\n        }\r\n        if (height < width) {\r\n            y -= (diff / 2);\r\n            height += diff;\r\n        }\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.rescale = function (s) {\r\n        var scaleX = isDimensions(s) ? s.width : s;\r\n        var scaleY = isDimensions(s) ? s.height : s;\r\n        return new Box({\r\n            x: this.x * scaleX,\r\n            y: this.y * scaleY,\r\n            width: this.width * scaleX,\r\n            height: this.height * scaleY\r\n        });\r\n    };\r\n    Box.prototype.pad = function (padX, padY) {\r\n        var _a = [\r\n            this.x - (padX / 2),\r\n            this.y - (padY / 2),\r\n            this.width + padX,\r\n            this.height + padY\r\n        ], x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {\r\n        var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;\r\n        var clippedX = Math.max(x, 0);\r\n        var clippedY = Math.max(y, 0);\r\n        var newWidth = right - clippedX;\r\n        var newHeight = bottom - clippedY;\r\n        var clippedWidth = Math.min(newWidth, imgWidth - clippedX);\r\n        var clippedHeight = Math.min(newHeight, imgHeight - clippedY);\r\n        return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();\r\n    };\r\n    Box.prototype.shift = function (sx, sy) {\r\n        var _a = this, width = _a.width, height = _a.height;\r\n        var x = this.x + sx;\r\n        var y = this.y + sy;\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.padAtBorders = function (imageHeight, imageWidth) {\r\n        var w = this.width + 1;\r\n        var h = this.height + 1;\r\n        var dx = 1;\r\n        var dy = 1;\r\n        var edx = w;\r\n        var edy = h;\r\n        var x = this.left;\r\n        var y = this.top;\r\n        var ex = this.right;\r\n        var ey = this.bottom;\r\n        if (ex > imageWidth) {\r\n            edx = -ex + imageWidth + w;\r\n            ex = imageWidth;\r\n        }\r\n        if (ey > imageHeight) {\r\n            edy = -ey + imageHeight + h;\r\n            ey = imageHeight;\r\n        }\r\n        if (x < 1) {\r\n            edy = 2 - x;\r\n            x = 1;\r\n        }\r\n        if (y < 1) {\r\n            edy = 2 - y;\r\n            y = 1;\r\n        }\r\n        return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };\r\n    };\r\n    Box.prototype.calibrate = function (region) {\r\n        return new Box({\r\n            left: this.left + (region.left * this.width),\r\n            top: this.top + (region.top * this.height),\r\n            right: this.right + (region.right * this.width),\r\n            bottom: this.bottom + (region.bottom * this.height)\r\n        }).toSquare().round();\r\n    };\r\n    return Box;\r\n}());\r\nexport { Box };\r\n//# sourceMappingURL=Box.js.map","import { __extends } from \"tslib\";\r\nimport { Box } from './Box';\r\nvar BoundingBox = /** @class */ (function (_super) {\r\n    __extends(BoundingBox, _super);\r\n    function BoundingBox(left, top, right, bottom, allowNegativeDimensions) {\r\n        if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }\r\n        return _super.call(this, { left: left, top: top, right: right, bottom: bottom }, allowNegativeDimensions) || this;\r\n    }\r\n    return BoundingBox;\r\n}(Box));\r\nexport { BoundingBox };\r\n//# sourceMappingURL=BoundingBox.js.map","import { Box } from './Box';\r\nimport { Dimensions } from './Dimensions';\r\nvar ObjectDetection = /** @class */ (function () {\r\n    function ObjectDetection(score, classScore, className, relativeBox, imageDims) {\r\n        this._imageDims = new Dimensions(imageDims.width, imageDims.height);\r\n        this._score = score;\r\n        this._classScore = classScore;\r\n        this._className = className;\r\n        this._box = new Box(relativeBox).rescale(this._imageDims);\r\n    }\r\n    Object.defineProperty(ObjectDetection.prototype, \"score\", {\r\n        get: function () { return this._score; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"classScore\", {\r\n        get: function () { return this._classScore; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"className\", {\r\n        get: function () { return this._className; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"box\", {\r\n        get: function () { return this._box; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"imageDims\", {\r\n        get: function () { return this._imageDims; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"imageWidth\", {\r\n        get: function () { return this.imageDims.width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"imageHeight\", {\r\n        get: function () { return this.imageDims.height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"relativeBox\", {\r\n        get: function () { return new Box(this._box).rescale(this.imageDims.reverse()); },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    ObjectDetection.prototype.forSize = function (width, height) {\r\n        return new ObjectDetection(this.score, this.classScore, this.className, this.relativeBox, { width: width, height: height });\r\n    };\r\n    return ObjectDetection;\r\n}());\r\nexport { ObjectDetection };\r\n//# sourceMappingURL=ObjectDetection.js.map","import { __extends } from \"tslib\";\r\nimport { ObjectDetection } from './ObjectDetection';\r\nvar FaceDetection = /** @class */ (function (_super) {\r\n    __extends(FaceDetection, _super);\r\n    function FaceDetection(score, relativeBox, imageDims) {\r\n        return _super.call(this, score, score, '', relativeBox, imageDims) || this;\r\n    }\r\n    FaceDetection.prototype.forSize = function (width, height) {\r\n        var _a = _super.prototype.forSize.call(this, width, height), score = _a.score, relativeBox = _a.relativeBox, imageDims = _a.imageDims;\r\n        return new FaceDetection(score, relativeBox, imageDims);\r\n    };\r\n    return FaceDetection;\r\n}(ObjectDetection));\r\nexport { FaceDetection };\r\n//# sourceMappingURL=FaceDetection.js.map","export function iou(box1, box2, isIOU) {\r\n    if (isIOU === void 0) { isIOU = true; }\r\n    var width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\r\n    var height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\r\n    var interSection = width * height;\r\n    return isIOU\r\n        ? interSection / (box1.area + box2.area - interSection)\r\n        : interSection / Math.min(box1.area, box2.area);\r\n}\r\n//# sourceMappingURL=iou.js.map","import { BoundingBox } from '../classes';\r\nexport function minBbox(pts) {\r\n    var xs = pts.map(function (pt) { return pt.x; });\r\n    var ys = pts.map(function (pt) { return pt.y; });\r\n    var minX = xs.reduce(function (min, x) { return x < min ? x : min; }, Infinity);\r\n    var minY = ys.reduce(function (min, y) { return y < min ? y : min; }, Infinity);\r\n    var maxX = xs.reduce(function (max, x) { return max < x ? x : max; }, 0);\r\n    var maxY = ys.reduce(function (max, y) { return max < y ? y : max; }, 0);\r\n    return new BoundingBox(minX, minY, maxX, maxY);\r\n}\r\n//# sourceMappingURL=minBbox.js.map","import { iou } from './iou';\r\nexport function nonMaxSuppression(boxes, scores, iouThreshold, isIOU) {\r\n    if (isIOU === void 0) { isIOU = true; }\r\n    var indicesSortedByScore = scores\r\n        .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })\r\n        .sort(function (c1, c2) { return c1.score - c2.score; })\r\n        .map(function (c) { return c.boxIndex; });\r\n    var pick = [];\r\n    var _loop_1 = function () {\r\n        var curr = indicesSortedByScore.pop();\r\n        pick.push(curr);\r\n        var indices = indicesSortedByScore;\r\n        var outputs = [];\r\n        for (var i = 0; i < indices.length; i++) {\r\n            var idx = indices[i];\r\n            var currBox = boxes[curr];\r\n            var idxBox = boxes[idx];\r\n            outputs.push(iou(currBox, idxBox, isIOU));\r\n        }\r\n        indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });\r\n    };\r\n    while (indicesSortedByScore.length > 0) {\r\n        _loop_1();\r\n    }\r\n    return pick;\r\n}\r\n//# sourceMappingURL=nonMaxSuppression.js.map","import { __spreadArrays } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nexport function normalize(x, meanRgb) {\r\n    return tf.tidy(function () {\r\n        var r = meanRgb[0], g = meanRgb[1], b = meanRgb[2];\r\n        var avg_r = tf.fill(__spreadArrays(x.shape.slice(0, 3), [1]), r);\r\n        var avg_g = tf.fill(__spreadArrays(x.shape.slice(0, 3), [1]), g);\r\n        var avg_b = tf.fill(__spreadArrays(x.shape.slice(0, 3), [1]), b);\r\n        var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\r\n        return tf.sub(x, avg_rgb);\r\n    });\r\n}\r\n//# sourceMappingURL=normalize.js.map","import * as tf from '@tensorflow/tfjs-core';\r\n/**\r\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\r\n *\r\n * @param imgTensor The image tensor.\r\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\r\n * both sides of the minor dimension oof the image.\r\n * @returns The padded tensor with width === height.\r\n */\r\nexport function padToSquare(imgTensor, isCenterImage) {\r\n    if (isCenterImage === void 0) { isCenterImage = false; }\r\n    return tf.tidy(function () {\r\n        var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];\r\n        if (height === width) {\r\n            return imgTensor;\r\n        }\r\n        var dimDiff = Math.abs(height - width);\r\n        var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\r\n        var paddingAxis = height > width ? 2 : 1;\r\n        var createPaddingTensor = function (paddingAmount) {\r\n            var paddingTensorShape = imgTensor.shape.slice();\r\n            paddingTensorShape[paddingAxis] = paddingAmount;\r\n            return tf.fill(paddingTensorShape, 0);\r\n        };\r\n        var paddingTensorAppend = createPaddingTensor(paddingAmount);\r\n        var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];\r\n        var paddingTensorPrepend = isCenterImage && remainingPaddingAmount\r\n            ? createPaddingTensor(remainingPaddingAmount)\r\n            : null;\r\n        var tensorsToStack = [\r\n            paddingTensorPrepend,\r\n            imgTensor,\r\n            paddingTensorAppend\r\n        ]\r\n            .filter(function (t) { return !!t; })\r\n            .map(function (t) { return t.toFloat(); });\r\n        return tf.concat(tensorsToStack, paddingAxis);\r\n    });\r\n}\r\n//# sourceMappingURL=padToSquare.js.map","export * from './iou';\r\nexport * from './minBbox';\r\nexport * from './nonMaxSuppression';\r\nexport * from './normalize';\r\nexport * from './padToSquare';\r\nexport * from './shuffleArray';\r\nexport function sigmoid(x) {\r\n    return 1 / (1 + Math.exp(-x));\r\n}\r\nexport function inverseSigmoid(x) {\r\n    return Math.log(x / (1 - x));\r\n}\r\n//# sourceMappingURL=index.js.map","import { __extends } from \"tslib\";\r\nimport { Box } from './Box';\r\nvar Rect = /** @class */ (function (_super) {\r\n    __extends(Rect, _super);\r\n    function Rect(x, y, width, height, allowNegativeDimensions) {\r\n        if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }\r\n        return _super.call(this, { x: x, y: y, width: width, height: height }, allowNegativeDimensions) || this;\r\n    }\r\n    return Rect;\r\n}(Box));\r\nexport { Rect };\r\n//# sourceMappingURL=Rect.js.map","import { minBbox } from '../ops';\r\nimport { getCenterPoint } from '../utils';\r\nimport { Box } from './Box';\r\nimport { Dimensions } from './Dimensions';\r\nimport { FaceDetection } from './FaceDetection';\r\nimport { Point } from './Point';\r\nimport { Rect } from './Rect';\r\n// face alignment constants\r\nvar relX = 0.5;\r\nvar relY = 0.43;\r\nvar relScale = 0.45;\r\nvar FaceLandmarks = /** @class */ (function () {\r\n    function FaceLandmarks(relativeFaceLandmarkPositions, imgDims, shift) {\r\n        if (shift === void 0) { shift = new Point(0, 0); }\r\n        var width = imgDims.width, height = imgDims.height;\r\n        this._imgDims = new Dimensions(width, height);\r\n        this._shift = shift;\r\n        this._positions = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point(width, height)).add(shift); });\r\n    }\r\n    Object.defineProperty(FaceLandmarks.prototype, \"shift\", {\r\n        get: function () { return new Point(this._shift.x, this._shift.y); },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(FaceLandmarks.prototype, \"imageWidth\", {\r\n        get: function () { return this._imgDims.width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(FaceLandmarks.prototype, \"imageHeight\", {\r\n        get: function () { return this._imgDims.height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(FaceLandmarks.prototype, \"positions\", {\r\n        get: function () { return this._positions; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(FaceLandmarks.prototype, \"relativePositions\", {\r\n        get: function () {\r\n            var _this = this;\r\n            return this._positions.map(function (pt) { return pt.sub(_this._shift).div(new Point(_this.imageWidth, _this.imageHeight)); });\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    FaceLandmarks.prototype.forSize = function (width, height) {\r\n        return new this.constructor(this.relativePositions, { width: width, height: height });\r\n    };\r\n    FaceLandmarks.prototype.shiftBy = function (x, y) {\r\n        return new this.constructor(this.relativePositions, this._imgDims, new Point(x, y));\r\n    };\r\n    FaceLandmarks.prototype.shiftByPoint = function (pt) {\r\n        return this.shiftBy(pt.x, pt.y);\r\n    };\r\n    /**\r\n     * Aligns the face landmarks after face detection from the relative positions of the faces\r\n     * bounding box, or it's current shift. This function should be used to align the face images\r\n     * after face detection has been performed, before they are passed to the face recognition net.\r\n     * This will make the computed face descriptor more accurate.\r\n     *\r\n     * @param detection (optional) The bounding box of the face or the face detection result. If\r\n     * no argument was passed the position of the face landmarks are assumed to be relative to\r\n     * it's current shift.\r\n     * @returns The bounding box of the aligned face.\r\n     */\r\n    FaceLandmarks.prototype.align = function (detection, options) {\r\n        if (options === void 0) { options = {}; }\r\n        if (detection) {\r\n            var box = detection instanceof FaceDetection\r\n                ? detection.box.floor()\r\n                : new Box(detection);\r\n            return this.shiftBy(box.x, box.y).align(null, options);\r\n        }\r\n        var _a = Object.assign({}, { useDlibAlignment: false, minBoxPadding: 0.2 }, options), useDlibAlignment = _a.useDlibAlignment, minBoxPadding = _a.minBoxPadding;\r\n        if (useDlibAlignment) {\r\n            return this.alignDlib();\r\n        }\r\n        return this.alignMinBbox(minBoxPadding);\r\n    };\r\n    FaceLandmarks.prototype.alignDlib = function () {\r\n        var centers = this.getRefPointsForAlignment();\r\n        var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];\r\n        var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };\r\n        var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;\r\n        var size = Math.floor(eyeToMouthDist / relScale);\r\n        var refPoint = getCenterPoint(centers);\r\n        // TODO: pad in case rectangle is out of image bounds\r\n        var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));\r\n        var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));\r\n        return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));\r\n    };\r\n    FaceLandmarks.prototype.alignMinBbox = function (padding) {\r\n        var box = minBbox(this.positions);\r\n        return box.pad(box.width * padding, box.height * padding);\r\n    };\r\n    FaceLandmarks.prototype.getRefPointsForAlignment = function () {\r\n        throw new Error('getRefPointsForAlignment not implemented by base class');\r\n    };\r\n    return FaceLandmarks;\r\n}());\r\nexport { FaceLandmarks };\r\n//# sourceMappingURL=FaceLandmarks.js.map","import { __extends } from \"tslib\";\r\nimport { getCenterPoint } from '../utils';\r\nimport { FaceLandmarks } from './FaceLandmarks';\r\nvar FaceLandmarks5 = /** @class */ (function (_super) {\r\n    __extends(FaceLandmarks5, _super);\r\n    function FaceLandmarks5() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    FaceLandmarks5.prototype.getRefPointsForAlignment = function () {\r\n        var pts = this.positions;\r\n        return [\r\n            pts[0],\r\n            pts[1],\r\n            getCenterPoint([pts[3], pts[4]])\r\n        ];\r\n    };\r\n    return FaceLandmarks5;\r\n}(FaceLandmarks));\r\nexport { FaceLandmarks5 };\r\n//# sourceMappingURL=FaceLandmarks5.js.map","import { __extends } from \"tslib\";\r\nimport { getCenterPoint } from '../utils';\r\nimport { FaceLandmarks } from './FaceLandmarks';\r\nvar FaceLandmarks68 = /** @class */ (function (_super) {\r\n    __extends(FaceLandmarks68, _super);\r\n    function FaceLandmarks68() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    FaceLandmarks68.prototype.getJawOutline = function () {\r\n        return this.positions.slice(0, 17);\r\n    };\r\n    FaceLandmarks68.prototype.getLeftEyeBrow = function () {\r\n        return this.positions.slice(17, 22);\r\n    };\r\n    FaceLandmarks68.prototype.getRightEyeBrow = function () {\r\n        return this.positions.slice(22, 27);\r\n    };\r\n    FaceLandmarks68.prototype.getNose = function () {\r\n        return this.positions.slice(27, 36);\r\n    };\r\n    FaceLandmarks68.prototype.getLeftEye = function () {\r\n        return this.positions.slice(36, 42);\r\n    };\r\n    FaceLandmarks68.prototype.getRightEye = function () {\r\n        return this.positions.slice(42, 48);\r\n    };\r\n    FaceLandmarks68.prototype.getMouth = function () {\r\n        return this.positions.slice(48, 68);\r\n    };\r\n    FaceLandmarks68.prototype.getRefPointsForAlignment = function () {\r\n        return [\r\n            this.getLeftEye(),\r\n            this.getRightEye(),\r\n            this.getMouth()\r\n        ].map(getCenterPoint);\r\n    };\r\n    return FaceLandmarks68;\r\n}(FaceLandmarks));\r\nexport { FaceLandmarks68 };\r\n//# sourceMappingURL=FaceLandmarks68.js.map","import { round } from '../utils';\r\nvar FaceMatch = /** @class */ (function () {\r\n    function FaceMatch(label, distance) {\r\n        this._label = label;\r\n        this._distance = distance;\r\n    }\r\n    Object.defineProperty(FaceMatch.prototype, \"label\", {\r\n        get: function () { return this._label; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(FaceMatch.prototype, \"distance\", {\r\n        get: function () { return this._distance; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    FaceMatch.prototype.toString = function (withDistance) {\r\n        if (withDistance === void 0) { withDistance = true; }\r\n        return \"\" + this.label + (withDistance ? \" (\" + round(this.distance) + \")\" : '');\r\n    };\r\n    return FaceMatch;\r\n}());\r\nexport { FaceMatch };\r\n//# sourceMappingURL=FaceMatch.js.map","import { __extends } from \"tslib\";\r\nimport { isValidNumber } from '../utils';\r\nimport { Box } from './Box';\r\nvar LabeledBox = /** @class */ (function (_super) {\r\n    __extends(LabeledBox, _super);\r\n    function LabeledBox(box, label) {\r\n        var _this = _super.call(this, box) || this;\r\n        _this._label = label;\r\n        return _this;\r\n    }\r\n    LabeledBox.assertIsValidLabeledBox = function (box, callee) {\r\n        Box.assertIsValidBox(box, callee);\r\n        if (!isValidNumber(box.label)) {\r\n            throw new Error(callee + \" - expected property label (\" + box.label + \") to be a number\");\r\n        }\r\n    };\r\n    Object.defineProperty(LabeledBox.prototype, \"label\", {\r\n        get: function () { return this._label; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return LabeledBox;\r\n}(Box));\r\nexport { LabeledBox };\r\n//# sourceMappingURL=LabeledBox.js.map","var LabeledFaceDescriptors = /** @class */ (function () {\r\n    function LabeledFaceDescriptors(label, descriptors) {\r\n        if (!(typeof label === 'string')) {\r\n            throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');\r\n        }\r\n        if (!Array.isArray(descriptors) || descriptors.some(function (desc) { return !(desc instanceof Float32Array); })) {\r\n            throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');\r\n        }\r\n        this._label = label;\r\n        this._descriptors = descriptors;\r\n    }\r\n    Object.defineProperty(LabeledFaceDescriptors.prototype, \"label\", {\r\n        get: function () { return this._label; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(LabeledFaceDescriptors.prototype, \"descriptors\", {\r\n        get: function () { return this._descriptors; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    LabeledFaceDescriptors.prototype.toJSON = function () {\r\n        return {\r\n            label: this.label,\r\n            descriptors: this.descriptors.map(function (d) { return Array.from(d); })\r\n        };\r\n    };\r\n    LabeledFaceDescriptors.fromJSON = function (json) {\r\n        var descriptors = json.descriptors.map(function (d) {\r\n            return new Float32Array(d);\r\n        });\r\n        return new LabeledFaceDescriptors(json.label, descriptors);\r\n    };\r\n    return LabeledFaceDescriptors;\r\n}());\r\nexport { LabeledFaceDescriptors };\r\n//# sourceMappingURL=LabeledFaceDescriptors.js.map","import { __extends } from \"tslib\";\r\nimport { isValidProbablitiy } from '../utils';\r\nimport { LabeledBox } from './LabeledBox';\r\nvar PredictedBox = /** @class */ (function (_super) {\r\n    __extends(PredictedBox, _super);\r\n    function PredictedBox(box, label, score, classScore) {\r\n        var _this = _super.call(this, box, label) || this;\r\n        _this._score = score;\r\n        _this._classScore = classScore;\r\n        return _this;\r\n    }\r\n    PredictedBox.assertIsValidPredictedBox = function (box, callee) {\r\n        LabeledBox.assertIsValidLabeledBox(box, callee);\r\n        if (!isValidProbablitiy(box.score)\r\n            || !isValidProbablitiy(box.classScore)) {\r\n            throw new Error(callee + \" - expected properties score (\" + box.score + \") and (\" + box.classScore + \") to be a number between [0, 1]\");\r\n        }\r\n    };\r\n    Object.defineProperty(PredictedBox.prototype, \"score\", {\r\n        get: function () { return this._score; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(PredictedBox.prototype, \"classScore\", {\r\n        get: function () { return this._classScore; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return PredictedBox;\r\n}(LabeledBox));\r\nexport { PredictedBox };\r\n//# sourceMappingURL=PredictedBox.js.map","import { FaceDetection } from '../classes/FaceDetection';\r\nexport function isWithFaceDetection(obj) {\r\n    return obj['detection'] instanceof FaceDetection;\r\n}\r\nexport function extendWithFaceDetection(sourceObj, detection) {\r\n    var extension = { detection: detection };\r\n    return Object.assign({}, sourceObj, extension);\r\n}\r\n//# sourceMappingURL=WithFaceDetection.js.map","export function createBrowserEnv() {\r\n    var fetch = window['fetch'] || function () {\r\n        throw new Error('fetch - missing fetch implementation for browser environment');\r\n    };\r\n    var readFile = function () {\r\n        throw new Error('readFile - filesystem not available for browser environment');\r\n    };\r\n    return {\r\n        Canvas: HTMLCanvasElement,\r\n        CanvasRenderingContext2D: CanvasRenderingContext2D,\r\n        Image: HTMLImageElement,\r\n        ImageData: ImageData,\r\n        Video: HTMLVideoElement,\r\n        createCanvasElement: function () { return document.createElement('canvas'); },\r\n        createImageElement: function () { return document.createElement('img'); },\r\n        fetch: fetch,\r\n        readFile: readFile\r\n    };\r\n}\r\n//# sourceMappingURL=createBrowserEnv.js.map","export function isBrowser() {\r\n    return typeof window === 'object'\r\n        && typeof document !== 'undefined'\r\n        && typeof HTMLImageElement !== 'undefined'\r\n        && typeof HTMLCanvasElement !== 'undefined'\r\n        && typeof HTMLVideoElement !== 'undefined'\r\n        && typeof ImageData !== 'undefined'\r\n        && typeof CanvasRenderingContext2D !== 'undefined';\r\n}\r\n//# sourceMappingURL=isBrowser.js.map","import { createBrowserEnv } from './createBrowserEnv';\r\nimport { createFileSystem } from './createFileSystem';\r\nimport { createNodejsEnv } from './createNodejsEnv';\r\nimport { isBrowser } from './isBrowser';\r\nimport { isNodejs } from './isNodejs';\r\nvar environment;\r\nfunction getEnv() {\r\n    if (!environment) {\r\n        throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\r\n    }\r\n    return environment;\r\n}\r\nfunction setEnv(env) {\r\n    environment = env;\r\n}\r\nfunction initialize() {\r\n    // check for isBrowser() first to prevent electron renderer process\r\n    // to be initialized with wrong environment due to isNodejs() returning true\r\n    if (isBrowser()) {\r\n        setEnv(createBrowserEnv());\r\n    }\r\n    if (isNodejs()) {\r\n        setEnv(createNodejsEnv());\r\n    }\r\n}\r\nfunction monkeyPatch(env) {\r\n    if (!environment) {\r\n        initialize();\r\n    }\r\n    if (!environment) {\r\n        throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\r\n    }\r\n    var _a = env.Canvas, Canvas = _a === void 0 ? environment.Canvas : _a, _b = env.Image, Image = _b === void 0 ? environment.Image : _b;\r\n    environment.Canvas = Canvas;\r\n    environment.Image = Image;\r\n    environment.createCanvasElement = env.createCanvasElement || (function () { return new Canvas(); });\r\n    environment.createImageElement = env.createImageElement || (function () { return new Image(); });\r\n    environment.ImageData = env.ImageData || environment.ImageData;\r\n    environment.Video = env.Video || environment.Video;\r\n    environment.fetch = env.fetch || environment.fetch;\r\n    environment.readFile = env.readFile || environment.readFile;\r\n}\r\nexport var env = {\r\n    getEnv: getEnv,\r\n    setEnv: setEnv,\r\n    initialize: initialize,\r\n    createBrowserEnv: createBrowserEnv,\r\n    createFileSystem: createFileSystem,\r\n    createNodejsEnv: createNodejsEnv,\r\n    monkeyPatch: monkeyPatch,\r\n    isBrowser: isBrowser,\r\n    isNodejs: isNodejs\r\n};\r\ninitialize();\r\n//# sourceMappingURL=index.js.map","import { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\r\nimport { resolveInput } from '../dom/resolveInput';\r\nexport var AnchorPosition;\r\n(function (AnchorPosition) {\r\n    AnchorPosition[\"TOP_LEFT\"] = \"TOP_LEFT\";\r\n    AnchorPosition[\"TOP_RIGHT\"] = \"TOP_RIGHT\";\r\n    AnchorPosition[\"BOTTOM_LEFT\"] = \"BOTTOM_LEFT\";\r\n    AnchorPosition[\"BOTTOM_RIGHT\"] = \"BOTTOM_RIGHT\";\r\n})(AnchorPosition || (AnchorPosition = {}));\r\nvar DrawTextFieldOptions = /** @class */ (function () {\r\n    function DrawTextFieldOptions(options) {\r\n        if (options === void 0) { options = {}; }\r\n        var anchorPosition = options.anchorPosition, backgroundColor = options.backgroundColor, fontColor = options.fontColor, fontSize = options.fontSize, fontStyle = options.fontStyle, padding = options.padding;\r\n        this.anchorPosition = anchorPosition || AnchorPosition.TOP_LEFT;\r\n        this.backgroundColor = backgroundColor || 'rgba(0, 0, 0, 0.5)';\r\n        this.fontColor = fontColor || 'rgba(255, 255, 255, 1)';\r\n        this.fontSize = fontSize || 14;\r\n        this.fontStyle = fontStyle || 'Georgia';\r\n        this.padding = padding || 4;\r\n    }\r\n    return DrawTextFieldOptions;\r\n}());\r\nexport { DrawTextFieldOptions };\r\nvar DrawTextField = /** @class */ (function () {\r\n    function DrawTextField(text, anchor, options) {\r\n        if (options === void 0) { options = {}; }\r\n        this.text = typeof text === 'string'\r\n            ? [text]\r\n            : (text instanceof DrawTextField ? text.text : text);\r\n        this.anchor = anchor;\r\n        this.options = new DrawTextFieldOptions(options);\r\n    }\r\n    DrawTextField.prototype.measureWidth = function (ctx) {\r\n        var padding = this.options.padding;\r\n        return this.text.map(function (l) { return ctx.measureText(l).width; }).reduce(function (w0, w1) { return w0 < w1 ? w1 : w0; }, 0) + (2 * padding);\r\n    };\r\n    DrawTextField.prototype.measureHeight = function () {\r\n        var _a = this.options, fontSize = _a.fontSize, padding = _a.padding;\r\n        return this.text.length * fontSize + (2 * padding);\r\n    };\r\n    DrawTextField.prototype.getUpperLeft = function (ctx, canvasDims) {\r\n        var anchorPosition = this.options.anchorPosition;\r\n        var isShiftLeft = anchorPosition === AnchorPosition.BOTTOM_RIGHT || anchorPosition === AnchorPosition.TOP_RIGHT;\r\n        var isShiftTop = anchorPosition === AnchorPosition.BOTTOM_LEFT || anchorPosition === AnchorPosition.BOTTOM_RIGHT;\r\n        var textFieldWidth = this.measureWidth(ctx);\r\n        var textFieldHeight = this.measureHeight();\r\n        var x = (isShiftLeft ? this.anchor.x - textFieldWidth : this.anchor.x);\r\n        var y = isShiftTop ? this.anchor.y - textFieldHeight : this.anchor.y;\r\n        // adjust anchor if text box exceeds canvas borders\r\n        if (canvasDims) {\r\n            var width = canvasDims.width, height = canvasDims.height;\r\n            var newX = Math.max(Math.min(x, width - textFieldWidth), 0);\r\n            var newY = Math.max(Math.min(y, height - textFieldHeight), 0);\r\n            return { x: newX, y: newY };\r\n        }\r\n        return { x: x, y: y };\r\n    };\r\n    DrawTextField.prototype.draw = function (canvasArg) {\r\n        var canvas = resolveInput(canvasArg);\r\n        var ctx = getContext2dOrThrow(canvas);\r\n        var _a = this.options, backgroundColor = _a.backgroundColor, fontColor = _a.fontColor, fontSize = _a.fontSize, fontStyle = _a.fontStyle, padding = _a.padding;\r\n        ctx.font = fontSize + \"px \" + fontStyle;\r\n        var maxTextWidth = this.measureWidth(ctx);\r\n        var textHeight = this.measureHeight();\r\n        ctx.fillStyle = backgroundColor;\r\n        var upperLeft = this.getUpperLeft(ctx, canvas);\r\n        ctx.fillRect(upperLeft.x, upperLeft.y, maxTextWidth, textHeight);\r\n        ctx.fillStyle = fontColor;\r\n        this.text.forEach(function (textLine, i) {\r\n            var x = padding + upperLeft.x;\r\n            var y = padding + upperLeft.y + ((i + 1) * fontSize);\r\n            ctx.fillText(textLine, x, y);\r\n        });\r\n    };\r\n    return DrawTextField;\r\n}());\r\nexport { DrawTextField };\r\n//# sourceMappingURL=DrawTextField.js.map","import { env } from '../env';\r\nexport function resolveInput(arg) {\r\n    if (!env.isNodejs() && typeof arg === 'string') {\r\n        return document.getElementById(arg);\r\n    }\r\n    return arg;\r\n}\r\n//# sourceMappingURL=resolveInput.js.map","import { env } from '../env';\r\nimport { resolveInput } from './resolveInput';\r\nexport function getContext2dOrThrow(canvasArg) {\r\n    var _a = env.getEnv(), Canvas = _a.Canvas, CanvasRenderingContext2D = _a.CanvasRenderingContext2D;\r\n    if (canvasArg instanceof CanvasRenderingContext2D) {\r\n        return canvasArg;\r\n    }\r\n    var canvas = resolveInput(canvasArg);\r\n    if (!(canvas instanceof Canvas)) {\r\n        throw new Error('resolveContext2d - expected canvas to be of instance of Canvas');\r\n    }\r\n    var ctx = canvas.getContext('2d');\r\n    if (!ctx) {\r\n        throw new Error('resolveContext2d - canvas 2d context is null');\r\n    }\r\n    return ctx;\r\n}\r\n//# sourceMappingURL=getContext2dOrThrow.js.map","import { Box } from '../classes';\r\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\r\nimport { AnchorPosition, DrawTextField, DrawTextFieldOptions } from './DrawTextField';\r\nvar DrawBoxOptions = /** @class */ (function () {\r\n    function DrawBoxOptions(options) {\r\n        if (options === void 0) { options = {}; }\r\n        var boxColor = options.boxColor, lineWidth = options.lineWidth, label = options.label, drawLabelOptions = options.drawLabelOptions;\r\n        this.boxColor = boxColor || 'rgba(0, 0, 255, 1)';\r\n        this.lineWidth = lineWidth || 2;\r\n        this.label = label;\r\n        var defaultDrawLabelOptions = {\r\n            anchorPosition: AnchorPosition.BOTTOM_LEFT,\r\n            backgroundColor: this.boxColor\r\n        };\r\n        this.drawLabelOptions = new DrawTextFieldOptions(Object.assign({}, defaultDrawLabelOptions, drawLabelOptions));\r\n    }\r\n    return DrawBoxOptions;\r\n}());\r\nexport { DrawBoxOptions };\r\nvar DrawBox = /** @class */ (function () {\r\n    function DrawBox(box, options) {\r\n        if (options === void 0) { options = {}; }\r\n        this.box = new Box(box);\r\n        this.options = new DrawBoxOptions(options);\r\n    }\r\n    DrawBox.prototype.draw = function (canvasArg) {\r\n        var ctx = getContext2dOrThrow(canvasArg);\r\n        var _a = this.options, boxColor = _a.boxColor, lineWidth = _a.lineWidth;\r\n        var _b = this.box, x = _b.x, y = _b.y, width = _b.width, height = _b.height;\r\n        ctx.strokeStyle = boxColor;\r\n        ctx.lineWidth = lineWidth;\r\n        ctx.strokeRect(x, y, width, height);\r\n        var label = this.options.label;\r\n        if (label) {\r\n            new DrawTextField([label], { x: x - (lineWidth / 2), y: y }, this.options.drawLabelOptions).draw(canvasArg);\r\n        }\r\n    };\r\n    return DrawBox;\r\n}());\r\nexport { DrawBox };\r\n//# sourceMappingURL=DrawBox.js.map","import { Box } from '../classes';\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { round } from '../utils';\r\nimport { DrawBox } from './DrawBox';\r\nexport function drawDetections(canvasArg, detections) {\r\n    var detectionsArray = Array.isArray(detections) ? detections : [detections];\r\n    detectionsArray.forEach(function (det) {\r\n        var score = det instanceof FaceDetection\r\n            ? det.score\r\n            : (isWithFaceDetection(det) ? det.detection.score : undefined);\r\n        var box = det instanceof FaceDetection\r\n            ? det.box\r\n            : (isWithFaceDetection(det) ? det.detection.box : new Box(det));\r\n        var label = score ? \"\" + round(score) : undefined;\r\n        new DrawBox(box, { label: label }).draw(canvasArg);\r\n    });\r\n}\r\n//# sourceMappingURL=drawDetections.js.map","import { env } from '../env';\r\nexport function isMediaLoaded(media) {\r\n    var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;\r\n    return (media instanceof Image && media.complete)\r\n        || (media instanceof Video && media.readyState >= 3);\r\n}\r\n//# sourceMappingURL=isMediaLoaded.js.map","import { env } from '../env';\r\nimport { isMediaLoaded } from './isMediaLoaded';\r\nexport function awaitMediaLoaded(media) {\r\n    return new Promise(function (resolve, reject) {\r\n        if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\r\n            return resolve();\r\n        }\r\n        function onLoad(e) {\r\n            if (!e.currentTarget)\r\n                return;\r\n            e.currentTarget.removeEventListener('load', onLoad);\r\n            e.currentTarget.removeEventListener('error', onError);\r\n            resolve(e);\r\n        }\r\n        function onError(e) {\r\n            if (!e.currentTarget)\r\n                return;\r\n            e.currentTarget.removeEventListener('load', onLoad);\r\n            e.currentTarget.removeEventListener('error', onError);\r\n            reject(e);\r\n        }\r\n        media.addEventListener('load', onLoad);\r\n        media.addEventListener('error', onError);\r\n    });\r\n}\r\n//# sourceMappingURL=awaitMediaLoaded.js.map","import { env } from '../env';\r\nexport function bufferToImage(buf) {\r\n    return new Promise(function (resolve, reject) {\r\n        if (!(buf instanceof Blob)) {\r\n            return reject('bufferToImage - expected buf to be of type: Blob');\r\n        }\r\n        var reader = new FileReader();\r\n        reader.onload = function () {\r\n            if (typeof reader.result !== 'string') {\r\n                return reject('bufferToImage - expected reader.result to be a string, in onload');\r\n            }\r\n            var img = env.getEnv().createImageElement();\r\n            img.onload = function () { return resolve(img); };\r\n            img.onerror = reject;\r\n            img.src = reader.result;\r\n        };\r\n        reader.onerror = reject;\r\n        reader.readAsDataURL(buf);\r\n    });\r\n}\r\n//# sourceMappingURL=bufferToImage.js.map","import { Dimensions } from '../classes/Dimensions';\r\nimport { env } from '../env';\r\nexport function getMediaDimensions(input) {\r\n    var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;\r\n    if (input instanceof Image) {\r\n        return new Dimensions(input.naturalWidth, input.naturalHeight);\r\n    }\r\n    if (input instanceof Video) {\r\n        return new Dimensions(input.videoWidth, input.videoHeight);\r\n    }\r\n    return new Dimensions(input.width, input.height);\r\n}\r\n//# sourceMappingURL=getMediaDimensions.js.map","import { env } from '../env';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { getMediaDimensions } from './getMediaDimensions';\r\nimport { isMediaLoaded } from './isMediaLoaded';\r\nexport function createCanvas(_a) {\r\n    var width = _a.width, height = _a.height;\r\n    var createCanvasElement = env.getEnv().createCanvasElement;\r\n    var canvas = createCanvasElement();\r\n    canvas.width = width;\r\n    canvas.height = height;\r\n    return canvas;\r\n}\r\nexport function createCanvasFromMedia(media, dims) {\r\n    var ImageData = env.getEnv().ImageData;\r\n    if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\r\n        throw new Error('createCanvasFromMedia - media has not finished loading yet');\r\n    }\r\n    var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;\r\n    var canvas = createCanvas({ width: width, height: height });\r\n    if (media instanceof ImageData) {\r\n        getContext2dOrThrow(canvas).putImageData(media, 0, 0);\r\n    }\r\n    else {\r\n        getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\r\n    }\r\n    return canvas;\r\n}\r\n//# sourceMappingURL=createCanvas.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { env } from '../env';\r\nimport { isTensor4D } from '../utils';\r\nexport function imageTensorToCanvas(imgTensor, canvas) {\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        var targetCanvas, _a, height, width, numChannels, imgTensor3D;\r\n        return __generator(this, function (_b) {\r\n            switch (_b.label) {\r\n                case 0:\r\n                    targetCanvas = canvas || env.getEnv().createCanvasElement();\r\n                    _a = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];\r\n                    imgTensor3D = tf.tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });\r\n                    return [4 /*yield*/, tf.browser.toPixels(imgTensor3D, targetCanvas)];\r\n                case 1:\r\n                    _b.sent();\r\n                    imgTensor3D.dispose();\r\n                    return [2 /*return*/, targetCanvas];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=imageTensorToCanvas.js.map","import { env } from '../env';\r\nexport function isMediaElement(input) {\r\n    var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas, Video = _a.Video;\r\n    return input instanceof Image\r\n        || input instanceof Canvas\r\n        || input instanceof Video;\r\n}\r\n//# sourceMappingURL=isMediaElement.js.map","import { env } from '../env';\r\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { getMediaDimensions } from './getMediaDimensions';\r\nexport function imageToSquare(input, inputSize, centerImage) {\r\n    if (centerImage === void 0) { centerImage = false; }\r\n    var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas;\r\n    if (!(input instanceof Image || input instanceof Canvas)) {\r\n        throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\r\n    }\r\n    var dims = getMediaDimensions(input);\r\n    var scale = inputSize / Math.max(dims.height, dims.width);\r\n    var width = scale * dims.width;\r\n    var height = scale * dims.height;\r\n    var targetCanvas = createCanvas({ width: inputSize, height: inputSize });\r\n    var inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\r\n    var offset = Math.abs(width - height) / 2;\r\n    var dx = centerImage && width < height ? offset : 0;\r\n    var dy = centerImage && height < width ? offset : 0;\r\n    getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\r\n    return targetCanvas;\r\n}\r\n//# sourceMappingURL=imageToSquare.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { env } from '../env';\r\nimport { padToSquare } from '../ops/padToSquare';\r\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\r\nimport { createCanvasFromMedia } from './createCanvas';\r\nimport { imageToSquare } from './imageToSquare';\r\nvar NetInput = /** @class */ (function () {\r\n    function NetInput(inputs, treatAsBatchInput) {\r\n        var _this = this;\r\n        if (treatAsBatchInput === void 0) { treatAsBatchInput = false; }\r\n        this._imageTensors = [];\r\n        this._canvases = [];\r\n        this._treatAsBatchInput = false;\r\n        this._inputDimensions = [];\r\n        if (!Array.isArray(inputs)) {\r\n            throw new Error(\"NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have \" + inputs);\r\n        }\r\n        this._treatAsBatchInput = treatAsBatchInput;\r\n        this._batchSize = inputs.length;\r\n        inputs.forEach(function (input, idx) {\r\n            if (isTensor3D(input)) {\r\n                _this._imageTensors[idx] = input;\r\n                _this._inputDimensions[idx] = input.shape;\r\n                return;\r\n            }\r\n            if (isTensor4D(input)) {\r\n                var batchSize = input.shape[0];\r\n                if (batchSize !== 1) {\r\n                    throw new Error(\"NetInput - tf.Tensor4D with batchSize \" + batchSize + \" passed, but not supported in input array\");\r\n                }\r\n                _this._imageTensors[idx] = input;\r\n                _this._inputDimensions[idx] = input.shape.slice(1);\r\n                return;\r\n            }\r\n            var canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\r\n            _this._canvases[idx] = canvas;\r\n            _this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\r\n        });\r\n    }\r\n    Object.defineProperty(NetInput.prototype, \"imageTensors\", {\r\n        get: function () {\r\n            return this._imageTensors;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"canvases\", {\r\n        get: function () {\r\n            return this._canvases;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"isBatchInput\", {\r\n        get: function () {\r\n            return this.batchSize > 1 || this._treatAsBatchInput;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"batchSize\", {\r\n        get: function () {\r\n            return this._batchSize;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"inputDimensions\", {\r\n        get: function () {\r\n            return this._inputDimensions;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"inputSize\", {\r\n        get: function () {\r\n            return this._inputSize;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"reshapedInputDimensions\", {\r\n        get: function () {\r\n            var _this = this;\r\n            return range(this.batchSize, 0, 1).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    NetInput.prototype.getInput = function (batchIdx) {\r\n        return this.canvases[batchIdx] || this.imageTensors[batchIdx];\r\n    };\r\n    NetInput.prototype.getInputDimensions = function (batchIdx) {\r\n        return this._inputDimensions[batchIdx];\r\n    };\r\n    NetInput.prototype.getInputHeight = function (batchIdx) {\r\n        return this._inputDimensions[batchIdx][0];\r\n    };\r\n    NetInput.prototype.getInputWidth = function (batchIdx) {\r\n        return this._inputDimensions[batchIdx][1];\r\n    };\r\n    NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {\r\n        if (typeof this.inputSize !== 'number') {\r\n            throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\r\n        }\r\n        var width = this.getInputWidth(batchIdx);\r\n        var height = this.getInputHeight(batchIdx);\r\n        return computeReshapedDimensions({ width: width, height: height }, this.inputSize);\r\n    };\r\n    /**\r\n     * Create a batch tensor from all input canvases and tensors\r\n     * with size [batchSize, inputSize, inputSize, 3].\r\n     *\r\n     * @param inputSize Height and width of the tensor.\r\n     * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\r\n     * both sides of the minor dimension oof the image.\r\n     * @returns The batch tensor.\r\n     */\r\n    NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {\r\n        var _this = this;\r\n        if (isCenterInputs === void 0) { isCenterInputs = true; }\r\n        this._inputSize = inputSize;\r\n        return tf.tidy(function () {\r\n            var inputTensors = range(_this.batchSize, 0, 1).map(function (batchIdx) {\r\n                var input = _this.getInput(batchIdx);\r\n                if (input instanceof tf.Tensor) {\r\n                    var imgTensor = isTensor4D(input) ? input : input.expandDims();\r\n                    imgTensor = padToSquare(imgTensor, isCenterInputs);\r\n                    if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\r\n                        imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\r\n                    }\r\n                    return imgTensor.as3D(inputSize, inputSize, 3);\r\n                }\r\n                if (input instanceof env.getEnv().Canvas) {\r\n                    return tf.browser.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\r\n                }\r\n                throw new Error(\"toBatchTensor - at batchIdx \" + batchIdx + \", expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have \" + input);\r\n            });\r\n            var batchTensor = tf.stack(inputTensors.map(function (t) { return t.toFloat(); })).as4D(_this.batchSize, inputSize, inputSize, 3);\r\n            return batchTensor;\r\n        });\r\n    };\r\n    return NetInput;\r\n}());\r\nexport { NetInput };\r\n//# sourceMappingURL=NetInput.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport { isTensor3D, isTensor4D } from '../utils';\r\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\r\nimport { isMediaElement } from './isMediaElement';\r\nimport { NetInput } from './NetInput';\r\nimport { resolveInput } from './resolveInput';\r\n/**\r\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\r\n * to be finished loading.\r\n *\r\n * @param input The input, which can be a media element or an array of different media elements.\r\n * @returns A NetInput instance, which can be passed into one of the neural networks.\r\n */\r\nexport function toNetInput(inputs) {\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        var inputArgArray, getIdxHint, inputArray;\r\n        return __generator(this, function (_a) {\r\n            switch (_a.label) {\r\n                case 0:\r\n                    if (inputs instanceof NetInput) {\r\n                        return [2 /*return*/, inputs];\r\n                    }\r\n                    inputArgArray = Array.isArray(inputs)\r\n                        ? inputs\r\n                        : [inputs];\r\n                    if (!inputArgArray.length) {\r\n                        throw new Error('toNetInput - empty array passed as input');\r\n                    }\r\n                    getIdxHint = function (idx) { return Array.isArray(inputs) ? \" at input index \" + idx + \":\" : ''; };\r\n                    inputArray = inputArgArray.map(resolveInput);\r\n                    inputArray.forEach(function (input, i) {\r\n                        if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\r\n                            if (typeof inputArgArray[i] === 'string') {\r\n                                throw new Error(\"toNetInput -\" + getIdxHint(i) + \" string passed, but could not resolve HTMLElement for element id \" + inputArgArray[i]);\r\n                            }\r\n                            throw new Error(\"toNetInput -\" + getIdxHint(i) + \" expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id\");\r\n                        }\r\n                        if (isTensor4D(input)) {\r\n                            // if tf.Tensor4D is passed in the input array, the batch size has to be 1\r\n                            var batchSize = input.shape[0];\r\n                            if (batchSize !== 1) {\r\n                                throw new Error(\"toNetInput -\" + getIdxHint(i) + \" tf.Tensor4D with batchSize \" + batchSize + \" passed, but not supported in input array\");\r\n                            }\r\n                        }\r\n                    });\r\n                    // wait for all media elements being loaded\r\n                    return [4 /*yield*/, Promise.all(inputArray.map(function (input) { return isMediaElement(input) && awaitMediaLoaded(input); }))];\r\n                case 1:\r\n                    // wait for all media elements being loaded\r\n                    _a.sent();\r\n                    return [2 /*return*/, new NetInput(inputArray, Array.isArray(inputs))];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=toNetInput.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { env } from '../env';\r\nimport { createCanvas } from './createCanvas';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { imageTensorToCanvas } from './imageTensorToCanvas';\r\nimport { toNetInput } from './toNetInput';\r\n/**\r\n * Extracts the image regions containing the detected faces.\r\n *\r\n * @param input The image that face detection has been performed on.\r\n * @param detections The face detection results or face bounding boxes for that image.\r\n * @returns The Canvases of the corresponding image region for each detected face.\r\n */\r\nexport function extractFaces(input, detections) {\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        var Canvas, canvas, netInput, tensorOrCanvas, _a, ctx, boxes;\r\n        return __generator(this, function (_b) {\r\n            switch (_b.label) {\r\n                case 0:\r\n                    Canvas = env.getEnv().Canvas;\r\n                    canvas = input;\r\n                    if (!!(input instanceof Canvas)) return [3 /*break*/, 5];\r\n                    return [4 /*yield*/, toNetInput(input)];\r\n                case 1:\r\n                    netInput = _b.sent();\r\n                    if (netInput.batchSize > 1) {\r\n                        throw new Error('extractFaces - batchSize > 1 not supported');\r\n                    }\r\n                    tensorOrCanvas = netInput.getInput(0);\r\n                    if (!(tensorOrCanvas instanceof Canvas)) return [3 /*break*/, 2];\r\n                    _a = tensorOrCanvas;\r\n                    return [3 /*break*/, 4];\r\n                case 2: return [4 /*yield*/, imageTensorToCanvas(tensorOrCanvas)];\r\n                case 3:\r\n                    _a = _b.sent();\r\n                    _b.label = 4;\r\n                case 4:\r\n                    canvas = _a;\r\n                    _b.label = 5;\r\n                case 5:\r\n                    ctx = getContext2dOrThrow(canvas);\r\n                    boxes = detections.map(function (det) { return det instanceof FaceDetection\r\n                        ? det.forSize(canvas.width, canvas.height).box.floor()\r\n                        : det; })\r\n                        .map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });\r\n                    return [2 /*return*/, boxes.map(function (_a) {\r\n                            var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n                            var faceImg = createCanvas({ width: width, height: height });\r\n                            getContext2dOrThrow(faceImg)\r\n                                .putImageData(ctx.getImageData(x, y, width, height), 0, 0);\r\n                            return faceImg;\r\n                        })];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=extractFaces.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { isTensor3D, isTensor4D } from '../utils';\r\n/**\r\n * Extracts the tensors of the image regions containing the detected faces.\r\n * Useful if you want to compute the face descriptors for the face images.\r\n * Using this method is faster then extracting a canvas for each face and\r\n * converting them to tensors individually.\r\n *\r\n * @param imageTensor The image tensor that face detection has been performed on.\r\n * @param detections The face detection results or face bounding boxes for that image.\r\n * @returns Tensors of the corresponding image region for each detected face.\r\n */\r\nexport function extractFaceTensors(imageTensor, detections) {\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        return __generator(this, function (_a) {\r\n            if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\r\n                throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');\r\n            }\r\n            if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\r\n                throw new Error('extractFaceTensors - batchSize > 1 not supported');\r\n            }\r\n            return [2 /*return*/, tf.tidy(function () {\r\n                    var _a = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];\r\n                    var boxes = detections.map(function (det) { return det instanceof FaceDetection\r\n                        ? det.forSize(imgWidth, imgHeight).box\r\n                        : det; })\r\n                        .map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });\r\n                    var faceTensors = boxes.map(function (_a) {\r\n                        var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n                        return tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);\r\n                    });\r\n                    return faceTensors;\r\n                })];\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=extractFaceTensors.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport { env } from '../env';\r\nexport function fetchOrThrow(url, init) {\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        var fetch, res;\r\n        return __generator(this, function (_a) {\r\n            switch (_a.label) {\r\n                case 0:\r\n                    fetch = env.getEnv().fetch;\r\n                    return [4 /*yield*/, fetch(url, init)];\r\n                case 1:\r\n                    res = _a.sent();\r\n                    if (!(res.status < 400)) {\r\n                        throw new Error(\"failed to fetch: (\" + res.status + \") \" + res.statusText + \", from url: \" + res.url);\r\n                    }\r\n                    return [2 /*return*/, res];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=fetchOrThrow.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport { fetchOrThrow } from './fetchOrThrow';\r\nexport function fetchJson(uri) {\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        return __generator(this, function (_a) {\r\n            switch (_a.label) {\r\n                case 0: return [4 /*yield*/, fetchOrThrow(uri)];\r\n                case 1: return [2 /*return*/, (_a.sent()).json()];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=fetchJson.js.map","export function getModelUris(uri, defaultModelName) {\r\n    var defaultManifestFilename = defaultModelName + \"-weights_manifest.json\";\r\n    if (!uri) {\r\n        return {\r\n            modelBaseUri: '',\r\n            manifestUri: defaultManifestFilename\r\n        };\r\n    }\r\n    if (uri === '/') {\r\n        return {\r\n            modelBaseUri: '/',\r\n            manifestUri: \"/\" + defaultManifestFilename\r\n        };\r\n    }\r\n    var protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\r\n    uri = uri.replace(protocol, '');\r\n    var parts = uri.split('/').filter(function (s) { return s; });\r\n    var manifestFile = uri.endsWith('.json')\r\n        ? parts[parts.length - 1]\r\n        : defaultManifestFilename;\r\n    var modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\r\n    modelBaseUri = uri.startsWith('/') ? \"/\" + modelBaseUri : modelBaseUri;\r\n    return {\r\n        modelBaseUri: modelBaseUri,\r\n        manifestUri: modelBaseUri === '/' ? \"/\" + manifestFile : modelBaseUri + \"/\" + manifestFile\r\n    };\r\n}\r\n//# sourceMappingURL=getModelUris.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { getModelUris } from '../common/getModelUris';\r\nimport { fetchJson } from './fetchJson';\r\nexport function loadWeightMap(uri, defaultModelName) {\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        var _a, manifestUri, modelBaseUri, manifest;\r\n        return __generator(this, function (_b) {\r\n            switch (_b.label) {\r\n                case 0:\r\n                    _a = getModelUris(uri, defaultModelName), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;\r\n                    return [4 /*yield*/, fetchJson(manifestUri)];\r\n                case 1:\r\n                    manifest = _b.sent();\r\n                    return [2 /*return*/, tf.io.loadWeights(manifest, modelBaseUri)];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=loadWeightMap.js.map","import { getMediaDimensions } from './getMediaDimensions';\r\nexport function matchDimensions(input, reference, useMediaDimensions) {\r\n    if (useMediaDimensions === void 0) { useMediaDimensions = false; }\r\n    var _a = useMediaDimensions\r\n        ? getMediaDimensions(reference)\r\n        : reference, width = _a.width, height = _a.height;\r\n    input.width = width;\r\n    input.height = height;\r\n    return { width: width, height: height };\r\n}\r\n//# sourceMappingURL=matchDimensions.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { getModelUris } from './common/getModelUris';\r\nimport { loadWeightMap } from './dom';\r\nimport { env } from './env';\r\nvar NeuralNetwork = /** @class */ (function () {\r\n    function NeuralNetwork(_name) {\r\n        this._name = _name;\r\n        this._params = undefined;\r\n        this._paramMappings = [];\r\n    }\r\n    Object.defineProperty(NeuralNetwork.prototype, \"params\", {\r\n        get: function () { return this._params; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NeuralNetwork.prototype, \"paramMappings\", {\r\n        get: function () { return this._paramMappings; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NeuralNetwork.prototype, \"isLoaded\", {\r\n        get: function () { return !!this.params; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    NeuralNetwork.prototype.getParamFromPath = function (paramPath) {\r\n        var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;\r\n        return obj[objProp];\r\n    };\r\n    NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor) {\r\n        var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;\r\n        obj[objProp].dispose();\r\n        obj[objProp] = tensor;\r\n    };\r\n    NeuralNetwork.prototype.getParamList = function () {\r\n        var _this = this;\r\n        return this._paramMappings.map(function (_a) {\r\n            var paramPath = _a.paramPath;\r\n            return ({\r\n                path: paramPath,\r\n                tensor: _this.getParamFromPath(paramPath)\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.getTrainableParams = function () {\r\n        return this.getParamList().filter(function (param) { return param.tensor instanceof tf.Variable; });\r\n    };\r\n    NeuralNetwork.prototype.getFrozenParams = function () {\r\n        return this.getParamList().filter(function (param) { return !(param.tensor instanceof tf.Variable); });\r\n    };\r\n    NeuralNetwork.prototype.variable = function () {\r\n        var _this = this;\r\n        this.getFrozenParams().forEach(function (_a) {\r\n            var path = _a.path, tensor = _a.tensor;\r\n            _this.reassignParamFromPath(path, tensor.variable());\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.freeze = function () {\r\n        var _this = this;\r\n        this.getTrainableParams().forEach(function (_a) {\r\n            var path = _a.path, variable = _a.tensor;\r\n            var tensor = tf.tensor(variable.dataSync());\r\n            variable.dispose();\r\n            _this.reassignParamFromPath(path, tensor);\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.dispose = function (throwOnRedispose) {\r\n        if (throwOnRedispose === void 0) { throwOnRedispose = true; }\r\n        this.getParamList().forEach(function (param) {\r\n            if (throwOnRedispose && param.tensor.isDisposed) {\r\n                throw new Error(\"param tensor has already been disposed for path \" + param.path);\r\n            }\r\n            param.tensor.dispose();\r\n        });\r\n        this._params = undefined;\r\n    };\r\n    NeuralNetwork.prototype.serializeParams = function () {\r\n        return new Float32Array(this.getParamList()\r\n            .map(function (_a) {\r\n            var tensor = _a.tensor;\r\n            return Array.from(tensor.dataSync());\r\n        })\r\n            .reduce(function (flat, arr) { return flat.concat(arr); }));\r\n    };\r\n    NeuralNetwork.prototype.load = function (weightsOrUrl) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0:\r\n                        if (weightsOrUrl instanceof Float32Array) {\r\n                            this.extractWeights(weightsOrUrl);\r\n                            return [2 /*return*/];\r\n                        }\r\n                        return [4 /*yield*/, this.loadFromUri(weightsOrUrl)];\r\n                    case 1:\r\n                        _a.sent();\r\n                        return [2 /*return*/];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.loadFromUri = function (uri) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var weightMap;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0:\r\n                        if (uri && typeof uri !== 'string') {\r\n                            throw new Error(this._name + \".loadFromUri - expected model uri\");\r\n                        }\r\n                        return [4 /*yield*/, loadWeightMap(uri, this.getDefaultModelName())];\r\n                    case 1:\r\n                        weightMap = _a.sent();\r\n                        this.loadFromWeightMap(weightMap);\r\n                        return [2 /*return*/];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.loadFromDisk = function (filePath) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var readFile, _a, manifestUri, modelBaseUri, fetchWeightsFromDisk, loadWeights, manifest, _b, _c, weightMap;\r\n            return __generator(this, function (_d) {\r\n                switch (_d.label) {\r\n                    case 0:\r\n                        if (filePath && typeof filePath !== 'string') {\r\n                            throw new Error(this._name + \".loadFromDisk - expected model file path\");\r\n                        }\r\n                        readFile = env.getEnv().readFile;\r\n                        _a = getModelUris(filePath, this.getDefaultModelName()), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;\r\n                        fetchWeightsFromDisk = function (filePaths) { return Promise.all(filePaths.map(function (filePath) { return readFile(filePath).then(function (buf) { return buf.buffer; }); })); };\r\n                        loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\r\n                        _c = (_b = JSON).parse;\r\n                        return [4 /*yield*/, readFile(manifestUri)];\r\n                    case 1:\r\n                        manifest = _c.apply(_b, [(_d.sent()).toString()]);\r\n                        return [4 /*yield*/, loadWeights(manifest, modelBaseUri)];\r\n                    case 2:\r\n                        weightMap = _d.sent();\r\n                        this.loadFromWeightMap(weightMap);\r\n                        return [2 /*return*/];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.loadFromWeightMap = function (weightMap) {\r\n        var _a = this.extractParamsFromWeigthMap(weightMap), paramMappings = _a.paramMappings, params = _a.params;\r\n        this._paramMappings = paramMappings;\r\n        this._params = params;\r\n    };\r\n    NeuralNetwork.prototype.extractWeights = function (weights) {\r\n        var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;\r\n        this._paramMappings = paramMappings;\r\n        this._params = params;\r\n    };\r\n    NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {\r\n        if (!this.params) {\r\n            throw new Error(\"traversePropertyPath - model has no loaded params\");\r\n        }\r\n        var result = paramPath.split('/').reduce(function (res, objProp) {\r\n            if (!res.nextObj.hasOwnProperty(objProp)) {\r\n                throw new Error(\"traversePropertyPath - object does not have property \" + objProp + \", for path \" + paramPath);\r\n            }\r\n            return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };\r\n        }, { nextObj: this.params });\r\n        var obj = result.obj, objProp = result.objProp;\r\n        if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\r\n            throw new Error(\"traversePropertyPath - parameter is not a tensor, for path \" + paramPath);\r\n        }\r\n        return { obj: obj, objProp: objProp };\r\n    };\r\n    return NeuralNetwork;\r\n}());\r\nexport { NeuralNetwork };\r\n//# sourceMappingURL=NeuralNetwork.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function depthwiseSeparableConv(x, params, stride) {\r\n    return tf.tidy(function () {\r\n        var out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');\r\n        out = tf.add(out, params.bias);\r\n        return out;\r\n    });\r\n}\r\n//# sourceMappingURL=depthwiseSeparableConv.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { depthwiseSeparableConv } from '../common/depthwiseSeparableConv';\r\nexport function denseBlock3(x, denseBlockParams, isFirstLayer) {\r\n    if (isFirstLayer === void 0) { isFirstLayer = false; }\r\n    return tf.tidy(function () {\r\n        var out1 = tf.relu(isFirstLayer\r\n            ? tf.add(tf.conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)\r\n            : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));\r\n        var out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\r\n        var in3 = tf.relu(tf.add(out1, out2));\r\n        var out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\r\n        return tf.relu(tf.add(out1, tf.add(out2, out3)));\r\n    });\r\n}\r\nexport function denseBlock4(x, denseBlockParams, isFirstLayer, isScaleDown) {\r\n    if (isFirstLayer === void 0) { isFirstLayer = false; }\r\n    if (isScaleDown === void 0) { isScaleDown = true; }\r\n    return tf.tidy(function () {\r\n        var out1 = tf.relu(isFirstLayer\r\n            ? tf.add(tf.conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], 'same'), denseBlockParams.conv0.bias)\r\n            : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]));\r\n        var out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);\r\n        var in3 = tf.relu(tf.add(out1, out2));\r\n        var out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);\r\n        var in4 = tf.relu(tf.add(out1, tf.add(out2, out3)));\r\n        var out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);\r\n        return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4))));\r\n    });\r\n}\r\n//# sourceMappingURL=denseBlock.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function convLayer(x, params, padding, withRelu) {\r\n    if (padding === void 0) { padding = 'same'; }\r\n    if (withRelu === void 0) { withRelu = false; }\r\n    return tf.tidy(function () {\r\n        var out = tf.add(tf.conv2d(x, params.filters, [1, 1], padding), params.bias);\r\n        return withRelu ? tf.relu(out) : out;\r\n    });\r\n}\r\n//# sourceMappingURL=convLayer.js.map","export function disposeUnusedWeightTensors(weightMap, paramMappings) {\r\n    Object.keys(weightMap).forEach(function (path) {\r\n        if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {\r\n            weightMap[path].dispose();\r\n        }\r\n    });\r\n}\r\n//# sourceMappingURL=disposeUnusedWeightTensors.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function extractConvParamsFactory(extractWeights, paramMappings) {\r\n    return function (channelsIn, channelsOut, filterSize, mappedPrefix) {\r\n        var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);\r\n        var bias = tf.tensor1d(extractWeights(channelsOut));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/filters\" }, { paramPath: mappedPrefix + \"/bias\" });\r\n        return { filters: filters, bias: bias };\r\n    };\r\n}\r\n//# sourceMappingURL=extractConvParamsFactory.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function extractFCParamsFactory(extractWeights, paramMappings) {\r\n    return function (channelsIn, channelsOut, mappedPrefix) {\r\n        var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\r\n        var fc_bias = tf.tensor1d(extractWeights(channelsOut));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/weights\" }, { paramPath: mappedPrefix + \"/bias\" });\r\n        return {\r\n            weights: fc_weights,\r\n            bias: fc_bias\r\n        };\r\n    };\r\n}\r\n//# sourceMappingURL=extractFCParamsFactory.js.map","var SeparableConvParams = /** @class */ (function () {\r\n    function SeparableConvParams(depthwise_filter, pointwise_filter, bias) {\r\n        this.depthwise_filter = depthwise_filter;\r\n        this.pointwise_filter = pointwise_filter;\r\n        this.bias = bias;\r\n    }\r\n    return SeparableConvParams;\r\n}());\r\nexport { SeparableConvParams };\r\n//# sourceMappingURL=types.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { SeparableConvParams } from './types';\r\nexport function extractSeparableConvParamsFactory(extractWeights, paramMappings) {\r\n    return function (channelsIn, channelsOut, mappedPrefix) {\r\n        var depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\r\n        var pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\r\n        var bias = tf.tensor1d(extractWeights(channelsOut));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/depthwise_filter\" }, { paramPath: mappedPrefix + \"/pointwise_filter\" }, { paramPath: mappedPrefix + \"/bias\" });\r\n        return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);\r\n    };\r\n}\r\nexport function loadSeparableConvParamsFactory(extractWeightEntry) {\r\n    return function (prefix) {\r\n        var depthwise_filter = extractWeightEntry(prefix + \"/depthwise_filter\", 4);\r\n        var pointwise_filter = extractWeightEntry(prefix + \"/pointwise_filter\", 4);\r\n        var bias = extractWeightEntry(prefix + \"/bias\", 1);\r\n        return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);\r\n    };\r\n}\r\n//# sourceMappingURL=extractSeparableConvParamsFactory.js.map","import { isTensor } from '../utils';\r\nexport function extractWeightEntryFactory(weightMap, paramMappings) {\r\n    return function (originalPath, paramRank, mappedPath) {\r\n        var tensor = weightMap[originalPath];\r\n        if (!isTensor(tensor, paramRank)) {\r\n            throw new Error(\"expected weightMap[\" + originalPath + \"] to be a Tensor\" + paramRank + \"D, instead have \" + tensor);\r\n        }\r\n        paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });\r\n        return tensor;\r\n    };\r\n}\r\n//# sourceMappingURL=extractWeightEntryFactory.js.map","export function extractWeightsFactory(weights) {\r\n    var remainingWeights = weights;\r\n    function extractWeights(numWeights) {\r\n        var ret = remainingWeights.slice(0, numWeights);\r\n        remainingWeights = remainingWeights.slice(numWeights);\r\n        return ret;\r\n    }\r\n    function getRemainingWeights() {\r\n        return remainingWeights;\r\n    }\r\n    return {\r\n        extractWeights: extractWeights,\r\n        getRemainingWeights: getRemainingWeights\r\n    };\r\n}\r\n//# sourceMappingURL=extractWeightsFactory.js.map","import { extractConvParamsFactory, extractSeparableConvParamsFactory, } from '../common';\r\nexport function extractorsFactory(extractWeights, paramMappings) {\r\n    var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\r\n    var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\r\n    function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {\r\n        if (isFirstLayer === void 0) { isFirstLayer = false; }\r\n        var conv0 = isFirstLayer\r\n            ? extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + \"/conv0\")\r\n            : extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix + \"/conv0\");\r\n        var conv1 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + \"/conv1\");\r\n        var conv2 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + \"/conv2\");\r\n        return { conv0: conv0, conv1: conv1, conv2: conv2 };\r\n    }\r\n    function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {\r\n        if (isFirstLayer === void 0) { isFirstLayer = false; }\r\n        var _a = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer), conv0 = _a.conv0, conv1 = _a.conv1, conv2 = _a.conv2;\r\n        var conv3 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + \"/conv3\");\r\n        return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };\r\n    }\r\n    return {\r\n        extractDenseBlock3Params: extractDenseBlock3Params,\r\n        extractDenseBlock4Params: extractDenseBlock4Params\r\n    };\r\n}\r\n//# sourceMappingURL=extractorsFactory.js.map","import { extractWeightsFactory } from '../common';\r\nimport { extractorsFactory } from './extractorsFactory';\r\nexport function extractParams(weights) {\r\n    var paramMappings = [];\r\n    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;\r\n    var extractDenseBlock4Params = extractorsFactory(extractWeights, paramMappings).extractDenseBlock4Params;\r\n    var dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);\r\n    var dense1 = extractDenseBlock4Params(32, 64, 'dense1');\r\n    var dense2 = extractDenseBlock4Params(64, 128, 'dense2');\r\n    var dense3 = extractDenseBlock4Params(128, 256, 'dense3');\r\n    if (getRemainingWeights().length !== 0) {\r\n        throw new Error(\"weights remaing after extract: \" + getRemainingWeights().length);\r\n    }\r\n    return {\r\n        paramMappings: paramMappings,\r\n        params: { dense0: dense0, dense1: dense1, dense2: dense2, dense3: dense3 }\r\n    };\r\n}\r\n//# sourceMappingURL=extractParams.js.map","export function loadConvParamsFactory(extractWeightEntry) {\r\n    return function (prefix) {\r\n        var filters = extractWeightEntry(prefix + \"/filters\", 4);\r\n        var bias = extractWeightEntry(prefix + \"/bias\", 1);\r\n        return { filters: filters, bias: bias };\r\n    };\r\n}\r\n//# sourceMappingURL=loadConvParamsFactory.js.map","import { extractWeightEntryFactory, loadSeparableConvParamsFactory } from '../common';\r\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\r\nexport function loadParamsFactory(weightMap, paramMappings) {\r\n    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\r\n    var extractConvParams = loadConvParamsFactory(extractWeightEntry);\r\n    var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\r\n    function extractDenseBlock3Params(prefix, isFirstLayer) {\r\n        if (isFirstLayer === void 0) { isFirstLayer = false; }\r\n        var conv0 = isFirstLayer\r\n            ? extractConvParams(prefix + \"/conv0\")\r\n            : extractSeparableConvParams(prefix + \"/conv0\");\r\n        var conv1 = extractSeparableConvParams(prefix + \"/conv1\");\r\n        var conv2 = extractSeparableConvParams(prefix + \"/conv2\");\r\n        return { conv0: conv0, conv1: conv1, conv2: conv2 };\r\n    }\r\n    function extractDenseBlock4Params(prefix, isFirstLayer) {\r\n        if (isFirstLayer === void 0) { isFirstLayer = false; }\r\n        var conv0 = isFirstLayer\r\n            ? extractConvParams(prefix + \"/conv0\")\r\n            : extractSeparableConvParams(prefix + \"/conv0\");\r\n        var conv1 = extractSeparableConvParams(prefix + \"/conv1\");\r\n        var conv2 = extractSeparableConvParams(prefix + \"/conv2\");\r\n        var conv3 = extractSeparableConvParams(prefix + \"/conv3\");\r\n        return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };\r\n    }\r\n    return {\r\n        extractDenseBlock3Params: extractDenseBlock3Params,\r\n        extractDenseBlock4Params: extractDenseBlock4Params\r\n    };\r\n}\r\n//# sourceMappingURL=loadParamsFactory.js.map","import { disposeUnusedWeightTensors } from '../common';\r\nimport { loadParamsFactory } from './loadParamsFactory';\r\nexport function extractParamsFromWeigthMap(weightMap) {\r\n    var paramMappings = [];\r\n    var extractDenseBlock4Params = loadParamsFactory(weightMap, paramMappings).extractDenseBlock4Params;\r\n    var params = {\r\n        dense0: extractDenseBlock4Params('dense0', true),\r\n        dense1: extractDenseBlock4Params('dense1'),\r\n        dense2: extractDenseBlock4Params('dense2'),\r\n        dense3: extractDenseBlock4Params('dense3')\r\n    };\r\n    disposeUnusedWeightTensors(weightMap, paramMappings);\r\n    return { params: params, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParamsFromWeigthMap.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { toNetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { normalize } from '../ops';\r\nimport { denseBlock4 } from './denseBlock';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nvar FaceFeatureExtractor = /** @class */ (function (_super) {\r\n    __extends(FaceFeatureExtractor, _super);\r\n    function FaceFeatureExtractor() {\r\n        return _super.call(this, 'FaceFeatureExtractor') || this;\r\n    }\r\n    FaceFeatureExtractor.prototype.forwardInput = function (input) {\r\n        var params = this.params;\r\n        if (!params) {\r\n            throw new Error('FaceFeatureExtractor - load model before inference');\r\n        }\r\n        return tf.tidy(function () {\r\n            var batchTensor = input.toBatchTensor(112, true);\r\n            var meanRgb = [122.782, 117.001, 104.298];\r\n            var normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255));\r\n            var out = denseBlock4(normalized, params.dense0, true);\r\n            out = denseBlock4(out, params.dense1);\r\n            out = denseBlock4(out, params.dense2);\r\n            out = denseBlock4(out, params.dense3);\r\n            out = tf.avgPool(out, [7, 7], [2, 2], 'valid');\r\n            return out;\r\n        });\r\n    };\r\n    FaceFeatureExtractor.prototype.forward = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    FaceFeatureExtractor.prototype.getDefaultModelName = function () {\r\n        return 'face_feature_extractor_model';\r\n    };\r\n    FaceFeatureExtractor.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        return extractParamsFromWeigthMap(weightMap);\r\n    };\r\n    FaceFeatureExtractor.prototype.extractParams = function (weights) {\r\n        return extractParams(weights);\r\n    };\r\n    return FaceFeatureExtractor;\r\n}(NeuralNetwork));\r\nexport { FaceFeatureExtractor };\r\n//# sourceMappingURL=FaceFeatureExtractor.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function fullyConnectedLayer(x, params) {\r\n    return tf.tidy(function () {\r\n        return tf.add(tf.matMul(x, params.weights), params.bias);\r\n    });\r\n}\r\n//# sourceMappingURL=fullyConnectedLayer.js.map","import { extractFCParamsFactory, extractWeightsFactory } from '../common';\r\nexport function extractParams(weights, channelsIn, channelsOut) {\r\n    var paramMappings = [];\r\n    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;\r\n    var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\r\n    var fc = extractFCParams(channelsIn, channelsOut, 'fc');\r\n    if (getRemainingWeights().length !== 0) {\r\n        throw new Error(\"weights remaing after extract: \" + getRemainingWeights().length);\r\n    }\r\n    return {\r\n        paramMappings: paramMappings,\r\n        params: { fc: fc }\r\n    };\r\n}\r\n//# sourceMappingURL=extractParams.js.map","import { disposeUnusedWeightTensors, extractWeightEntryFactory } from '../common';\r\nexport function extractParamsFromWeigthMap(weightMap) {\r\n    var paramMappings = [];\r\n    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\r\n    function extractFcParams(prefix) {\r\n        var weights = extractWeightEntry(prefix + \"/weights\", 2);\r\n        var bias = extractWeightEntry(prefix + \"/bias\", 1);\r\n        return { weights: weights, bias: bias };\r\n    }\r\n    var params = {\r\n        fc: extractFcParams('fc')\r\n    };\r\n    disposeUnusedWeightTensors(weightMap, paramMappings);\r\n    return { params: params, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParamsFromWeigthMap.js.map","export function seperateWeightMaps(weightMap) {\r\n    var featureExtractorMap = {};\r\n    var classifierMap = {};\r\n    Object.keys(weightMap).forEach(function (key) {\r\n        var map = key.startsWith('fc') ? classifierMap : featureExtractorMap;\r\n        map[key] = weightMap[key];\r\n    });\r\n    return { featureExtractorMap: featureExtractorMap, classifierMap: classifierMap };\r\n}\r\n//# sourceMappingURL=util.js.map","import { __extends } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\r\nimport { NetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { seperateWeightMaps } from './util';\r\nvar FaceProcessor = /** @class */ (function (_super) {\r\n    __extends(FaceProcessor, _super);\r\n    function FaceProcessor(_name, faceFeatureExtractor) {\r\n        var _this = _super.call(this, _name) || this;\r\n        _this._faceFeatureExtractor = faceFeatureExtractor;\r\n        return _this;\r\n    }\r\n    Object.defineProperty(FaceProcessor.prototype, \"faceFeatureExtractor\", {\r\n        get: function () {\r\n            return this._faceFeatureExtractor;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    FaceProcessor.prototype.runNet = function (input) {\r\n        var _this = this;\r\n        var params = this.params;\r\n        if (!params) {\r\n            throw new Error(this._name + \" - load model before inference\");\r\n        }\r\n        return tf.tidy(function () {\r\n            var bottleneckFeatures = input instanceof NetInput\r\n                ? _this.faceFeatureExtractor.forwardInput(input)\r\n                : input;\r\n            return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);\r\n        });\r\n    };\r\n    FaceProcessor.prototype.dispose = function (throwOnRedispose) {\r\n        if (throwOnRedispose === void 0) { throwOnRedispose = true; }\r\n        this.faceFeatureExtractor.dispose(throwOnRedispose);\r\n        _super.prototype.dispose.call(this, throwOnRedispose);\r\n    };\r\n    FaceProcessor.prototype.loadClassifierParams = function (weights) {\r\n        var _a = this.extractClassifierParams(weights), params = _a.params, paramMappings = _a.paramMappings;\r\n        this._params = params;\r\n        this._paramMappings = paramMappings;\r\n    };\r\n    FaceProcessor.prototype.extractClassifierParams = function (weights) {\r\n        return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());\r\n    };\r\n    FaceProcessor.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        var _a = seperateWeightMaps(weightMap), featureExtractorMap = _a.featureExtractorMap, classifierMap = _a.classifierMap;\r\n        this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\r\n        return extractParamsFromWeigthMap(classifierMap);\r\n    };\r\n    FaceProcessor.prototype.extractParams = function (weights) {\r\n        var cIn = this.getClassifierChannelsIn();\r\n        var cOut = this.getClassifierChannelsOut();\r\n        var classifierWeightSize = (cOut * cIn) + cOut;\r\n        var featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\r\n        var classifierWeights = weights.slice(weights.length - classifierWeightSize);\r\n        this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\r\n        return this.extractClassifierParams(classifierWeights);\r\n    };\r\n    return FaceProcessor;\r\n}(NeuralNetwork));\r\nexport { FaceProcessor };\r\n//# sourceMappingURL=FaceProcessor.js.map","export var FACE_EXPRESSION_LABELS = ['neutral', 'happy', 'sad', 'angry', 'fearful', 'disgusted', 'surprised'];\r\nvar FaceExpressions = /** @class */ (function () {\r\n    function FaceExpressions(probabilities) {\r\n        var _this = this;\r\n        if (probabilities.length !== 7) {\r\n            throw new Error(\"FaceExpressions.constructor - expected probabilities.length to be 7, have: \" + probabilities.length);\r\n        }\r\n        FACE_EXPRESSION_LABELS.forEach(function (expression, idx) {\r\n            _this[expression] = probabilities[idx];\r\n        });\r\n    }\r\n    FaceExpressions.prototype.asSortedArray = function () {\r\n        var _this = this;\r\n        return FACE_EXPRESSION_LABELS\r\n            .map(function (expression) { return ({ expression: expression, probability: _this[expression] }); })\r\n            .sort(function (e0, e1) { return e1.probability - e0.probability; });\r\n    };\r\n    return FaceExpressions;\r\n}());\r\nexport { FaceExpressions };\r\n//# sourceMappingURL=FaceExpressions.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { toNetInput } from '../dom';\r\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\r\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\r\nimport { FaceExpressions } from './FaceExpressions';\r\nvar FaceExpressionNet = /** @class */ (function (_super) {\r\n    __extends(FaceExpressionNet, _super);\r\n    function FaceExpressionNet(faceFeatureExtractor) {\r\n        if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new FaceFeatureExtractor(); }\r\n        return _super.call(this, 'FaceExpressionNet', faceFeatureExtractor) || this;\r\n    }\r\n    FaceExpressionNet.prototype.forwardInput = function (input) {\r\n        var _this = this;\r\n        return tf.tidy(function () { return tf.softmax(_this.runNet(input)); });\r\n    };\r\n    FaceExpressionNet.prototype.forward = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    FaceExpressionNet.prototype.predictExpressions = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var netInput, out, probabilitesByBatch, predictionsByBatch;\r\n            var _this = this;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, toNetInput(input)];\r\n                    case 1:\r\n                        netInput = _a.sent();\r\n                        return [4 /*yield*/, this.forwardInput(netInput)];\r\n                    case 2:\r\n                        out = _a.sent();\r\n                        return [4 /*yield*/, Promise.all(tf.unstack(out).map(function (t) { return __awaiter(_this, void 0, void 0, function () {\r\n                                var data;\r\n                                return __generator(this, function (_a) {\r\n                                    switch (_a.label) {\r\n                                        case 0: return [4 /*yield*/, t.data()];\r\n                                        case 1:\r\n                                            data = _a.sent();\r\n                                            t.dispose();\r\n                                            return [2 /*return*/, data];\r\n                                    }\r\n                                });\r\n                            }); }))];\r\n                    case 3:\r\n                        probabilitesByBatch = _a.sent();\r\n                        out.dispose();\r\n                        predictionsByBatch = probabilitesByBatch\r\n                            .map(function (probabilites) { return new FaceExpressions(probabilites); });\r\n                        return [2 /*return*/, netInput.isBatchInput\r\n                                ? predictionsByBatch\r\n                                : predictionsByBatch[0]];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    FaceExpressionNet.prototype.getDefaultModelName = function () {\r\n        return 'face_expression_model';\r\n    };\r\n    FaceExpressionNet.prototype.getClassifierChannelsIn = function () {\r\n        return 256;\r\n    };\r\n    FaceExpressionNet.prototype.getClassifierChannelsOut = function () {\r\n        return 7;\r\n    };\r\n    return FaceExpressionNet;\r\n}(FaceProcessor));\r\nexport { FaceExpressionNet };\r\n//# sourceMappingURL=FaceExpressionNet.js.map","import { FaceExpressions } from '../faceExpressionNet/FaceExpressions';\r\nexport function isWithFaceExpressions(obj) {\r\n    return obj['expressions'] instanceof FaceExpressions;\r\n}\r\nexport function extendWithFaceExpressions(sourceObj, expressions) {\r\n    var extension = { expressions: expressions };\r\n    return Object.assign({}, sourceObj, extension);\r\n}\r\n//# sourceMappingURL=WithFaceExpressions.js.map","import { Point } from '../classes';\r\nimport { FaceExpressions } from '../faceExpressionNet';\r\nimport { isWithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { isWithFaceExpressions } from '../factories/WithFaceExpressions';\r\nimport { round } from '../utils';\r\nimport { DrawTextField } from './DrawTextField';\r\nexport function drawFaceExpressions(canvasArg, faceExpressions, minConfidence, textFieldAnchor) {\r\n    if (minConfidence === void 0) { minConfidence = 0.1; }\r\n    var faceExpressionsArray = Array.isArray(faceExpressions) ? faceExpressions : [faceExpressions];\r\n    faceExpressionsArray.forEach(function (e) {\r\n        var expr = e instanceof FaceExpressions\r\n            ? e\r\n            : (isWithFaceExpressions(e) ? e.expressions : undefined);\r\n        if (!expr) {\r\n            throw new Error('drawFaceExpressions - expected faceExpressions to be FaceExpressions | WithFaceExpressions<{}> or array thereof');\r\n        }\r\n        var sorted = expr.asSortedArray();\r\n        var resultsToDisplay = sorted.filter(function (expr) { return expr.probability > minConfidence; });\r\n        var anchor = isWithFaceDetection(e)\r\n            ? e.detection.box.bottomLeft\r\n            : (textFieldAnchor || new Point(0, 0));\r\n        var drawTextField = new DrawTextField(resultsToDisplay.map(function (expr) { return expr.expression + \" (\" + round(expr.probability) + \")\"; }), anchor);\r\n        drawTextField.draw(canvasArg);\r\n    });\r\n}\r\n//# sourceMappingURL=drawFaceExpressions.js.map","import { FaceDetection } from '../classes/FaceDetection';\r\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\r\nimport { isWithFaceDetection } from './WithFaceDetection';\r\nexport function isWithFaceLandmarks(obj) {\r\n    return isWithFaceDetection(obj)\r\n        && obj['landmarks'] instanceof FaceLandmarks\r\n        && obj['unshiftedLandmarks'] instanceof FaceLandmarks\r\n        && obj['alignedRect'] instanceof FaceDetection;\r\n}\r\nexport function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) {\r\n    var shift = sourceObj.detection.box;\r\n    var landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);\r\n    var rect = landmarks.align();\r\n    var imageDims = sourceObj.detection.imageDims;\r\n    var alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);\r\n    var extension = {\r\n        landmarks: landmarks,\r\n        unshiftedLandmarks: unshiftedLandmarks,\r\n        alignedRect: alignedRect\r\n    };\r\n    return Object.assign({}, sourceObj, extension);\r\n}\r\n//# sourceMappingURL=WithFaceLandmarks.js.map","import { FaceLandmarks } from '../classes/FaceLandmarks';\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { getContext2dOrThrow } from '../dom/getContext2dOrThrow';\r\nimport { isWithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nimport { drawContour } from './drawContour';\r\nvar DrawFaceLandmarksOptions = /** @class */ (function () {\r\n    function DrawFaceLandmarksOptions(options) {\r\n        if (options === void 0) { options = {}; }\r\n        var _a = options.drawLines, drawLines = _a === void 0 ? true : _a, _b = options.drawPoints, drawPoints = _b === void 0 ? true : _b, lineWidth = options.lineWidth, lineColor = options.lineColor, pointSize = options.pointSize, pointColor = options.pointColor;\r\n        this.drawLines = drawLines;\r\n        this.drawPoints = drawPoints;\r\n        this.lineWidth = lineWidth || 1;\r\n        this.pointSize = pointSize || 2;\r\n        this.lineColor = lineColor || 'rgba(0, 255, 255, 1)';\r\n        this.pointColor = pointColor || 'rgba(255, 0, 255, 1)';\r\n    }\r\n    return DrawFaceLandmarksOptions;\r\n}());\r\nexport { DrawFaceLandmarksOptions };\r\nvar DrawFaceLandmarks = /** @class */ (function () {\r\n    function DrawFaceLandmarks(faceLandmarks, options) {\r\n        if (options === void 0) { options = {}; }\r\n        this.faceLandmarks = faceLandmarks;\r\n        this.options = new DrawFaceLandmarksOptions(options);\r\n    }\r\n    DrawFaceLandmarks.prototype.draw = function (canvasArg) {\r\n        var ctx = getContext2dOrThrow(canvasArg);\r\n        var _a = this.options, drawLines = _a.drawLines, drawPoints = _a.drawPoints, lineWidth = _a.lineWidth, lineColor = _a.lineColor, pointSize = _a.pointSize, pointColor = _a.pointColor;\r\n        if (drawLines && this.faceLandmarks instanceof FaceLandmarks68) {\r\n            ctx.strokeStyle = lineColor;\r\n            ctx.lineWidth = lineWidth;\r\n            drawContour(ctx, this.faceLandmarks.getJawOutline());\r\n            drawContour(ctx, this.faceLandmarks.getLeftEyeBrow());\r\n            drawContour(ctx, this.faceLandmarks.getRightEyeBrow());\r\n            drawContour(ctx, this.faceLandmarks.getNose());\r\n            drawContour(ctx, this.faceLandmarks.getLeftEye(), true);\r\n            drawContour(ctx, this.faceLandmarks.getRightEye(), true);\r\n            drawContour(ctx, this.faceLandmarks.getMouth(), true);\r\n        }\r\n        if (drawPoints) {\r\n            ctx.strokeStyle = pointColor;\r\n            ctx.fillStyle = pointColor;\r\n            var drawPoint = function (pt) {\r\n                ctx.beginPath();\r\n                ctx.arc(pt.x, pt.y, pointSize, 0, 2 * Math.PI);\r\n                ctx.fill();\r\n            };\r\n            this.faceLandmarks.positions.forEach(drawPoint);\r\n        }\r\n    };\r\n    return DrawFaceLandmarks;\r\n}());\r\nexport { DrawFaceLandmarks };\r\nexport function drawFaceLandmarks(canvasArg, faceLandmarks) {\r\n    var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];\r\n    faceLandmarksArray.forEach(function (f) {\r\n        var landmarks = f instanceof FaceLandmarks\r\n            ? f\r\n            : (isWithFaceLandmarks(f) ? f.landmarks : undefined);\r\n        if (!landmarks) {\r\n            throw new Error('drawFaceLandmarks - expected faceExpressions to be FaceLandmarks | WithFaceLandmarks<WithFaceDetection<{}>> or array thereof');\r\n        }\r\n        new DrawFaceLandmarks(landmarks).draw(canvasArg);\r\n    });\r\n}\r\n//# sourceMappingURL=DrawFaceLandmarks.js.map","import { extractConvParamsFactory, extractSeparableConvParamsFactory, extractWeightsFactory } from '../common';\r\nimport { range } from '../utils';\r\nfunction extractorsFactory(extractWeights, paramMappings) {\r\n    var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\r\n    var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\r\n    function extractReductionBlockParams(channelsIn, channelsOut, mappedPrefix) {\r\n        var separable_conv0 = extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix + \"/separable_conv0\");\r\n        var separable_conv1 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + \"/separable_conv1\");\r\n        var expansion_conv = extractConvParams(channelsIn, channelsOut, 1, mappedPrefix + \"/expansion_conv\");\r\n        return { separable_conv0: separable_conv0, separable_conv1: separable_conv1, expansion_conv: expansion_conv };\r\n    }\r\n    function extractMainBlockParams(channels, mappedPrefix) {\r\n        var separable_conv0 = extractSeparableConvParams(channels, channels, mappedPrefix + \"/separable_conv0\");\r\n        var separable_conv1 = extractSeparableConvParams(channels, channels, mappedPrefix + \"/separable_conv1\");\r\n        var separable_conv2 = extractSeparableConvParams(channels, channels, mappedPrefix + \"/separable_conv2\");\r\n        return { separable_conv0: separable_conv0, separable_conv1: separable_conv1, separable_conv2: separable_conv2 };\r\n    }\r\n    return {\r\n        extractConvParams: extractConvParams,\r\n        extractSeparableConvParams: extractSeparableConvParams,\r\n        extractReductionBlockParams: extractReductionBlockParams,\r\n        extractMainBlockParams: extractMainBlockParams\r\n    };\r\n}\r\nexport function extractParams(weights, numMainBlocks) {\r\n    var paramMappings = [];\r\n    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;\r\n    var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractSeparableConvParams = _b.extractSeparableConvParams, extractReductionBlockParams = _b.extractReductionBlockParams, extractMainBlockParams = _b.extractMainBlockParams;\r\n    var entry_flow_conv_in = extractConvParams(3, 32, 3, 'entry_flow/conv_in');\r\n    var entry_flow_reduction_block_0 = extractReductionBlockParams(32, 64, 'entry_flow/reduction_block_0');\r\n    var entry_flow_reduction_block_1 = extractReductionBlockParams(64, 128, 'entry_flow/reduction_block_1');\r\n    var entry_flow = {\r\n        conv_in: entry_flow_conv_in,\r\n        reduction_block_0: entry_flow_reduction_block_0,\r\n        reduction_block_1: entry_flow_reduction_block_1\r\n    };\r\n    var middle_flow = {};\r\n    range(numMainBlocks, 0, 1).forEach(function (idx) {\r\n        middle_flow[\"main_block_\" + idx] = extractMainBlockParams(128, \"middle_flow/main_block_\" + idx);\r\n    });\r\n    var exit_flow_reduction_block = extractReductionBlockParams(128, 256, 'exit_flow/reduction_block');\r\n    var exit_flow_separable_conv = extractSeparableConvParams(256, 512, 'exit_flow/separable_conv');\r\n    var exit_flow = {\r\n        reduction_block: exit_flow_reduction_block,\r\n        separable_conv: exit_flow_separable_conv\r\n    };\r\n    if (getRemainingWeights().length !== 0) {\r\n        throw new Error(\"weights remaing after extract: \" + getRemainingWeights().length);\r\n    }\r\n    return {\r\n        paramMappings: paramMappings,\r\n        params: { entry_flow: entry_flow, middle_flow: middle_flow, exit_flow: exit_flow }\r\n    };\r\n}\r\n//# sourceMappingURL=extractParams.js.map","import { disposeUnusedWeightTensors, extractWeightEntryFactory, loadSeparableConvParamsFactory, } from '../common';\r\nimport { loadConvParamsFactory } from '../common/loadConvParamsFactory';\r\nimport { range } from '../utils';\r\nfunction loadParamsFactory(weightMap, paramMappings) {\r\n    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\r\n    var extractConvParams = loadConvParamsFactory(extractWeightEntry);\r\n    var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\r\n    function extractReductionBlockParams(mappedPrefix) {\r\n        var separable_conv0 = extractSeparableConvParams(mappedPrefix + \"/separable_conv0\");\r\n        var separable_conv1 = extractSeparableConvParams(mappedPrefix + \"/separable_conv1\");\r\n        var expansion_conv = extractConvParams(mappedPrefix + \"/expansion_conv\");\r\n        return { separable_conv0: separable_conv0, separable_conv1: separable_conv1, expansion_conv: expansion_conv };\r\n    }\r\n    function extractMainBlockParams(mappedPrefix) {\r\n        var separable_conv0 = extractSeparableConvParams(mappedPrefix + \"/separable_conv0\");\r\n        var separable_conv1 = extractSeparableConvParams(mappedPrefix + \"/separable_conv1\");\r\n        var separable_conv2 = extractSeparableConvParams(mappedPrefix + \"/separable_conv2\");\r\n        return { separable_conv0: separable_conv0, separable_conv1: separable_conv1, separable_conv2: separable_conv2 };\r\n    }\r\n    return {\r\n        extractConvParams: extractConvParams,\r\n        extractSeparableConvParams: extractSeparableConvParams,\r\n        extractReductionBlockParams: extractReductionBlockParams,\r\n        extractMainBlockParams: extractMainBlockParams\r\n    };\r\n}\r\nexport function extractParamsFromWeigthMap(weightMap, numMainBlocks) {\r\n    var paramMappings = [];\r\n    var _a = loadParamsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractSeparableConvParams = _a.extractSeparableConvParams, extractReductionBlockParams = _a.extractReductionBlockParams, extractMainBlockParams = _a.extractMainBlockParams;\r\n    var entry_flow_conv_in = extractConvParams('entry_flow/conv_in');\r\n    var entry_flow_reduction_block_0 = extractReductionBlockParams('entry_flow/reduction_block_0');\r\n    var entry_flow_reduction_block_1 = extractReductionBlockParams('entry_flow/reduction_block_1');\r\n    var entry_flow = {\r\n        conv_in: entry_flow_conv_in,\r\n        reduction_block_0: entry_flow_reduction_block_0,\r\n        reduction_block_1: entry_flow_reduction_block_1\r\n    };\r\n    var middle_flow = {};\r\n    range(numMainBlocks, 0, 1).forEach(function (idx) {\r\n        middle_flow[\"main_block_\" + idx] = extractMainBlockParams(\"middle_flow/main_block_\" + idx);\r\n    });\r\n    var exit_flow_reduction_block = extractReductionBlockParams('exit_flow/reduction_block');\r\n    var exit_flow_separable_conv = extractSeparableConvParams('exit_flow/separable_conv');\r\n    var exit_flow = {\r\n        reduction_block: exit_flow_reduction_block,\r\n        separable_conv: exit_flow_separable_conv\r\n    };\r\n    disposeUnusedWeightTensors(weightMap, paramMappings);\r\n    return { params: { entry_flow: entry_flow, middle_flow: middle_flow, exit_flow: exit_flow }, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParamsFromWeigthMap.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { depthwiseSeparableConv } from '../common';\r\nimport { toNetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { normalize } from '../ops';\r\nimport { range } from '../utils';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nfunction conv(x, params, stride) {\r\n    return tf.add(tf.conv2d(x, params.filters, stride, 'same'), params.bias);\r\n}\r\nfunction reductionBlock(x, params, isActivateInput) {\r\n    if (isActivateInput === void 0) { isActivateInput = true; }\r\n    var out = isActivateInput ? tf.relu(x) : x;\r\n    out = depthwiseSeparableConv(out, params.separable_conv0, [1, 1]);\r\n    out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\r\n    out = tf.maxPool(out, [3, 3], [2, 2], 'same');\r\n    out = tf.add(out, conv(x, params.expansion_conv, [2, 2]));\r\n    return out;\r\n}\r\nfunction mainBlock(x, params) {\r\n    var out = depthwiseSeparableConv(tf.relu(x), params.separable_conv0, [1, 1]);\r\n    out = depthwiseSeparableConv(tf.relu(out), params.separable_conv1, [1, 1]);\r\n    out = depthwiseSeparableConv(tf.relu(out), params.separable_conv2, [1, 1]);\r\n    out = tf.add(out, x);\r\n    return out;\r\n}\r\nvar TinyXception = /** @class */ (function (_super) {\r\n    __extends(TinyXception, _super);\r\n    function TinyXception(numMainBlocks) {\r\n        var _this = _super.call(this, 'TinyXception') || this;\r\n        _this._numMainBlocks = numMainBlocks;\r\n        return _this;\r\n    }\r\n    TinyXception.prototype.forwardInput = function (input) {\r\n        var _this = this;\r\n        var params = this.params;\r\n        if (!params) {\r\n            throw new Error('TinyXception - load model before inference');\r\n        }\r\n        return tf.tidy(function () {\r\n            var batchTensor = input.toBatchTensor(112, true);\r\n            var meanRgb = [122.782, 117.001, 104.298];\r\n            var normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256));\r\n            var out = tf.relu(conv(normalized, params.entry_flow.conv_in, [2, 2]));\r\n            out = reductionBlock(out, params.entry_flow.reduction_block_0, false);\r\n            out = reductionBlock(out, params.entry_flow.reduction_block_1);\r\n            range(_this._numMainBlocks, 0, 1).forEach(function (idx) {\r\n                out = mainBlock(out, params.middle_flow[\"main_block_\" + idx]);\r\n            });\r\n            out = reductionBlock(out, params.exit_flow.reduction_block);\r\n            out = tf.relu(depthwiseSeparableConv(out, params.exit_flow.separable_conv, [1, 1]));\r\n            return out;\r\n        });\r\n    };\r\n    TinyXception.prototype.forward = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    TinyXception.prototype.getDefaultModelName = function () {\r\n        return 'tiny_xception_model';\r\n    };\r\n    TinyXception.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        return extractParamsFromWeigthMap(weightMap, this._numMainBlocks);\r\n    };\r\n    TinyXception.prototype.extractParams = function (weights) {\r\n        return extractParams(weights, this._numMainBlocks);\r\n    };\r\n    return TinyXception;\r\n}(NeuralNetwork));\r\nexport { TinyXception };\r\n//# sourceMappingURL=TinyXception.js.map","export var Gender;\r\n(function (Gender) {\r\n    Gender[\"FEMALE\"] = \"female\";\r\n    Gender[\"MALE\"] = \"male\";\r\n})(Gender || (Gender = {}));\r\n//# sourceMappingURL=types.js.map","import { extractFCParamsFactory, extractWeightsFactory } from '../common';\r\nexport function extractParams(weights) {\r\n    var paramMappings = [];\r\n    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;\r\n    var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\r\n    var age = extractFCParams(512, 1, 'fc/age');\r\n    var gender = extractFCParams(512, 2, 'fc/gender');\r\n    if (getRemainingWeights().length !== 0) {\r\n        throw new Error(\"weights remaing after extract: \" + getRemainingWeights().length);\r\n    }\r\n    return {\r\n        paramMappings: paramMappings,\r\n        params: { fc: { age: age, gender: gender } }\r\n    };\r\n}\r\n//# sourceMappingURL=extractParams.js.map","import { disposeUnusedWeightTensors, extractWeightEntryFactory } from '../common';\r\nexport function extractParamsFromWeigthMap(weightMap) {\r\n    var paramMappings = [];\r\n    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\r\n    function extractFcParams(prefix) {\r\n        var weights = extractWeightEntry(prefix + \"/weights\", 2);\r\n        var bias = extractWeightEntry(prefix + \"/bias\", 1);\r\n        return { weights: weights, bias: bias };\r\n    }\r\n    var params = {\r\n        fc: {\r\n            age: extractFcParams('fc/age'),\r\n            gender: extractFcParams('fc/gender')\r\n        }\r\n    };\r\n    disposeUnusedWeightTensors(weightMap, paramMappings);\r\n    return { params: params, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParamsFromWeigthMap.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\r\nimport { seperateWeightMaps } from '../faceProcessor/util';\r\nimport { TinyXception } from '../xception/TinyXception';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { Gender } from './types';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { NetInput, toNetInput } from '../dom';\r\nvar AgeGenderNet = /** @class */ (function (_super) {\r\n    __extends(AgeGenderNet, _super);\r\n    function AgeGenderNet(faceFeatureExtractor) {\r\n        if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new TinyXception(2); }\r\n        var _this = _super.call(this, 'AgeGenderNet') || this;\r\n        _this._faceFeatureExtractor = faceFeatureExtractor;\r\n        return _this;\r\n    }\r\n    Object.defineProperty(AgeGenderNet.prototype, \"faceFeatureExtractor\", {\r\n        get: function () {\r\n            return this._faceFeatureExtractor;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    AgeGenderNet.prototype.runNet = function (input) {\r\n        var _this = this;\r\n        var params = this.params;\r\n        if (!params) {\r\n            throw new Error(this._name + \" - load model before inference\");\r\n        }\r\n        return tf.tidy(function () {\r\n            var bottleneckFeatures = input instanceof NetInput\r\n                ? _this.faceFeatureExtractor.forwardInput(input)\r\n                : input;\r\n            var pooled = tf.avgPool(bottleneckFeatures, [7, 7], [2, 2], 'valid').as2D(bottleneckFeatures.shape[0], -1);\r\n            var age = fullyConnectedLayer(pooled, params.fc.age).as1D();\r\n            var gender = fullyConnectedLayer(pooled, params.fc.gender);\r\n            return { age: age, gender: gender };\r\n        });\r\n    };\r\n    AgeGenderNet.prototype.forwardInput = function (input) {\r\n        var _this = this;\r\n        return tf.tidy(function () {\r\n            var _a = _this.runNet(input), age = _a.age, gender = _a.gender;\r\n            return { age: age, gender: tf.softmax(gender) };\r\n        });\r\n    };\r\n    AgeGenderNet.prototype.forward = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    AgeGenderNet.prototype.predictAgeAndGender = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var netInput, out, ages, genders, ageAndGenderTensors, predictionsByBatch;\r\n            var _this = this;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, toNetInput(input)];\r\n                    case 1:\r\n                        netInput = _a.sent();\r\n                        return [4 /*yield*/, this.forwardInput(netInput)];\r\n                    case 2:\r\n                        out = _a.sent();\r\n                        ages = tf.unstack(out.age);\r\n                        genders = tf.unstack(out.gender);\r\n                        ageAndGenderTensors = ages.map(function (ageTensor, i) { return ({\r\n                            ageTensor: ageTensor,\r\n                            genderTensor: genders[i]\r\n                        }); });\r\n                        return [4 /*yield*/, Promise.all(ageAndGenderTensors.map(function (_a) {\r\n                                var ageTensor = _a.ageTensor, genderTensor = _a.genderTensor;\r\n                                return __awaiter(_this, void 0, void 0, function () {\r\n                                    var age, probMale, isMale, gender, genderProbability;\r\n                                    return __generator(this, function (_b) {\r\n                                        switch (_b.label) {\r\n                                            case 0: return [4 /*yield*/, ageTensor.data()];\r\n                                            case 1:\r\n                                                age = (_b.sent())[0];\r\n                                                return [4 /*yield*/, genderTensor.data()];\r\n                                            case 2:\r\n                                                probMale = (_b.sent())[0];\r\n                                                isMale = probMale > 0.5;\r\n                                                gender = isMale ? Gender.MALE : Gender.FEMALE;\r\n                                                genderProbability = isMale ? probMale : (1 - probMale);\r\n                                                ageTensor.dispose();\r\n                                                genderTensor.dispose();\r\n                                                return [2 /*return*/, { age: age, gender: gender, genderProbability: genderProbability }];\r\n                                        }\r\n                                    });\r\n                                });\r\n                            }))];\r\n                    case 3:\r\n                        predictionsByBatch = _a.sent();\r\n                        out.age.dispose();\r\n                        out.gender.dispose();\r\n                        return [2 /*return*/, netInput.isBatchInput\r\n                                ? predictionsByBatch\r\n                                : predictionsByBatch[0]];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    AgeGenderNet.prototype.getDefaultModelName = function () {\r\n        return 'age_gender_model';\r\n    };\r\n    AgeGenderNet.prototype.dispose = function (throwOnRedispose) {\r\n        if (throwOnRedispose === void 0) { throwOnRedispose = true; }\r\n        this.faceFeatureExtractor.dispose(throwOnRedispose);\r\n        _super.prototype.dispose.call(this, throwOnRedispose);\r\n    };\r\n    AgeGenderNet.prototype.loadClassifierParams = function (weights) {\r\n        var _a = this.extractClassifierParams(weights), params = _a.params, paramMappings = _a.paramMappings;\r\n        this._params = params;\r\n        this._paramMappings = paramMappings;\r\n    };\r\n    AgeGenderNet.prototype.extractClassifierParams = function (weights) {\r\n        return extractParams(weights);\r\n    };\r\n    AgeGenderNet.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        var _a = seperateWeightMaps(weightMap), featureExtractorMap = _a.featureExtractorMap, classifierMap = _a.classifierMap;\r\n        this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);\r\n        return extractParamsFromWeigthMap(classifierMap);\r\n    };\r\n    AgeGenderNet.prototype.extractParams = function (weights) {\r\n        var classifierWeightSize = (512 * 1 + 1) + (512 * 2 + 2);\r\n        var featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);\r\n        var classifierWeights = weights.slice(weights.length - classifierWeightSize);\r\n        this.faceFeatureExtractor.extractWeights(featureExtractorWeights);\r\n        return this.extractClassifierParams(classifierWeights);\r\n    };\r\n    return AgeGenderNet;\r\n}(NeuralNetwork));\r\nexport { AgeGenderNet };\r\n//# sourceMappingURL=AgeGenderNet.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { Point } from '../classes';\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { toNetInput } from '../dom';\r\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\r\nimport { isEven } from '../utils';\r\nvar FaceLandmark68NetBase = /** @class */ (function (_super) {\r\n    __extends(FaceLandmark68NetBase, _super);\r\n    function FaceLandmark68NetBase() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    FaceLandmark68NetBase.prototype.postProcess = function (output, inputSize, originalDimensions) {\r\n        var inputDimensions = originalDimensions.map(function (_a) {\r\n            var width = _a.width, height = _a.height;\r\n            var scale = inputSize / Math.max(height, width);\r\n            return {\r\n                width: width * scale,\r\n                height: height * scale\r\n            };\r\n        });\r\n        var batchSize = inputDimensions.length;\r\n        return tf.tidy(function () {\r\n            var createInterleavedTensor = function (fillX, fillY) {\r\n                return tf.stack([\r\n                    tf.fill([68], fillX),\r\n                    tf.fill([68], fillY)\r\n                ], 1).as2D(1, 136).as1D();\r\n            };\r\n            var getPadding = function (batchIdx, cond) {\r\n                var _a = inputDimensions[batchIdx], width = _a.width, height = _a.height;\r\n                return cond(width, height) ? Math.abs(width - height) / 2 : 0;\r\n            };\r\n            var getPaddingX = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return w < h; }); };\r\n            var getPaddingY = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return h < w; }); };\r\n            var landmarkTensors = output\r\n                .mul(tf.fill([batchSize, 136], inputSize))\r\n                .sub(tf.stack(Array.from(Array(batchSize), function (_, batchIdx) {\r\n                return createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx));\r\n            })))\r\n                .div(tf.stack(Array.from(Array(batchSize), function (_, batchIdx) {\r\n                return createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height);\r\n            })));\r\n            return landmarkTensors;\r\n        });\r\n    };\r\n    FaceLandmark68NetBase.prototype.forwardInput = function (input) {\r\n        var _this = this;\r\n        return tf.tidy(function () {\r\n            var out = _this.runNet(input);\r\n            return _this.postProcess(out, input.inputSize, input.inputDimensions.map(function (_a) {\r\n                var height = _a[0], width = _a[1];\r\n                return ({ height: height, width: width });\r\n            }));\r\n        });\r\n    };\r\n    FaceLandmark68NetBase.prototype.forward = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    FaceLandmark68NetBase.prototype.detectLandmarks = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var netInput, landmarkTensors, landmarksForBatch;\r\n            var _this = this;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, toNetInput(input)];\r\n                    case 1:\r\n                        netInput = _a.sent();\r\n                        landmarkTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });\r\n                        return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return __awaiter(_this, void 0, void 0, function () {\r\n                                var landmarksArray, _a, _b, xCoords, yCoords;\r\n                                return __generator(this, function (_c) {\r\n                                    switch (_c.label) {\r\n                                        case 0:\r\n                                            _b = (_a = Array).from;\r\n                                            return [4 /*yield*/, landmarkTensor.data()];\r\n                                        case 1:\r\n                                            landmarksArray = _b.apply(_a, [_c.sent()]);\r\n                                            xCoords = landmarksArray.filter(function (_, i) { return isEven(i); });\r\n                                            yCoords = landmarksArray.filter(function (_, i) { return !isEven(i); });\r\n                                            return [2 /*return*/, new FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), {\r\n                                                    height: netInput.getInputHeight(batchIdx),\r\n                                                    width: netInput.getInputWidth(batchIdx),\r\n                                                })];\r\n                                    }\r\n                                });\r\n                            }); }))];\r\n                    case 2:\r\n                        landmarksForBatch = _a.sent();\r\n                        landmarkTensors.forEach(function (t) { return t.dispose(); });\r\n                        return [2 /*return*/, netInput.isBatchInput\r\n                                ? landmarksForBatch\r\n                                : landmarksForBatch[0]];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    FaceLandmark68NetBase.prototype.getClassifierChannelsOut = function () {\r\n        return 136;\r\n    };\r\n    return FaceLandmark68NetBase;\r\n}(FaceProcessor));\r\nexport { FaceLandmark68NetBase };\r\n//# sourceMappingURL=FaceLandmark68NetBase.js.map","import { __extends } from \"tslib\";\r\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\r\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\r\nvar FaceLandmark68Net = /** @class */ (function (_super) {\r\n    __extends(FaceLandmark68Net, _super);\r\n    function FaceLandmark68Net(faceFeatureExtractor) {\r\n        if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new FaceFeatureExtractor(); }\r\n        return _super.call(this, 'FaceLandmark68Net', faceFeatureExtractor) || this;\r\n    }\r\n    FaceLandmark68Net.prototype.getDefaultModelName = function () {\r\n        return 'face_landmark_68_model';\r\n    };\r\n    FaceLandmark68Net.prototype.getClassifierChannelsIn = function () {\r\n        return 256;\r\n    };\r\n    return FaceLandmark68Net;\r\n}(FaceLandmark68NetBase));\r\nexport { FaceLandmark68Net };\r\n//# sourceMappingURL=FaceLandmark68Net.js.map","import { disposeUnusedWeightTensors } from '../common';\r\nimport { loadParamsFactory } from './loadParamsFactory';\r\nexport function extractParamsFromWeigthMapTiny(weightMap) {\r\n    var paramMappings = [];\r\n    var extractDenseBlock3Params = loadParamsFactory(weightMap, paramMappings).extractDenseBlock3Params;\r\n    var params = {\r\n        dense0: extractDenseBlock3Params('dense0', true),\r\n        dense1: extractDenseBlock3Params('dense1'),\r\n        dense2: extractDenseBlock3Params('dense2')\r\n    };\r\n    disposeUnusedWeightTensors(weightMap, paramMappings);\r\n    return { params: params, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParamsFromWeigthMapTiny.js.map","import { extractWeightsFactory } from '../common';\r\nimport { extractorsFactory } from './extractorsFactory';\r\nexport function extractParamsTiny(weights) {\r\n    var paramMappings = [];\r\n    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;\r\n    var extractDenseBlock3Params = extractorsFactory(extractWeights, paramMappings).extractDenseBlock3Params;\r\n    var dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);\r\n    var dense1 = extractDenseBlock3Params(32, 64, 'dense1');\r\n    var dense2 = extractDenseBlock3Params(64, 128, 'dense2');\r\n    if (getRemainingWeights().length !== 0) {\r\n        throw new Error(\"weights remaing after extract: \" + getRemainingWeights().length);\r\n    }\r\n    return {\r\n        paramMappings: paramMappings,\r\n        params: { dense0: dense0, dense1: dense1, dense2: dense2 }\r\n    };\r\n}\r\n//# sourceMappingURL=extractParamsTiny.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { toNetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { normalize } from '../ops';\r\nimport { denseBlock3 } from './denseBlock';\r\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\r\nimport { extractParamsTiny } from './extractParamsTiny';\r\nvar TinyFaceFeatureExtractor = /** @class */ (function (_super) {\r\n    __extends(TinyFaceFeatureExtractor, _super);\r\n    function TinyFaceFeatureExtractor() {\r\n        return _super.call(this, 'TinyFaceFeatureExtractor') || this;\r\n    }\r\n    TinyFaceFeatureExtractor.prototype.forwardInput = function (input) {\r\n        var params = this.params;\r\n        if (!params) {\r\n            throw new Error('TinyFaceFeatureExtractor - load model before inference');\r\n        }\r\n        return tf.tidy(function () {\r\n            var batchTensor = input.toBatchTensor(112, true);\r\n            var meanRgb = [122.782, 117.001, 104.298];\r\n            var normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255));\r\n            var out = denseBlock3(normalized, params.dense0, true);\r\n            out = denseBlock3(out, params.dense1);\r\n            out = denseBlock3(out, params.dense2);\r\n            out = tf.avgPool(out, [14, 14], [2, 2], 'valid');\r\n            return out;\r\n        });\r\n    };\r\n    TinyFaceFeatureExtractor.prototype.forward = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    TinyFaceFeatureExtractor.prototype.getDefaultModelName = function () {\r\n        return 'face_feature_extractor_tiny_model';\r\n    };\r\n    TinyFaceFeatureExtractor.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        return extractParamsFromWeigthMapTiny(weightMap);\r\n    };\r\n    TinyFaceFeatureExtractor.prototype.extractParams = function (weights) {\r\n        return extractParamsTiny(weights);\r\n    };\r\n    return TinyFaceFeatureExtractor;\r\n}(NeuralNetwork));\r\nexport { TinyFaceFeatureExtractor };\r\n//# sourceMappingURL=TinyFaceFeatureExtractor.js.map","import { __extends } from \"tslib\";\r\nimport { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\r\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\r\nvar FaceLandmark68TinyNet = /** @class */ (function (_super) {\r\n    __extends(FaceLandmark68TinyNet, _super);\r\n    function FaceLandmark68TinyNet(faceFeatureExtractor) {\r\n        if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new TinyFaceFeatureExtractor(); }\r\n        return _super.call(this, 'FaceLandmark68TinyNet', faceFeatureExtractor) || this;\r\n    }\r\n    FaceLandmark68TinyNet.prototype.getDefaultModelName = function () {\r\n        return 'face_landmark_68_tiny_model';\r\n    };\r\n    FaceLandmark68TinyNet.prototype.getClassifierChannelsIn = function () {\r\n        return 128;\r\n    };\r\n    return FaceLandmark68TinyNet;\r\n}(FaceLandmark68NetBase));\r\nexport { FaceLandmark68TinyNet };\r\n//# sourceMappingURL=FaceLandmark68TinyNet.js.map","import { __extends } from \"tslib\";\r\nimport { FaceLandmark68Net } from './FaceLandmark68Net';\r\nexport * from './FaceLandmark68Net';\r\nexport * from './FaceLandmark68TinyNet';\r\nvar FaceLandmarkNet = /** @class */ (function (_super) {\r\n    __extends(FaceLandmarkNet, _super);\r\n    function FaceLandmarkNet() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    return FaceLandmarkNet;\r\n}(FaceLandmark68Net));\r\nexport { FaceLandmarkNet };\r\n//# sourceMappingURL=index.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function scale(x, params) {\r\n    return tf.add(tf.mul(x, params.weights), params.biases);\r\n}\r\n//# sourceMappingURL=scaleLayer.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { scale } from './scaleLayer';\r\nfunction convLayer(x, params, strides, withRelu, padding) {\r\n    if (padding === void 0) { padding = 'same'; }\r\n    var _a = params.conv, filters = _a.filters, bias = _a.bias;\r\n    var out = tf.conv2d(x, filters, strides, padding);\r\n    out = tf.add(out, bias);\r\n    out = scale(out, params.scale);\r\n    return withRelu ? tf.relu(out) : out;\r\n}\r\nexport function conv(x, params) {\r\n    return convLayer(x, params, [1, 1], true);\r\n}\r\nexport function convNoRelu(x, params) {\r\n    return convLayer(x, params, [1, 1], false);\r\n}\r\nexport function convDown(x, params) {\r\n    return convLayer(x, params, [2, 2], true, 'valid');\r\n}\r\n//# sourceMappingURL=convLayer.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { extractWeightsFactory } from '../common';\r\nimport { isFloat } from '../utils';\r\nfunction extractorsFactory(extractWeights, paramMappings) {\r\n    function extractFilterValues(numFilterValues, numFilters, filterSize) {\r\n        var weights = extractWeights(numFilterValues);\r\n        var depth = weights.length / (numFilters * filterSize * filterSize);\r\n        if (isFloat(depth)) {\r\n            throw new Error(\"depth has to be an integer: \" + depth + \", weights.length: \" + weights.length + \", numFilters: \" + numFilters + \", filterSize: \" + filterSize);\r\n        }\r\n        return tf.tidy(function () { return tf.transpose(tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); });\r\n    }\r\n    function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {\r\n        var filters = extractFilterValues(numFilterValues, numFilters, filterSize);\r\n        var bias = tf.tensor1d(extractWeights(numFilters));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/filters\" }, { paramPath: mappedPrefix + \"/bias\" });\r\n        return { filters: filters, bias: bias };\r\n    }\r\n    function extractScaleLayerParams(numWeights, mappedPrefix) {\r\n        var weights = tf.tensor1d(extractWeights(numWeights));\r\n        var biases = tf.tensor1d(extractWeights(numWeights));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/weights\" }, { paramPath: mappedPrefix + \"/biases\" });\r\n        return {\r\n            weights: weights,\r\n            biases: biases\r\n        };\r\n    }\r\n    function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {\r\n        var conv = extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix + \"/conv\");\r\n        var scale = extractScaleLayerParams(numFilters, mappedPrefix + \"/scale\");\r\n        return { conv: conv, scale: scale };\r\n    }\r\n    function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown) {\r\n        if (isDown === void 0) { isDown = false; }\r\n        var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, mappedPrefix + \"/conv1\");\r\n        var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix + \"/conv2\");\r\n        return { conv1: conv1, conv2: conv2 };\r\n    }\r\n    return {\r\n        extractConvLayerParams: extractConvLayerParams,\r\n        extractResidualLayerParams: extractResidualLayerParams\r\n    };\r\n}\r\nexport function extractParams(weights) {\r\n    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;\r\n    var paramMappings = [];\r\n    var _b = extractorsFactory(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;\r\n    var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');\r\n    var conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');\r\n    var conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');\r\n    var conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');\r\n    var conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);\r\n    var conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');\r\n    var conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');\r\n    var conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');\r\n    var conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);\r\n    var conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');\r\n    var conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');\r\n    var conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);\r\n    var conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');\r\n    var conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');\r\n    var conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');\r\n    var fc = tf.tidy(function () { return tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]); });\r\n    paramMappings.push({ paramPath: \"fc\" });\r\n    if (getRemainingWeights().length !== 0) {\r\n        throw new Error(\"weights remaing after extract: \" + getRemainingWeights().length);\r\n    }\r\n    var params = {\r\n        conv32_down: conv32_down,\r\n        conv32_1: conv32_1,\r\n        conv32_2: conv32_2,\r\n        conv32_3: conv32_3,\r\n        conv64_down: conv64_down,\r\n        conv64_1: conv64_1,\r\n        conv64_2: conv64_2,\r\n        conv64_3: conv64_3,\r\n        conv128_down: conv128_down,\r\n        conv128_1: conv128_1,\r\n        conv128_2: conv128_2,\r\n        conv256_down: conv256_down,\r\n        conv256_1: conv256_1,\r\n        conv256_2: conv256_2,\r\n        conv256_down_out: conv256_down_out,\r\n        fc: fc\r\n    };\r\n    return { params: params, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParams.js.map","import { disposeUnusedWeightTensors, extractWeightEntryFactory } from '../common';\r\nimport { isTensor2D } from '../utils';\r\nfunction extractorsFactory(weightMap, paramMappings) {\r\n    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\r\n    function extractScaleLayerParams(prefix) {\r\n        var weights = extractWeightEntry(prefix + \"/scale/weights\", 1);\r\n        var biases = extractWeightEntry(prefix + \"/scale/biases\", 1);\r\n        return { weights: weights, biases: biases };\r\n    }\r\n    function extractConvLayerParams(prefix) {\r\n        var filters = extractWeightEntry(prefix + \"/conv/filters\", 4);\r\n        var bias = extractWeightEntry(prefix + \"/conv/bias\", 1);\r\n        var scale = extractScaleLayerParams(prefix);\r\n        return { conv: { filters: filters, bias: bias }, scale: scale };\r\n    }\r\n    function extractResidualLayerParams(prefix) {\r\n        return {\r\n            conv1: extractConvLayerParams(prefix + \"/conv1\"),\r\n            conv2: extractConvLayerParams(prefix + \"/conv2\")\r\n        };\r\n    }\r\n    return {\r\n        extractConvLayerParams: extractConvLayerParams,\r\n        extractResidualLayerParams: extractResidualLayerParams\r\n    };\r\n}\r\nexport function extractParamsFromWeigthMap(weightMap) {\r\n    var paramMappings = [];\r\n    var _a = extractorsFactory(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;\r\n    var conv32_down = extractConvLayerParams('conv32_down');\r\n    var conv32_1 = extractResidualLayerParams('conv32_1');\r\n    var conv32_2 = extractResidualLayerParams('conv32_2');\r\n    var conv32_3 = extractResidualLayerParams('conv32_3');\r\n    var conv64_down = extractResidualLayerParams('conv64_down');\r\n    var conv64_1 = extractResidualLayerParams('conv64_1');\r\n    var conv64_2 = extractResidualLayerParams('conv64_2');\r\n    var conv64_3 = extractResidualLayerParams('conv64_3');\r\n    var conv128_down = extractResidualLayerParams('conv128_down');\r\n    var conv128_1 = extractResidualLayerParams('conv128_1');\r\n    var conv128_2 = extractResidualLayerParams('conv128_2');\r\n    var conv256_down = extractResidualLayerParams('conv256_down');\r\n    var conv256_1 = extractResidualLayerParams('conv256_1');\r\n    var conv256_2 = extractResidualLayerParams('conv256_2');\r\n    var conv256_down_out = extractResidualLayerParams('conv256_down_out');\r\n    var fc = weightMap['fc'];\r\n    paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });\r\n    if (!isTensor2D(fc)) {\r\n        throw new Error(\"expected weightMap[fc] to be a Tensor2D, instead have \" + fc);\r\n    }\r\n    var params = {\r\n        conv32_down: conv32_down,\r\n        conv32_1: conv32_1,\r\n        conv32_2: conv32_2,\r\n        conv32_3: conv32_3,\r\n        conv64_down: conv64_down,\r\n        conv64_1: conv64_1,\r\n        conv64_2: conv64_2,\r\n        conv64_3: conv64_3,\r\n        conv128_down: conv128_down,\r\n        conv128_1: conv128_1,\r\n        conv128_2: conv128_2,\r\n        conv256_down: conv256_down,\r\n        conv256_1: conv256_1,\r\n        conv256_2: conv256_2,\r\n        conv256_down_out: conv256_down_out,\r\n        fc: fc\r\n    };\r\n    disposeUnusedWeightTensors(weightMap, paramMappings);\r\n    return { params: params, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParamsFromWeigthMap.js.map","import { __spreadArrays } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { conv, convDown, convNoRelu } from './convLayer';\r\nexport function residual(x, params) {\r\n    var out = conv(x, params.conv1);\r\n    out = convNoRelu(out, params.conv2);\r\n    out = tf.add(out, x);\r\n    out = tf.relu(out);\r\n    return out;\r\n}\r\nexport function residualDown(x, params) {\r\n    var out = convDown(x, params.conv1);\r\n    out = convNoRelu(out, params.conv2);\r\n    var pooled = tf.avgPool(x, 2, 2, 'valid');\r\n    var zeros = tf.zeros(pooled.shape);\r\n    var isPad = pooled.shape[3] !== out.shape[3];\r\n    var isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];\r\n    if (isAdjustShape) {\r\n        var padShapeX = __spreadArrays(out.shape);\r\n        padShapeX[1] = 1;\r\n        var zerosW = tf.zeros(padShapeX);\r\n        out = tf.concat([out, zerosW], 1);\r\n        var padShapeY = __spreadArrays(out.shape);\r\n        padShapeY[2] = 1;\r\n        var zerosH = tf.zeros(padShapeY);\r\n        out = tf.concat([out, zerosH], 2);\r\n    }\r\n    pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled;\r\n    out = tf.add(pooled, out);\r\n    out = tf.relu(out);\r\n    return out;\r\n}\r\n//# sourceMappingURL=residualLayer.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { toNetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { normalize } from '../ops';\r\nimport { convDown } from './convLayer';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { residual, residualDown } from './residualLayer';\r\nvar FaceRecognitionNet = /** @class */ (function (_super) {\r\n    __extends(FaceRecognitionNet, _super);\r\n    function FaceRecognitionNet() {\r\n        return _super.call(this, 'FaceRecognitionNet') || this;\r\n    }\r\n    FaceRecognitionNet.prototype.forwardInput = function (input) {\r\n        var params = this.params;\r\n        if (!params) {\r\n            throw new Error('FaceRecognitionNet - load model before inference');\r\n        }\r\n        return tf.tidy(function () {\r\n            var batchTensor = input.toBatchTensor(150, true).toFloat();\r\n            var meanRgb = [122.782, 117.001, 104.298];\r\n            var normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256));\r\n            var out = convDown(normalized, params.conv32_down);\r\n            out = tf.maxPool(out, 3, 2, 'valid');\r\n            out = residual(out, params.conv32_1);\r\n            out = residual(out, params.conv32_2);\r\n            out = residual(out, params.conv32_3);\r\n            out = residualDown(out, params.conv64_down);\r\n            out = residual(out, params.conv64_1);\r\n            out = residual(out, params.conv64_2);\r\n            out = residual(out, params.conv64_3);\r\n            out = residualDown(out, params.conv128_down);\r\n            out = residual(out, params.conv128_1);\r\n            out = residual(out, params.conv128_2);\r\n            out = residualDown(out, params.conv256_down);\r\n            out = residual(out, params.conv256_1);\r\n            out = residual(out, params.conv256_2);\r\n            out = residualDown(out, params.conv256_down_out);\r\n            var globalAvg = out.mean([1, 2]);\r\n            var fullyConnected = tf.matMul(globalAvg, params.fc);\r\n            return fullyConnected;\r\n        });\r\n    };\r\n    FaceRecognitionNet.prototype.forward = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var netInput, faceDescriptorTensors, faceDescriptorsForBatch;\r\n            var _this = this;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, toNetInput(input)];\r\n                    case 1:\r\n                        netInput = _a.sent();\r\n                        faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });\r\n                        return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];\r\n                    case 2:\r\n                        faceDescriptorsForBatch = _a.sent();\r\n                        faceDescriptorTensors.forEach(function (t) { return t.dispose(); });\r\n                        return [2 /*return*/, netInput.isBatchInput\r\n                                ? faceDescriptorsForBatch\r\n                                : faceDescriptorsForBatch[0]];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    FaceRecognitionNet.prototype.getDefaultModelName = function () {\r\n        return 'face_recognition_model';\r\n    };\r\n    FaceRecognitionNet.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        return extractParamsFromWeigthMap(weightMap);\r\n    };\r\n    FaceRecognitionNet.prototype.extractParams = function (weights) {\r\n        return extractParams(weights);\r\n    };\r\n    return FaceRecognitionNet;\r\n}(NeuralNetwork));\r\nexport { FaceRecognitionNet };\r\n//# sourceMappingURL=FaceRecognitionNet.js.map","export function extendWithFaceDescriptor(sourceObj, descriptor) {\r\n    var extension = { descriptor: descriptor };\r\n    return Object.assign({}, sourceObj, extension);\r\n}\r\n//# sourceMappingURL=WithFaceDescriptor.js.map","export function isWithAge(obj) {\r\n    return typeof obj['age'] === 'number';\r\n}\r\nexport function extendWithAge(sourceObj, age) {\r\n    var extension = { age: age };\r\n    return Object.assign({}, sourceObj, extension);\r\n}\r\n//# sourceMappingURL=WithAge.js.map","import { Gender } from '../ageGenderNet/types';\r\nimport { isValidProbablitiy } from '../utils';\r\nexport function isWithGender(obj) {\r\n    return (obj['gender'] === Gender.MALE || obj['gender'] === Gender.FEMALE)\r\n        && isValidProbablitiy(obj['genderProbability']);\r\n}\r\nexport function extendWithGender(sourceObj, gender, genderProbability) {\r\n    var extension = { gender: gender, genderProbability: genderProbability };\r\n    return Object.assign({}, sourceObj, extension);\r\n}\r\n//# sourceMappingURL=WithGender.js.map","var MtcnnOptions = /** @class */ (function () {\r\n    function MtcnnOptions(_a) {\r\n        var _b = _a === void 0 ? {} : _a, minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;\r\n        this._name = 'MtcnnOptions';\r\n        this._minFaceSize = minFaceSize || 20;\r\n        this._scaleFactor = scaleFactor || 0.709;\r\n        this._maxNumScales = maxNumScales || 10;\r\n        this._scoreThresholds = scoreThresholds || [0.6, 0.7, 0.7];\r\n        this._scaleSteps = scaleSteps;\r\n        if (typeof this._minFaceSize !== 'number' || this._minFaceSize < 0) {\r\n            throw new Error(this._name + \" - expected minFaceSize to be a number > 0\");\r\n        }\r\n        if (typeof this._scaleFactor !== 'number' || this._scaleFactor <= 0 || this._scaleFactor >= 1) {\r\n            throw new Error(this._name + \" - expected scaleFactor to be a number between 0 and 1\");\r\n        }\r\n        if (typeof this._maxNumScales !== 'number' || this._maxNumScales < 0) {\r\n            throw new Error(this._name + \" - expected maxNumScales to be a number > 0\");\r\n        }\r\n        if (!Array.isArray(this._scoreThresholds)\r\n            || this._scoreThresholds.length !== 3\r\n            || this._scoreThresholds.some(function (th) { return typeof th !== 'number'; })) {\r\n            throw new Error(this._name + \" - expected scoreThresholds to be an array of numbers of length 3\");\r\n        }\r\n        if (this._scaleSteps\r\n            && (!Array.isArray(this._scaleSteps) || this._scaleSteps.some(function (th) { return typeof th !== 'number'; }))) {\r\n            throw new Error(this._name + \" - expected scaleSteps to be an array of numbers\");\r\n        }\r\n    }\r\n    Object.defineProperty(MtcnnOptions.prototype, \"minFaceSize\", {\r\n        get: function () { return this._minFaceSize; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(MtcnnOptions.prototype, \"scaleFactor\", {\r\n        get: function () { return this._scaleFactor; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(MtcnnOptions.prototype, \"maxNumScales\", {\r\n        get: function () { return this._maxNumScales; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(MtcnnOptions.prototype, \"scoreThresholds\", {\r\n        get: function () { return this._scoreThresholds; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(MtcnnOptions.prototype, \"scaleSteps\", {\r\n        get: function () { return this._scaleSteps; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return MtcnnOptions;\r\n}());\r\nexport { MtcnnOptions };\r\n//# sourceMappingURL=MtcnnOptions.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { extractWeightsFactory } from '../common';\r\nfunction extractorsFactory(extractWeights, paramMappings) {\r\n    function extractDepthwiseConvParams(numChannels, mappedPrefix) {\r\n        var filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);\r\n        var batch_norm_scale = tf.tensor1d(extractWeights(numChannels));\r\n        var batch_norm_offset = tf.tensor1d(extractWeights(numChannels));\r\n        var batch_norm_mean = tf.tensor1d(extractWeights(numChannels));\r\n        var batch_norm_variance = tf.tensor1d(extractWeights(numChannels));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/filters\" }, { paramPath: mappedPrefix + \"/batch_norm_scale\" }, { paramPath: mappedPrefix + \"/batch_norm_offset\" }, { paramPath: mappedPrefix + \"/batch_norm_mean\" }, { paramPath: mappedPrefix + \"/batch_norm_variance\" });\r\n        return {\r\n            filters: filters,\r\n            batch_norm_scale: batch_norm_scale,\r\n            batch_norm_offset: batch_norm_offset,\r\n            batch_norm_mean: batch_norm_mean,\r\n            batch_norm_variance: batch_norm_variance\r\n        };\r\n    }\r\n    function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {\r\n        var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);\r\n        var bias = tf.tensor1d(extractWeights(channelsOut));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/filters\" }, { paramPath: mappedPrefix + \"/\" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });\r\n        return { filters: filters, bias: bias };\r\n    }\r\n    function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {\r\n        var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;\r\n        return {\r\n            filters: filters,\r\n            batch_norm_offset: bias\r\n        };\r\n    }\r\n    function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {\r\n        var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + \"/depthwise_conv\");\r\n        var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + \"/pointwise_conv\");\r\n        return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };\r\n    }\r\n    function extractMobilenetV1Params() {\r\n        var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');\r\n        var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');\r\n        var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');\r\n        var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');\r\n        var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');\r\n        var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');\r\n        var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');\r\n        var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');\r\n        var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');\r\n        var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');\r\n        var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');\r\n        var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');\r\n        var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');\r\n        var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');\r\n        return {\r\n            conv_0: conv_0,\r\n            conv_1: conv_1,\r\n            conv_2: conv_2,\r\n            conv_3: conv_3,\r\n            conv_4: conv_4,\r\n            conv_5: conv_5,\r\n            conv_6: conv_6,\r\n            conv_7: conv_7,\r\n            conv_8: conv_8,\r\n            conv_9: conv_9,\r\n            conv_10: conv_10,\r\n            conv_11: conv_11,\r\n            conv_12: conv_12,\r\n            conv_13: conv_13\r\n        };\r\n    }\r\n    function extractPredictionLayerParams() {\r\n        var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');\r\n        var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');\r\n        var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');\r\n        var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');\r\n        var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');\r\n        var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');\r\n        var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');\r\n        var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');\r\n        var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');\r\n        var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');\r\n        var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');\r\n        var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');\r\n        var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');\r\n        var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');\r\n        var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');\r\n        var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');\r\n        var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');\r\n        var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');\r\n        var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');\r\n        var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');\r\n        var box_predictor_0 = {\r\n            box_encoding_predictor: box_encoding_0_predictor,\r\n            class_predictor: class_predictor_0\r\n        };\r\n        var box_predictor_1 = {\r\n            box_encoding_predictor: box_encoding_1_predictor,\r\n            class_predictor: class_predictor_1\r\n        };\r\n        var box_predictor_2 = {\r\n            box_encoding_predictor: box_encoding_2_predictor,\r\n            class_predictor: class_predictor_2\r\n        };\r\n        var box_predictor_3 = {\r\n            box_encoding_predictor: box_encoding_3_predictor,\r\n            class_predictor: class_predictor_3\r\n        };\r\n        var box_predictor_4 = {\r\n            box_encoding_predictor: box_encoding_4_predictor,\r\n            class_predictor: class_predictor_4\r\n        };\r\n        var box_predictor_5 = {\r\n            box_encoding_predictor: box_encoding_5_predictor,\r\n            class_predictor: class_predictor_5\r\n        };\r\n        return {\r\n            conv_0: conv_0,\r\n            conv_1: conv_1,\r\n            conv_2: conv_2,\r\n            conv_3: conv_3,\r\n            conv_4: conv_4,\r\n            conv_5: conv_5,\r\n            conv_6: conv_6,\r\n            conv_7: conv_7,\r\n            box_predictor_0: box_predictor_0,\r\n            box_predictor_1: box_predictor_1,\r\n            box_predictor_2: box_predictor_2,\r\n            box_predictor_3: box_predictor_3,\r\n            box_predictor_4: box_predictor_4,\r\n            box_predictor_5: box_predictor_5\r\n        };\r\n    }\r\n    return {\r\n        extractMobilenetV1Params: extractMobilenetV1Params,\r\n        extractPredictionLayerParams: extractPredictionLayerParams\r\n    };\r\n}\r\nexport function extractParams(weights) {\r\n    var paramMappings = [];\r\n    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;\r\n    var _b = extractorsFactory(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;\r\n    var mobilenetv1 = extractMobilenetV1Params();\r\n    var prediction_layer = extractPredictionLayerParams();\r\n    var extra_dim = tf.tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);\r\n    var output_layer = {\r\n        extra_dim: extra_dim\r\n    };\r\n    paramMappings.push({ paramPath: 'output_layer/extra_dim' });\r\n    if (getRemainingWeights().length !== 0) {\r\n        throw new Error(\"weights remaing after extract: \" + getRemainingWeights().length);\r\n    }\r\n    return {\r\n        params: {\r\n            mobilenetv1: mobilenetv1,\r\n            prediction_layer: prediction_layer,\r\n            output_layer: output_layer\r\n        },\r\n        paramMappings: paramMappings\r\n    };\r\n}\r\n//# sourceMappingURL=extractParams.js.map","import { disposeUnusedWeightTensors, extractWeightEntryFactory } from '../common';\r\nimport { isTensor3D } from '../utils';\r\nfunction extractorsFactory(weightMap, paramMappings) {\r\n    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\r\n    function extractPointwiseConvParams(prefix, idx, mappedPrefix) {\r\n        var filters = extractWeightEntry(prefix + \"/Conv2d_\" + idx + \"_pointwise/weights\", 4, mappedPrefix + \"/filters\");\r\n        var batch_norm_offset = extractWeightEntry(prefix + \"/Conv2d_\" + idx + \"_pointwise/convolution_bn_offset\", 1, mappedPrefix + \"/batch_norm_offset\");\r\n        return { filters: filters, batch_norm_offset: batch_norm_offset };\r\n    }\r\n    function extractConvPairParams(idx) {\r\n        var mappedPrefix = \"mobilenetv1/conv_\" + idx;\r\n        var prefixDepthwiseConv = \"MobilenetV1/Conv2d_\" + idx + \"_depthwise\";\r\n        var mappedPrefixDepthwiseConv = mappedPrefix + \"/depthwise_conv\";\r\n        var mappedPrefixPointwiseConv = mappedPrefix + \"/pointwise_conv\";\r\n        var filters = extractWeightEntry(prefixDepthwiseConv + \"/depthwise_weights\", 4, mappedPrefixDepthwiseConv + \"/filters\");\r\n        var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + \"/BatchNorm/gamma\", 1, mappedPrefixDepthwiseConv + \"/batch_norm_scale\");\r\n        var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + \"/BatchNorm/beta\", 1, mappedPrefixDepthwiseConv + \"/batch_norm_offset\");\r\n        var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + \"/BatchNorm/moving_mean\", 1, mappedPrefixDepthwiseConv + \"/batch_norm_mean\");\r\n        var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + \"/BatchNorm/moving_variance\", 1, mappedPrefixDepthwiseConv + \"/batch_norm_variance\");\r\n        return {\r\n            depthwise_conv: {\r\n                filters: filters,\r\n                batch_norm_scale: batch_norm_scale,\r\n                batch_norm_offset: batch_norm_offset,\r\n                batch_norm_mean: batch_norm_mean,\r\n                batch_norm_variance: batch_norm_variance\r\n            },\r\n            pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\r\n        };\r\n    }\r\n    function extractMobilenetV1Params() {\r\n        return {\r\n            conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\r\n            conv_1: extractConvPairParams(1),\r\n            conv_2: extractConvPairParams(2),\r\n            conv_3: extractConvPairParams(3),\r\n            conv_4: extractConvPairParams(4),\r\n            conv_5: extractConvPairParams(5),\r\n            conv_6: extractConvPairParams(6),\r\n            conv_7: extractConvPairParams(7),\r\n            conv_8: extractConvPairParams(8),\r\n            conv_9: extractConvPairParams(9),\r\n            conv_10: extractConvPairParams(10),\r\n            conv_11: extractConvPairParams(11),\r\n            conv_12: extractConvPairParams(12),\r\n            conv_13: extractConvPairParams(13)\r\n        };\r\n    }\r\n    function extractConvParams(prefix, mappedPrefix) {\r\n        var filters = extractWeightEntry(prefix + \"/weights\", 4, mappedPrefix + \"/filters\");\r\n        var bias = extractWeightEntry(prefix + \"/biases\", 1, mappedPrefix + \"/bias\");\r\n        return { filters: filters, bias: bias };\r\n    }\r\n    function extractBoxPredictorParams(idx) {\r\n        var box_encoding_predictor = extractConvParams(\"Prediction/BoxPredictor_\" + idx + \"/BoxEncodingPredictor\", \"prediction_layer/box_predictor_\" + idx + \"/box_encoding_predictor\");\r\n        var class_predictor = extractConvParams(\"Prediction/BoxPredictor_\" + idx + \"/ClassPredictor\", \"prediction_layer/box_predictor_\" + idx + \"/class_predictor\");\r\n        return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };\r\n    }\r\n    function extractPredictionLayerParams() {\r\n        return {\r\n            conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\r\n            conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\r\n            conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\r\n            conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\r\n            conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\r\n            conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\r\n            conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\r\n            conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\r\n            box_predictor_0: extractBoxPredictorParams(0),\r\n            box_predictor_1: extractBoxPredictorParams(1),\r\n            box_predictor_2: extractBoxPredictorParams(2),\r\n            box_predictor_3: extractBoxPredictorParams(3),\r\n            box_predictor_4: extractBoxPredictorParams(4),\r\n            box_predictor_5: extractBoxPredictorParams(5)\r\n        };\r\n    }\r\n    return {\r\n        extractMobilenetV1Params: extractMobilenetV1Params,\r\n        extractPredictionLayerParams: extractPredictionLayerParams\r\n    };\r\n}\r\nexport function extractParamsFromWeigthMap(weightMap) {\r\n    var paramMappings = [];\r\n    var _a = extractorsFactory(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;\r\n    var extra_dim = weightMap['Output/extra_dim'];\r\n    paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });\r\n    if (!isTensor3D(extra_dim)) {\r\n        throw new Error(\"expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have \" + extra_dim);\r\n    }\r\n    var params = {\r\n        mobilenetv1: extractMobilenetV1Params(),\r\n        prediction_layer: extractPredictionLayerParams(),\r\n        output_layer: {\r\n            extra_dim: extra_dim\r\n        }\r\n    };\r\n    disposeUnusedWeightTensors(weightMap, paramMappings);\r\n    return { params: params, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParamsFromWeigthMap.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function pointwiseConvLayer(x, params, strides) {\r\n    return tf.tidy(function () {\r\n        var out = tf.conv2d(x, params.filters, strides, 'same');\r\n        out = tf.add(out, params.batch_norm_offset);\r\n        return tf.clipByValue(out, 0, 6);\r\n    });\r\n}\r\n//# sourceMappingURL=pointwiseConvLayer.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\r\nvar epsilon = 0.0010000000474974513;\r\nfunction depthwiseConvLayer(x, params, strides) {\r\n    return tf.tidy(function () {\r\n        var out = tf.depthwiseConv2d(x, params.filters, strides, 'same');\r\n        out = tf.batchNorm(out, params.batch_norm_mean, params.batch_norm_variance, params.batch_norm_offset, params.batch_norm_scale, epsilon);\r\n        return tf.clipByValue(out, 0, 6);\r\n    });\r\n}\r\nfunction getStridesForLayerIdx(layerIdx) {\r\n    return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];\r\n}\r\nexport function mobileNetV1(x, params) {\r\n    return tf.tidy(function () {\r\n        var conv11 = null;\r\n        var out = pointwiseConvLayer(x, params.conv_0, [2, 2]);\r\n        var convPairParams = [\r\n            params.conv_1,\r\n            params.conv_2,\r\n            params.conv_3,\r\n            params.conv_4,\r\n            params.conv_5,\r\n            params.conv_6,\r\n            params.conv_7,\r\n            params.conv_8,\r\n            params.conv_9,\r\n            params.conv_10,\r\n            params.conv_11,\r\n            params.conv_12,\r\n            params.conv_13\r\n        ];\r\n        convPairParams.forEach(function (param, i) {\r\n            var layerIdx = i + 1;\r\n            var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);\r\n            out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);\r\n            out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);\r\n            if (layerIdx === 11) {\r\n                conv11 = out;\r\n            }\r\n        });\r\n        if (conv11 === null) {\r\n            throw new Error('mobileNetV1 - output of conv layer 11 is null');\r\n        }\r\n        return {\r\n            out: out,\r\n            conv11: conv11\r\n        };\r\n    });\r\n}\r\n//# sourceMappingURL=mobileNetV1.js.map","export function nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\r\n    var numBoxes = boxes.shape[0];\r\n    var outputSize = Math.min(maxOutputSize, numBoxes);\r\n    var candidates = scores\r\n        .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })\r\n        .filter(function (c) { return c.score > scoreThreshold; })\r\n        .sort(function (c1, c2) { return c2.score - c1.score; });\r\n    var suppressFunc = function (x) { return x <= iouThreshold ? 1 : 0; };\r\n    var selected = [];\r\n    candidates.forEach(function (c) {\r\n        if (selected.length >= outputSize) {\r\n            return;\r\n        }\r\n        var originalScore = c.score;\r\n        for (var j = selected.length - 1; j >= 0; --j) {\r\n            var iou = IOU(boxes, c.boxIndex, selected[j]);\r\n            if (iou === 0.0) {\r\n                continue;\r\n            }\r\n            c.score *= suppressFunc(iou);\r\n            if (c.score <= scoreThreshold) {\r\n                break;\r\n            }\r\n        }\r\n        if (originalScore === c.score) {\r\n            selected.push(c.boxIndex);\r\n        }\r\n    });\r\n    return selected;\r\n}\r\nfunction IOU(boxes, i, j) {\r\n    var boxesData = boxes.arraySync();\r\n    var yminI = Math.min(boxesData[i][0], boxesData[i][2]);\r\n    var xminI = Math.min(boxesData[i][1], boxesData[i][3]);\r\n    var ymaxI = Math.max(boxesData[i][0], boxesData[i][2]);\r\n    var xmaxI = Math.max(boxesData[i][1], boxesData[i][3]);\r\n    var yminJ = Math.min(boxesData[j][0], boxesData[j][2]);\r\n    var xminJ = Math.min(boxesData[j][1], boxesData[j][3]);\r\n    var ymaxJ = Math.max(boxesData[j][0], boxesData[j][2]);\r\n    var xmaxJ = Math.max(boxesData[j][1], boxesData[j][3]);\r\n    var areaI = (ymaxI - yminI) * (xmaxI - xminI);\r\n    var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\r\n    if (areaI <= 0 || areaJ <= 0) {\r\n        return 0.0;\r\n    }\r\n    var intersectionYmin = Math.max(yminI, yminJ);\r\n    var intersectionXmin = Math.max(xminI, xminJ);\r\n    var intersectionYmax = Math.min(ymaxI, ymaxJ);\r\n    var intersectionXmax = Math.min(xmaxI, xmaxJ);\r\n    var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\r\n        Math.max(intersectionXmax - intersectionXmin, 0.0);\r\n    return intersectionArea / (areaI + areaJ - intersectionArea);\r\n}\r\n//# sourceMappingURL=nonMaxSuppression.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nfunction getCenterCoordinatesAndSizesLayer(x) {\r\n    var vec = tf.unstack(tf.transpose(x, [1, 0]));\r\n    var sizes = [\r\n        tf.sub(vec[2], vec[0]),\r\n        tf.sub(vec[3], vec[1])\r\n    ];\r\n    var centers = [\r\n        tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\r\n        tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\r\n    ];\r\n    return {\r\n        sizes: sizes,\r\n        centers: centers\r\n    };\r\n}\r\nfunction decodeBoxesLayer(x0, x1) {\r\n    var _a = getCenterCoordinatesAndSizesLayer(x0), sizes = _a.sizes, centers = _a.centers;\r\n    var vec = tf.unstack(tf.transpose(x1, [1, 0]));\r\n    var div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2));\r\n    var add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0]);\r\n    var div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2));\r\n    var add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1]);\r\n    return tf.transpose(tf.stack([\r\n        tf.sub(add0_out, div0_out),\r\n        tf.sub(add1_out, div1_out),\r\n        tf.add(add0_out, div0_out),\r\n        tf.add(add1_out, div1_out)\r\n    ]), [1, 0]);\r\n}\r\nexport function outputLayer(boxPredictions, classPredictions, params) {\r\n    return tf.tidy(function () {\r\n        var batchSize = boxPredictions.shape[0];\r\n        var boxes = decodeBoxesLayer(tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), tf.reshape(boxPredictions, [-1, 4]));\r\n        boxes = tf.reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);\r\n        var scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]));\r\n        var scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);\r\n        scores = tf.reshape(scores, [batchSize, scores.shape[1]]);\r\n        var boxesByBatch = tf.unstack(boxes);\r\n        var scoresByBatch = tf.unstack(scores);\r\n        return {\r\n            boxes: boxesByBatch,\r\n            scores: scoresByBatch\r\n        };\r\n    });\r\n}\r\n//# sourceMappingURL=outputLayer.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { convLayer } from '../common';\r\nexport function boxPredictionLayer(x, params) {\r\n    return tf.tidy(function () {\r\n        var batchSize = x.shape[0];\r\n        var boxPredictionEncoding = tf.reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);\r\n        var classPrediction = tf.reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);\r\n        return {\r\n            boxPredictionEncoding: boxPredictionEncoding,\r\n            classPrediction: classPrediction\r\n        };\r\n    });\r\n}\r\n//# sourceMappingURL=boxPredictionLayer.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { boxPredictionLayer } from './boxPredictionLayer';\r\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\r\nexport function predictionLayer(x, conv11, params) {\r\n    return tf.tidy(function () {\r\n        var conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);\r\n        var conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);\r\n        var conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);\r\n        var conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);\r\n        var conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);\r\n        var conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);\r\n        var conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);\r\n        var conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);\r\n        var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);\r\n        var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);\r\n        var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);\r\n        var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);\r\n        var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);\r\n        var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);\r\n        var boxPredictions = tf.concat([\r\n            boxPrediction0.boxPredictionEncoding,\r\n            boxPrediction1.boxPredictionEncoding,\r\n            boxPrediction2.boxPredictionEncoding,\r\n            boxPrediction3.boxPredictionEncoding,\r\n            boxPrediction4.boxPredictionEncoding,\r\n            boxPrediction5.boxPredictionEncoding\r\n        ], 1);\r\n        var classPredictions = tf.concat([\r\n            boxPrediction0.classPrediction,\r\n            boxPrediction1.classPrediction,\r\n            boxPrediction2.classPrediction,\r\n            boxPrediction3.classPrediction,\r\n            boxPrediction4.classPrediction,\r\n            boxPrediction5.classPrediction\r\n        ], 1);\r\n        return {\r\n            boxPredictions: boxPredictions,\r\n            classPredictions: classPredictions\r\n        };\r\n    });\r\n}\r\n//# sourceMappingURL=predictionLayer.js.map","var SsdMobilenetv1Options = /** @class */ (function () {\r\n    function SsdMobilenetv1Options(_a) {\r\n        var _b = _a === void 0 ? {} : _a, minConfidence = _b.minConfidence, maxResults = _b.maxResults;\r\n        this._name = 'SsdMobilenetv1Options';\r\n        this._minConfidence = minConfidence || 0.5;\r\n        this._maxResults = maxResults || 100;\r\n        if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\r\n            throw new Error(this._name + \" - expected minConfidence to be a number between 0 and 1\");\r\n        }\r\n        if (typeof this._maxResults !== 'number') {\r\n            throw new Error(this._name + \" - expected maxResults to be a number\");\r\n        }\r\n    }\r\n    Object.defineProperty(SsdMobilenetv1Options.prototype, \"minConfidence\", {\r\n        get: function () { return this._minConfidence; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(SsdMobilenetv1Options.prototype, \"maxResults\", {\r\n        get: function () { return this._maxResults; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return SsdMobilenetv1Options;\r\n}());\r\nexport { SsdMobilenetv1Options };\r\n//# sourceMappingURL=SsdMobilenetv1Options.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { Rect } from '../classes';\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { toNetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { mobileNetV1 } from './mobileNetV1';\r\nimport { nonMaxSuppression } from './nonMaxSuppression';\r\nimport { outputLayer } from './outputLayer';\r\nimport { predictionLayer } from './predictionLayer';\r\nimport { SsdMobilenetv1Options } from './SsdMobilenetv1Options';\r\nvar SsdMobilenetv1 = /** @class */ (function (_super) {\r\n    __extends(SsdMobilenetv1, _super);\r\n    function SsdMobilenetv1() {\r\n        return _super.call(this, 'SsdMobilenetv1') || this;\r\n    }\r\n    SsdMobilenetv1.prototype.forwardInput = function (input) {\r\n        var params = this.params;\r\n        if (!params) {\r\n            throw new Error('SsdMobilenetv1 - load model before inference');\r\n        }\r\n        return tf.tidy(function () {\r\n            var batchTensor = input.toBatchTensor(512, false).toFloat();\r\n            var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));\r\n            var features = mobileNetV1(x, params.mobilenetv1);\r\n            var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;\r\n            return outputLayer(boxPredictions, classPredictions, params.output_layer);\r\n        });\r\n    };\r\n    SsdMobilenetv1.prototype.forward = function (input) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    SsdMobilenetv1.prototype.locateFaces = function (input, options) {\r\n        if (options === void 0) { options = {}; }\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a, maxResults, minConfidence, netInput, _b, _boxes, _scores, boxes, scores, i, scoresData, _c, _d, iouThreshold, indices, reshapedDims, inputSize, padX, padY, boxesData, results;\r\n            return __generator(this, function (_e) {\r\n                switch (_e.label) {\r\n                    case 0:\r\n                        _a = new SsdMobilenetv1Options(options), maxResults = _a.maxResults, minConfidence = _a.minConfidence;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1:\r\n                        netInput = _e.sent();\r\n                        _b = this.forwardInput(netInput), _boxes = _b.boxes, _scores = _b.scores;\r\n                        boxes = _boxes[0];\r\n                        scores = _scores[0];\r\n                        for (i = 1; i < _boxes.length; i++) {\r\n                            _boxes[i].dispose();\r\n                            _scores[i].dispose();\r\n                        }\r\n                        _d = (_c = Array).from;\r\n                        return [4 /*yield*/, scores.data()];\r\n                    case 2:\r\n                        scoresData = _d.apply(_c, [_e.sent()]);\r\n                        iouThreshold = 0.5;\r\n                        indices = nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);\r\n                        reshapedDims = netInput.getReshapedInputDimensions(0);\r\n                        inputSize = netInput.inputSize;\r\n                        padX = inputSize / reshapedDims.width;\r\n                        padY = inputSize / reshapedDims.height;\r\n                        boxesData = boxes.arraySync();\r\n                        results = indices\r\n                            .map(function (idx) {\r\n                            var _a = [\r\n                                Math.max(0, boxesData[idx][0]),\r\n                                Math.min(1.0, boxesData[idx][2])\r\n                            ].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];\r\n                            var _b = [\r\n                                Math.max(0, boxesData[idx][1]),\r\n                                Math.min(1.0, boxesData[idx][3])\r\n                            ].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];\r\n                            return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {\r\n                                height: netInput.getInputHeight(0),\r\n                                width: netInput.getInputWidth(0)\r\n                            });\r\n                        });\r\n                        boxes.dispose();\r\n                        scores.dispose();\r\n                        return [2 /*return*/, results];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    SsdMobilenetv1.prototype.getDefaultModelName = function () {\r\n        return 'ssd_mobilenetv1_model';\r\n    };\r\n    SsdMobilenetv1.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        return extractParamsFromWeigthMap(weightMap);\r\n    };\r\n    SsdMobilenetv1.prototype.extractParams = function (weights) {\r\n        return extractParams(weights);\r\n    };\r\n    return SsdMobilenetv1;\r\n}(NeuralNetwork));\r\nexport { SsdMobilenetv1 };\r\n//# sourceMappingURL=SsdMobilenetv1.js.map","import { __extends } from \"tslib\";\r\nimport { SsdMobilenetv1 } from './SsdMobilenetv1';\r\nexport * from './SsdMobilenetv1';\r\nexport * from './SsdMobilenetv1Options';\r\nexport function createSsdMobilenetv1(weights) {\r\n    var net = new SsdMobilenetv1();\r\n    net.extractWeights(weights);\r\n    return net;\r\n}\r\nexport function createFaceDetectionNet(weights) {\r\n    return createSsdMobilenetv1(weights);\r\n}\r\n// alias for backward compatibily\r\nvar FaceDetectionNet = /** @class */ (function (_super) {\r\n    __extends(FaceDetectionNet, _super);\r\n    function FaceDetectionNet() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    return FaceDetectionNet;\r\n}(SsdMobilenetv1));\r\nexport { FaceDetectionNet };\r\n//# sourceMappingURL=index.js.map","export var TinyYolov2SizeType;\r\n(function (TinyYolov2SizeType) {\r\n    TinyYolov2SizeType[TinyYolov2SizeType[\"XS\"] = 224] = \"XS\";\r\n    TinyYolov2SizeType[TinyYolov2SizeType[\"SM\"] = 320] = \"SM\";\r\n    TinyYolov2SizeType[TinyYolov2SizeType[\"MD\"] = 416] = \"MD\";\r\n    TinyYolov2SizeType[TinyYolov2SizeType[\"LG\"] = 608] = \"LG\";\r\n})(TinyYolov2SizeType || (TinyYolov2SizeType = {}));\r\nvar TinyYolov2Options = /** @class */ (function () {\r\n    function TinyYolov2Options(_a) {\r\n        var _b = _a === void 0 ? {} : _a, inputSize = _b.inputSize, scoreThreshold = _b.scoreThreshold;\r\n        this._name = 'TinyYolov2Options';\r\n        this._inputSize = inputSize || 416;\r\n        this._scoreThreshold = scoreThreshold || 0.5;\r\n        if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\r\n            throw new Error(this._name + \" - expected inputSize to be a number divisible by 32\");\r\n        }\r\n        if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\r\n            throw new Error(this._name + \" - expected scoreThreshold to be a number between 0 and 1\");\r\n        }\r\n    }\r\n    Object.defineProperty(TinyYolov2Options.prototype, \"inputSize\", {\r\n        get: function () { return this._inputSize; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2Options.prototype, \"scoreThreshold\", {\r\n        get: function () { return this._scoreThreshold; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return TinyYolov2Options;\r\n}());\r\nexport { TinyYolov2Options };\r\n//# sourceMappingURL=TinyYolov2Options.js.map","import { Point } from '../classes';\r\nexport var IOU_THRESHOLD = 0.4;\r\nexport var BOX_ANCHORS = [\r\n    new Point(0.738768, 0.874946),\r\n    new Point(2.42204, 2.65704),\r\n    new Point(4.30971, 7.04493),\r\n    new Point(10.246, 4.59428),\r\n    new Point(12.6868, 11.8741)\r\n];\r\nexport var BOX_ANCHORS_SEPARABLE = [\r\n    new Point(1.603231, 2.094468),\r\n    new Point(6.041143, 7.080126),\r\n    new Point(2.882459, 3.518061),\r\n    new Point(4.266906, 5.178857),\r\n    new Point(9.041765, 10.66308)\r\n];\r\nexport var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];\r\nexport var DEFAULT_MODEL_NAME = 'tiny_yolov2_model';\r\nexport var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';\r\n//# sourceMappingURL=const.js.map","var isNumber = function (arg) { return typeof arg === 'number'; };\r\nexport function validateConfig(config) {\r\n    if (!config) {\r\n        throw new Error(\"invalid config: \" + config);\r\n    }\r\n    if (typeof config.withSeparableConvs !== 'boolean') {\r\n        throw new Error(\"config.withSeparableConvs has to be a boolean, have: \" + config.withSeparableConvs);\r\n    }\r\n    if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\r\n        throw new Error(\"config.iouThreshold has to be a number between [0, 1], have: \" + config.iouThreshold);\r\n    }\r\n    if (!Array.isArray(config.classes)\r\n        || !config.classes.length\r\n        || !config.classes.every(function (c) { return typeof c === 'string'; })) {\r\n        throw new Error(\"config.classes has to be an array class names: string[], have: \" + JSON.stringify(config.classes));\r\n    }\r\n    if (!Array.isArray(config.anchors)\r\n        || !config.anchors.length\r\n        || !config.anchors.map(function (a) { return a || {}; }).every(function (a) { return isNumber(a.x) && isNumber(a.y); })) {\r\n        throw new Error(\"config.anchors has to be an array of { x: number, y: number }, have: \" + JSON.stringify(config.anchors));\r\n    }\r\n    if (config.meanRgb && (!Array.isArray(config.meanRgb)\r\n        || config.meanRgb.length !== 3\r\n        || !config.meanRgb.every(isNumber))) {\r\n        throw new Error(\"config.meanRgb has to be an array of shape [number, number, number], have: \" + JSON.stringify(config.meanRgb));\r\n    }\r\n}\r\n//# sourceMappingURL=config.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function leaky(x) {\r\n    return tf.tidy(function () {\r\n        var min = tf.mul(x, tf.scalar(0.10000000149011612));\r\n        return tf.add(tf.relu(tf.sub(x, min)), min);\r\n        //return tf.maximum(x, min)\r\n    });\r\n}\r\n//# sourceMappingURL=leaky.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { leaky } from './leaky';\r\nexport function convWithBatchNorm(x, params) {\r\n    return tf.tidy(function () {\r\n        var out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);\r\n        out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\r\n        out = tf.sub(out, params.bn.sub);\r\n        out = tf.mul(out, params.bn.truediv);\r\n        out = tf.add(out, params.conv.bias);\r\n        return leaky(out);\r\n    });\r\n}\r\n//# sourceMappingURL=convWithBatchNorm.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { leaky } from './leaky';\r\nexport function depthwiseSeparableConv(x, params) {\r\n    return tf.tidy(function () {\r\n        var out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);\r\n        out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\r\n        out = tf.add(out, params.bias);\r\n        return leaky(out);\r\n    });\r\n}\r\n//# sourceMappingURL=depthwiseSeparableConv.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { extractConvParamsFactory } from '../common';\r\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\r\nimport { extractWeightsFactory } from '../common/extractWeightsFactory';\r\nfunction extractorsFactory(extractWeights, paramMappings) {\r\n    var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\r\n    function extractBatchNormParams(size, mappedPrefix) {\r\n        var sub = tf.tensor1d(extractWeights(size));\r\n        var truediv = tf.tensor1d(extractWeights(size));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/sub\" }, { paramPath: mappedPrefix + \"/truediv\" });\r\n        return { sub: sub, truediv: truediv };\r\n    }\r\n    function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {\r\n        var conv = extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + \"/conv\");\r\n        var bn = extractBatchNormParams(channelsOut, mappedPrefix + \"/bn\");\r\n        return { conv: conv, bn: bn };\r\n    }\r\n    var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\r\n    return {\r\n        extractConvParams: extractConvParams,\r\n        extractConvWithBatchNormParams: extractConvWithBatchNormParams,\r\n        extractSeparableConvParams: extractSeparableConvParams\r\n    };\r\n}\r\nexport function extractParams(weights, config, boxEncodingSize, filterSizes) {\r\n    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;\r\n    var paramMappings = [];\r\n    var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;\r\n    var params;\r\n    if (config.withSeparableConvs) {\r\n        var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];\r\n        var conv0 = config.isFirstLayerConv2d\r\n            ? extractConvParams(s0, s1, 3, 'conv0')\r\n            : extractSeparableConvParams(s0, s1, 'conv0');\r\n        var conv1 = extractSeparableConvParams(s1, s2, 'conv1');\r\n        var conv2 = extractSeparableConvParams(s2, s3, 'conv2');\r\n        var conv3 = extractSeparableConvParams(s3, s4, 'conv3');\r\n        var conv4 = extractSeparableConvParams(s4, s5, 'conv4');\r\n        var conv5 = extractSeparableConvParams(s5, s6, 'conv5');\r\n        var conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\r\n        var conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\r\n        var conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\r\n        params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };\r\n    }\r\n    else {\r\n        var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];\r\n        var conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\r\n        var conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\r\n        var conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\r\n        var conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\r\n        var conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\r\n        var conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\r\n        var conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\r\n        var conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\r\n        var conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\r\n        params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };\r\n    }\r\n    if (getRemainingWeights().length !== 0) {\r\n        throw new Error(\"weights remaing after extract: \" + getRemainingWeights().length);\r\n    }\r\n    return { params: params, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParams.js.map","import { disposeUnusedWeightTensors } from '../common/disposeUnusedWeightTensors';\r\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\r\nimport { extractWeightEntryFactory } from '../common/extractWeightEntryFactory';\r\nfunction extractorsFactory(weightMap, paramMappings) {\r\n    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\r\n    function extractBatchNormParams(prefix) {\r\n        var sub = extractWeightEntry(prefix + \"/sub\", 1);\r\n        var truediv = extractWeightEntry(prefix + \"/truediv\", 1);\r\n        return { sub: sub, truediv: truediv };\r\n    }\r\n    function extractConvParams(prefix) {\r\n        var filters = extractWeightEntry(prefix + \"/filters\", 4);\r\n        var bias = extractWeightEntry(prefix + \"/bias\", 1);\r\n        return { filters: filters, bias: bias };\r\n    }\r\n    function extractConvWithBatchNormParams(prefix) {\r\n        var conv = extractConvParams(prefix + \"/conv\");\r\n        var bn = extractBatchNormParams(prefix + \"/bn\");\r\n        return { conv: conv, bn: bn };\r\n    }\r\n    var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\r\n    return {\r\n        extractConvParams: extractConvParams,\r\n        extractConvWithBatchNormParams: extractConvWithBatchNormParams,\r\n        extractSeparableConvParams: extractSeparableConvParams\r\n    };\r\n}\r\nexport function extractParamsFromWeigthMap(weightMap, config) {\r\n    var paramMappings = [];\r\n    var _a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams, extractSeparableConvParams = _a.extractSeparableConvParams;\r\n    var params;\r\n    if (config.withSeparableConvs) {\r\n        var numFilters = (config.filterSizes && config.filterSizes.length || 9);\r\n        params = {\r\n            conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\r\n            conv1: extractSeparableConvParams('conv1'),\r\n            conv2: extractSeparableConvParams('conv2'),\r\n            conv3: extractSeparableConvParams('conv3'),\r\n            conv4: extractSeparableConvParams('conv4'),\r\n            conv5: extractSeparableConvParams('conv5'),\r\n            conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\r\n            conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\r\n            conv8: extractConvParams('conv8')\r\n        };\r\n    }\r\n    else {\r\n        params = {\r\n            conv0: extractConvWithBatchNormParams('conv0'),\r\n            conv1: extractConvWithBatchNormParams('conv1'),\r\n            conv2: extractConvWithBatchNormParams('conv2'),\r\n            conv3: extractConvWithBatchNormParams('conv3'),\r\n            conv4: extractConvWithBatchNormParams('conv4'),\r\n            conv5: extractConvWithBatchNormParams('conv5'),\r\n            conv6: extractConvWithBatchNormParams('conv6'),\r\n            conv7: extractConvWithBatchNormParams('conv7'),\r\n            conv8: extractConvParams('conv8')\r\n        };\r\n    }\r\n    disposeUnusedWeightTensors(weightMap, paramMappings);\r\n    return { params: params, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParamsFromWeigthMap.js.map","import { __assign, __awaiter, __extends, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { BoundingBox } from '../classes/BoundingBox';\r\nimport { ObjectDetection } from '../classes/ObjectDetection';\r\nimport { convLayer } from '../common';\r\nimport { toNetInput } from '../dom';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { sigmoid } from '../ops';\r\nimport { nonMaxSuppression } from '../ops/nonMaxSuppression';\r\nimport { normalize } from '../ops/normalize';\r\nimport { validateConfig } from './config';\r\nimport { convWithBatchNorm } from './convWithBatchNorm';\r\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { leaky } from './leaky';\r\nimport { TinyYolov2Options } from './TinyYolov2Options';\r\nvar TinyYolov2Base = /** @class */ (function (_super) {\r\n    __extends(TinyYolov2Base, _super);\r\n    function TinyYolov2Base(config) {\r\n        var _this = _super.call(this, 'TinyYolov2') || this;\r\n        validateConfig(config);\r\n        _this._config = config;\r\n        return _this;\r\n    }\r\n    Object.defineProperty(TinyYolov2Base.prototype, \"config\", {\r\n        get: function () {\r\n            return this._config;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2Base.prototype, \"withClassScores\", {\r\n        get: function () {\r\n            return this.config.withClassScores || this.config.classes.length > 1;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2Base.prototype, \"boxEncodingSize\", {\r\n        get: function () {\r\n            return 5 + (this.withClassScores ? this.config.classes.length : 0);\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    TinyYolov2Base.prototype.runTinyYolov2 = function (x, params) {\r\n        var out = convWithBatchNorm(x, params.conv0);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = convWithBatchNorm(out, params.conv1);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = convWithBatchNorm(out, params.conv2);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = convWithBatchNorm(out, params.conv3);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = convWithBatchNorm(out, params.conv4);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = convWithBatchNorm(out, params.conv5);\r\n        out = tf.maxPool(out, [2, 2], [1, 1], 'same');\r\n        out = convWithBatchNorm(out, params.conv6);\r\n        out = convWithBatchNorm(out, params.conv7);\r\n        return convLayer(out, params.conv8, 'valid', false);\r\n    };\r\n    TinyYolov2Base.prototype.runMobilenet = function (x, params) {\r\n        var out = this.config.isFirstLayerConv2d\r\n            ? leaky(convLayer(x, params.conv0, 'valid', false))\r\n            : depthwiseSeparableConv(x, params.conv0);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = depthwiseSeparableConv(out, params.conv1);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = depthwiseSeparableConv(out, params.conv2);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = depthwiseSeparableConv(out, params.conv3);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = depthwiseSeparableConv(out, params.conv4);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = depthwiseSeparableConv(out, params.conv5);\r\n        out = tf.maxPool(out, [2, 2], [1, 1], 'same');\r\n        out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\r\n        out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\r\n        return convLayer(out, params.conv8, 'valid', false);\r\n    };\r\n    TinyYolov2Base.prototype.forwardInput = function (input, inputSize) {\r\n        var _this = this;\r\n        var params = this.params;\r\n        if (!params) {\r\n            throw new Error('TinyYolov2 - load model before inference');\r\n        }\r\n        return tf.tidy(function () {\r\n            var batchTensor = input.toBatchTensor(inputSize, false).toFloat();\r\n            batchTensor = _this.config.meanRgb\r\n                ? normalize(batchTensor, _this.config.meanRgb)\r\n                : batchTensor;\r\n            batchTensor = batchTensor.div(tf.scalar(256));\r\n            return _this.config.withSeparableConvs\r\n                ? _this.runMobilenet(batchTensor, params)\r\n                : _this.runTinyYolov2(batchTensor, params);\r\n        });\r\n    };\r\n    TinyYolov2Base.prototype.forward = function (input, inputSize) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];\r\n                    case 2: return [2 /*return*/, _b.sent()];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    TinyYolov2Base.prototype.detect = function (input, forwardParams) {\r\n        if (forwardParams === void 0) { forwardParams = {}; }\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a, inputSize, scoreThreshold, netInput, out, out0, inputDimensions, results, boxes, scores, classScores, classNames, indices, detections;\r\n            var _this = this;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = new TinyYolov2Options(forwardParams), inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1:\r\n                        netInput = _b.sent();\r\n                        return [4 /*yield*/, this.forwardInput(netInput, inputSize)];\r\n                    case 2:\r\n                        out = _b.sent();\r\n                        out0 = tf.tidy(function () { return tf.unstack(out)[0].expandDims(); });\r\n                        inputDimensions = {\r\n                            width: netInput.getInputWidth(0),\r\n                            height: netInput.getInputHeight(0)\r\n                        };\r\n                        return [4 /*yield*/, this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)];\r\n                    case 3:\r\n                        results = _b.sent();\r\n                        out.dispose();\r\n                        out0.dispose();\r\n                        boxes = results.map(function (res) { return res.box; });\r\n                        scores = results.map(function (res) { return res.score; });\r\n                        classScores = results.map(function (res) { return res.classScore; });\r\n                        classNames = results.map(function (res) { return _this.config.classes[res.label]; });\r\n                        indices = nonMaxSuppression(boxes.map(function (box) { return box.rescale(inputSize); }), scores, this.config.iouThreshold, true);\r\n                        detections = indices.map(function (idx) {\r\n                            return new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions);\r\n                        });\r\n                        return [2 /*return*/, detections];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    TinyYolov2Base.prototype.getDefaultModelName = function () {\r\n        return '';\r\n    };\r\n    TinyYolov2Base.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        return extractParamsFromWeigthMap(weightMap, this.config);\r\n    };\r\n    TinyYolov2Base.prototype.extractParams = function (weights) {\r\n        var filterSizes = this.config.filterSizes || TinyYolov2Base.DEFAULT_FILTER_SIZES;\r\n        var numFilters = filterSizes ? filterSizes.length : undefined;\r\n        if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\r\n            throw new Error(\"TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found \" + numFilters + \" filterSizes in config\");\r\n        }\r\n        return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\r\n    };\r\n    TinyYolov2Base.prototype.extractBoxes = function (outputTensor, inputBlobDimensions, scoreThreshold) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var width, height, inputSize, correctionFactorX, correctionFactorY, numCells, numBoxes, _a, boxesTensor, scoresTensor, classScoresTensor, results, scoresData, boxesData, row, col, anchor, score, ctX, ctY, width_1, height_1, x, y, pos, _b, classScore, label, _c;\r\n            var _this = this;\r\n            return __generator(this, function (_d) {\r\n                switch (_d.label) {\r\n                    case 0:\r\n                        width = inputBlobDimensions.width, height = inputBlobDimensions.height;\r\n                        inputSize = Math.max(width, height);\r\n                        correctionFactorX = inputSize / width;\r\n                        correctionFactorY = inputSize / height;\r\n                        numCells = outputTensor.shape[1];\r\n                        numBoxes = this.config.anchors.length;\r\n                        _a = tf.tidy(function () {\r\n                            var reshaped = outputTensor.reshape([numCells, numCells, numBoxes, _this.boxEncodingSize]);\r\n                            var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\r\n                            var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\r\n                            var classScores = _this.withClassScores\r\n                                ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, _this.config.classes.length]), 3)\r\n                                : tf.scalar(0);\r\n                            return [boxes, scores, classScores];\r\n                        }), boxesTensor = _a[0], scoresTensor = _a[1], classScoresTensor = _a[2];\r\n                        results = [];\r\n                        return [4 /*yield*/, scoresTensor.array()];\r\n                    case 1:\r\n                        scoresData = _d.sent();\r\n                        return [4 /*yield*/, boxesTensor.array()];\r\n                    case 2:\r\n                        boxesData = _d.sent();\r\n                        row = 0;\r\n                        _d.label = 3;\r\n                    case 3:\r\n                        if (!(row < numCells)) return [3 /*break*/, 12];\r\n                        col = 0;\r\n                        _d.label = 4;\r\n                    case 4:\r\n                        if (!(col < numCells)) return [3 /*break*/, 11];\r\n                        anchor = 0;\r\n                        _d.label = 5;\r\n                    case 5:\r\n                        if (!(anchor < numBoxes)) return [3 /*break*/, 10];\r\n                        score = sigmoid(scoresData[row][col][anchor][0]);\r\n                        if (!(!scoreThreshold || score > scoreThreshold)) return [3 /*break*/, 9];\r\n                        ctX = ((col + sigmoid(boxesData[row][col][anchor][0])) / numCells) * correctionFactorX;\r\n                        ctY = ((row + sigmoid(boxesData[row][col][anchor][1])) / numCells) * correctionFactorY;\r\n                        width_1 = ((Math.exp(boxesData[row][col][anchor][2]) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\r\n                        height_1 = ((Math.exp(boxesData[row][col][anchor][3]) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\r\n                        x = (ctX - (width_1 / 2));\r\n                        y = (ctY - (height_1 / 2));\r\n                        pos = { row: row, col: col, anchor: anchor };\r\n                        if (!this.withClassScores) return [3 /*break*/, 7];\r\n                        return [4 /*yield*/, this.extractPredictedClass(classScoresTensor, pos)];\r\n                    case 6:\r\n                        _c = _d.sent();\r\n                        return [3 /*break*/, 8];\r\n                    case 7:\r\n                        _c = { classScore: 1, label: 0 };\r\n                        _d.label = 8;\r\n                    case 8:\r\n                        _b = _c, classScore = _b.classScore, label = _b.label;\r\n                        results.push(__assign({ box: new BoundingBox(x, y, x + width_1, y + height_1), score: score, classScore: score * classScore, label: label }, pos));\r\n                        _d.label = 9;\r\n                    case 9:\r\n                        anchor++;\r\n                        return [3 /*break*/, 5];\r\n                    case 10:\r\n                        col++;\r\n                        return [3 /*break*/, 4];\r\n                    case 11:\r\n                        row++;\r\n                        return [3 /*break*/, 3];\r\n                    case 12:\r\n                        boxesTensor.dispose();\r\n                        scoresTensor.dispose();\r\n                        classScoresTensor.dispose();\r\n                        return [2 /*return*/, results];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    TinyYolov2Base.prototype.extractPredictedClass = function (classesTensor, pos) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var row, col, anchor, classesData;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0:\r\n                        row = pos.row, col = pos.col, anchor = pos.anchor;\r\n                        return [4 /*yield*/, classesTensor.array()];\r\n                    case 1:\r\n                        classesData = _a.sent();\r\n                        return [2 /*return*/, Array(this.config.classes.length).fill(0)\r\n                                .map(function (_, i) { return classesData[row][col][anchor][i]; })\r\n                                .map(function (classScore, label) { return ({\r\n                                classScore: classScore,\r\n                                label: label\r\n                            }); })\r\n                                .reduce(function (max, curr) { return max.classScore > curr.classScore ? max : curr; })];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    TinyYolov2Base.DEFAULT_FILTER_SIZES = [\r\n        3, 16, 32, 64, 128, 256, 512, 1024, 1024\r\n    ];\r\n    return TinyYolov2Base;\r\n}(NeuralNetwork));\r\nexport { TinyYolov2Base };\r\n//# sourceMappingURL=TinyYolov2Base.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport { FaceDetection } from '../classes';\r\nimport { BOX_ANCHORS, BOX_ANCHORS_SEPARABLE, DEFAULT_MODEL_NAME, DEFAULT_MODEL_NAME_SEPARABLE_CONV, IOU_THRESHOLD, MEAN_RGB_SEPARABLE, } from './const';\r\nimport { TinyYolov2Base } from './TinyYolov2Base';\r\nvar TinyYolov2 = /** @class */ (function (_super) {\r\n    __extends(TinyYolov2, _super);\r\n    function TinyYolov2(withSeparableConvs) {\r\n        if (withSeparableConvs === void 0) { withSeparableConvs = true; }\r\n        var _this = this;\r\n        var config = Object.assign({}, {\r\n            withSeparableConvs: withSeparableConvs,\r\n            iouThreshold: IOU_THRESHOLD,\r\n            classes: ['face']\r\n        }, withSeparableConvs\r\n            ? {\r\n                anchors: BOX_ANCHORS_SEPARABLE,\r\n                meanRgb: MEAN_RGB_SEPARABLE\r\n            }\r\n            : {\r\n                anchors: BOX_ANCHORS,\r\n                withClassScores: true\r\n            });\r\n        _this = _super.call(this, config) || this;\r\n        return _this;\r\n    }\r\n    Object.defineProperty(TinyYolov2.prototype, \"withSeparableConvs\", {\r\n        get: function () {\r\n            return this.config.withSeparableConvs;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2.prototype, \"anchors\", {\r\n        get: function () {\r\n            return this.config.anchors;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    TinyYolov2.prototype.locateFaces = function (input, forwardParams) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var objectDetections;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, this.detect(input, forwardParams)];\r\n                    case 1:\r\n                        objectDetections = _a.sent();\r\n                        return [2 /*return*/, objectDetections.map(function (det) { return new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    TinyYolov2.prototype.getDefaultModelName = function () {\r\n        return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;\r\n    };\r\n    TinyYolov2.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        return _super.prototype.extractParamsFromWeigthMap.call(this, weightMap);\r\n    };\r\n    return TinyYolov2;\r\n}(TinyYolov2Base));\r\nexport { TinyYolov2 };\r\n//# sourceMappingURL=TinyYolov2.js.map","import { __extends } from \"tslib\";\r\nimport { TinyYolov2Options } from '../tinyYolov2';\r\nvar TinyFaceDetectorOptions = /** @class */ (function (_super) {\r\n    __extends(TinyFaceDetectorOptions, _super);\r\n    function TinyFaceDetectorOptions() {\r\n        var _this = _super !== null && _super.apply(this, arguments) || this;\r\n        _this._name = 'TinyFaceDetectorOptions';\r\n        return _this;\r\n    }\r\n    return TinyFaceDetectorOptions;\r\n}(TinyYolov2Options));\r\nexport { TinyFaceDetectorOptions };\r\n//# sourceMappingURL=TinyFaceDetectorOptions.js.map","import { __awaiter, __generator } from \"tslib\";\r\nvar ComposableTask = /** @class */ (function () {\r\n    function ComposableTask() {\r\n    }\r\n    ComposableTask.prototype.then = function (onfulfilled) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = onfulfilled;\r\n                        return [4 /*yield*/, this.run()];\r\n                    case 1: return [2 /*return*/, _a.apply(void 0, [_b.sent()])];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    ComposableTask.prototype.run = function () {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            return __generator(this, function (_a) {\r\n                throw new Error('ComposableTask - run is not implemented');\r\n            });\r\n        });\r\n    };\r\n    return ComposableTask;\r\n}());\r\nexport { ComposableTask };\r\n//# sourceMappingURL=ComposableTask.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { extractFaces, extractFaceTensors } from '../dom';\r\nimport { isWithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nexport function extractAllFacesAndComputeResults(parentResults, input, computeResults, extractedFaces, getRectForAlignment) {\r\n    if (getRectForAlignment === void 0) { getRectForAlignment = function (_a) {\r\n        var alignedRect = _a.alignedRect;\r\n        return alignedRect;\r\n    }; }\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        var faceBoxes, faces, _a, _b, results;\r\n        return __generator(this, function (_c) {\r\n            switch (_c.label) {\r\n                case 0:\r\n                    faceBoxes = parentResults.map(function (parentResult) {\r\n                        return isWithFaceLandmarks(parentResult)\r\n                            ? getRectForAlignment(parentResult)\r\n                            : parentResult.detection;\r\n                    });\r\n                    _a = extractedFaces;\r\n                    if (_a) return [3 /*break*/, 5];\r\n                    if (!(input instanceof tf.Tensor)) return [3 /*break*/, 2];\r\n                    return [4 /*yield*/, extractFaceTensors(input, faceBoxes)];\r\n                case 1:\r\n                    _b = _c.sent();\r\n                    return [3 /*break*/, 4];\r\n                case 2: return [4 /*yield*/, extractFaces(input, faceBoxes)];\r\n                case 3:\r\n                    _b = _c.sent();\r\n                    _c.label = 4;\r\n                case 4:\r\n                    _a = (_b);\r\n                    _c.label = 5;\r\n                case 5:\r\n                    faces = _a;\r\n                    return [4 /*yield*/, computeResults(faces)];\r\n                case 6:\r\n                    results = _c.sent();\r\n                    faces.forEach(function (f) { return f instanceof tf.Tensor && f.dispose(); });\r\n                    return [2 /*return*/, results];\r\n            }\r\n        });\r\n    });\r\n}\r\nexport function extractSingleFaceAndComputeResult(parentResult, input, computeResult, extractedFaces, getRectForAlignment) {\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        var _this = this;\r\n        return __generator(this, function (_a) {\r\n            return [2 /*return*/, extractAllFacesAndComputeResults([parentResult], input, function (faces) { return __awaiter(_this, void 0, void 0, function () { return __generator(this, function (_a) {\r\n                    return [2 /*return*/, computeResult(faces[0])];\r\n                }); }); }, extractedFaces, getRectForAlignment)];\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=extractFacesAndComputeResults.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function bgrToRgbTensor(tensor) {\r\n    return tf.tidy(function () { return tf.stack(tf.unstack(tensor, 3).reverse(), 3); });\r\n}\r\n//# sourceMappingURL=bgrToRgbTensor.js.map","export var CELL_STRIDE = 2;\r\nexport var CELL_SIZE = 12;\r\n//# sourceMappingURL=config.js.map","import { __assign } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { extractConvParamsFactory, extractFCParamsFactory, extractWeightsFactory, } from '../common';\r\nfunction extractorsFactory(extractWeights, paramMappings) {\r\n    var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\r\n    var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);\r\n    function extractPReluParams(size, paramPath) {\r\n        var alpha = tf.tensor1d(extractWeights(size));\r\n        paramMappings.push({ paramPath: paramPath });\r\n        return alpha;\r\n    }\r\n    function extractSharedParams(numFilters, mappedPrefix, isRnet) {\r\n        if (isRnet === void 0) { isRnet = false; }\r\n        var conv1 = extractConvParams(numFilters[0], numFilters[1], 3, mappedPrefix + \"/conv1\");\r\n        var prelu1_alpha = extractPReluParams(numFilters[1], mappedPrefix + \"/prelu1_alpha\");\r\n        var conv2 = extractConvParams(numFilters[1], numFilters[2], 3, mappedPrefix + \"/conv2\");\r\n        var prelu2_alpha = extractPReluParams(numFilters[2], mappedPrefix + \"/prelu2_alpha\");\r\n        var conv3 = extractConvParams(numFilters[2], numFilters[3], isRnet ? 2 : 3, mappedPrefix + \"/conv3\");\r\n        var prelu3_alpha = extractPReluParams(numFilters[3], mappedPrefix + \"/prelu3_alpha\");\r\n        return { conv1: conv1, prelu1_alpha: prelu1_alpha, conv2: conv2, prelu2_alpha: prelu2_alpha, conv3: conv3, prelu3_alpha: prelu3_alpha };\r\n    }\r\n    function extractPNetParams() {\r\n        var sharedParams = extractSharedParams([3, 10, 16, 32], 'pnet');\r\n        var conv4_1 = extractConvParams(32, 2, 1, 'pnet/conv4_1');\r\n        var conv4_2 = extractConvParams(32, 4, 1, 'pnet/conv4_2');\r\n        return __assign(__assign({}, sharedParams), { conv4_1: conv4_1, conv4_2: conv4_2 });\r\n    }\r\n    function extractRNetParams() {\r\n        var sharedParams = extractSharedParams([3, 28, 48, 64], 'rnet', true);\r\n        var fc1 = extractFCParams(576, 128, 'rnet/fc1');\r\n        var prelu4_alpha = extractPReluParams(128, 'rnet/prelu4_alpha');\r\n        var fc2_1 = extractFCParams(128, 2, 'rnet/fc2_1');\r\n        var fc2_2 = extractFCParams(128, 4, 'rnet/fc2_2');\r\n        return __assign(__assign({}, sharedParams), { fc1: fc1, prelu4_alpha: prelu4_alpha, fc2_1: fc2_1, fc2_2: fc2_2 });\r\n    }\r\n    function extractONetParams() {\r\n        var sharedParams = extractSharedParams([3, 32, 64, 64], 'onet');\r\n        var conv4 = extractConvParams(64, 128, 2, 'onet/conv4');\r\n        var prelu4_alpha = extractPReluParams(128, 'onet/prelu4_alpha');\r\n        var fc1 = extractFCParams(1152, 256, 'onet/fc1');\r\n        var prelu5_alpha = extractPReluParams(256, 'onet/prelu5_alpha');\r\n        var fc2_1 = extractFCParams(256, 2, 'onet/fc2_1');\r\n        var fc2_2 = extractFCParams(256, 4, 'onet/fc2_2');\r\n        var fc2_3 = extractFCParams(256, 10, 'onet/fc2_3');\r\n        return __assign(__assign({}, sharedParams), { conv4: conv4, prelu4_alpha: prelu4_alpha, fc1: fc1, prelu5_alpha: prelu5_alpha, fc2_1: fc2_1, fc2_2: fc2_2, fc2_3: fc2_3 });\r\n    }\r\n    return {\r\n        extractPNetParams: extractPNetParams,\r\n        extractRNetParams: extractRNetParams,\r\n        extractONetParams: extractONetParams\r\n    };\r\n}\r\nexport function extractParams(weights) {\r\n    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;\r\n    var paramMappings = [];\r\n    var _b = extractorsFactory(extractWeights, paramMappings), extractPNetParams = _b.extractPNetParams, extractRNetParams = _b.extractRNetParams, extractONetParams = _b.extractONetParams;\r\n    var pnet = extractPNetParams();\r\n    var rnet = extractRNetParams();\r\n    var onet = extractONetParams();\r\n    if (getRemainingWeights().length !== 0) {\r\n        throw new Error(\"weights remaing after extract: \" + getRemainingWeights().length);\r\n    }\r\n    return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParams.js.map","import { __assign } from \"tslib\";\r\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory } from '../common';\r\nfunction extractorsFactory(weightMap, paramMappings) {\r\n    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\r\n    function extractConvParams(prefix) {\r\n        var filters = extractWeightEntry(prefix + \"/weights\", 4, prefix + \"/filters\");\r\n        var bias = extractWeightEntry(prefix + \"/bias\", 1);\r\n        return { filters: filters, bias: bias };\r\n    }\r\n    function extractFCParams(prefix) {\r\n        var weights = extractWeightEntry(prefix + \"/weights\", 2);\r\n        var bias = extractWeightEntry(prefix + \"/bias\", 1);\r\n        return { weights: weights, bias: bias };\r\n    }\r\n    function extractPReluParams(paramPath) {\r\n        return extractWeightEntry(paramPath, 1);\r\n    }\r\n    function extractSharedParams(prefix) {\r\n        var conv1 = extractConvParams(prefix + \"/conv1\");\r\n        var prelu1_alpha = extractPReluParams(prefix + \"/prelu1_alpha\");\r\n        var conv2 = extractConvParams(prefix + \"/conv2\");\r\n        var prelu2_alpha = extractPReluParams(prefix + \"/prelu2_alpha\");\r\n        var conv3 = extractConvParams(prefix + \"/conv3\");\r\n        var prelu3_alpha = extractPReluParams(prefix + \"/prelu3_alpha\");\r\n        return { conv1: conv1, prelu1_alpha: prelu1_alpha, conv2: conv2, prelu2_alpha: prelu2_alpha, conv3: conv3, prelu3_alpha: prelu3_alpha };\r\n    }\r\n    function extractPNetParams() {\r\n        var sharedParams = extractSharedParams('pnet');\r\n        var conv4_1 = extractConvParams('pnet/conv4_1');\r\n        var conv4_2 = extractConvParams('pnet/conv4_2');\r\n        return __assign(__assign({}, sharedParams), { conv4_1: conv4_1, conv4_2: conv4_2 });\r\n    }\r\n    function extractRNetParams() {\r\n        var sharedParams = extractSharedParams('rnet');\r\n        var fc1 = extractFCParams('rnet/fc1');\r\n        var prelu4_alpha = extractPReluParams('rnet/prelu4_alpha');\r\n        var fc2_1 = extractFCParams('rnet/fc2_1');\r\n        var fc2_2 = extractFCParams('rnet/fc2_2');\r\n        return __assign(__assign({}, sharedParams), { fc1: fc1, prelu4_alpha: prelu4_alpha, fc2_1: fc2_1, fc2_2: fc2_2 });\r\n    }\r\n    function extractONetParams() {\r\n        var sharedParams = extractSharedParams('onet');\r\n        var conv4 = extractConvParams('onet/conv4');\r\n        var prelu4_alpha = extractPReluParams('onet/prelu4_alpha');\r\n        var fc1 = extractFCParams('onet/fc1');\r\n        var prelu5_alpha = extractPReluParams('onet/prelu5_alpha');\r\n        var fc2_1 = extractFCParams('onet/fc2_1');\r\n        var fc2_2 = extractFCParams('onet/fc2_2');\r\n        var fc2_3 = extractFCParams('onet/fc2_3');\r\n        return __assign(__assign({}, sharedParams), { conv4: conv4, prelu4_alpha: prelu4_alpha, fc1: fc1, prelu5_alpha: prelu5_alpha, fc2_1: fc2_1, fc2_2: fc2_2, fc2_3: fc2_3 });\r\n    }\r\n    return {\r\n        extractPNetParams: extractPNetParams,\r\n        extractRNetParams: extractRNetParams,\r\n        extractONetParams: extractONetParams\r\n    };\r\n}\r\nexport function extractParamsFromWeigthMap(weightMap) {\r\n    var paramMappings = [];\r\n    var _a = extractorsFactory(weightMap, paramMappings), extractPNetParams = _a.extractPNetParams, extractRNetParams = _a.extractRNetParams, extractONetParams = _a.extractONetParams;\r\n    var pnet = extractPNetParams();\r\n    var rnet = extractRNetParams();\r\n    var onet = extractONetParams();\r\n    disposeUnusedWeightTensors(weightMap, paramMappings);\r\n    return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParamsFromWeigthMap.js.map","export function getSizesForScale(scale, _a) {\r\n    var height = _a[0], width = _a[1];\r\n    return {\r\n        height: Math.floor(height * scale),\r\n        width: Math.floor(width * scale)\r\n    };\r\n}\r\n//# sourceMappingURL=getSizesForScale.js.map","import { CELL_SIZE } from './config';\r\nexport function pyramidDown(minFaceSize, scaleFactor, dims) {\r\n    var height = dims[0], width = dims[1];\r\n    var m = CELL_SIZE / minFaceSize;\r\n    var scales = [];\r\n    var minLayer = Math.min(height, width) * m;\r\n    var exp = 0;\r\n    while (minLayer >= 12) {\r\n        scales.push(m * Math.pow(scaleFactor, exp));\r\n        minLayer = minLayer * scaleFactor;\r\n        exp += 1;\r\n    }\r\n    return scales;\r\n}\r\n//# sourceMappingURL=pyramidDown.js.map","import { __extends } from \"tslib\";\r\nimport { Box } from '../classes';\r\nvar MtcnnBox = /** @class */ (function (_super) {\r\n    __extends(MtcnnBox, _super);\r\n    function MtcnnBox(left, top, right, bottom) {\r\n        return _super.call(this, { left: left, top: top, right: right, bottom: bottom }, true) || this;\r\n    }\r\n    return MtcnnBox;\r\n}(Box));\r\nexport { MtcnnBox };\r\n//# sourceMappingURL=MtcnnBox.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function normalize(x) {\r\n    return tf.tidy(function () { return tf.mul(tf.sub(x, tf.scalar(127.5)), tf.scalar(0.0078125)); });\r\n}\r\n//# sourceMappingURL=normalize.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function prelu(x, alpha) {\r\n    return tf.tidy(function () {\r\n        return tf.add(tf.relu(x), tf.mul(alpha, tf.neg(tf.relu(tf.neg(x)))));\r\n    });\r\n}\r\n//# sourceMappingURL=prelu.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { convLayer } from '../common';\r\nimport { prelu } from './prelu';\r\nexport function sharedLayer(x, params, isPnet) {\r\n    if (isPnet === void 0) { isPnet = false; }\r\n    return tf.tidy(function () {\r\n        var out = convLayer(x, params.conv1, 'valid');\r\n        out = prelu(out, params.prelu1_alpha);\r\n        out = tf.maxPool(out, isPnet ? [2, 2] : [3, 3], [2, 2], 'same');\r\n        out = convLayer(out, params.conv2, 'valid');\r\n        out = prelu(out, params.prelu2_alpha);\r\n        out = isPnet ? out : tf.maxPool(out, [3, 3], [2, 2], 'valid');\r\n        out = convLayer(out, params.conv3, 'valid');\r\n        out = prelu(out, params.prelu3_alpha);\r\n        return out;\r\n    });\r\n}\r\n//# sourceMappingURL=sharedLayers.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { convLayer } from '../common';\r\nimport { sharedLayer } from './sharedLayers';\r\nexport function PNet(x, params) {\r\n    return tf.tidy(function () {\r\n        var out = sharedLayer(x, params, true);\r\n        var conv = convLayer(out, params.conv4_1, 'valid');\r\n        var max = tf.expandDims(tf.max(conv, 3), 3);\r\n        var prob = tf.softmax(tf.sub(conv, max), 3);\r\n        var regions = convLayer(out, params.conv4_2, 'valid');\r\n        return { prob: prob, regions: regions };\r\n    });\r\n}\r\n//# sourceMappingURL=PNet.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { BoundingBox, Point } from '../classes';\r\nimport { nonMaxSuppression } from '../ops';\r\nimport { CELL_SIZE, CELL_STRIDE } from './config';\r\nimport { getSizesForScale } from './getSizesForScale';\r\nimport { MtcnnBox } from './MtcnnBox';\r\nimport { normalize } from './normalize';\r\nimport { PNet } from './PNet';\r\nfunction rescaleAndNormalize(x, scale) {\r\n    return tf.tidy(function () {\r\n        var _a = getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;\r\n        var resized = tf.image.resizeBilinear(x, [height, width]);\r\n        var normalized = normalize(resized);\r\n        return tf.transpose(normalized, [0, 2, 1, 3]);\r\n    });\r\n}\r\nfunction extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold) {\r\n    // TODO: fix this!, maybe better to use tf.gather here\r\n    var indices = [];\r\n    var scoresData = scoresTensor.arraySync();\r\n    for (var y = 0; y < scoresTensor.shape[0]; y++) {\r\n        for (var x = 0; x < scoresTensor.shape[1]; x++) {\r\n            if (scoresData[y][x] >= scoreThreshold) {\r\n                indices.push(new Point(x, y));\r\n            }\r\n        }\r\n    }\r\n    var boundingBoxes = indices.map(function (idx) {\r\n        var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale));\r\n        var score = scoresData[idx.y][idx.x];\r\n        var regionsData = regionsTensor.arraySync();\r\n        var region = new MtcnnBox(regionsData[idx.y][idx.x][0], regionsData[idx.y][idx.x][1], regionsData[idx.y][idx.x][2], regionsData[idx.y][idx.x][3]);\r\n        return {\r\n            cell: cell,\r\n            score: score,\r\n            region: region\r\n        };\r\n    });\r\n    return boundingBoxes;\r\n}\r\nexport function stage1(imgTensor, scales, scoreThreshold, params, stats) {\r\n    stats.stage1 = [];\r\n    var pnetOutputs = scales.map(function (scale) { return tf.tidy(function () {\r\n        var statsForScale = { scale: scale };\r\n        var resized = rescaleAndNormalize(imgTensor, scale);\r\n        var ts = Date.now();\r\n        var _a = PNet(resized, params), prob = _a.prob, regions = _a.regions;\r\n        statsForScale.pnet = Date.now() - ts;\r\n        var scoresTensor = tf.unstack(tf.unstack(prob, 3)[1])[0];\r\n        var regionsTensor = tf.unstack(regions)[0];\r\n        return {\r\n            scoresTensor: scoresTensor,\r\n            regionsTensor: regionsTensor,\r\n            scale: scale,\r\n            statsForScale: statsForScale\r\n        };\r\n    }); });\r\n    var boxesForScale = pnetOutputs.map(function (_a) {\r\n        var scoresTensor = _a.scoresTensor, regionsTensor = _a.regionsTensor, scale = _a.scale, statsForScale = _a.statsForScale;\r\n        var boundingBoxes = extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold);\r\n        scoresTensor.dispose();\r\n        regionsTensor.dispose();\r\n        if (!boundingBoxes.length) {\r\n            stats.stage1.push(statsForScale);\r\n            return [];\r\n        }\r\n        var ts = Date.now();\r\n        var indices = nonMaxSuppression(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);\r\n        statsForScale.nms = Date.now() - ts;\r\n        statsForScale.numBoxes = indices.length;\r\n        stats.stage1.push(statsForScale);\r\n        return indices.map(function (boxIdx) { return boundingBoxes[boxIdx]; });\r\n    });\r\n    var allBoxes = boxesForScale.reduce(function (all, boxes) { return all.concat(boxes); }, []);\r\n    var finalBoxes = [];\r\n    var finalScores = [];\r\n    if (allBoxes.length > 0) {\r\n        var ts = Date.now();\r\n        var indices = nonMaxSuppression(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);\r\n        stats.stage1_nms = Date.now() - ts;\r\n        finalScores = indices.map(function (idx) { return allBoxes[idx].score; });\r\n        finalBoxes = indices\r\n            .map(function (idx) { return allBoxes[idx]; })\r\n            .map(function (_a) {\r\n            var cell = _a.cell, region = _a.region;\r\n            return new BoundingBox(cell.left + (region.left * cell.width), cell.top + (region.top * cell.height), cell.right + (region.right * cell.width), cell.bottom + (region.bottom * cell.height)).toSquare().round();\r\n        });\r\n    }\r\n    return {\r\n        boxes: finalBoxes,\r\n        scores: finalScores\r\n    };\r\n}\r\n//# sourceMappingURL=stage1.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { createCanvas, createCanvasFromMedia, getContext2dOrThrow } from '../dom';\r\nimport { env } from '../env';\r\nimport { normalize } from './normalize';\r\nexport function extractImagePatches(img, boxes, _a) {\r\n    var width = _a.width, height = _a.height;\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        var imgCtx, bitmaps, imagePatchesDatas;\r\n        var _this = this;\r\n        return __generator(this, function (_b) {\r\n            switch (_b.label) {\r\n                case 0:\r\n                    imgCtx = getContext2dOrThrow(img);\r\n                    return [4 /*yield*/, Promise.all(boxes.map(function (box) { return __awaiter(_this, void 0, void 0, function () {\r\n                            var _a, y, ey, x, ex, fromX, fromY, imgData;\r\n                            return __generator(this, function (_b) {\r\n                                _a = box.padAtBorders(img.height, img.width), y = _a.y, ey = _a.ey, x = _a.x, ex = _a.ex;\r\n                                fromX = x - 1;\r\n                                fromY = y - 1;\r\n                                imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY));\r\n                                return [2 /*return*/, env.isNodejs() ? createCanvasFromMedia(imgData) : createImageBitmap(imgData)];\r\n                            });\r\n                        }); }))];\r\n                case 1:\r\n                    bitmaps = _b.sent();\r\n                    imagePatchesDatas = [];\r\n                    bitmaps.forEach(function (bmp) {\r\n                        var patch = createCanvas({ width: width, height: height });\r\n                        var patchCtx = getContext2dOrThrow(patch);\r\n                        patchCtx.drawImage(bmp, 0, 0, width, height);\r\n                        var data = patchCtx.getImageData(0, 0, width, height).data;\r\n                        var currData = [];\r\n                        // RGBA -> BGR\r\n                        for (var i = 0; i < data.length; i += 4) {\r\n                            currData.push(data[i + 2]);\r\n                            currData.push(data[i + 1]);\r\n                            currData.push(data[i]);\r\n                        }\r\n                        imagePatchesDatas.push(currData);\r\n                    });\r\n                    return [2 /*return*/, imagePatchesDatas.map(function (data) {\r\n                            var t = tf.tidy(function () {\r\n                                var imagePatchTensor = tf.transpose(tf.tensor4d(data, [1, width, height, 3]), [0, 2, 1, 3]).toFloat();\r\n                                return normalize(imagePatchTensor);\r\n                            });\r\n                            return t;\r\n                        })];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=extractImagePatches.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\r\nimport { prelu } from './prelu';\r\nimport { sharedLayer } from './sharedLayers';\r\nexport function RNet(x, params) {\r\n    return tf.tidy(function () {\r\n        var convOut = sharedLayer(x, params);\r\n        var vectorized = tf.reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]);\r\n        var fc1 = fullyConnectedLayer(vectorized, params.fc1);\r\n        var prelu4 = prelu(fc1, params.prelu4_alpha);\r\n        var fc2_1 = fullyConnectedLayer(prelu4, params.fc2_1);\r\n        var max = tf.expandDims(tf.max(fc2_1, 1), 1);\r\n        var prob = tf.softmax(tf.sub(fc2_1, max), 1);\r\n        var regions = fullyConnectedLayer(prelu4, params.fc2_2);\r\n        var scores = tf.unstack(prob, 1)[1];\r\n        return { scores: scores, regions: regions };\r\n    });\r\n}\r\n//# sourceMappingURL=RNet.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { nonMaxSuppression } from '../ops';\r\nimport { extractImagePatches } from './extractImagePatches';\r\nimport { MtcnnBox } from './MtcnnBox';\r\nimport { RNet } from './RNet';\r\nexport function stage2(img, inputBoxes, scoreThreshold, params, stats) {\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        var ts, rnetInputs, rnetOuts, scoresTensor, scores, _a, _b, indices, filteredBoxes, filteredScores, finalBoxes, finalScores, indicesNms, regions_1;\r\n        return __generator(this, function (_c) {\r\n            switch (_c.label) {\r\n                case 0:\r\n                    ts = Date.now();\r\n                    return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 24, height: 24 })];\r\n                case 1:\r\n                    rnetInputs = _c.sent();\r\n                    stats.stage2_extractImagePatches = Date.now() - ts;\r\n                    ts = Date.now();\r\n                    rnetOuts = rnetInputs.map(function (rnetInput) {\r\n                        var out = RNet(rnetInput, params);\r\n                        rnetInput.dispose();\r\n                        return out;\r\n                    });\r\n                    stats.stage2_rnet = Date.now() - ts;\r\n                    scoresTensor = rnetOuts.length > 1\r\n                        ? tf.concat(rnetOuts.map(function (out) { return out.scores; }))\r\n                        : rnetOuts[0].scores;\r\n                    _b = (_a = Array).from;\r\n                    return [4 /*yield*/, scoresTensor.data()];\r\n                case 2:\r\n                    scores = _b.apply(_a, [_c.sent()]);\r\n                    scoresTensor.dispose();\r\n                    indices = scores\r\n                        .map(function (score, idx) { return ({ score: score, idx: idx }); })\r\n                        .filter(function (c) { return c.score > scoreThreshold; })\r\n                        .map(function (_a) {\r\n                        var idx = _a.idx;\r\n                        return idx;\r\n                    });\r\n                    filteredBoxes = indices.map(function (idx) { return inputBoxes[idx]; });\r\n                    filteredScores = indices.map(function (idx) { return scores[idx]; });\r\n                    finalBoxes = [];\r\n                    finalScores = [];\r\n                    if (filteredBoxes.length > 0) {\r\n                        ts = Date.now();\r\n                        indicesNms = nonMaxSuppression(filteredBoxes, filteredScores, 0.7);\r\n                        stats.stage2_nms = Date.now() - ts;\r\n                        regions_1 = indicesNms.map(function (idx) {\r\n                            var regionsData = rnetOuts[indices[idx]].regions.arraySync();\r\n                            return new MtcnnBox(regionsData[0][0], regionsData[0][1], regionsData[0][2], regionsData[0][3]);\r\n                        });\r\n                        finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });\r\n                        finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });\r\n                    }\r\n                    rnetOuts.forEach(function (t) {\r\n                        t.regions.dispose();\r\n                        t.scores.dispose();\r\n                    });\r\n                    return [2 /*return*/, {\r\n                            boxes: finalBoxes,\r\n                            scores: finalScores\r\n                        }];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=stage2.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { convLayer } from '../common';\r\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\r\nimport { prelu } from './prelu';\r\nimport { sharedLayer } from './sharedLayers';\r\nexport function ONet(x, params) {\r\n    return tf.tidy(function () {\r\n        var out = sharedLayer(x, params);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = convLayer(out, params.conv4, 'valid');\r\n        out = prelu(out, params.prelu4_alpha);\r\n        var vectorized = tf.reshape(out, [out.shape[0], params.fc1.weights.shape[0]]);\r\n        var fc1 = fullyConnectedLayer(vectorized, params.fc1);\r\n        var prelu5 = prelu(fc1, params.prelu5_alpha);\r\n        var fc2_1 = fullyConnectedLayer(prelu5, params.fc2_1);\r\n        var max = tf.expandDims(tf.max(fc2_1, 1), 1);\r\n        var prob = tf.softmax(tf.sub(fc2_1, max), 1);\r\n        var regions = fullyConnectedLayer(prelu5, params.fc2_2);\r\n        var points = fullyConnectedLayer(prelu5, params.fc2_3);\r\n        var scores = tf.unstack(prob, 1)[1];\r\n        return { scores: scores, regions: regions, points: points };\r\n    });\r\n}\r\n//# sourceMappingURL=ONet.js.map","import { __awaiter, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { Point } from '../classes';\r\nimport { nonMaxSuppression } from '../ops';\r\nimport { extractImagePatches } from './extractImagePatches';\r\nimport { MtcnnBox } from './MtcnnBox';\r\nimport { ONet } from './ONet';\r\nexport function stage3(img, inputBoxes, scoreThreshold, params, stats) {\r\n    return __awaiter(this, void 0, void 0, function () {\r\n        var ts, onetInputs, onetOuts, scoresTensor, scores, _a, _b, indices, filteredRegions, filteredBoxes, filteredScores, finalBoxes, finalScores, points, indicesNms;\r\n        return __generator(this, function (_c) {\r\n            switch (_c.label) {\r\n                case 0:\r\n                    ts = Date.now();\r\n                    return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 48, height: 48 })];\r\n                case 1:\r\n                    onetInputs = _c.sent();\r\n                    stats.stage3_extractImagePatches = Date.now() - ts;\r\n                    ts = Date.now();\r\n                    onetOuts = onetInputs.map(function (onetInput) {\r\n                        var out = ONet(onetInput, params);\r\n                        onetInput.dispose();\r\n                        return out;\r\n                    });\r\n                    stats.stage3_onet = Date.now() - ts;\r\n                    scoresTensor = onetOuts.length > 1\r\n                        ? tf.concat(onetOuts.map(function (out) { return out.scores; }))\r\n                        : onetOuts[0].scores;\r\n                    _b = (_a = Array).from;\r\n                    return [4 /*yield*/, scoresTensor.data()];\r\n                case 2:\r\n                    scores = _b.apply(_a, [_c.sent()]);\r\n                    scoresTensor.dispose();\r\n                    indices = scores\r\n                        .map(function (score, idx) { return ({ score: score, idx: idx }); })\r\n                        .filter(function (c) { return c.score > scoreThreshold; })\r\n                        .map(function (_a) {\r\n                        var idx = _a.idx;\r\n                        return idx;\r\n                    });\r\n                    filteredRegions = indices.map(function (idx) {\r\n                        var regionsData = onetOuts[idx].regions.arraySync();\r\n                        return new MtcnnBox(regionsData[0][0], regionsData[0][1], regionsData[0][2], regionsData[0][3]);\r\n                    });\r\n                    filteredBoxes = indices\r\n                        .map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });\r\n                    filteredScores = indices.map(function (idx) { return scores[idx]; });\r\n                    finalBoxes = [];\r\n                    finalScores = [];\r\n                    points = [];\r\n                    if (filteredBoxes.length > 0) {\r\n                        ts = Date.now();\r\n                        indicesNms = nonMaxSuppression(filteredBoxes, filteredScores, 0.7, false);\r\n                        stats.stage3_nms = Date.now() - ts;\r\n                        finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });\r\n                        finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });\r\n                        points = indicesNms.map(function (idx, i) {\r\n                            return Array(5).fill(0).map(function (_, ptIdx) {\r\n                                var pointsData = onetOuts[idx].points.arraySync();\r\n                                return new Point(((pointsData[0][ptIdx] * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((pointsData[0][ptIdx + 5] * (finalBoxes[i].height + 1)) + finalBoxes[i].top));\r\n                            });\r\n                        });\r\n                    }\r\n                    onetOuts.forEach(function (t) {\r\n                        t.regions.dispose();\r\n                        t.scores.dispose();\r\n                        t.points.dispose();\r\n                    });\r\n                    return [2 /*return*/, {\r\n                            boxes: finalBoxes,\r\n                            scores: finalScores,\r\n                            points: points\r\n                        }];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=stage3.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { Point, Rect } from '../classes';\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\r\nimport { toNetInput } from '../dom';\r\nimport { extendWithFaceDetection, extendWithFaceLandmarks } from '../factories';\r\nimport { NeuralNetwork } from '../NeuralNetwork';\r\nimport { bgrToRgbTensor } from './bgrToRgbTensor';\r\nimport { CELL_SIZE } from './config';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { getSizesForScale } from './getSizesForScale';\r\nimport { MtcnnOptions } from './MtcnnOptions';\r\nimport { pyramidDown } from './pyramidDown';\r\nimport { stage1 } from './stage1';\r\nimport { stage2 } from './stage2';\r\nimport { stage3 } from './stage3';\r\nvar Mtcnn = /** @class */ (function (_super) {\r\n    __extends(Mtcnn, _super);\r\n    function Mtcnn() {\r\n        return _super.call(this, 'Mtcnn') || this;\r\n    }\r\n    Mtcnn.prototype.load = function (weightsOrUrl) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            return __generator(this, function (_a) {\r\n                console.warn('mtcnn is deprecated and will be removed soon');\r\n                return [2 /*return*/, _super.prototype.load.call(this, weightsOrUrl)];\r\n            });\r\n        });\r\n    };\r\n    Mtcnn.prototype.loadFromDisk = function (filePath) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            return __generator(this, function (_a) {\r\n                console.warn('mtcnn is deprecated and will be removed soon');\r\n                return [2 /*return*/, _super.prototype.loadFromDisk.call(this, filePath)];\r\n            });\r\n        });\r\n    };\r\n    Mtcnn.prototype.forwardInput = function (input, forwardParams) {\r\n        if (forwardParams === void 0) { forwardParams = {}; }\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var params, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;\r\n            return __generator(this, function (_c) {\r\n                switch (_c.label) {\r\n                    case 0:\r\n                        params = this.params;\r\n                        if (!params) {\r\n                            throw new Error('Mtcnn - load model before inference');\r\n                        }\r\n                        inputCanvas = input.canvases[0];\r\n                        if (!inputCanvas) {\r\n                            throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.');\r\n                        }\r\n                        stats = {};\r\n                        tsTotal = Date.now();\r\n                        imgTensor = tf.tidy(function () {\r\n                            return bgrToRgbTensor(tf.expandDims(tf.browser.fromPixels(inputCanvas)).toFloat());\r\n                        });\r\n                        onReturn = function (results) {\r\n                            // dispose tensors on return\r\n                            imgTensor.dispose();\r\n                            stats.total = Date.now() - tsTotal;\r\n                            return results;\r\n                        };\r\n                        _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];\r\n                        _b = new MtcnnOptions(forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;\r\n                        scales = (scaleSteps || pyramidDown(minFaceSize, scaleFactor, [height, width]))\r\n                            .filter(function (scale) {\r\n                            var sizes = getSizesForScale(scale, [height, width]);\r\n                            return Math.min(sizes.width, sizes.height) > CELL_SIZE;\r\n                        })\r\n                            .slice(0, maxNumScales);\r\n                        stats.scales = scales;\r\n                        stats.pyramid = scales.map(function (scale) { return getSizesForScale(scale, [height, width]); });\r\n                        ts = Date.now();\r\n                        return [4 /*yield*/, stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)];\r\n                    case 1:\r\n                        out1 = _c.sent();\r\n                        stats.total_stage1 = Date.now() - ts;\r\n                        if (!out1.boxes.length) {\r\n                            return [2 /*return*/, onReturn({ results: [], stats: stats })];\r\n                        }\r\n                        stats.stage2_numInputBoxes = out1.boxes.length;\r\n                        // using the inputCanvas to extract and resize the image patches, since it is faster\r\n                        // than doing this on the gpu\r\n                        ts = Date.now();\r\n                        return [4 /*yield*/, stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)];\r\n                    case 2:\r\n                        out2 = _c.sent();\r\n                        stats.total_stage2 = Date.now() - ts;\r\n                        if (!out2.boxes.length) {\r\n                            return [2 /*return*/, onReturn({ results: [], stats: stats })];\r\n                        }\r\n                        stats.stage3_numInputBoxes = out2.boxes.length;\r\n                        ts = Date.now();\r\n                        return [4 /*yield*/, stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)];\r\n                    case 3:\r\n                        out3 = _c.sent();\r\n                        stats.total_stage3 = Date.now() - ts;\r\n                        results = out3.boxes.map(function (box, idx) { return extendWithFaceLandmarks(extendWithFaceDetection({}, new FaceDetection(out3.scores[idx], new Rect(box.left / width, box.top / height, box.width / width, box.height / height), {\r\n                            height: height,\r\n                            width: width\r\n                        })), new FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.sub(new Point(box.left, box.top)).div(new Point(box.width, box.height)); }), { width: box.width, height: box.height })); });\r\n                        return [2 /*return*/, onReturn({ results: results, stats: stats })];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    Mtcnn.prototype.forward = function (input, forwardParams) {\r\n        if (forwardParams === void 0) { forwardParams = {}; }\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),\r\n                            forwardParams])];\r\n                    case 2: return [2 /*return*/, (_b.sent()).results];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    Mtcnn.prototype.forwardWithStats = function (input, forwardParams) {\r\n        if (forwardParams === void 0) { forwardParams = {}; }\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),\r\n                            forwardParams])];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    Mtcnn.prototype.getDefaultModelName = function () {\r\n        return 'mtcnn_model';\r\n    };\r\n    Mtcnn.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        return extractParamsFromWeigthMap(weightMap);\r\n    };\r\n    Mtcnn.prototype.extractParams = function (weights) {\r\n        return extractParams(weights);\r\n    };\r\n    return Mtcnn;\r\n}(NeuralNetwork));\r\nexport { Mtcnn };\r\n//# sourceMappingURL=Mtcnn.js.map","import { Point } from '../classes';\r\nexport var IOU_THRESHOLD = 0.4;\r\nexport var BOX_ANCHORS = [\r\n    new Point(1.603231, 2.094468),\r\n    new Point(6.041143, 7.080126),\r\n    new Point(2.882459, 3.518061),\r\n    new Point(4.266906, 5.178857),\r\n    new Point(9.041765, 10.66308)\r\n];\r\nexport var MEAN_RGB = [117.001, 114.697, 97.404];\r\n//# sourceMappingURL=const.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport { FaceDetection } from '../classes';\r\nimport { TinyYolov2Base } from '../tinyYolov2/TinyYolov2Base';\r\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\r\nvar TinyFaceDetector = /** @class */ (function (_super) {\r\n    __extends(TinyFaceDetector, _super);\r\n    function TinyFaceDetector() {\r\n        var _this = this;\r\n        var config = {\r\n            withSeparableConvs: true,\r\n            iouThreshold: IOU_THRESHOLD,\r\n            classes: ['face'],\r\n            anchors: BOX_ANCHORS,\r\n            meanRgb: MEAN_RGB,\r\n            isFirstLayerConv2d: true,\r\n            filterSizes: [3, 16, 32, 64, 128, 256, 512]\r\n        };\r\n        _this = _super.call(this, config) || this;\r\n        return _this;\r\n    }\r\n    Object.defineProperty(TinyFaceDetector.prototype, \"anchors\", {\r\n        get: function () {\r\n            return this.config.anchors;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    TinyFaceDetector.prototype.locateFaces = function (input, forwardParams) {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var objectDetections;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, this.detect(input, forwardParams)];\r\n                    case 1:\r\n                        objectDetections = _a.sent();\r\n                        return [2 /*return*/, objectDetections.map(function (det) { return new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    TinyFaceDetector.prototype.getDefaultModelName = function () {\r\n        return 'tiny_face_detector_model';\r\n    };\r\n    TinyFaceDetector.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        return _super.prototype.extractParamsFromWeigthMap.call(this, weightMap);\r\n    };\r\n    return TinyFaceDetector;\r\n}(TinyYolov2Base));\r\nexport { TinyFaceDetector };\r\n//# sourceMappingURL=TinyFaceDetector.js.map","import { AgeGenderNet } from '../ageGenderNet/AgeGenderNet';\r\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\r\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\r\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\r\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\r\nimport { Mtcnn } from '../mtcnn/Mtcnn';\r\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\r\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\r\nimport { TinyYolov2 } from '../tinyYolov2';\r\nexport var nets = {\r\n    ssdMobilenetv1: new SsdMobilenetv1(),\r\n    tinyFaceDetector: new TinyFaceDetector(),\r\n    tinyYolov2: new TinyYolov2(),\r\n    mtcnn: new Mtcnn(),\r\n    faceLandmark68Net: new FaceLandmark68Net(),\r\n    faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\r\n    faceRecognitionNet: new FaceRecognitionNet(),\r\n    faceExpressionNet: new FaceExpressionNet(),\r\n    ageGenderNet: new AgeGenderNet()\r\n};\r\n/**\r\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\r\n * @returns Bounding box of each face with score.\r\n */\r\nexport var ssdMobilenetv1 = function (input, options) {\r\n    return nets.ssdMobilenetv1.locateFaces(input, options);\r\n};\r\n/**\r\n * Attempts to detect all faces in an image using the Tiny Face Detector.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\r\n * @returns Bounding box of each face with score.\r\n */\r\nexport var tinyFaceDetector = function (input, options) {\r\n    return nets.tinyFaceDetector.locateFaces(input, options);\r\n};\r\n/**\r\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\r\n * @returns Bounding box of each face with score.\r\n */\r\nexport var tinyYolov2 = function (input, options) {\r\n    return nets.tinyYolov2.locateFaces(input, options);\r\n};\r\n/**\r\n * Attempts to detect all faces in an image and the 5 point face landmarks\r\n * of each detected face using the MTCNN Network.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see MtcnnOptions constructor for default parameters).\r\n * @returns Bounding box of each face with score and 5 point face landmarks.\r\n */\r\nexport var mtcnn = function (input, options) {\r\n    return nets.mtcnn.forward(input, options);\r\n};\r\n/**\r\n * Detects the 68 point face landmark positions of the face shown in an image.\r\n *\r\n * @param inputs The face image extracted from the bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns 68 point face landmarks or array thereof in case of batch input.\r\n */\r\nexport var detectFaceLandmarks = function (input) {\r\n    return nets.faceLandmark68Net.detectLandmarks(input);\r\n};\r\n/**\r\n * Detects the 68 point face landmark positions of the face shown in an image\r\n * using a tinier version of the 68 point face landmark model, which is slightly\r\n * faster at inference, but also slightly less accurate.\r\n *\r\n * @param inputs The face image extracted from the bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns 68 point face landmarks or array thereof in case of batch input.\r\n */\r\nexport var detectFaceLandmarksTiny = function (input) {\r\n    return nets.faceLandmark68TinyNet.detectLandmarks(input);\r\n};\r\n/**\r\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\r\n * which uniquely represents the features of that persons face. The computed face descriptor can\r\n * be used to measure the similarity between faces, by computing the euclidean distance of two\r\n * face descriptors.\r\n *\r\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\r\n */\r\nexport var computeFaceDescriptor = function (input) {\r\n    return nets.faceRecognitionNet.computeFaceDescriptor(input);\r\n};\r\n/**\r\n * Recognizes the facial expressions from a face image.\r\n *\r\n * @param inputs The face image extracted from the bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns Facial expressions with corresponding probabilities or array thereof in case of batch input.\r\n */\r\nexport var recognizeFaceExpressions = function (input) {\r\n    return nets.faceExpressionNet.predictExpressions(input);\r\n};\r\n/**\r\n * Predicts age and gender from a face image.\r\n *\r\n * @param inputs The face image extracted from the bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns Predictions with age, gender and gender probability or array thereof in case of batch input.\r\n */\r\nexport var predictAgeAndGender = function (input) {\r\n    return nets.ageGenderNet.predictAgeAndGender(input);\r\n};\r\nexport var loadSsdMobilenetv1Model = function (url) { return nets.ssdMobilenetv1.load(url); };\r\nexport var loadTinyFaceDetectorModel = function (url) { return nets.tinyFaceDetector.load(url); };\r\nexport var loadMtcnnModel = function (url) { return nets.mtcnn.load(url); };\r\nexport var loadTinyYolov2Model = function (url) { return nets.tinyYolov2.load(url); };\r\nexport var loadFaceLandmarkModel = function (url) { return nets.faceLandmark68Net.load(url); };\r\nexport var loadFaceLandmarkTinyModel = function (url) { return nets.faceLandmark68TinyNet.load(url); };\r\nexport var loadFaceRecognitionModel = function (url) { return nets.faceRecognitionNet.load(url); };\r\nexport var loadFaceExpressionModel = function (url) { return nets.faceExpressionNet.load(url); };\r\nexport var loadAgeGenderModel = function (url) { return nets.ageGenderNet.load(url); };\r\n// backward compatibility\r\nexport var loadFaceDetectionModel = loadSsdMobilenetv1Model;\r\nexport var locateFaces = ssdMobilenetv1;\r\nexport var detectLandmarks = detectFaceLandmarks;\r\n//# sourceMappingURL=nets.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport { extendWithFaceExpressions } from '../factories/WithFaceExpressions';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\r\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\r\nimport { nets } from './nets';\r\nimport { PredictAllAgeAndGenderTask, PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderTask, PredictSingleAgeAndGenderWithFaceAlignmentTask, } from './PredictAgeAndGenderTask';\r\nvar PredictFaceExpressionsTaskBase = /** @class */ (function (_super) {\r\n    __extends(PredictFaceExpressionsTaskBase, _super);\r\n    function PredictFaceExpressionsTaskBase(parentTask, input, extractedFaces) {\r\n        var _this = _super.call(this) || this;\r\n        _this.parentTask = parentTask;\r\n        _this.input = input;\r\n        _this.extractedFaces = extractedFaces;\r\n        return _this;\r\n    }\r\n    return PredictFaceExpressionsTaskBase;\r\n}(ComposableTask));\r\nexport { PredictFaceExpressionsTaskBase };\r\nvar PredictAllFaceExpressionsTask = /** @class */ (function (_super) {\r\n    __extends(PredictAllFaceExpressionsTask, _super);\r\n    function PredictAllFaceExpressionsTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    PredictAllFaceExpressionsTask.prototype.run = function () {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var parentResults, faceExpressionsByFace;\r\n            var _this = this;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, this.parentTask];\r\n                    case 1:\r\n                        parentResults = _a.sent();\r\n                        return [4 /*yield*/, extractAllFacesAndComputeResults(parentResults, this.input, function (faces) { return __awaiter(_this, void 0, void 0, function () {\r\n                                return __generator(this, function (_a) {\r\n                                    switch (_a.label) {\r\n                                        case 0: return [4 /*yield*/, Promise.all(faces.map(function (face) { return nets.faceExpressionNet.predictExpressions(face); }))];\r\n                                        case 1: return [2 /*return*/, _a.sent()];\r\n                                    }\r\n                                });\r\n                            }); }, this.extractedFaces)];\r\n                    case 2:\r\n                        faceExpressionsByFace = _a.sent();\r\n                        return [2 /*return*/, parentResults.map(function (parentResult, i) { return extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]); })];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    PredictAllFaceExpressionsTask.prototype.withAgeAndGender = function () {\r\n        return new PredictAllAgeAndGenderTask(this, this.input);\r\n    };\r\n    return PredictAllFaceExpressionsTask;\r\n}(PredictFaceExpressionsTaskBase));\r\nexport { PredictAllFaceExpressionsTask };\r\nvar PredictSingleFaceExpressionsTask = /** @class */ (function (_super) {\r\n    __extends(PredictSingleFaceExpressionsTask, _super);\r\n    function PredictSingleFaceExpressionsTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    PredictSingleFaceExpressionsTask.prototype.run = function () {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var parentResult, faceExpressions;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, this.parentTask];\r\n                    case 1:\r\n                        parentResult = _a.sent();\r\n                        if (!parentResult) {\r\n                            return [2 /*return*/];\r\n                        }\r\n                        return [4 /*yield*/, extractSingleFaceAndComputeResult(parentResult, this.input, function (face) { return nets.faceExpressionNet.predictExpressions(face); }, this.extractedFaces)];\r\n                    case 2:\r\n                        faceExpressions = _a.sent();\r\n                        return [2 /*return*/, extendWithFaceExpressions(parentResult, faceExpressions)];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    PredictSingleFaceExpressionsTask.prototype.withAgeAndGender = function () {\r\n        return new PredictSingleAgeAndGenderTask(this, this.input);\r\n    };\r\n    return PredictSingleFaceExpressionsTask;\r\n}(PredictFaceExpressionsTaskBase));\r\nexport { PredictSingleFaceExpressionsTask };\r\nvar PredictAllFaceExpressionsWithFaceAlignmentTask = /** @class */ (function (_super) {\r\n    __extends(PredictAllFaceExpressionsWithFaceAlignmentTask, _super);\r\n    function PredictAllFaceExpressionsWithFaceAlignmentTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    PredictAllFaceExpressionsWithFaceAlignmentTask.prototype.withAgeAndGender = function () {\r\n        return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    PredictAllFaceExpressionsWithFaceAlignmentTask.prototype.withFaceDescriptors = function () {\r\n        return new ComputeAllFaceDescriptorsTask(this, this.input);\r\n    };\r\n    return PredictAllFaceExpressionsWithFaceAlignmentTask;\r\n}(PredictAllFaceExpressionsTask));\r\nexport { PredictAllFaceExpressionsWithFaceAlignmentTask };\r\nvar PredictSingleFaceExpressionsWithFaceAlignmentTask = /** @class */ (function (_super) {\r\n    __extends(PredictSingleFaceExpressionsWithFaceAlignmentTask, _super);\r\n    function PredictSingleFaceExpressionsWithFaceAlignmentTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    PredictSingleFaceExpressionsWithFaceAlignmentTask.prototype.withAgeAndGender = function () {\r\n        return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    PredictSingleFaceExpressionsWithFaceAlignmentTask.prototype.withFaceDescriptor = function () {\r\n        return new ComputeSingleFaceDescriptorTask(this, this.input);\r\n    };\r\n    return PredictSingleFaceExpressionsWithFaceAlignmentTask;\r\n}(PredictSingleFaceExpressionsTask));\r\nexport { PredictSingleFaceExpressionsWithFaceAlignmentTask };\r\n//# sourceMappingURL=PredictFaceExpressionsTask.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport { extendWithAge } from '../factories/WithAge';\r\nimport { extendWithGender } from '../factories/WithGender';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\r\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\r\nimport { nets } from './nets';\r\nimport { PredictAllFaceExpressionsTask, PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsTask, PredictSingleFaceExpressionsWithFaceAlignmentTask, } from './PredictFaceExpressionsTask';\r\nvar PredictAgeAndGenderTaskBase = /** @class */ (function (_super) {\r\n    __extends(PredictAgeAndGenderTaskBase, _super);\r\n    function PredictAgeAndGenderTaskBase(parentTask, input, extractedFaces) {\r\n        var _this = _super.call(this) || this;\r\n        _this.parentTask = parentTask;\r\n        _this.input = input;\r\n        _this.extractedFaces = extractedFaces;\r\n        return _this;\r\n    }\r\n    return PredictAgeAndGenderTaskBase;\r\n}(ComposableTask));\r\nexport { PredictAgeAndGenderTaskBase };\r\nvar PredictAllAgeAndGenderTask = /** @class */ (function (_super) {\r\n    __extends(PredictAllAgeAndGenderTask, _super);\r\n    function PredictAllAgeAndGenderTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    PredictAllAgeAndGenderTask.prototype.run = function () {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var parentResults, ageAndGenderByFace;\r\n            var _this = this;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, this.parentTask];\r\n                    case 1:\r\n                        parentResults = _a.sent();\r\n                        return [4 /*yield*/, extractAllFacesAndComputeResults(parentResults, this.input, function (faces) { return __awaiter(_this, void 0, void 0, function () {\r\n                                return __generator(this, function (_a) {\r\n                                    switch (_a.label) {\r\n                                        case 0: return [4 /*yield*/, Promise.all(faces.map(function (face) { return nets.ageGenderNet.predictAgeAndGender(face); }))];\r\n                                        case 1: return [2 /*return*/, _a.sent()];\r\n                                    }\r\n                                });\r\n                            }); }, this.extractedFaces)];\r\n                    case 2:\r\n                        ageAndGenderByFace = _a.sent();\r\n                        return [2 /*return*/, parentResults.map(function (parentResult, i) {\r\n                                var _a = ageAndGenderByFace[i], age = _a.age, gender = _a.gender, genderProbability = _a.genderProbability;\r\n                                return extendWithAge(extendWithGender(parentResult, gender, genderProbability), age);\r\n                            })];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    PredictAllAgeAndGenderTask.prototype.withFaceExpressions = function () {\r\n        return new PredictAllFaceExpressionsTask(this, this.input);\r\n    };\r\n    return PredictAllAgeAndGenderTask;\r\n}(PredictAgeAndGenderTaskBase));\r\nexport { PredictAllAgeAndGenderTask };\r\nvar PredictSingleAgeAndGenderTask = /** @class */ (function (_super) {\r\n    __extends(PredictSingleAgeAndGenderTask, _super);\r\n    function PredictSingleAgeAndGenderTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    PredictSingleAgeAndGenderTask.prototype.run = function () {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var parentResult, _a, age, gender, genderProbability;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0: return [4 /*yield*/, this.parentTask];\r\n                    case 1:\r\n                        parentResult = _b.sent();\r\n                        if (!parentResult) {\r\n                            return [2 /*return*/];\r\n                        }\r\n                        return [4 /*yield*/, extractSingleFaceAndComputeResult(parentResult, this.input, function (face) { return nets.ageGenderNet.predictAgeAndGender(face); }, this.extractedFaces)];\r\n                    case 2:\r\n                        _a = _b.sent(), age = _a.age, gender = _a.gender, genderProbability = _a.genderProbability;\r\n                        return [2 /*return*/, extendWithAge(extendWithGender(parentResult, gender, genderProbability), age)];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    PredictSingleAgeAndGenderTask.prototype.withFaceExpressions = function () {\r\n        return new PredictSingleFaceExpressionsTask(this, this.input);\r\n    };\r\n    return PredictSingleAgeAndGenderTask;\r\n}(PredictAgeAndGenderTaskBase));\r\nexport { PredictSingleAgeAndGenderTask };\r\nvar PredictAllAgeAndGenderWithFaceAlignmentTask = /** @class */ (function (_super) {\r\n    __extends(PredictAllAgeAndGenderWithFaceAlignmentTask, _super);\r\n    function PredictAllAgeAndGenderWithFaceAlignmentTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    PredictAllAgeAndGenderWithFaceAlignmentTask.prototype.withFaceExpressions = function () {\r\n        return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    PredictAllAgeAndGenderWithFaceAlignmentTask.prototype.withFaceDescriptors = function () {\r\n        return new ComputeAllFaceDescriptorsTask(this, this.input);\r\n    };\r\n    return PredictAllAgeAndGenderWithFaceAlignmentTask;\r\n}(PredictAllAgeAndGenderTask));\r\nexport { PredictAllAgeAndGenderWithFaceAlignmentTask };\r\nvar PredictSingleAgeAndGenderWithFaceAlignmentTask = /** @class */ (function (_super) {\r\n    __extends(PredictSingleAgeAndGenderWithFaceAlignmentTask, _super);\r\n    function PredictSingleAgeAndGenderWithFaceAlignmentTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    PredictSingleAgeAndGenderWithFaceAlignmentTask.prototype.withFaceExpressions = function () {\r\n        return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    PredictSingleAgeAndGenderWithFaceAlignmentTask.prototype.withFaceDescriptor = function () {\r\n        return new ComputeSingleFaceDescriptorTask(this, this.input);\r\n    };\r\n    return PredictSingleAgeAndGenderWithFaceAlignmentTask;\r\n}(PredictSingleAgeAndGenderTask));\r\nexport { PredictSingleAgeAndGenderWithFaceAlignmentTask };\r\n//# sourceMappingURL=PredictAgeAndGenderTask.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport { extendWithFaceDescriptor } from '../factories/WithFaceDescriptor';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { extractAllFacesAndComputeResults, extractSingleFaceAndComputeResult } from './extractFacesAndComputeResults';\r\nimport { nets } from './nets';\r\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask, } from './PredictAgeAndGenderTask';\r\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask, } from './PredictFaceExpressionsTask';\r\nvar ComputeFaceDescriptorsTaskBase = /** @class */ (function (_super) {\r\n    __extends(ComputeFaceDescriptorsTaskBase, _super);\r\n    function ComputeFaceDescriptorsTaskBase(parentTask, input) {\r\n        var _this = _super.call(this) || this;\r\n        _this.parentTask = parentTask;\r\n        _this.input = input;\r\n        return _this;\r\n    }\r\n    return ComputeFaceDescriptorsTaskBase;\r\n}(ComposableTask));\r\nexport { ComputeFaceDescriptorsTaskBase };\r\nvar ComputeAllFaceDescriptorsTask = /** @class */ (function (_super) {\r\n    __extends(ComputeAllFaceDescriptorsTask, _super);\r\n    function ComputeAllFaceDescriptorsTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    ComputeAllFaceDescriptorsTask.prototype.run = function () {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var parentResults, descriptors;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, this.parentTask];\r\n                    case 1:\r\n                        parentResults = _a.sent();\r\n                        return [4 /*yield*/, extractAllFacesAndComputeResults(parentResults, this.input, function (faces) { return Promise.all(faces.map(function (face) {\r\n                                return nets.faceRecognitionNet.computeFaceDescriptor(face);\r\n                            })); }, null, function (parentResult) { return parentResult.landmarks.align(null, { useDlibAlignment: true }); })];\r\n                    case 2:\r\n                        descriptors = _a.sent();\r\n                        return [2 /*return*/, descriptors.map(function (descriptor, i) { return extendWithFaceDescriptor(parentResults[i], descriptor); })];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    ComputeAllFaceDescriptorsTask.prototype.withFaceExpressions = function () {\r\n        return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    ComputeAllFaceDescriptorsTask.prototype.withAgeAndGender = function () {\r\n        return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    return ComputeAllFaceDescriptorsTask;\r\n}(ComputeFaceDescriptorsTaskBase));\r\nexport { ComputeAllFaceDescriptorsTask };\r\nvar ComputeSingleFaceDescriptorTask = /** @class */ (function (_super) {\r\n    __extends(ComputeSingleFaceDescriptorTask, _super);\r\n    function ComputeSingleFaceDescriptorTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    ComputeSingleFaceDescriptorTask.prototype.run = function () {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var parentResult, descriptor;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, this.parentTask];\r\n                    case 1:\r\n                        parentResult = _a.sent();\r\n                        if (!parentResult) {\r\n                            return [2 /*return*/];\r\n                        }\r\n                        return [4 /*yield*/, extractSingleFaceAndComputeResult(parentResult, this.input, function (face) { return nets.faceRecognitionNet.computeFaceDescriptor(face); }, null, function (parentResult) { return parentResult.landmarks.align(null, { useDlibAlignment: true }); })];\r\n                    case 2:\r\n                        descriptor = _a.sent();\r\n                        return [2 /*return*/, extendWithFaceDescriptor(parentResult, descriptor)];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    ComputeSingleFaceDescriptorTask.prototype.withFaceExpressions = function () {\r\n        return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    ComputeSingleFaceDescriptorTask.prototype.withAgeAndGender = function () {\r\n        return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    return ComputeSingleFaceDescriptorTask;\r\n}(ComputeFaceDescriptorsTaskBase));\r\nexport { ComputeSingleFaceDescriptorTask };\r\n//# sourceMappingURL=ComputeFaceDescriptorsTasks.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { extractFaces, extractFaceTensors } from '../dom';\r\nimport { extendWithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\r\nimport { nets } from './nets';\r\nimport { PredictAllAgeAndGenderWithFaceAlignmentTask, PredictSingleAgeAndGenderWithFaceAlignmentTask, } from './PredictAgeAndGenderTask';\r\nimport { PredictAllFaceExpressionsWithFaceAlignmentTask, PredictSingleFaceExpressionsWithFaceAlignmentTask, } from './PredictFaceExpressionsTask';\r\nvar DetectFaceLandmarksTaskBase = /** @class */ (function (_super) {\r\n    __extends(DetectFaceLandmarksTaskBase, _super);\r\n    function DetectFaceLandmarksTaskBase(parentTask, input, useTinyLandmarkNet) {\r\n        var _this = _super.call(this) || this;\r\n        _this.parentTask = parentTask;\r\n        _this.input = input;\r\n        _this.useTinyLandmarkNet = useTinyLandmarkNet;\r\n        return _this;\r\n    }\r\n    Object.defineProperty(DetectFaceLandmarksTaskBase.prototype, \"landmarkNet\", {\r\n        get: function () {\r\n            return this.useTinyLandmarkNet\r\n                ? nets.faceLandmark68TinyNet\r\n                : nets.faceLandmark68Net;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return DetectFaceLandmarksTaskBase;\r\n}(ComposableTask));\r\nexport { DetectFaceLandmarksTaskBase };\r\nvar DetectAllFaceLandmarksTask = /** @class */ (function (_super) {\r\n    __extends(DetectAllFaceLandmarksTask, _super);\r\n    function DetectAllFaceLandmarksTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    DetectAllFaceLandmarksTask.prototype.run = function () {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var parentResults, detections, faces, _a, faceLandmarksByFace;\r\n            var _this = this;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0: return [4 /*yield*/, this.parentTask];\r\n                    case 1:\r\n                        parentResults = _b.sent();\r\n                        detections = parentResults.map(function (res) { return res.detection; });\r\n                        if (!(this.input instanceof tf.Tensor)) return [3 /*break*/, 3];\r\n                        return [4 /*yield*/, extractFaceTensors(this.input, detections)];\r\n                    case 2:\r\n                        _a = _b.sent();\r\n                        return [3 /*break*/, 5];\r\n                    case 3: return [4 /*yield*/, extractFaces(this.input, detections)];\r\n                    case 4:\r\n                        _a = _b.sent();\r\n                        _b.label = 5;\r\n                    case 5:\r\n                        faces = _a;\r\n                        return [4 /*yield*/, Promise.all(faces.map(function (face) { return _this.landmarkNet.detectLandmarks(face); }))];\r\n                    case 6:\r\n                        faceLandmarksByFace = _b.sent();\r\n                        faces.forEach(function (f) { return f instanceof tf.Tensor && f.dispose(); });\r\n                        return [2 /*return*/, parentResults.map(function (parentResult, i) {\r\n                                return extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]);\r\n                            })];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    DetectAllFaceLandmarksTask.prototype.withFaceExpressions = function () {\r\n        return new PredictAllFaceExpressionsWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    DetectAllFaceLandmarksTask.prototype.withAgeAndGender = function () {\r\n        return new PredictAllAgeAndGenderWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    DetectAllFaceLandmarksTask.prototype.withFaceDescriptors = function () {\r\n        return new ComputeAllFaceDescriptorsTask(this, this.input);\r\n    };\r\n    return DetectAllFaceLandmarksTask;\r\n}(DetectFaceLandmarksTaskBase));\r\nexport { DetectAllFaceLandmarksTask };\r\nvar DetectSingleFaceLandmarksTask = /** @class */ (function (_super) {\r\n    __extends(DetectSingleFaceLandmarksTask, _super);\r\n    function DetectSingleFaceLandmarksTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    DetectSingleFaceLandmarksTask.prototype.run = function () {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var parentResult, detection, faces, _a, landmarks;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0: return [4 /*yield*/, this.parentTask];\r\n                    case 1:\r\n                        parentResult = _b.sent();\r\n                        if (!parentResult) {\r\n                            return [2 /*return*/];\r\n                        }\r\n                        detection = parentResult.detection;\r\n                        if (!(this.input instanceof tf.Tensor)) return [3 /*break*/, 3];\r\n                        return [4 /*yield*/, extractFaceTensors(this.input, [detection])];\r\n                    case 2:\r\n                        _a = _b.sent();\r\n                        return [3 /*break*/, 5];\r\n                    case 3: return [4 /*yield*/, extractFaces(this.input, [detection])];\r\n                    case 4:\r\n                        _a = _b.sent();\r\n                        _b.label = 5;\r\n                    case 5:\r\n                        faces = _a;\r\n                        return [4 /*yield*/, this.landmarkNet.detectLandmarks(faces[0])];\r\n                    case 6:\r\n                        landmarks = _b.sent();\r\n                        faces.forEach(function (f) { return f instanceof tf.Tensor && f.dispose(); });\r\n                        return [2 /*return*/, extendWithFaceLandmarks(parentResult, landmarks)];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    DetectSingleFaceLandmarksTask.prototype.withFaceExpressions = function () {\r\n        return new PredictSingleFaceExpressionsWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    DetectSingleFaceLandmarksTask.prototype.withAgeAndGender = function () {\r\n        return new PredictSingleAgeAndGenderWithFaceAlignmentTask(this, this.input);\r\n    };\r\n    DetectSingleFaceLandmarksTask.prototype.withFaceDescriptor = function () {\r\n        return new ComputeSingleFaceDescriptorTask(this, this.input);\r\n    };\r\n    return DetectSingleFaceLandmarksTask;\r\n}(DetectFaceLandmarksTaskBase));\r\nexport { DetectSingleFaceLandmarksTask };\r\n//# sourceMappingURL=DetectFaceLandmarksTasks.js.map","import { __awaiter, __extends, __generator } from \"tslib\";\r\nimport { extendWithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { MtcnnOptions } from '../mtcnn/MtcnnOptions';\r\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\r\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\r\nimport { TinyYolov2Options } from '../tinyYolov2';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\r\nimport { nets } from './nets';\r\nimport { PredictAllAgeAndGenderTask, PredictSingleAgeAndGenderTask } from './PredictAgeAndGenderTask';\r\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionsTask } from './PredictFaceExpressionsTask';\r\nvar DetectFacesTaskBase = /** @class */ (function (_super) {\r\n    __extends(DetectFacesTaskBase, _super);\r\n    function DetectFacesTaskBase(input, options) {\r\n        if (options === void 0) { options = new SsdMobilenetv1Options(); }\r\n        var _this = _super.call(this) || this;\r\n        _this.input = input;\r\n        _this.options = options;\r\n        return _this;\r\n    }\r\n    return DetectFacesTaskBase;\r\n}(ComposableTask));\r\nexport { DetectFacesTaskBase };\r\nvar DetectAllFacesTask = /** @class */ (function (_super) {\r\n    __extends(DetectAllFacesTask, _super);\r\n    function DetectAllFacesTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    DetectAllFacesTask.prototype.run = function () {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var _a, input, options, faceDetectionFunction;\r\n            return __generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this, input = _a.input, options = _a.options;\r\n                        if (!(options instanceof MtcnnOptions)) return [3 /*break*/, 2];\r\n                        return [4 /*yield*/, nets.mtcnn.forward(input, options)];\r\n                    case 1: return [2 /*return*/, (_b.sent())\r\n                            .map(function (result) { return result.detection; })];\r\n                    case 2:\r\n                        faceDetectionFunction = options instanceof TinyFaceDetectorOptions\r\n                            ? function (input) { return nets.tinyFaceDetector.locateFaces(input, options); }\r\n                            : (options instanceof SsdMobilenetv1Options\r\n                                ? function (input) { return nets.ssdMobilenetv1.locateFaces(input, options); }\r\n                                : (options instanceof TinyYolov2Options\r\n                                    ? function (input) { return nets.tinyYolov2.locateFaces(input, options); }\r\n                                    : null));\r\n                        if (!faceDetectionFunction) {\r\n                            throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');\r\n                        }\r\n                        return [2 /*return*/, faceDetectionFunction(input)];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    DetectAllFacesTask.prototype.runAndExtendWithFaceDetections = function () {\r\n        var _this = this;\r\n        return new Promise(function (res) { return __awaiter(_this, void 0, void 0, function () {\r\n            var detections;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, this.run()];\r\n                    case 1:\r\n                        detections = _a.sent();\r\n                        return [2 /*return*/, res(detections.map(function (detection) { return extendWithFaceDetection({}, detection); }))];\r\n                }\r\n            });\r\n        }); });\r\n    };\r\n    DetectAllFacesTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {\r\n        if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }\r\n        return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(), this.input, useTinyLandmarkNet);\r\n    };\r\n    DetectAllFacesTask.prototype.withFaceExpressions = function () {\r\n        return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(), this.input);\r\n    };\r\n    DetectAllFacesTask.prototype.withAgeAndGender = function () {\r\n        return new PredictAllAgeAndGenderTask(this.runAndExtendWithFaceDetections(), this.input);\r\n    };\r\n    return DetectAllFacesTask;\r\n}(DetectFacesTaskBase));\r\nexport { DetectAllFacesTask };\r\nvar DetectSingleFaceTask = /** @class */ (function (_super) {\r\n    __extends(DetectSingleFaceTask, _super);\r\n    function DetectSingleFaceTask() {\r\n        return _super !== null && _super.apply(this, arguments) || this;\r\n    }\r\n    DetectSingleFaceTask.prototype.run = function () {\r\n        return __awaiter(this, void 0, void 0, function () {\r\n            var faceDetections, faceDetectionWithHighestScore;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, new DetectAllFacesTask(this.input, this.options)];\r\n                    case 1:\r\n                        faceDetections = _a.sent();\r\n                        faceDetectionWithHighestScore = faceDetections[0];\r\n                        faceDetections.forEach(function (faceDetection) {\r\n                            if (faceDetection.score > faceDetectionWithHighestScore.score) {\r\n                                faceDetectionWithHighestScore = faceDetection;\r\n                            }\r\n                        });\r\n                        return [2 /*return*/, faceDetectionWithHighestScore];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    DetectSingleFaceTask.prototype.runAndExtendWithFaceDetection = function () {\r\n        var _this = this;\r\n        return new Promise(function (res) { return __awaiter(_this, void 0, void 0, function () {\r\n            var detection;\r\n            return __generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, this.run()];\r\n                    case 1:\r\n                        detection = _a.sent();\r\n                        return [2 /*return*/, res(detection ? extendWithFaceDetection({}, detection) : undefined)];\r\n                }\r\n            });\r\n        }); });\r\n    };\r\n    DetectSingleFaceTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {\r\n        if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }\r\n        return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(), this.input, useTinyLandmarkNet);\r\n    };\r\n    DetectSingleFaceTask.prototype.withFaceExpressions = function () {\r\n        return new PredictSingleFaceExpressionsTask(this.runAndExtendWithFaceDetection(), this.input);\r\n    };\r\n    DetectSingleFaceTask.prototype.withAgeAndGender = function () {\r\n        return new PredictSingleAgeAndGenderTask(this.runAndExtendWithFaceDetection(), this.input);\r\n    };\r\n    return DetectSingleFaceTask;\r\n}(DetectFacesTaskBase));\r\nexport { DetectSingleFaceTask };\r\n//# sourceMappingURL=DetectFacesTasks.js.map","import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\r\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\r\nexport function detectSingleFace(input, options) {\r\n    if (options === void 0) { options = new SsdMobilenetv1Options(); }\r\n    return new DetectSingleFaceTask(input, options);\r\n}\r\nexport function detectAllFaces(input, options) {\r\n    if (options === void 0) { options = new SsdMobilenetv1Options(); }\r\n    return new DetectAllFacesTask(input, options);\r\n}\r\n//# sourceMappingURL=detectFaces.js.map","export function euclideanDistance(arr1, arr2) {\r\n    if (arr1.length !== arr2.length)\r\n        throw new Error('euclideanDistance: arr1.length !== arr2.length');\r\n    var desc1 = Array.from(arr1);\r\n    var desc2 = Array.from(arr2);\r\n    return Math.sqrt(desc1\r\n        .map(function (val, i) { return val - desc2[i]; })\r\n        .reduce(function (res, diff) { return res + Math.pow(diff, 2); }, 0));\r\n}\r\n//# sourceMappingURL=euclideanDistance.js.map","import { FaceMatch } from '../classes/FaceMatch';\r\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\r\nimport { euclideanDistance } from '../euclideanDistance';\r\nvar FaceMatcher = /** @class */ (function () {\r\n    function FaceMatcher(inputs, distanceThreshold) {\r\n        if (distanceThreshold === void 0) { distanceThreshold = 0.6; }\r\n        this._distanceThreshold = distanceThreshold;\r\n        var inputArray = Array.isArray(inputs) ? inputs : [inputs];\r\n        if (!inputArray.length) {\r\n            throw new Error(\"FaceRecognizer.constructor - expected atleast one input\");\r\n        }\r\n        var count = 1;\r\n        var createUniqueLabel = function () { return \"person \" + count++; };\r\n        this._labeledDescriptors = inputArray.map(function (desc) {\r\n            if (desc instanceof LabeledFaceDescriptors) {\r\n                return desc;\r\n            }\r\n            if (desc instanceof Float32Array) {\r\n                return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);\r\n            }\r\n            if (desc.descriptor && desc.descriptor instanceof Float32Array) {\r\n                return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);\r\n            }\r\n            throw new Error(\"FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>\");\r\n        });\r\n    }\r\n    Object.defineProperty(FaceMatcher.prototype, \"labeledDescriptors\", {\r\n        get: function () { return this._labeledDescriptors; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(FaceMatcher.prototype, \"distanceThreshold\", {\r\n        get: function () { return this._distanceThreshold; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    FaceMatcher.prototype.computeMeanDistance = function (queryDescriptor, descriptors) {\r\n        return descriptors\r\n            .map(function (d) { return euclideanDistance(d, queryDescriptor); })\r\n            .reduce(function (d1, d2) { return d1 + d2; }, 0)\r\n            / (descriptors.length || 1);\r\n    };\r\n    FaceMatcher.prototype.matchDescriptor = function (queryDescriptor) {\r\n        var _this = this;\r\n        return this.labeledDescriptors\r\n            .map(function (_a) {\r\n            var descriptors = _a.descriptors, label = _a.label;\r\n            return new FaceMatch(label, _this.computeMeanDistance(queryDescriptor, descriptors));\r\n        })\r\n            .reduce(function (best, curr) { return best.distance < curr.distance ? best : curr; });\r\n    };\r\n    FaceMatcher.prototype.findBestMatch = function (queryDescriptor) {\r\n        var bestMatch = this.matchDescriptor(queryDescriptor);\r\n        return bestMatch.distance < this.distanceThreshold\r\n            ? bestMatch\r\n            : new FaceMatch('unknown', bestMatch.distance);\r\n    };\r\n    FaceMatcher.prototype.toJSON = function () {\r\n        return {\r\n            distanceThreshold: this.distanceThreshold,\r\n            labeledDescriptors: this.labeledDescriptors.map(function (ld) { return ld.toJSON(); })\r\n        };\r\n    };\r\n    FaceMatcher.fromJSON = function (json) {\r\n        var labeledDescriptors = json.labeledDescriptors\r\n            .map(function (ld) { return LabeledFaceDescriptors.fromJSON(ld); });\r\n        return new FaceMatcher(labeledDescriptors, json.distanceThreshold);\r\n    };\r\n    return FaceMatcher;\r\n}());\r\nexport { FaceMatcher };\r\n//# sourceMappingURL=FaceMatcher.js.map","import { Dimensions } from './classes';\r\nimport { FaceDetection } from './classes/FaceDetection';\r\nimport { FaceLandmarks } from './classes/FaceLandmarks';\r\nimport { extendWithFaceDetection, isWithFaceDetection } from './factories/WithFaceDetection';\r\nimport { extendWithFaceLandmarks, isWithFaceLandmarks } from './factories/WithFaceLandmarks';\r\nexport function resizeResults(results, dimensions) {\r\n    var _a = new Dimensions(dimensions.width, dimensions.height), width = _a.width, height = _a.height;\r\n    if (width <= 0 || height <= 0) {\r\n        throw new Error(\"resizeResults - invalid dimensions: \" + JSON.stringify({ width: width, height: height }));\r\n    }\r\n    if (Array.isArray(results)) {\r\n        return results.map(function (obj) { return resizeResults(obj, { width: width, height: height }); });\r\n    }\r\n    if (isWithFaceLandmarks(results)) {\r\n        var resizedDetection = results.detection.forSize(width, height);\r\n        var resizedLandmarks = results.unshiftedLandmarks.forSize(resizedDetection.box.width, resizedDetection.box.height);\r\n        return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);\r\n    }\r\n    if (isWithFaceDetection(results)) {\r\n        return extendWithFaceDetection(results, results.detection.forSize(width, height));\r\n    }\r\n    if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\r\n        return results.forSize(width, height);\r\n    }\r\n    return results;\r\n}\r\n//# sourceMappingURL=resizeResults.js.map","<template>\r\n    <div class=\"face_expression_recognition\">\r\n      <div class=\"left\">\r\n        <div class=\"Imagerecognition\">\r\n          <label>更换图片</label>\r\n          <div class=\"imgbg\">\r\n            <div class=\"see imgsee\" id=\"imgsee\" v-show=\"imgEl!=null\">\r\n              <img id=\"myImg\" src=\"http://210.5.159.76:8095//File/TemporaryFile/Uploads/images/publicize/1.png\" />\r\n              <canvas id=\"myCanvas\"></canvas>\r\n            </div>\r\n            <div class=\"default\" v-show=\"imgEl==null\"><img src=\"../../../public/img/img03.png\"></div>\r\n          </div>\r\n          <input class=\"updataimg\" type=\"file\" accept=\"image/png, image/jpeg\"  @change=\"fnChange($event)\" />\r\n        </div>\r\n\r\n        <div class=\"result\">\r\n          <div class=\"emote\" v-for=\"item in expression\">\r\n            <span>{{ item.Name }}</span>\r\n            <span>{{Math.floor(item.value*100)}}%</span>\r\n          </div>\r\n          <div class=\"gender item\">\r\n            <span>{{sex}}</span>\r\n            <label>性别</label>\r\n          </div>\r\n          <div class=\"age item\">\r\n            <span>{{years}}</span>\r\n            <label>年龄</label>\r\n          </div>\r\n        </div>\r\n\r\n      </div>\r\n\r\n      <div class=\"right\">\r\n        <div class=\"videorecognition\">\r\n            <!-- 视频 -->\r\n          <label>上传视频</label>\r\n          <div class=\"videosee\">\r\n            <video id=\"myVideo\" src=\"\" playsinline></video>\r\n            <canvas id=\"myvideoCanvas\"></canvas>\r\n          </div>\r\n          <div class=\"playicon\" v-show=\"myvideoCanvas==null\"><img src=\"../../../public/img/play.png\" ></div>\r\n          <input\r\n            type=\"file\"\r\n            accept=\"video/mp4, video/ogg, video/webm\"\r\n            @change=\"videoChange($event)\"\r\n          />\r\n\r\n          <!-- 显示内容 -->\r\n          <div class=\"Identifycontent\" v-show=\"myvideoCanvas!=null\">\r\n            <p class=\"title\">识别信息</p>\r\n            <div class=\"other\">\r\n              <p v-for=\"item in videoexpression\">表情：{{ item.Name }}  {{ item.value*100 }}</p>\r\n              <p>性别：{{ videosex }}</p>\r\n              <p>年龄：{{ videoyears }}</p>\r\n            </div>\r\n          </div>\r\n        </div>\r\n\r\n\r\n        <div class=\"camerarecognition\" @click=\"Openlive\">\r\n          <!-- 直播或者摄像头追踪微表情识别 -->\r\n          <label>打开摄像头</label>\r\n          <div class=\"livesee\">\r\n            <video id=\"mylive\" src=\"\" muted loop playsinline @loadedmetadata=\"liveRun\"></video>\r\n            <canvas id=\"myliveCanvas\"></canvas>\r\n          </div>\r\n          <div class=\"playicon\" v-show=\"myliveCanvas==null\"><img src=\"../../../public/img/img02.png\" ></div>\r\n          <!-- <button type=\"button\" class=\"play\"  @click=\"Openlive\">打开摄像头</button> -->\r\n          <!-- 显示内容 -->\r\n          <div class=\"Identifycontent\" v-show=\"myliveCanvas!=null\">\r\n            <p class=\"title\">识别信息</p>\r\n            <div class=\"other\">\r\n              <p v-for=\"item in cameraexpression\">表情：{{ item.Name }}  {{ Math.floor(item.value*100) }}</p>\r\n              <p>性别：{{ camerasex }}</p>\r\n              <p>年龄：{{ camerayears }}</p>\r\n            </div>\r\n          </div>\r\n        </div>\r\n      </div>\r\n    </div>\r\n</template>\r\n<script>\r\nimport * as faceapi from \"face-api.js\";\r\nconst MODELS_URL='/models';\r\n\r\nexport default {\r\n  name: \"tinyFaceDetector\",\r\n  data() {\r\n    return {\r\n      options: null, // 模型参数\r\n      imgEl:null,\r\n      canvasEl: null,\r\n      expression:[{Name:'未知',value:0}],\r\n      years:'未知',\r\n      sex:'未知',\r\n      expressionName:{\r\n        angry:'生气',\r\n        disgusted:'厌恶',\r\n        fearful:'担心',\r\n        happy:'开心',\r\n        neutral:'正常',\r\n        sad:'伤心',\r\n        surprised:'惊喜'\r\n      },\r\n\r\n      // 视频\r\n      timeout: 0,\r\n      videoEl:null,\r\n      myvideoCanvas:null,\r\n      videoexpression:[],\r\n      videoyears:'',\r\n      videosex:'',\r\n\r\n      // 直播\r\n      mylive:null,\r\n      myliveCanvas:null,\r\n      livetimeout:0,\r\n      cameraexpression:[],\r\n      camerayears:'',\r\n      camerasex:'',\r\n      // 视频媒体参数配置\r\n      constraints: {\r\n        audio: false,\r\n        video: {\r\n          // ideal（应用最理想的）\r\n          width: {\r\n            min: 320,\r\n            ideal: 1280,\r\n            max: 1920,\r\n          },\r\n          height: {\r\n            min: 240,\r\n            ideal: 720,\r\n            max: 1080,\r\n          },\r\n          // frameRate受限带宽传输时，低帧率可能更适宜\r\n          frameRate: {\r\n            min: 15,\r\n            ideal: 30,\r\n            max: 60,\r\n          },\r\n          // 显示模式前置后置\r\n          facingMode: \"environment\",\r\n        },\r\n      },\r\n    };\r\n  },\r\n  mounted() {\r\n    this.$nextTick(() => {\r\n      this.fnInit().then();\r\n    });\r\n  },\r\n  methods: {\r\n    // 初始化模型加载\r\n    async fnInit() {\r\n      await faceapi.nets['tinyFaceDetector'].loadFromUri(MODELS_URL);//算法模型\r\n      await faceapi.loadFaceLandmarkModel(MODELS_URL); // 轮廓模型\r\n      await faceapi.loadFaceExpressionModel(MODELS_URL); // 表情模型\r\n      await faceapi.loadAgeGenderModel(MODELS_URL); // 年龄模型\r\n      \r\n      // 节点属性化.\r\n      // this.imgEl = document.getElementById(\"myImg\");\r\n      // this.canvasEl = document.getElementById(\"myCanvas\");\r\n    },\r\n\r\n\r\n    // 执行识别绘制\r\n    async fnRun() {\r\n        // 根据模型参数识别调整结果\r\n        this.options = new faceapi.TinyFaceDetectorOptions({\r\n          inputSize: 224, // 160 224 320 416 512 608\r\n          scoreThreshold: 0.1, // 0.1 ~ 0.9\r\n        });\r\n      const results = await faceapi\r\n        .detectAllFaces(this.imgEl, this.options)\r\n        .withFaceLandmarks()\r\n        .withFaceExpressions()\r\n        .withAgeAndGender();\r\n      faceapi.matchDimensions(this.canvasEl, this.imgEl);\r\n      const resizedResults = faceapi.resizeResults(results, this.imgEl);\r\n      let statunum=0;\r\n      resizedResults.forEach((result) => {\r\n        for(let item in result.expressions){\r\n          if(this.expressionName[item]){\r\n            if(result.expressions[item]>statunum){\r\n              statunum=result.expressions[item];\r\n              this.expression=[{Name:this.expressionName[item],value:result.expressions[item].toFixed(2)}]\r\n            }\r\n          }\r\n        }\r\n        const { age, gender, genderProbability} = result;\r\n        this.years=Math.round(age,0);\r\n        this.sex=gender=='female'?'女':'男';\r\n\r\n        new faceapi.draw.DrawTextField(\r\n          [],\r\n          result.detection.box.bottomLeft\r\n        ).draw(this.canvasEl);\r\n      });\r\n      this.withBoxes\r\n        ? faceapi.draw.drawDetections(this.canvasEl, resizedResults)\r\n        : faceapi.draw.drawFaceLandmarks(this.canvasEl, resizedResults);\r\n\r\n    },\r\n    fnChange(e) {\r\n      if(!e.target.files.length) return;\r\n      this.imgEl = document.getElementById(\"myImg\");\r\n      this.canvasEl = document.getElementById(\"myCanvas\");\r\n      faceapi.bufferToImage(e.target.files[0]).then((img) => {\r\n        this.imgEl.src= img.src;\r\n        this.canvasEl.getContext(\"2d\").clearRect(0, 0, this.imgEl.width, this.imgEl.height);\r\n        this.fnRun();\r\n      });\r\n    },\r\n\r\n    // 视频相关\r\n    // 视频暂停播放\r\n    fnPaused() {\r\n      if (this.videoEl.paused) {\r\n        this.videoEl.play();\r\n        setTimeout(() => this.fnRunFaceAgeAndGender(), 300);\r\n      } else {\r\n        this.videoEl.pause();\r\n      }\r\n    },\r\n\r\n    // 年龄性别识别绘制\r\n    async fnRunFaceAgeAndGender() {\r\n      if (this.videoEl.paused) return clearTimeout(this.timeout);\r\n      // 根据模型参数识别调整结果\r\n      this.options = new faceapi.TinyFaceDetectorOptions({\r\n          inputSize: 512, // 160 224 320 416 512 608\r\n          scoreThreshold: 0.5, // 0.1 ~ 0.9\r\n        });\r\n      // 识别绘制人脸信息\r\n      const result = await faceapi\r\n        .detectAllFaces(this.videoEl, this.options)\r\n        .withFaceLandmarks()\r\n        .withFaceExpressions()\r\n        .withAgeAndGender();\r\n      if (result) {\r\n        const dims = faceapi.matchDimensions(this.myvideoCanvas, this.videoEl, true);\r\n        const resizeResult = faceapi.resizeResults(result, dims);\r\n        this.withBoxes?faceapi.draw.drawDetections(this.myvideoCanvas, resizeResult): faceapi.draw.drawFaceLandmarks(this.myvideoCanvas, resizeResult);\r\n        this.videoexpression=[];\r\n        let statunum=0;\r\n        if (Array.isArray(resizeResult)) {\r\n          resizeResult.forEach((result) => {\r\n            for(let item in result.expressions){\r\n              if(this.expressionName[item] && result.expressions[item]>statunum){\r\n                statunum=result.expressions[item];\r\n                this.videoexpression=[{Name:this.expressionName[item],value:result.expressions[item].toFixed(2)}];\r\n              }\r\n            }\r\n            const { age, gender, genderProbability} = result;\r\n            this.videoyears=Math.round(age,0);\r\n            this.videosex=gender=='female'?'女':'男';\r\n            new faceapi.draw.DrawTextField(\r\n              [],\r\n              result.detection.box.bottomLeft\r\n            ).draw(this.myvideoCanvas);\r\n          });\r\n        } else {\r\n          const { age, gender, genderProbability } = resizeResult;\r\n          new faceapi.draw.DrawTextField(\r\n            [\r\n              `${Math.round(age, 0)} years`,\r\n              `${gender} (${Math.round(genderProbability)})`,\r\n            ],\r\n            resizeResult.detection.box.bottomLeft\r\n          ).draw(this.myvideoCanvas);\r\n        }\r\n      } else {\r\n        this.myvideoCanvas\r\n          .getContext(\"2d\")\r\n          .clearRect(0, 0, this.myvideoCanvas.width, this.myvideoCanvas.height);\r\n      }\r\n      this.timeout = setTimeout(() => this.fnRunFaceAgeAndGender());\r\n    },\r\n\r\n    // 更换视频,清空绘制\r\n    videoChange(e) {\r\n      this.videoEl = document.getElementById(\"myVideo\");\r\n      this.myvideoCanvas = document.getElementById(\"myvideoCanvas\");\r\n      if (!e.target.files.length) return;\r\n      this.videoEl.pause();\r\n      \r\n      setTimeout(() => {\r\n        this.myvideoCanvas\r\n          .getContext(\"2d\")\r\n          .clearRect(0, 0, this.myvideoCanvas.width, this.myvideoCanvas.height);\r\n        this.videoEl.src = URL.createObjectURL(e.target.files[0]);\r\n        this.fnPaused();\r\n      }, 300);\r\n    },\r\n\r\n\r\n    // 直播摄像头相关\r\n    Openlive() {\r\n      if (typeof window.stream === \"object\") return;\r\n      clearTimeout(this.livetimeout);\r\n      this.timeout = setTimeout(() => {\r\n        clearTimeout(this.livetimeout);\r\n        if(navigator.mediaDevices){\r\n          navigator.mediaDevices\r\n          .getUserMedia(this.constraints)\r\n          .then(this.liveSuccess)\r\n          .catch(this.liveError);\r\n        }else{\r\n          alert(\"请开启摄像头权限，如不是https请前往https://www.cnblogs.com/tuofei13/p/16636145.html查看教程即可开启摄像头\")\r\n        }\r\n        \r\n      }, 300);\r\n    },\r\n\r\n    // 成功启动视频媒体流\r\n    liveSuccess(stream) {\r\n      this.mylive = document.getElementById(\"mylive\");\r\n      this.myliveCanvas = document.getElementById(\"myliveCanvas\");\r\n      window.stream = stream; // 使流对浏览器控制台可用\r\n      this.mylive.srcObject = stream;\r\n      this.mylive.play();\r\n      this.liveRun();\r\n    },\r\n    // 失败启动视频媒体流\r\n    liveError(error) {\r\n      console.log(error);\r\n      alert(\"视频媒体流获取错误\" + error);\r\n    },\r\n    // 年龄性别识别绘制\r\n    async liveRun() {\r\n      if (this.mylive.paused) return \r\n      clearTimeout(this.livetimeout);\r\n      this.options = new faceapi.TinyFaceDetectorOptions({\r\n          inputSize: 512, // 160 224 320 416 512 608\r\n          scoreThreshold: 0.5, // 0.1 ~ 0.9\r\n      });\r\n      // 识别绘制人脸信息\r\n      const result = await faceapi\r\n        .detectAllFaces(this.mylive, this.options)\r\n        .withFaceLandmarks()\r\n        .withFaceExpressions()\r\n        .withAgeAndGender();\r\n      if (result && !this.mylive.paused) {\r\n        const dims = faceapi.matchDimensions(this.myliveCanvas, this.mylive, true);\r\n        const resizeResults = faceapi.resizeResults(result, dims);\r\n        let statunum=0;\r\n        this.withBoxes\r\n          ? faceapi.draw.drawDetections(this.myliveCanvas, resizeResults)\r\n          : faceapi.draw.drawFaceLandmarks(this.myliveCanvas, resizeResults);\r\n        if (Array.isArray(resizeResults)) {\r\n          resizeResults.forEach((result) => {\r\n            for(let item in result.expressions){\r\n              if(this.expressionName[item] && result.expressions[item]>statunum){\r\n                statunum=result.expressions[item];\r\n                this.cameraexpression=[{Name:this.expressionName[item],value:result.expressions[item].toFixed(2)}];\r\n              }\r\n            }\r\n            const { age, gender, genderProbability } = result;\r\n            this.camerayears=Math.round(age, 0);\r\n            this.camerasex=gender=='female'?'女':'男'\r\n            new faceapi.draw.DrawTextField(\r\n              [],\r\n              result.detection.box.bottomLeft\r\n            ).draw(this.myliveCanvas);\r\n          });\r\n        } else {\r\n          const { age, gender, genderProbability } = resizeResults;\r\n          new faceapi.draw.DrawTextField(\r\n            [\r\n              `${Math.round(age, 0)} years`,\r\n              `${gender} (${Math.round(genderProbability)})`,\r\n            ],\r\n            resizeResults.detection.box.bottomLeft\r\n          ).draw(this.myliveCanvas);\r\n        }\r\n      } else {\r\n        this.myliveCanvas\r\n          .getContext(\"2d\")\r\n          .clearRect(0, 0, this.myliveCanvas.width, this.myliveCanvas.height);\r\n      }\r\n      this.livetimeout = setTimeout(() => this.liveRun());\r\n    },\r\n  },\r\n};\r\n</script>\r\n<style scoped>\r\n.face_expression_recognition{background: url(\"../../../public/img/chatgpt/face_api_bg.jpg\") 100% no-repeat;background-size: 100% 100%;background-position: top;display: flex;padding: 30px;overflow: hidden;}\r\n.Imagerecognition{background: rgba(33, 59, 112, 0.5);padding:24px;width: 305px;height: 576px;position: relative;display: inline-block;}\r\n.Imagerecognition label{background: #001E53;color: #fff;border: 1px solid #5C77AD;padding: 8px 0;text-align: center;display: block;}\r\n.Imagerecognition .imgbg{background:url('../../../public/img/bg04.png') 100%;background-size:305px 536px;height: 536px;}\r\n.Imagerecognition .imgsee{width:100%;position: relative;}\r\n.Imagerecognition .imgsee img{width: 100%; display: block;}\r\n.Imagerecognition .imgsee canvas{position: absolute;top: 0;left: 0;width: 100%;height: 100%;}\r\n.Imagerecognition .updataimg{position: absolute;top: 0;left: 0;width: 100%;height:100%;opacity:0;z-index:2;}\r\n.Imagerecognition .default{text-align: center;padding-top: 120px;}\r\n.face_expression_recognition .result{width: 293px;height: 101px;background: #001E53;margin-top:16px;padding:15px 30px;display: flex;align-items: center;}\r\n.face_expression_recognition .result .emote{width: 102px;height: 102px;position: relative;padding-top: 45px;}\r\n.face_expression_recognition .result .emote span{display: block;text-align: center;color: #4FE2FF;font-size: 20px;}\r\n.face_expression_recognition .result .emote::after{content: '';position:absolute;top:0;left:0;width: 100%;height:100%;background: url('../../../public/img/bg05.png') 100% no-repeat;background-size: 100%;}\r\n.face_expression_recognition .result .item{text-align: center;font-size: 16px;width:100px;}\r\n.face_expression_recognition .result .item::after{content: '';background: url('../../../public/img/img01.png') no-repeat;width: 65px;height:10px;background-size: 100%;display: block;margin: 10px auto 0;}\r\n.face_expression_recognition .result .item span{color: #4FE2FF;display: block;}\r\n.face_expression_recognition .result .item label{font-size: 16px;color: #BDD3FF;display: block;}\r\n\r\n.right{width:558px;display: inline-block;margin-left: 30px;}\r\n.right .videorecognition{padding:24px;background: #001E53;height: 325px;position: relative;}\r\n.right .videorecognition label{background: #001E53;color: #fff;border: 1px solid #5C77AD;padding: 8px 0;text-align: center;display: block;}\r\n.right .videorecognition .videosee{width:100%;height:287px;background:url('../../../public/img/bg01.png') 100% no-repeat;background-size: 100% 100%;position: relative;}\r\n#myVideo{width: 100%;height: 100%;}\r\n.right .videorecognition input{position: absolute;top: 0;left: 0;width: 100%;height:100%;opacity:0;z-index:2;}\r\n#myvideoCanvas{position: absolute;top: 0;left: 0;width: 100%;height:100%;}\r\n.right .videorecognition .playicon{position: absolute;width: 90px;top: 50%;left: 50%;transform: translate(-50%,-50%);}\r\n.right .camerarecognition{padding:24px;background: #001E53;height: 325px;position: relative;margin-top: 20px;}\r\n.right .camerarecognition label{background: #001E53;color: #fff;border: 1px solid #5C77AD;padding: 8px 0;text-align: center;display: block;}\r\n.right .camerarecognition .livesee{width:100%;height:287px;background:url('../../../public/img/bg01.png') 100% no-repeat;background-size: 100% 100%;}\r\n.right .camerarecognition .playicon{position: absolute;width: 90px;top: 50%;left: 50%;transform: translate(-50%,-50%);}\r\n.right .camerarecognition .livesee video{width:558px;height:287px;}\r\n\r\n.right .Identifycontent{position: absolute;top: 100px;width: 146px;height: 116px;}\r\n.right .Identifycontent .title{text-align: center;color: #fff;background:#5E6166;font-size: 16px;padding: 5px 0;}\r\n.right .Identifycontent .other{background: #31343D;color: #fff;padding: 20px;}\r\n.right .Identifycontent .other p{padding: 5px 0;}\r\n</style>","import mod from \"-!../../../node_modules/cache-loader/dist/cjs.js??ref--13-0!../../../node_modules/thread-loader/dist/cjs.js!../../../node_modules/babel-loader/lib/index.js!../../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Facerecognition.vue?vue&type=script&lang=js\"; export default mod; export * from \"-!../../../node_modules/cache-loader/dist/cjs.js??ref--13-0!../../../node_modules/thread-loader/dist/cjs.js!../../../node_modules/babel-loader/lib/index.js!../../../node_modules/cache-loader/dist/cjs.js??ref--1-0!../../../node_modules/vue-loader/lib/index.js??vue-loader-options!./Facerecognition.vue?vue&type=script&lang=js\"","import { render, staticRenderFns } from \"./Facerecognition.vue?vue&type=template&id=6cc2f6f3&scoped=true\"\nimport script from \"./Facerecognition.vue?vue&type=script&lang=js\"\nexport * from \"./Facerecognition.vue?vue&type=script&lang=js\"\nimport style0 from \"./Facerecognition.vue?vue&type=style&index=0&id=6cc2f6f3&prod&scoped=true&lang=css\"\n\n\n/* normalize component */\nimport normalizer from \"!../../../node_modules/vue-loader/lib/runtime/componentNormalizer.js\"\nvar component = normalizer(\n  script,\n  render,\n  staticRenderFns,\n  false,\n  null,\n  \"6cc2f6f3\",\n  null\n  \n)\n\nexport default component.exports","'use strict';\nvar $TypeError = TypeError;\n\nmodule.exports = function (passed, required) {\n  if (passed < required) throw new $TypeError('Not enough arguments');\n  return passed;\n};\n","export function createFileSystem(fs) {\r\n    var requireFsError = '';\r\n    if (!fs) {\r\n        try {\r\n            fs = require('fs');\r\n        }\r\n        catch (err) {\r\n            requireFsError = err.toString();\r\n        }\r\n    }\r\n    var readFile = fs\r\n        ? function (filePath) {\r\n            return new Promise(function (res, rej) {\r\n                fs.readFile(filePath, function (err, buffer) {\r\n                    return err ? rej(err) : res(buffer);\r\n                });\r\n            });\r\n        }\r\n        : function () {\r\n            throw new Error(\"readFile - failed to require fs in nodejs environment with error: \" + requireFsError);\r\n        };\r\n    return {\r\n        readFile: readFile\r\n    };\r\n}\r\n//# sourceMappingURL=createFileSystem.js.map"],"sourceRoot":""}