(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}g.FaceDetector = f()}})(function(){var define,module,exports;return (function(){function r(e,n,t){function o(i,f){if(!n[i]){if(!e[i]){var c="function"==typeof require&&require;if(!f&&c)return c(i,!0);if(u)return u(i,!0);var a=new Error("Cannot find module '"+i+"'");throw a.code="MODULE_NOT_FOUND",a}var p=n[i]={exports:{}};e[i][0].call(p.exports,function(r){var n=e[i][1][r];return o(n||r)},p,p.exports,r,e,n,t)}return n[i].exports}for(var u="function"==typeof require&&require,i=0;i<t.length;i++)o(t[i]);return o}return r})()({1:[function(require,module,exports){
var FaceDetector = require('./src/FaceDetector.js');


module.exports = FaceDetector;


},{"./src/FaceDetector.js":5}],2:[function(require,module,exports){
var LowpassFilter = require('./src/LowpassFilter');

//export the module
module.exports = LowpassFilter;


},{"./src/LowpassFilter":3}],3:[function(require,module,exports){
/*
 *  Copyright (c) 2010-2017 Tom Misawa(riversun.org@gmail.com)
 *  
 *  Permission is hereby granted, free of charge, to any person obtaining a
 *  copy of this software and associated documentation files (the "Software"),
 *  to deal in the Software without restriction, including without limitation
 *  the rights to use, copy, modify, merge, publish, distribute, sublicense,
 *  and/or sell copies of the Software, and to permit persons to whom the
 *  Software is furnished to do so, subject to the following conditions:
 *  
 *  The above copyright notice and this permission notice shall be included in
 *  all copies or substantial portions of the Software.
 *  
 *  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 *  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 *  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 *  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 *  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
 *  FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 *  DEALINGS IN THE SOFTWARE.
 *  
 */
var LowpassFilter = (function () {

        /**
         * Calculate Simple Moving Average
         * @param params
         * @constructor
         */
        function LowpassFilter(params) {
            this.mSamplingRange = 0;
            this.mSamplingBuffer = [];
            this.mCurrentFilterVal = 0;
            this.mCurrentDataNumber = 0;
            this.setSamplingRange(20);

            //set linear weight moving average logic as default
            this.calcLogic = this.LinearWeightAverage;
            //this.calcLogic = this.SimpleAverage;
        }

        LowpassFilter.prototype.setLogic = function (func) {
            var _this = this;
            _this.calcLogic = func;
        };

        LowpassFilter.prototype.LinearWeightAverage = function (numerator, range, n, samplingBuffer, denominator) {
            numerator += (range - n) * samplingBuffer[n];
            denominator += (range - n);
            return {numerator: numerator, denominator: denominator};
        };

        LowpassFilter.prototype.SimpleAverage = function (numerator, range, n, samplingBuffer, denominator) {
            numerator += samplingBuffer[n];
            denominator = range;
            return {numerator: numerator, denominator: denominator};
        };

        /**
         * Set sampling range(DEFAULT is 20)
         * @param range
         */
        LowpassFilter.prototype.setSamplingRange = function (range) {
            var _this = this;
            _this.mSamplingRange = range;
            _this.mSamplingBuffer = new Array(_this.mSamplingRange);
        };

        /**
         * Returns total count
         * @param range
         * @returns {number}
         */
        LowpassFilter.prototype.getTotalCount = function (range) {
            var _this = this;
            return _this.mCurrentDataNumber;
        };

        /**
         * Get the latest filtered(linear weighted) value
         *
         * @return
         */
        LowpassFilter.prototype.getFilteredValue = function () {
            var _this = this;
            var filteredVal = _this.mCurrentFilterVal;
            return filteredVal;
        };

        /**
         * Put the current value to filter
         *
         * @param val
         */
        LowpassFilter.prototype.putValue = function (val) {
            var _this = this;
            _this.mCurrentFilterVal = _this.filter(val, _this.mSamplingBuffer);
        };

        /**
         * Get raw buffer
         * @returns {Array}
         */
        LowpassFilter.prototype.getSampingBuffer = function () {
            var _this = this;
            return _this.mSamplingBuffer;
        };

        /**
         * Get sampling range you specified
         * @returns {number|*}
         */
        LowpassFilter.prototype.getSamplingRange = function () {
            var _this = this;
            return _this.mSamplingRange;
        };

        /**
         * Fill out buffer by specified value
         * @param val
         */
        LowpassFilter.prototype.fillValue = function (val) {
            var _this = this;
            for (var i = 0; i < _this.mSamplingBuffer.length; i++) {
                _this.mSamplingBuffer[i] = val;
            }
        };


        /**
         * Do filter
         * @param inputValue the latest(current) value
         * @param samplingBuffer
         * @returns {number}
         */
        LowpassFilter.prototype.filter = function (inputValue, samplingBuffer) {
            var _this = this;

            _this.mCurrentDataNumber++;

            var range = samplingBuffer.length;

            if (_this.mCurrentDataNumber < range) {
                range = _this.mCurrentDataNumber;
            }

            for (var i = (range - 1) - 1; i >= 0; i--) {
                // copy next element to prev element
                samplingBuffer[i + 1] = samplingBuffer[i];
            }

            // Add current value to last
            samplingBuffer[0] = inputValue;

            var numerator = 0;

            var denominator = 0;

            for (var n = 0; n < range; n++) {

                var result = _this.calcLogic(numerator, range, n, samplingBuffer, denominator);
                numerator = result.numerator;
                denominator = result.denominator;

            }

            var currentOutputVal = numerator / denominator;

            return currentOutputVal;

        };

        return LowpassFilter;
    }()
);

if (typeof(module) !== "undefined") {
    module.exports = LowpassFilter;
}
},{}],4:[function(require,module,exports){
/*
 *
 * Copyright 2016-2017 Tom Misawa, riversun.org@gmail.com
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy of
 * this software and associated documentation files (the "Software"), to deal in the
 * Software without restriction, including without limitation the rights to use,
 * copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
 * Software, and to permit persons to whom the Software is furnished to do so,
 * subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
 *  INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
 * PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
 * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
 * WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
 * IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 *
 */
var LowpassFilter = require('lowpassf');

/**
 * Face
 *
 * @author Tom Misawa
 */
var Face = (function () {

        function Face(params) {

            //Maximum number of samples when calculating moving average
            var LOWPASS_SAMPLING_RANGE = 10;

            //The maximum moving distance(ratio of the width of the screen)
            // that can be recognized as the same face.
            var DEFAULT_MAX_MOVING_DISTANCE_RATIO = 0.15;

            this.faceId = "NOINDEX";
            this.faceIndex = -1;
            this.x = 0;
            this.y = 0;
            this.width = 0;
            this.height = 0;

            //A flag indicating whether or not the position of the cached face has been updated is first set to false
            this._isUpdatedFacePosition = false;

            //setup lowpass filters
            this._lowpass4x = new LowpassFilter();
            this._lowpass4x.setSamplingRange(LOWPASS_SAMPLING_RANGE);
            this._lowpass4y = new LowpassFilter();
            this._lowpass4y.setSamplingRange(LOWPASS_SAMPLING_RANGE);

            this._lowpass4w = new LowpassFilter();
            this._lowpass4w.setSamplingRange(LOWPASS_SAMPLING_RANGE);

            this._lowpass4h = new LowpassFilter();
            this._lowpass4h.setSamplingRange(LOWPASS_SAMPLING_RANGE);


            //The maximum moving distance(ratio of the width of the screen)
            // that can be recognized as the same face.
            if (params && params.trackingDistanceRatio) {
                this.maxMovingDistanceRatio = params.trackingDistanceRatio;
            } else {
                this.maxMovingDistanceRatio = DEFAULT_MAX_MOVING_DISTANCE_RATIO;
            }

        }

        
        var latestUpdatedPosition;
        /**
         * Update coordinates
         * 
         * 1.Enter the latest coordinates(x,y) into the lowpass filter.
         * 2.Update coordinates(this.x,this.y) with a value filtered by lowpass filter.
         *
         * @param x
         * @param y
         */
        Face.prototype.updatePos = function (x, y) {
            if(this.latestUpdatedPosition && this.isClosingToPositionThan(
                {x: this._lowpass4x.getFilteredValue(), y: this._lowpass4y.getFilteredValue()},
                {x: x, y: y}, this.latestUpdatedPosition )) {
                var _this = this;

                // 1.Enter the latest coordinates(x,y) into the lowpass filter.
                _this._lowpass4x.putValue(this.latestUpdatedPosition.x);
                _this._lowpass4y.putValue(this.latestUpdatedPosition.y);

                // 2.Update coordinates(this.x,this.y) with a value filtered by lowpass filter.
                _this.x = _this._lowpass4x.getFilteredValue();
                _this.y = _this._lowpass4y.getFilteredValue();
            }
            this.latestUpdatedPosition = {x: x, y: y};
        };

        Face.prototype.isClosingToPositionThan = function (refPosition, newPosition, latestPosition) {
            var distRefNew = Math.sqrt(Math.pow(refPosition.x - newPosition.x, 2) + Math.pow(refPosition.y - newPosition.y, 2));
            var distRefLatest = Math.sqrt(Math.pow(refPosition.x - latestPosition.x, 2) + Math.pow(refPosition.y - latestPosition.y, 2));
            return distRefLatest < distRefNew;
        };

        //
        /**
         * Update width/height
         *
         * @see  #updatePos
         * @param width
         * @param height
         */
        Face.prototype.updateSize = function (width, height) {
            var _this = this;

            _this._lowpass4w.putValue(width);
            _this._lowpass4h.putValue(height);

            _this.width = _this._lowpass4w.getFilteredValue();
            _this.height = _this._lowpass4h.getFilteredValue();
        };


        /**
         * Update cacehed face's position.
         * The detected face's position CLOSEST to the cached face's position is considered as the destination of the cached face.
         * 
         * @param detectedNewFaces latest faces detected(array)
         * @returns {boolean}
         */
        Face.prototype.updateFace = function (detectedNewFaces) {

            var _this = this;
            var minDistance = Number.MAX_SAFE_INTEGER;
            var minDistanceFace = null;


            if (detectedNewFaces[0]) {
                _this.updatePos(detectedNewFaces[0].x, detectedNewFaces[0].y);
                _this.updateSize(detectedNewFaces[0].width, detectedNewFaces[0].height);
            }


        };
        return Face;
    }()
);

module.exports = Face;

},{"lowpassf":2}],5:[function(require,module,exports){
/*
 *
 * Copyright 2016-2017 Tom Misawa, riversun.org@gmail.com
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy of
 * this software and associated documentation files (the "Software"), to deal in the
 * Software without restriction, including without limitation the rights to use,
 * copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
 * Software, and to permit persons to whom the Software is furnished to do so,
 * subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
 *  INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
 * PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
 * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
 * WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
 * IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 *
 */
var LowpassFilter = require('lowpassf');
var Face = require('./Face.js');
var ObjectDetect = require('./objectdetect.js');
var FrontalFaceAlt = require('./objectdetect.frontalface_alt.js');

/**
 * Face detector
 *
 * @author Tom Misawa
 */
var FaceDetector = (function () {

        /**
         * Multi Face Detector / Tracker
         *
         * @param params
         * @constructor
         */
        function FaceDetector(params) {

            //After detecting the face for more than a certain number(default is 50) of times,
            // when it stabilizes, the face integration logic starts.
            this.MIN_NUM_OF_PREPARE_DETECTION = 50;

            this.DEFAULT_MAX_MOVING_DISTANCE_RATIO = 0.15;

            //For example, if detectHeight = 60,
            //face search uses the rectangle image that is shrinked to 60pixels height.
            //The longer the length of the detectHeight, the greater the load , longer the processing time.

            this.detectHeight = 60;
            
            if (params && params.detectHeight) {
                this.detectHeight = params.detectHeight;
            }

            //this value will be automatically adjusted.
            this.detectWidth = null;

            this.video = params.video;

            //Booleanvalue for mirroring
            this.flipLeftRight = params.flipUpsideDown;

            //Boolean value for upside-down
            this.flipUpsideDown = params.flipUpsideDown;

            this._onFaceUpdatedCallback = null;
            this._onFaceAddedCallback = null;
            this._onFaceLostCallback = null;

            this._lowpassFilter4faceCount = new LowpassFilter();
            this._lowpassFilter4faceCount.setSamplingRange(200);

            this._cachedFaces = {};

            this._onSnapShotCallback = null;
            this._snapShotCanvas = null;
            this._snapShotContext = null;

            this._ttlFaceIndex = 0;

            //NodeJS timer (interval)
            this.updateTimer = undefined;
            //Start with a low update delay, in order to quickly have the 50 first pictures
            this.updateDelayLocal = 1;
            //To register the specified update delay
            this.updateDelayParam = undefined;

        }

        //[begin]setter for facial detection callback ///////////////////////////////

        /**
         * Set callback when face detection processing is updated
         *
         * @param callbackFunc
         */
        FaceDetector.prototype.setOnFaceUpdatedCallback = function (callbackFunc) {
            var _this = this;
            _this._onFaceUpdatedCallback = callbackFunc;
        };

        /**
         * Set callback when new face is found
         *
         * @param callbackFunc
         */
        FaceDetector.prototype.setOnFaceAddedCallback = function (callbackFunc) {
            var _this = this;
            _this._onFaceAddedCallback = callbackFunc;
        };

        /**
         * Set callback when face is lost
         *
         * @param callbackFunc
         */
        FaceDetector.prototype.setOnFaceLostCallback = function (callbackFunc) {
            var _this = this;
            _this._onFaceLostCallback = callbackFunc;
        };
        //[end]setter for facial detection callback ///////////////////////////////

        /**
         * Start face detection
         */
        FaceDetector.prototype.startDetecting = function (updateDelay) {
            if (!updateDelay) {
                updateDelay = 60;
            }
            this.updateDelayParam = updateDelay;
            if (this.updateTimer) {
                clearInterval(this.updateTimer);
            }
            var _this = this;
            this.updateTimer = setInterval(function () {
                _this.doRawDetectionLoop(_this)
            }, _this.updateDelayLocal);
        };
        
        /**
         * Stop face detection
         */
        FaceDetector.prototype.stop = function () {
            if (this.updateTimer) {
                clearInterval(this.updateTimer);
                this.updateTimer = undefined;
            }
            if(this._onFaceLostCallback) {
                this._onFaceLostCallback();
            }
        };

        FaceDetector.prototype.isRunning = function () {
            return this.updateTimer !== undefined;
        }

        FaceDetector.prototype.doRawDetectionLoop = function (_this) {

            //(Pay attention to call with "bind" on the caller so that following "this" points "FaceDetector")
            //var _this = this;

            //requestAnimationFrame(_this.doRawDetectionLoop.bind(_this));

            var video = _this.video;

            if (video.readyState === video.HAVE_ENOUGH_DATA && video.videoWidth > 0) {
                // - if video stream is ready

                if (!_this.detector) {
                    // - if detector is not instanciated

                    //Use the tilde (~~) to round the decimal point of the value
                    _this.detectWidth = ~~(_this.detectHeight * video.videoWidth / video.videoHeight);

                    //Once know the size of the video, prepare the detector:
                    _this.detector = new ObjectDetect.detector(_this.detectWidth, _this.detectHeight, 1.1, new FrontalFaceAlt());
                }


                if (_this._onSnapShotCallback) {
                    // - if snapshotCallback set

                    //capture image from media stream.
                    _this._snapShotContext.drawImage(video, 0, 0, _this._snapShotCanvas.width, _this._snapShotCanvas.height);
                    var snapshotImageData = _this._snapShotContext.getImageData(0, 0, _this._snapShotCanvas.width, _this._snapShotCanvas.height);

                    this._onSnapShotCallback(snapshotImageData);
                }

                // Perform the actual detection

                
                var coords = _this.detector.detect(video, 1);

                //detected faces
                var rawDetectedFaces = [];

                if (coords[0]) {
                    //- when one or more faces are found

                    var numOfFacesFound = coords.length;

                    for (var i = 0; i < numOfFacesFound; i++) {

                        //Coordinate information of the i-th face
                        var coord = coords[i];

                        var faceArea = {};

                        if (_this.flipUpsideDown) {

                            // - if do upside-down
                            // then coordinate (0,0) is set at (left,bottom)

                            if (_this.flipLeftRight) {
                                // - if flip left-right
                                // then the position that becomes the starting point of the position (x, y) is (left, bottom) of the face area.
                                faceArea = {
                                    x: (_this.detectWidth - coord[0]) - coord[2],
                                    y: (_this.detectHeight - (coord[1] + coord[3])),
                                    width: coord[2],
                                    height: coord[3],
                                    rawX: coord[0],
                                    rawY: coord[1],
                                };

                            } else {
                                //- if normal(non-mirrored)
                                faceArea = {
                                    x: coord[0],
                                    y: (_this.detectHeight - (coord[1] + coord[3])),
                                    width: coord[2],
                                    height: coord[3],
                                    rawX: coord[0],
                                    rawY: coord[1],
                                };

                            }
                        } else {

                            //- if normal(do not upside-down)

                            if (_this.flipLeftRight) {
                                // - if flip left-right
                                // then the position that becomes the starting point of the position (x, y) is (left, bottom) of the face area.
                                faceArea = {
                                    x: (_this.detectWidth - coord[0]) - coord[2],
                                    y: coord[1],
                                    width: coord[2],
                                    height: coord[3],
                                    rawX: coord[0],
                                    rawY: coord[1],
                                };

                            } else {
                                //- if normal(non-mirrored)
                                faceArea = {
                                    x: coord[0],
                                    y: coord[1],
                                    width: coord[2],
                                    height: coord[3],
                                    rawX: coord[0],
                                    rawY: coord[1],
                                };

                            }
                        }

                        //set coordinate range to (0.0-1.0)
                        faceArea.x = (faceArea.x / _this.detectWidth);
                        faceArea.y = (faceArea.y / _this.detectHeight);
                        faceArea.width = (faceArea.width / _this.detectWidth);
                        faceArea.height = (faceArea.height / _this.detectHeight);

                        if ((0 < faceArea.x && faceArea.x < 1.0 ) &&
                            (0 < faceArea.y && faceArea.y < 1.0)) {
                            //- When x, y are within the specified range
                            rawDetectedFaces.push(faceArea);
                        }

                    }

                    //Sort in ascending(small number->big number) order of x coordinates
                    rawDetectedFaces.sort(function (a, b) {
                        if (a.x < b.x) return -1;
                        if (a.x > b.x) return 1;
                        return 0;
                    });

                    _this.doTrackDetectedFaces(rawDetectedFaces);


                } else {

                    //- If there are 0 face detection
                    // return an empty array
                    _this.doTrackDetectedFaces([]);
                }
            }

        };


        /**
         * Track(follow) the movement of the face as much as possible
         * @param rawDetectedFaces (array)
         */
        FaceDetector.prototype.doTrackDetectedFaces = function (rawDetectedFaces) {
            var _this = this;

            //Number of faces detected NOW
            var numOfCrrDetectedFaces = rawDetectedFaces.length;

            //Smooth the number of detected faces by using lowpass filter(moving average filter)
            //Because a face may sometimes be lost or detect for a moment due to the influence of detection noise
            _this._lowpassFilter4faceCount.putValue(numOfCrrDetectedFaces);


            //After detecting the face for more than a certain number of times,
            // when it stabilizes, the face integration logic starts.
            if (_this._lowpassFilter4faceCount.getTotalCount() > _this.MIN_NUM_OF_PREPARE_DETECTION) {

                //Reload the process in order to take in account the new rate
                if(_this.updateDelayParam !== _this.updateDelayLocal) {
                    _this.updateDelayLocal = _this.updateDelayParam;
                    _this.startDetecting(_this.updateDelayParam);
                }

                //Calculate moving average by chronologically counting the number of detected faces, and let it be the number of faces.
                //(The reason why the weighted average is adopted for the number of faces
                // is because the face suddenly increases or decreases due to noise / chattering.
                var averageFaceCount = _this._lowpassFilter4faceCount.getFilteredValue();

                //Round the average face number, if it is 1.5 or more, make two faces visible
                var averageFaceCountRounded = Math.round(averageFaceCount);

                var newFace, cachedFace;

                var trackingFaces = [];

                var cacheKey;

                for (cacheKey in _this._cachedFaces) {

                    cachedFace = _this._cachedFaces[cacheKey];

                    //A flag indicating whether or not the position of the cached face has been updated is first set to false
                    cachedFace._isUpdatedFacePosition = false;

                    // Update cacehed face's position.
                    // The detected face's position CLOSEST to the cached face's position is considered as the destination of the cached face.
                    var updateSuccess = cachedFace.updateFace(rawDetectedFaces);

                    if (updateSuccess) {
                        // - If cached face position update success

                        // then update flag is set to true
                        cachedFace._isUpdatedFacePosition = true;
                        trackingFaces.push(cachedFace);
                    }

                }

                //When lost face for a moment
                for (cacheKey in _this._cachedFaces) {

                    cachedFace = _this._cachedFaces[cacheKey];

                    if (cachedFace._isUpdatedFacePosition == false) {
                        //- If there are faces that have not been matched yet at this point

                        //Judging that it was lost for a moment, return currently cached face.
                        trackingFaces.push(cachedFace);
                    }

                }

                //Sort
                trackingFaces.sort(function (a, b) {
                    if (a.x < b.x) return -1;
                    if (a.x > b.x) return 1;
                    return 0;
                });


                //Confirm increase and decrease of face
                if (Object.keys(_this._cachedFaces).length < averageFaceCountRounded) {
                    //- when the number of faces increases
                    // (When the number of detected faces is larger than the cached face)

                    var addedFaces = [];

                    for (var i = 0; i < numOfCrrDetectedFaces; i++) {

                        newFace = rawDetectedFaces[i];

                        if (newFace.consumed) {
                            //- When this face has already been processed (After being registered to a cached face list)
                            continue;
                        } else {
                            //- Not registered yet
                            newFace.consumed = true;
                        }

                        //create Face object for newly discovered face
                        var newFaceToCache = new Face(
                            {trackingDistanceRatio: _this.DEFAULT_MAX_MOVING_DISTANCE_RATIO}
                        );

                        newFaceToCache.updatePos(newFace.x, newFace.y);
                        newFaceToCache.updateSize(newFace.width, newFace.height);

                        //Assign the face number for tracking
                        newFaceToCache.faceId = "FACE_" + _this._ttlFaceIndex;
                        newFaceToCache.faceIndex = _this._ttlFaceIndex;

                        //Increment the total number of faces found  (Since it is not identifying individual face,
                        // even if they are the same (person's) face. So after the same person goes out of the camera screen,
                        // and once they come back again, this library regard it as different face)
                        _this._ttlFaceIndex++;

                        _this._cachedFaces[newFaceToCache.faceId] = newFaceToCache;

                        //Cache a newly discovered face
                        addedFaces.push(newFaceToCache);
                    }

                    if (_this._onFaceAddedCallback) {
                        if (addedFaces.length > 0) {
                            _this._onFaceAddedCallback(addedFaces, trackingFaces);
                        }
                    }

                } else if (Object.keys(_this._cachedFaces).length > averageFaceCountRounded) {
                    //- When the number of faces has decreased
                    // (If the number of faces detected is less than the cached face)

                    var lostFaces = [];

                    for (cacheKey in _this._cachedFaces) {

                        // "cachedFaces" means "tracking faces"
                        cachedFace = _this._cachedFaces[cacheKey];

                        if (cachedFace._isUpdatedFacePosition == false) {
                            delete _this._cachedFaces[cacheKey];
                            lostFaces.push(cachedFace);
                        }
                    }


                    if (_this._onFaceLostCallback) {
                        if (lostFaces.length > 0) {
                            _this._onFaceLostCallback(lostFaces, trackingFaces);
                        }
                    }

                    //When losing a face, stop processing here and prevent calling "onFaceUpdatecallback"
                    return;
                }

                if (_this._onFaceUpdatedCallback) {
                    if (trackingFaces.length > 0) {
                        _this._onFaceUpdatedCallback(trackingFaces);
                    }
                }

            }
        };

        /**
         *
         * Set still image snapshot callback
         * @param callbackFunc
         * @param width width of captured image
         * @param height height of captured image
         */
        FaceDetector.prototype.setOnSnapShotCallback = function (callbackFunc, width, height) {
            var _this = this;
            _this._onSnapShotCallback = callbackFunc;

            this._snapShotCanvas = document.createElement("canvas");
            this._snapShotCanvas.width = width;
            this._snapShotCanvas.height = height;
            this._snapShotContext = this._snapShotCanvas.getContext("2d");

        };

        /**
         * (Optional)
         * Detect skin area from Input Image
         * @param inputImage
         * @param outputImage detected skin-area is set to 255,non-skin-area is set to zero.
         * @returns {*}
         *
         * Example code for creating outputImage
         *  <code>
         *    var ctx = canvas.getContext("2d");
         *    var outputImage=ctx.createImageData(inputImage);
         *  </code>
         */
        FaceDetector.prototype.detectSkinArea = function (inputImage, outputImage) {
            var inp = inputImage.data, out = outputImage.data;


            var r, g, b, h, s, v, colorVal;
            var i;
            for (i = 0; i < inp.length; i += 4) {

                r = inp[i];
                g = inp[i + 1];
                b = inp[i + 2];

                var h, // 0-360
                    s, // 0.0-1.0
                    v, // 0.0-1.0
                    max = Math.max(Math.max(r, g), b),
                    min = Math.min(Math.min(r, g), b);

                if (max == min) {
                    h = 0;
                } else if (max == r) {
                    h = 60 * (g - b) / (max - min) + 0;
                } else if (max == g) {
                    h = (60 * (b - r) / (max - min)) + 120;
                } else {
                    h = (60 * (r - g) / (max - min)) + 240;
                }

                while (h < 0) {
                    h += 360;
                }
                s = (max == 0) ? 0 : (max - min) / max;//* 255;
                v = max / 255;

                colorVal = 0;

                //specify skin-color range
                if (0 < h && h < 50 && 0.23 < s && s < 0.68) {
                    colorVal = 255;
                } else {
                    colorVal = 128;
                }

                out[i] = colorVal;     //red
                out[i + 1] = colorVal; //green
                out[i + 2] = colorVal; //blue
                out[i + 3] = 255;      //alpha
            }

            outputImage.width = inputImage.width;
            outputImage.height = inputImage.height;

            return outputImage;
        };

        return FaceDetector;
    }()
);

module.exports = FaceDetector;
},{"./Face.js":4,"./objectdetect.frontalface_alt.js":6,"./objectdetect.js":7,"lowpassf":2}],6:[function(require,module,exports){
/**
 * Stump-based 20x20 gentle adaboost frontal face detector. Created by Rainer Lienhart.

 IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.

 By downloading, copying, installing or using the software you agree to this license.
 If you do not agree to this license, do not download, install,
 copy or use the software.


 Intel License Agreement
 For Open Source Computer Vision Library

 Copyright (C) 2000, Intel Corporation, all rights reserved.
 Third party copyrights are property of their respective owners.

 Redistribution and use in source and binary forms, with or without modification,
 are permitted provided that the following conditions are met:

 * Redistribution's of source code must retain the above copyright notice,
 this list of conditions and the following disclaimer.

 * Redistribution's in binary form must reproduce the above copyright notice,
 this list of conditions and the following disclaimer in the documentation
 and/or other materials provided with the distribution.

 * The name of Intel Corporation may not be used to endorse or promote products
 derived from this software without specific prior written permission.

 This software is provided by the copyright holders and contributors "as is" and
 any express or implied warranties, including, but not limited to, the implied
 warranties of merchantability and fitness for a particular purpose are disclaimed.
 In no event shall the Intel Corporation or contributors be liable for any direct,
 indirect, incidental, special, exemplary, or consequential damages
 (including, but not limited to, procurement of substitute goods or services;
 loss of use, data, or profits; or business interruption) however caused
 and on any theory of liability, whether in contract, strict liability,
 or tort (including negligence or otherwise) arising in any way out of
 the use of this software, even if advised of the possibility of such damage.
 */
var ClassifierFrontalFace=(function () {

        function ClassifierFrontalFace() {

            var classifier = [20, 20, 0.8226894140243530, 3, 0, 2, 3, 7, 14, 4, -1., 3, 9, 14, 2, 2., 4.0141958743333817e-003, 0.0337941907346249, 0.8378106951713562, 0, 2, 1, 2, 18, 4, -1., 7, 2, 6, 4, 3., 0.0151513395830989, 0.1514132022857666, 0.7488812208175659, 0, 2, 1, 7, 15, 9, -1., 1, 10, 15, 3, 3., 4.2109931819140911e-003, 0.0900492817163467, 0.6374819874763489, 6.9566087722778320, 16, 0, 2, 5, 6, 2, 6, -1., 5, 9, 2, 3, 2., 1.6227109590545297e-003, 0.0693085864186287, 0.7110946178436279, 0, 2, 7, 5, 6, 3, -1., 9, 5, 2, 3, 3., 2.2906649392098188e-003, 0.1795803010463715, 0.6668692231178284, 0, 2, 4, 0, 12, 9, -1., 4, 3, 12, 3, 3., 5.0025708042085171e-003, 0.1693672984838486, 0.6554006934165955, 0, 2, 6, 9, 10, 8, -1., 6, 13, 10, 4, 2., 7.9659894108772278e-003, 0.5866332054138184, 0.0914145186543465, 0, 2, 3, 6, 14, 8, -1., 3, 10, 14, 4, 2., -3.5227010957896709e-003, 0.1413166970014572, 0.6031895875930786, 0, 2, 14, 1, 6, 10, -1., 14, 1, 3, 10, 2., 0.0366676896810532, 0.3675672113895416, 0.7920318245887756, 0, 2, 7, 8, 5, 12, -1., 7, 12, 5, 4, 3., 9.3361474573612213e-003, 0.6161385774612427, 0.2088509947061539, 0, 2, 1, 1, 18, 3, -1., 7, 1, 6, 3, 3., 8.6961314082145691e-003, 0.2836230993270874, 0.6360273957252502, 0, 2, 1, 8, 17, 2, -1., 1, 9, 17, 1, 2., 1.1488880263641477e-003, 0.2223580926656723, 0.5800700783729553, 0, 2, 16, 6, 4, 2, -1., 16, 7, 4, 1, 2., -2.1484689787030220e-003, 0.2406464070081711, 0.5787054896354675, 0, 2, 5, 17, 2, 2, -1., 5, 18, 2, 1, 2., 2.1219060290604830e-003, 0.5559654831886292, 0.1362237036228180, 0, 2, 14, 2, 6, 12, -1., 14, 2, 3, 12, 2., -0.0939491465687752, 0.8502737283706665, 0.4717740118503571, 0, 3, 4, 0, 4, 12, -1., 4, 0, 2, 6, 2., 6, 6, 2, 6, 2., 1.3777789426967502e-003, 0.5993673801422119, 0.2834529876708984, 0, 2, 2, 11, 18, 8, -1., 8, 11, 6, 8, 3., 0.0730631574988365, 0.4341886043548584, 0.7060034275054932, 0, 2, 5, 7, 10, 2, -1., 5, 8, 10, 1, 2., 3.6767389974556863e-004, 0.3027887940406799, 0.6051574945449829, 0, 2, 15, 11, 5, 3, -1., 15, 12, 5, 1, 3., -6.0479710809886456e-003, 0.1798433959484100, 0.5675256848335266, 9.4985427856445313, 21, 0, 2, 5, 3, 10, 9, -1., 5, 6, 10, 3, 3., -0.0165106896311045, 0.6644225120544434, 0.1424857974052429, 0, 2, 9, 4, 2, 14, -1., 9, 11, 2, 7, 2., 2.7052499353885651e-003, 0.6325352191925049, 0.1288477033376694, 0, 2, 3, 5, 4, 12, -1., 3, 9, 4, 4, 3., 2.8069869149476290e-003, 0.1240288019180298, 0.6193193197250366, 0, 2, 4, 5, 12, 5, -1., 8, 5, 4, 5, 3., -1.5402400167658925e-003, 0.1432143002748489, 0.5670015811920166, 0, 2, 5, 6, 10, 8, -1., 5, 10, 10, 4, 2., -5.6386279175058007e-004, 0.1657433062791824, 0.5905207991600037, 0, 2, 8, 0, 6, 9, -1., 8, 3, 6, 3, 3., 1.9253729842603207e-003, 0.2695507109165192, 0.5738824009895325, 0, 2, 9, 12, 1, 8, -1., 9, 16, 1, 4, 2., -5.0214841030538082e-003, 0.1893538981676102, 0.5782774090766907, 0, 2, 0, 7, 20, 6, -1., 0, 9, 20, 2, 3., 2.6365420781075954e-003, 0.2309329062700272, 0.5695425868034363, 0, 2, 7, 0, 6, 17, -1., 9, 0, 2, 17, 3., -1.5127769438549876e-003, 0.2759602069854736, 0.5956642031669617, 0, 2, 9, 0, 6, 4, -1., 11, 0, 2, 4, 3., -0.0101574398577213, 0.1732538044452667, 0.5522047281265259, 0, 2, 5, 1, 6, 4, -1., 7, 1, 2, 4, 3., -0.0119536602869630, 0.1339409947395325, 0.5559014081954956, 0, 2, 12, 1, 6, 16, -1., 14, 1, 2, 16, 3., 4.8859491944313049e-003, 0.3628703951835632, 0.6188849210739136, 0, 3, 0, 5, 18, 8, -1., 0, 5, 9, 4, 2., 9, 9, 9, 4, 2., -0.0801329165697098, 0.0912110507488251, 0.5475944876670837, 0, 3, 8, 15, 10, 4, -1., 13, 15, 5, 2, 2., 8, 17, 5, 2, 2., 1.0643280111253262e-003, 0.3715142905712128, 0.5711399912834168, 0, 3, 3, 1, 4, 8, -1., 3, 1, 2, 4, 2., 5, 5, 2, 4, 2., -1.3419450260698795e-003, 0.5953313708305359, 0.3318097889423370, 0, 3, 3, 6, 14, 10, -1., 10, 6, 7, 5, 2., 3, 11, 7, 5, 2., -0.0546011403203011, 0.1844065934419632, 0.5602846145629883, 0, 2, 2, 1, 6, 16, -1., 4, 1, 2, 16, 3., 2.9071690514683723e-003, 0.3594244122505188, 0.6131715178489685, 0, 2, 0, 18, 20, 2, -1., 0, 19, 20, 1, 2., 7.4718717951327562e-004, 0.5994353294372559, 0.3459562957286835, 0, 2, 8, 13, 4, 3, -1., 8, 14, 4, 1, 3., 4.3013808317482471e-003, 0.4172652065753937, 0.6990845203399658, 0, 2, 9, 14, 2, 3, -1., 9, 15, 2, 1, 3., 4.5017572119832039e-003, 0.4509715139865875, 0.7801457047462463, 0, 2, 0, 12, 9, 6, -1., 0, 14, 9, 2, 3., 0.0241385009139776, 0.5438212752342224, 0.1319826990365982, 18.4129695892333980, 39, 0, 2, 5, 7, 3, 4, -1., 5, 9, 3, 2, 2., 1.9212230108678341e-003, 0.1415266990661621, 0.6199870705604553, 0, 2, 9, 3, 2, 16, -1., 9, 11, 2, 8, 2., -1.2748669541906565e-004, 0.6191074252128601, 0.1884928941726685, 0, 2, 3, 6, 13, 8, -1., 3, 10, 13, 4, 2., 5.1409931620582938e-004, 0.1487396955490112, 0.5857927799224854, 0, 2, 12, 3, 8, 2, -1., 12, 3, 4, 2, 2., 4.1878609918057919e-003, 0.2746909856796265, 0.6359239816665649, 0, 2, 8, 8, 4, 12, -1., 8, 12, 4, 4, 3., 5.1015717908740044e-003, 0.5870851278305054, 0.2175628989934921, 0, 3, 11, 3, 8, 6, -1., 15, 3, 4, 3, 2., 11, 6, 4, 3, 2., -2.1448440384119749e-003, 0.5880944728851318, 0.2979590892791748, 0, 2, 7, 1, 6, 19, -1., 9, 1, 2, 19, 3., -2.8977119363844395e-003, 0.2373327016830444, 0.5876647233963013, 0, 2, 9, 0, 6, 4, -1., 11, 0, 2, 4, 3., -0.0216106791049242, 0.1220654994249344, 0.5194202065467835, 0, 2, 3, 1, 9, 3, -1., 6, 1, 3, 3, 3., -4.6299318782985210e-003, 0.2631230950355530, 0.5817409157752991, 0, 3, 8, 15, 10, 4, -1., 13, 15, 5, 2, 2., 8, 17, 5, 2, 2., 5.9393711853772402e-004, 0.3638620078563690, 0.5698544979095459, 0, 2, 0, 3, 6, 10, -1., 3, 3, 3, 10, 2., 0.0538786612451077, 0.4303531050682068, 0.7559366226196289, 0, 2, 3, 4, 15, 15, -1., 3, 9, 15, 5, 3., 1.8887349870055914e-003, 0.2122603058815002, 0.5613427162170410, 0, 2, 6, 5, 8, 6, -1., 6, 7, 8, 2, 3., -2.3635339457541704e-003, 0.5631849169731140, 0.2642767131328583, 0, 3, 4, 4, 12, 10, -1., 10, 4, 6, 5, 2., 4, 9, 6, 5, 2., 0.0240177996456623, 0.5797107815742493, 0.2751705944538117, 0, 2, 6, 4, 4, 4, -1., 8, 4, 2, 4, 2., 2.0543030404951423e-004, 0.2705242037773132, 0.5752568840980530, 0, 2, 15, 11, 1, 2, -1., 15, 12, 1, 1, 2., 8.4790197433903813e-004, 0.5435624718666077, 0.2334876954555512, 0, 2, 3, 11, 2, 2, -1., 3, 12, 2, 1, 2., 1.4091329649090767e-003, 0.5319424867630005, 0.2063155025243759, 0, 2, 16, 11, 1, 3, -1., 16, 12, 1, 1, 3., 1.4642629539594054e-003, 0.5418980717658997, 0.3068861067295075, 0, 3, 3, 15, 6, 4, -1., 3, 15, 3, 2, 2., 6, 17, 3, 2, 2., 1.6352549428120255e-003, 0.3695372939109802, 0.6112868189811707, 0, 2, 6, 7, 8, 2, -1., 6, 8, 8, 1, 2., 8.3172752056270838e-004, 0.3565036952495575, 0.6025236248970032, 0, 2, 3, 11, 1, 3, -1., 3, 12, 1, 1, 3., -2.0998890977352858e-003, 0.1913982033729553, 0.5362827181816101, 0, 2, 6, 0, 12, 2, -1., 6, 1, 12, 1, 2., -7.4213981861248612e-004, 0.3835555016994476, 0.5529310107231140, 0, 2, 9, 14, 2, 3, -1., 9, 15, 2, 1, 3., 3.2655049581080675e-003, 0.4312896132469177, 0.7101895809173584, 0, 2, 7, 15, 6, 2, -1., 7, 16, 6, 1, 2., 8.9134991867467761e-004, 0.3984830975532532, 0.6391963958740234, 0, 2, 0, 5, 4, 6, -1., 0, 7, 4, 2, 3., -0.0152841797098517, 0.2366732954978943, 0.5433713793754578, 0, 2, 4, 12, 12, 2, -1., 8, 12, 4, 2, 3., 4.8381411470472813e-003, 0.5817500948905945, 0.3239189088344574, 0, 2, 6, 3, 1, 9, -1., 6, 6, 1, 3, 3., -9.1093179071322083e-004, 0.5540593862533569, 0.2911868989467621, 0, 2, 10, 17, 3, 2, -1., 11, 17, 1, 2, 3., -6.1275060288608074e-003, 0.1775255054235458, 0.5196629166603088, 0, 2, 9, 9, 2, 2, -1., 9, 10, 2, 1, 2., -4.4576259097084403e-004, 0.3024170100688934, 0.5533593893051148, 0, 2, 7, 6, 6, 4, -1., 9, 6, 2, 4, 3., 0.0226465407758951, 0.4414930939674377, 0.6975377202033997, 0, 2, 7, 17, 3, 2, -1., 8, 17, 1, 2, 3., -1.8804960418492556e-003, 0.2791394889354706, 0.5497952103614807, 0, 2, 10, 17, 3, 3, -1., 11, 17, 1, 3, 3., 7.0889107882976532e-003, 0.5263199210166931, 0.2385547012090683, 0, 2, 8, 12, 3, 2, -1., 8, 13, 3, 1, 2., 1.7318050377070904e-003, 0.4319379031658173, 0.6983600854873657, 0, 2, 9, 3, 6, 2, -1., 11, 3, 2, 2, 3., -6.8482700735330582e-003, 0.3082042932510376, 0.5390920042991638, 0, 2, 3, 11, 14, 4, -1., 3, 13, 14, 2, 2., -1.5062530110299122e-005, 0.5521922111511231, 0.3120366036891937, 0, 3, 1, 10, 18, 4, -1., 10, 10, 9, 2, 2., 1, 12, 9, 2, 2., 0.0294755697250366, 0.5401322841644287, 0.1770603060722351, 0, 2, 0, 10, 3, 3, -1., 0, 11, 3, 1, 3., 8.1387329846620560e-003, 0.5178617835044861, 0.1211019009351730, 0, 2, 9, 1, 6, 6, -1., 11, 1, 2, 6, 3., 0.0209429506212473, 0.5290294289588928, 0.3311221897602081, 0, 2, 8, 7, 3, 6, -1., 9, 7, 1, 6, 3., -9.5665529370307922e-003, 0.7471994161605835, 0.4451968967914581, 15.3241395950317380, 33, 0, 2, 1, 0, 18, 9, -1., 1, 3, 18, 3, 3., -2.8206960996612906e-004, 0.2064086049795151, 0.6076732277870178, 0, 2, 12, 10, 2, 6, -1., 12, 13, 2, 3, 2., 1.6790600493550301e-003, 0.5851997137069702, 0.1255383938550949, 0, 2, 0, 5, 19, 8, -1., 0, 9, 19, 4, 2., 6.9827912375330925e-004, 0.0940184295177460, 0.5728961229324341, 0, 2, 7, 0, 6, 9, -1., 9, 0, 2, 9, 3., 7.8959012171253562e-004, 0.1781987994909287, 0.5694308876991272, 0, 2, 5, 3, 6, 1, -1., 7, 3, 2, 1, 3., -2.8560499195009470e-003, 0.1638399064540863, 0.5788664817810059, 0, 2, 11, 3, 6, 1, -1., 13, 3, 2, 1, 3., -3.8122469559311867e-003, 0.2085440009832382, 0.5508564710617065, 0, 2, 5, 10, 4, 6, -1., 5, 13, 4, 3, 2., 1.5896620461717248e-003, 0.5702760815620422, 0.1857215017080307, 0, 2, 11, 3, 6, 1, -1., 13, 3, 2, 1, 3., 0.0100783398374915, 0.5116943120956421, 0.2189770042896271, 0, 2, 4, 4, 12, 6, -1., 4, 6, 12, 2, 3., -0.0635263025760651, 0.7131379842758179, 0.4043813049793243, 0, 2, 15, 12, 2, 6, -1., 15, 14, 2, 2, 3., -9.1031491756439209e-003, 0.2567181885242462, 0.5463973283767700, 0, 2, 9, 3, 2, 2, -1., 10, 3, 1, 2, 2., -2.4035000242292881e-003, 0.1700665950775147, 0.5590974092483521, 0, 2, 9, 3, 3, 1, -1., 10, 3, 1, 1, 3., 1.5226360410451889e-003, 0.5410556793212891, 0.2619054019451141, 0, 2, 1, 1, 4, 14, -1., 3, 1, 2, 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-0.0116449799388647, 0.3300260007381439, 0.5049629807472229, 0, 2, 8, 15, 4, 3, -1., 8, 16, 4, 1, 3., 0.0157643090933561, 0.4991598129272461, 0.7321153879165649, 0, 2, 8, 0, 3, 7, -1., 9, 0, 1, 7, 3., -1.3611479662358761e-003, 0.3911735117435455, 0.5160670876502991, 0, 2, 9, 5, 3, 4, -1., 10, 5, 1, 4, 3., -8.1522337859496474e-004, 0.5628911256790161, 0.4949719011783600, 0, 2, 8, 5, 3, 4, -1., 9, 5, 1, 4, 3., -6.0066272271797061e-004, 0.5853595137596130, 0.4550595879554749, 0, 2, 7, 6, 6, 1, -1., 9, 6, 2, 1, 3., 4.9715518252924085e-004, 0.4271470010280609, 0.5443599224090576, 0, 3, 7, 14, 4, 4, -1., 7, 14, 2, 2, 2., 9, 16, 2, 2, 2., 2.3475370835512877e-003, 0.5143110752105713, 0.3887656927108765, 0, 3, 13, 14, 4, 6, -1., 15, 14, 2, 3, 2., 13, 17, 2, 3, 2., -8.9261569082736969e-003, 0.6044502258300781, 0.4971720874309540, 0, 2, 7, 8, 1, 8, -1., 7, 12, 1, 4, 2., -0.0139199104160070, 0.2583160996437073, 0.5000367760658264, 0, 3, 16, 0, 2, 8, -1., 17, 0, 1, 4, 2., 16, 4, 1, 4, 2., 1.0209949687123299e-003, 0.4857374131679535, 0.5560358166694641, 0, 3, 2, 0, 2, 8, -1., 2, 0, 1, 4, 2., 3, 4, 1, 4, 2., -2.7441629208624363e-003, 0.5936884880065918, 0.4645777046680450, 0, 2, 6, 1, 14, 3, -1., 6, 2, 14, 1, 3., -0.0162001308053732, 0.3163014948368073, 0.5193495154380798, 0, 2, 7, 9, 3, 10, -1., 7, 14, 3, 5, 2., 4.3331980705261230e-003, 0.5061224102973938, 0.3458878993988037, 0, 2, 9, 14, 2, 2, -1., 9, 15, 2, 1, 2., 5.8497930876910686e-004, 0.4779017865657806, 0.5870177745819092, 0, 2, 7, 7, 6, 8, -1., 7, 11, 6, 4, 2., -2.2466450463980436e-003, 0.4297851026058197, 0.5374773144721985, 0, 2, 9, 7, 3, 6, -1., 9, 10, 3, 3, 2., 2.3146099410951138e-003, 0.5438671708106995, 0.4640969932079315, 0, 2, 7, 13, 3, 3, -1., 7, 14, 3, 1, 3., 8.7679121643304825e-003, 0.4726893007755280, 0.6771789789199829, 0, 2, 9, 9, 2, 2, -1., 9, 10, 2, 1, 2., -2.2448020172305405e-004, 0.4229173064231873, 0.5428048968315125, 0, 2, 0, 1, 18, 2, -1., 6, 1, 6, 2, 3., -7.4336021207273006e-003, 0.6098880767822266, 0.4683673977851868, 0, 2, 7, 1, 6, 14, -1., 7, 8, 6, 7, 2., -2.3189240600913763e-003, 0.5689436793327332, 0.4424242079257965, 0, 2, 1, 9, 18, 1, -1., 7, 9, 6, 1, 3., -2.1042178850620985e-003, 0.3762221038341522, 0.5187087059020996, 0, 2, 9, 7, 2, 2, -1., 9, 7, 1, 2, 2., 4.6034841216169298e-004, 0.4699405133724213, 0.5771207213401794, 0, 2, 9, 3, 2, 9, -1., 10, 3, 1, 9, 2., 1.0547629790380597e-003, 0.4465216994285584, 0.5601701736450195, 0, 2, 18, 14, 2, 3, -1., 18, 15, 2, 1, 3., 8.7148818420246243e-004, 0.5449805259704590, 0.3914709091186523, 0, 2, 7, 11, 3, 1, -1., 8, 11, 1, 1, 3., 3.3364820410497487e-004, 0.4564009010791779, 0.5645738840103149, 0, 2, 10, 8, 3, 4, -1., 11, 8, 1, 4, 3., -1.4853250468149781e-003, 0.5747377872467041, 0.4692778885364533, 0, 2, 7, 14, 3, 6, -1., 8, 14, 1, 6, 3., 3.0251620337367058e-003, 0.5166196823120117, 0.3762814104557037, 0, 2, 10, 8, 3, 4, -1., 11, 8, 1, 4, 3., 5.0280741415917873e-003, 0.5002111792564392, 0.6151527166366577, 0, 2, 7, 8, 3, 4, -1., 8, 8, 1, 4, 3., -5.8164511574432254e-004, 0.5394598245620728, 0.4390751123428345, 0, 2, 7, 9, 6, 9, -1., 7, 12, 6, 3, 3., 0.0451415292918682, 0.5188326835632324, 0.2063035964965820, 0, 2, 0, 14, 2, 3, -1., 0, 15, 2, 1, 3., -1.0795620037242770e-003, 0.3904685080051422, 0.5137907266616821, 0, 2, 11, 12, 1, 2, -1., 11, 13, 1, 1, 2., 1.5995999274309725e-004, 0.4895322918891907, 0.5427504181861877, 0, 2, 4, 3, 8, 3, -1., 8, 3, 4, 3, 2., -0.0193592701107264, 0.6975228786468506, 0.4773507118225098, 0, 2, 0, 4, 20, 6, -1., 0, 4, 10, 6, 2., 0.2072550952434540, 0.5233635902404785, 0.3034991919994354, 0, 2, 9, 14, 1, 3, -1., 9, 15, 1, 1, 3., -4.1953290929086506e-004, 0.5419396758079529, 0.4460186064243317, 0, 2, 8, 14, 4, 3, -1., 8, 15, 4, 1, 3., 2.2582069505006075e-003, 0.4815764129161835, 0.6027408838272095, 0, 2, 0, 15, 14, 4, -1., 0, 17, 14, 2, 2., -6.7811207845807076e-003, 0.3980278968811035, 0.5183305740356445, 0, 2, 1, 14, 18, 6, -1., 1, 17, 18, 3, 2., 0.0111543098464608, 0.5431231856346130, 0.4188759922981262, 0, 3, 0, 0, 10, 6, -1., 0, 0, 5, 3, 2., 5, 3, 5, 3, 2., 0.0431624315679073, 0.4738228023052216, 0.6522961258888245];

            this.data = new Float32Array(classifier);
            this.tilted = false;
        }

        return ClassifierFrontalFace;
    }()
);
module.exports = ClassifierFrontalFace;
},{}],7:[function(require,module,exports){
/**
 * Real-time object detector based on the Viola Jones Framework.
 * Compatible to OpenCV Haar Cascade Classifiers (stump based only).
 *
 * Copyright (c) 2012, Martin Tschirsich
 */
var od = (function () {
    "use strict";

    var /**
         * Converts from a 4-channel RGBA source image to a 1-channel grayscale
         * image. Corresponds to the CV_RGB2GRAY OpenCV color space conversion.
         *
         * @param {Array} src   4-channel 8-bit source image
         * @param {Array} [dst] 1-channel 32-bit destination image
         *
         * @return {Array} 1-channel 32-bit destination image
         */
        convertRgbaToGrayscale = function (src, dst) {
            var srcLength = src.length;
            if (!dst) dst = new Uint32Array(srcLength >> 2);

            for (var i = 0; i < srcLength; i += 2) {
                dst[i >> 2] = (src[i] * 4899 + src[++i] * 9617 + src[++i] * 1868 + 8192) >> 14;
            }
            return dst;
        },

        /**
         * Reduces the size of a given image by the given factor. Does NOT
         * perform interpolation. If interpolation is required, prefer using
         * the drawImage() method of the <canvas> element.
         *
         * @param {Array}  src       1-channel source image
         * @param {Number} srcWidth     Width of the source image
         * @param {Number} srcHeight Height of the source image
         * @param {Number} factor    Scaling down factor (> 1.0)
         * @param {Array}  [dst]     1-channel destination image
         *
         * @return {Array} 1-channel destination image
         */
        rescaleImage = function (src, srcWidth, srcHeight, factor, dst) {
            var srcLength = srcHeight * srcWidth,
                dstWidth = ~~(srcWidth / factor),
                dstHeight = ~~(srcHeight / factor);

            if (!dst) dst = new src.constructor(dstWidth * srcHeight);

            for (var x = 0; x < dstWidth; ++x) {
                var dstIndex = x;
                for (var srcIndex = ~~(x * factor), srcEnd = srcIndex + srcLength; srcIndex < srcEnd; srcIndex += srcWidth) {
                    dst[dstIndex] = src[srcIndex];
                    dstIndex += dstWidth;
                }
            }

            var dstIndex = 0;
            for (var y = 0, yEnd = dstHeight * factor; y < yEnd; y += factor) {
                for (var srcIndex = ~~y * dstWidth, srcEnd = srcIndex + dstWidth; srcIndex < srcEnd; ++srcIndex) {
                    dst[dstIndex] = dst[srcIndex];
                    ++dstIndex;
                }
            }
            return dst;
        },

        /**
         * Horizontally mirrors a 1-channel source image.
         *
         * @param {Array}  src       1-channel source image
         * @param {Number} srcWidth  Width of the source image
         * @param {Number} srcHeight Height of the source image
         * @param {Array} [dst]      1-channel destination image
         *
         * @return {Array} 1-channel destination image
         */
        mirrorImage = function (src, srcWidth, srcHeight, dst) {
            if (!dst) dst = new src.constructor(srcWidth * srcHeight);

            var index = 0;
            for (var y = 0; y < srcHeight; ++y) {
                for (var x = (srcWidth >> 1); x >= 0; --x) {
                    var swap = src[index + x];
                    dst[index + x] = src[index + srcWidth - 1 - x];
                    dst[index + srcWidth - 1 - x] = swap;
                }
                index += srcWidth;
            }
            return dst;
        },

        /**
         * Computes the gradient magnitude using a sobel filter after
         * applying gaussian smoothing (5x5 filter size). Useful for canny
         * pruning.
         *
         * @param {Array}  src      1-channel source image
         * @param {Number} srcWidth Width of the source image
         * @param {Number} srcWidth Height of the source image
         * @param {Array}  [dst]    1-channel destination image
         *
         * @return {Array} 1-channel destination image
         */
        computeCanny = function (src, srcWidth, srcHeight, dst) {
            var srcLength = srcWidth * srcHeight;
            if (!dst) dst = new src.constructor(srcLength);
            var buffer1 = dst === src ? new src.constructor(srcLength) : dst;
            var buffer2 = new src.constructor(srcLength);

            // Gaussian filter with size=5, sigma=sqrt(2) horizontal pass:
            for (var x = 2; x < srcWidth - 2; ++x) {
                var index = x;
                for (var y = 0; y < srcHeight; ++y) {
                    buffer1[index] =
                        0.1117 * src[index - 2] +
                        0.2365 * src[index - 1] +
                        0.3036 * src[index] +
                        0.2365 * src[index + 1] +
                        0.1117 * src[index + 2];
                    index += srcWidth;
                }
            }

            // Gaussian filter with size=5, sigma=sqrt(2) vertical pass:
            for (var x = 0; x < srcWidth; ++x) {
                var index = x + srcWidth;
                for (var y = 2; y < srcHeight - 2; ++y) {
                    index += srcWidth;
                    buffer2[index] =
                        0.1117 * buffer1[index - srcWidth - srcWidth] +
                        0.2365 * buffer1[index - srcWidth] +
                        0.3036 * buffer1[index] +
                        0.2365 * buffer1[index + srcWidth] +
                        0.1117 * buffer1[index + srcWidth + srcWidth];
                }
            }

            // Compute gradient:
            var abs = Math.abs;
            for (var x = 2; x < srcWidth - 2; ++x) {
                var index = x + srcWidth;
                for (var y = 2; y < srcHeight - 2; ++y) {
                    index += srcWidth;

                    dst[index] =
                        abs(-buffer2[index - 1 - srcWidth]
                            + buffer2[index + 1 - srcWidth]
                            - 2 * buffer2[index - 1]
                            + 2 * buffer2[index + 1]
                            - buffer2[index - 1 + srcWidth]
                            + buffer2[index + 1 + srcWidth]) +

                        abs(buffer2[index - 1 - srcWidth]
                            + 2 * buffer2[index - srcWidth]
                            + buffer2[index + 1 - srcWidth]
                            - buffer2[index - 1 + srcWidth]
                            - 2 * buffer2[index + srcWidth]
                            - buffer2[index + 1 + srcWidth]);
                }
            }
            return dst;
        },

        /**
         * Computes the integral image of a 1-channel image. Arithmetic
         * overflow may occur if the integral exceeds the limits for the
         * destination image values ([0, 2^32-1] for an unsigned 32-bit image).
         * The integral image is 1 pixel wider both in vertical and horizontal
         * dimension compared to the source image.
         *
         * SAT = Summed Area Table.
         *
         * @param {Array}       src       1-channel source image
         * @param {Number}      srcWidth  Width of the source image
         * @param {Number}      srcHeight Height of the source image
         * @param {Uint32Array} [dst]     1-channel destination image
         *
         * @return {Uint32Array} 1-channel destination image
         */
        computeSat = function (src, srcWidth, srcHeight, dst) {
            var dstWidth = srcWidth + 1;

            if (!dst) dst = new Uint32Array(srcWidth * srcHeight + dstWidth + srcHeight);

            for (var i = srcHeight * dstWidth; i >= 0; i -= dstWidth)
                dst[i] = 0;

            for (var x = 1; x <= srcWidth; ++x) {
                var column_sum = 0;
                var index = x;
                dst[x] = 0;

                for (var y = 1; y <= srcHeight; ++y) {
                    column_sum += src[index - y];
                    index += dstWidth;
                    dst[index] = dst[index - 1] + column_sum;
                }
            }
            return dst;
        },

        /**
         * Computes the squared integral image of a 1-channel image.
         * @see computeSat()
         *
         * @param {Array}       src       1-channel source image
         * @param {Number}      srcWidth  Width of the source image
         * @param {Number}      srcHeight Height of the source image
         * @param {Uint32Array} [dst]     1-channel destination image
         *
         * @return {Uint32Array} 1-channel destination image
         */
        computeSquaredSat = function (src, srcWidth, srcHeight, dst) {
            var dstWidth = srcWidth + 1;

            if (!dst) dst = new Uint32Array(srcWidth * srcHeight + dstWidth + srcHeight);

            for (var i = srcHeight * dstWidth; i >= 0; i -= dstWidth)
                dst[i] = 0;

            for (var x = 1; x <= srcWidth; ++x) {
                var column_sum = 0;
                var index = x;
                dst[x] = 0;
                for (var y = 1; y <= srcHeight; ++y) {
                    var val = src[index - y];
                    column_sum += val * val;
                    index += dstWidth;
                    dst[index] = dst[index - 1] + column_sum;
                }
            }
            return dst;
        },

        /**
         * Computes the rotated / tilted integral image of a 1-channel image.
         * @see computeSat()
         *
         * @param {Array}       src       1-channel source image
         * @param {Number}      srcWidth  Width of the source image
         * @param {Number}      srcHeight Height of the source image
         * @param {Uint32Array} [dst]     1-channel destination image
         *
         * @return {Uint32Array} 1-channel destination image
         */
        computeRsat = function (src, srcWidth, srcHeight, dst) {
            var dstWidth = srcWidth + 1,
                srcHeightTimesDstWidth = srcHeight * dstWidth;

            if (!dst) dst = new Uint32Array(srcWidth * srcHeight + dstWidth + srcHeight);

            for (var i = srcHeightTimesDstWidth; i >= 0; i -= dstWidth)
                dst[i] = 0;

            for (var i = 0; i < dstWidth; ++i)
                dst[i] = 0;

            var index = 0;
            for (var y = 0; y < srcHeight; ++y) {
                for (var x = 0; x < srcWidth; ++x) {
                    dst[index + dstWidth + 1] = src[index - y] + dst[index];
                    ++index;
                }
                dst[index + dstWidth] += dst[index];
                index++;
            }

            for (var x = srcWidth - 1; x > 0; --x) {
                var index = x + srcHeightTimesDstWidth;
                for (var y = srcHeight; y > 0; --y) {
                    index -= dstWidth;
                    dst[index + dstWidth] += dst[index] + dst[index + 1];
                }
            }

            return dst;
        },

        /**
         * Equalizes the histogram of an unsigned 1-channel image with integer
         * values in [0, 255]. Corresponds to the equalizeHist OpenCV function.
         *
         * @param {Array}  src   1-channel integer source image
         * @param {Number} step  Sampling stepsize, increase for performance
         * @param {Array}  [dst] 1-channel destination image
         *
         * @return {Array} 1-channel destination image
         */
        equalizeHistogram = function (src, step, dst) {
            var srcLength = src.length;
            if (!dst) dst = src;
            if (!step) step = 5;

            // Compute histogram and histogram sum:
            var hist = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0];

            for (var i = 0; i < srcLength; i += step) {
                ++hist[src[i]];
            }

            // Compute integral histogram:
            var norm = 255 * step / srcLength,
                prev = 0;
            for (var i = 0; i < 256; ++i) {
                var h = hist[i];
                prev = h += prev;
                hist[i] = h * norm; // For non-integer src: ~~(h * norm + 0.5);
            }

            // Equalize image:
            for (var i = 0; i < srcLength; ++i) {
                dst[i] = hist[src[i]];
            }
            return dst;
        },

        /**
         * Horizontally mirrors a cascase classifier. Useful to detect mirrored
         * objects such as opposite hands.
         *
         * @param {Array} dst Cascade classifier
         *
         * @return {Array} Mirrored cascade classifier
         */
        mirrorClassifier = function (src, dst) {
            if (!dst) dst = new src.constructor(src);
            var windowWidth = src[0];

            for (var i = 1, iEnd = src.length - 1; i < iEnd;) {
                ++i;
                for (var j = 0, jEnd = src[++i]; j < jEnd; ++j) {
                    if (src[++i]) {
                        // Simple classifier is tilted:
                        for (var kEnd = i + src[++i] * 5; i < kEnd;) {
                            dst[i + 1] = windowWidth - src[i + 1];
                            var width = src[i + 3];
                            dst[i + 3] = src[i + 4];
                            dst[i + 4] = width;
                            i += 5;
                        }
                    } else {
                        // Simple classifier is not tilted:
                        for (var kEnd = i + src[++i] * 5; i < kEnd;) {
                            dst[i + 1] = windowWidth - src[i + 1] - src[i + 3];
                            i += 5;
                        }
                    }
                    i += 3;
                }
            }
            return dst;
        },

        /**
         * Compiles a cascade classifier to be applicable to images
         * of given dimensions. Speeds-up the actual detection process later on.
         *
         * @param {Array}        src    Cascade classifier
         * @param {Number}       width  Width of the source image
         * @param {Float32Array} [dst]  Compiled cascade classifier
         *
         * @return {Float32Array} Compiled cascade classifier
         */
        compileClassifier = function (src, width, scale, dst) {
            width += 1;
            if (!dst) dst = new Float32Array(src.length);
            var dstUint32 = new Uint32Array(dst.buffer);

            dstUint32[0] = src[0];
            dstUint32[1] = src[1];
            var dstIndex = 1;
            for (var srcIndex = 1, iEnd = src.length - 1; srcIndex < iEnd;) {
                dst[++dstIndex] = src[++srcIndex];

                var numComplexClassifiers = dstUint32[++dstIndex] = src[++srcIndex];
                for (var j = 0, jEnd = numComplexClassifiers; j < jEnd; ++j) {

                    var tilted = dst[++dstIndex] = src[++srcIndex];
                    var numFeaturesTimes3 = dstUint32[++dstIndex] = src[++srcIndex] * 3;
                    if (tilted) {
                        for (var kEnd = dstIndex + numFeaturesTimes3; dstIndex < kEnd;) {
                            dstUint32[++dstIndex] = src[++srcIndex] + src[++srcIndex] * width;
                            dstUint32[++dstIndex] = src[++srcIndex] * (width + 1) + ((src[++srcIndex] * (width - 1)) << 16);
                            dst[++dstIndex] = src[++srcIndex];
                        }
                    } else {
                        for (var kEnd = dstIndex + numFeaturesTimes3; dstIndex < kEnd;) {
                            dstUint32[++dstIndex] = src[++srcIndex] + src[++srcIndex] * width;
                            dstUint32[++dstIndex] = src[++srcIndex] + ((src[++srcIndex] * width) << 16);
                            dst[++dstIndex] = src[++srcIndex];
                        }
                    }

                    var inverseClassifierThreshold = 1 / src[++srcIndex];
                    for (var k = 0; k < numFeaturesTimes3;) {
                        dst[dstIndex - k] *= inverseClassifierThreshold;
                        k += 3;
                    }

                    if (inverseClassifierThreshold < 0) {
                        dst[dstIndex + 2] = src[++srcIndex];
                        dst[dstIndex + 1] = src[++srcIndex];
                        dstIndex += 2;
                    } else {
                        dst[++dstIndex] = src[++srcIndex];
                        dst[++dstIndex] = src[++srcIndex];
                    }
                }
            }
            return dst.subarray(0, dstIndex + 1);
        },

        /**
         * Evaluates a compiled cascade classifier. Sliding window approach.
         *
         * @param {Uint32Array}  sat        SAT of the source image
         * @param {Uint32Array}  rsat       Rotated SAT of the source image
         * @param {Uint32Array}  ssat       Squared SAT of the source image
         * @param {Uint32Array}  [cannySat] SAT of the canny source image
         * @param {Number}       width      Width of the source image
         * @param {Number}       height     Height of the source image
         * @param {Number}       step       Stepsize, increase for performance
         * @param {Float32Array} classifier Compiled cascade classifier
         *
         * @return {Array} Rectangles representing detected objects
         */
        detect = function (sat, rsat, ssat, cannySat, width, height, step, classifier) {
            width += 1;
            height += 1;

            var classifierUint32 = new Uint32Array(classifier.buffer),
                windowWidth = classifierUint32[0],
                windowHeight = classifierUint32[1],
                windowHeightTimesWidth = windowHeight * width,
                area = windowWidth * windowHeight,
                inverseArea = 1 / area,
                widthTimesStep = width * step,
                rects = [];

            for (var x = 0; x + windowWidth < width; x += step) {
                var satIndex = x;
                for (var y = 0; y + windowHeight < height; y += step) {
                    var satIndex1 = satIndex + windowWidth,
                        satIndex2 = satIndex + windowHeightTimesWidth,
                        satIndex3 = satIndex2 + windowWidth,
                        canny = false;

                    // Canny test:
                    if (cannySat) {
                        var edgesDensity = (cannySat[satIndex] -
                            cannySat[satIndex1] -
                            cannySat[satIndex2] +
                            cannySat[satIndex3]) * inverseArea;
                        if (edgesDensity < 60 || edgesDensity > 200) {
                            canny = true;
                            satIndex += widthTimesStep;
                            continue;
                        }
                    }

                    // Normalize mean and variance of window area:
                    var mean = (sat[satIndex] -
                        sat[satIndex1] -
                        sat[satIndex2] +
                        sat[satIndex3]),

                        variance = (ssat[satIndex] -
                            ssat[satIndex1] -
                            ssat[satIndex2] +
                            ssat[satIndex3]) * area - mean * mean,

                        std = variance > 1 ? Math.sqrt(variance) : 1,
                        found = true;

                    // Evaluate cascade classifier aka 'stages':
                    for (var i = 1, iEnd = classifier.length - 1; i < iEnd;) {
                        var complexClassifierThreshold = classifier[++i];
                        // Evaluate complex classifiers aka 'trees':
                        var complexClassifierSum = 0;
                        for (var j = 0, jEnd = classifierUint32[++i]; j < jEnd; ++j) {

                            // Evaluate simple classifiers aka 'nodes':
                            var simpleClassifierSum = 0;

                            if (classifierUint32[++i]) {
                                // Simple classifier is tilted:
                                for (var kEnd = i + classifierUint32[++i]; i < kEnd;) {
                                    var f1 = satIndex + classifierUint32[++i],
                                        packed = classifierUint32[++i],
                                        f2 = f1 + (packed & 0xFFFF),
                                        f3 = f1 + (packed >> 16 & 0xFFFF);

                                    simpleClassifierSum += classifier[++i] *
                                        (rsat[f1] - rsat[f2] - rsat[f3] + rsat[f2 + f3 - f1]);
                                }
                            } else {
                                // Simple classifier is not tilted:
                                for (var kEnd = i + classifierUint32[++i]; i < kEnd;) {
                                    var f1 = satIndex + classifierUint32[++i],
                                        packed = classifierUint32[++i],
                                        f2 = f1 + (packed & 0xFFFF),
                                        f3 = f1 + (packed >> 16 & 0xFFFF);

                                    simpleClassifierSum += classifier[++i] *
                                        (sat[f1] - sat[f2] - sat[f3] + sat[f2 + f3 - f1]);
                                }
                            }
                            complexClassifierSum += classifier[i + (simpleClassifierSum > std ? 2 : 1)];
                            i += 2;
                        }
                        if (complexClassifierSum < complexClassifierThreshold) {
                            found = false;
                            break;
                        }
                    }
                    if (found) rects.push([x, y, windowWidth, windowHeight]);
                    satIndex += widthTimesStep;
                }
            }
            return rects;
        },

        /**
         * Groups rectangles together using a rectilinear distance metric. For
         * each group of related rectangles, a representative mean rectangle
         * is returned.
         *
         * @param {Array}  rects        Rectangles (Arrays of 4 floats)
         * @param {Number} minNeighbors Minimum neighbors for returned groups
         * @param {Number} confluence    Neighbor distance threshold factor
         * @return {Array} Mean rectangles (Arrays of 4 floats)
         */
        groupRectangles = function (rects, minNeighbors, confluence) {
            var rectsLength = rects.length;
            if (!confluence) confluence = 0.25;

            // Partition rects into similarity classes:
            var numClasses = 0;
            var labels = new Array(rectsLength);
            for (var i = 0; i < rectsLength; ++i) {
                labels[i] = 0;
            }

            var abs = Math.abs, min = Math.min;
            for (var i = 0; i < rectsLength; ++i) {
                var found = false;
                for (var j = 0; j < i; ++j) {

                    // Determine similarity:
                    var rect1 = rects[i];
                    var rect2 = rects[j];
                    var delta = confluence * (min(rect1[2], rect2[2]) + min(rect1[3], rect2[3]));
                    if (abs(rect1[0] - rect2[0]) <= delta &&
                        abs(rect1[1] - rect2[1]) <= delta &&
                        abs(rect1[0] + rect1[2] - rect2[0] - rect2[2]) <= delta &&
                        abs(rect1[1] + rect1[3] - rect2[1] - rect2[3]) <= delta) {

                        labels[i] = labels[j];
                        found = true;
                        break;
                    }
                }
                if (!found) {
                    labels[i] = numClasses++;
                }
            }

            // Compute average rectangle (group) for each cluster:
            var groups = new Array(numClasses);

            for (var i = 0; i < numClasses; ++i) {
                groups[i] = [0, 0, 0, 0, 0];
            }

            for (var i = 0; i < rectsLength; ++i) {
                var rect = rects[i],
                    group = groups[labels[i]];
                group[0] += rect[0];
                group[1] += rect[1];
                group[2] += rect[2];
                group[3] += rect[3];
                ++group[4];
            }

            for (var i = 0; i < numClasses; ++i) {
                var numNeighbors = groups[i][4];
                if (numNeighbors >= minNeighbors) {
                    var group = groups[i];
                    numNeighbors = 1 / numNeighbors;
                    group[0] *= numNeighbors;
                    group[1] *= numNeighbors;
                    group[2] *= numNeighbors;
                    group[3] *= numNeighbors;
                } else groups.splice(i, 1);
            }

            // Filter out small rectangles inside larger rectangles:
            var filteredGroups = [];
            for (var i = 0; i < numClasses; ++i) {
                var r1 = groups[i];

                for (var j = i + 1; j < numClasses; ++j) {
                    var r2 = groups[j];

                    var dx = r2[2] * confluence;// * 0.2;
                    var dy = r2[3] * confluence;// * 0.2;

                    // Not antisymmetric, must check both r1 > r2 and r2 > r1:
                    if ((r1[0] >= r2[0] - dx &&
                            r1[1] >= r2[1] - dy &&
                            r1[0] + r1[2] <= r2[0] + r2[2] + dx &&
                            r1[1] + r1[3] <= r2[1] + r2[3] + dy) ||
                        (r2[0] >= r1[0] - dx &&
                            r2[1] >= r1[1] - dy &&
                            r2[0] + r2[2] <= r1[0] + r1[2] + dx &&
                            r2[1] + r2[3] <= r1[1] + r1[3] + dy)) {
                        break;
                    }
                }

                if (j === numClasses) {
                    filteredGroups.push(r1);
                }
            }
            return filteredGroups;
        };

    var detector = (function () {

        /**
         * Creates a new detector - basically a convenient wrapper class around
         * the js-objectdetect functions and hides away the technical details
         * of multi-scale object detection on image, video or canvas elements.
         *
         * @param width       Width of the detector
         * @param height      Height of the detector
         * @param scaleFactor Scaling factor for multi-scale detection
         * @param classifier  Compiled cascade classifier
         */
        function detector(width, height, scaleFactor, classifier) {
            this.canvas = document.createElement('canvas');
            this.canvas.width = width;
            this.canvas.height = height;
            this.context = this.canvas.getContext('2d');
            this.tilted = classifier.tilted;
            this.scaleFactor = scaleFactor;
            this.numScales = ~~(Math.log(Math.min(width / classifier.data[0], height / classifier.data[1])) / Math.log(scaleFactor));
            this.scaledGray = new Uint32Array(width * height);
            this.compiledClassifiers = [];
            var scale = 1;
            for (var i = 0; i < this.numScales; ++i) {
                var scaledWidth = ~~(width / scale);
                this.compiledClassifiers[i] = od.compileClassifier(classifier.data, scaledWidth);
                scale *= scaleFactor;
            }
        }

        /**
         * Multi-scale object detection on image, video or canvas elements.
         *
         * @param image          HTML image, video or canvas element
         * @param [group]        Detection results will be grouped by proximity
         * @param [stepSize]     Increase for performance
         * @param [roi]          Region of interest, i.e. search window
         *
         * @return Grouped rectangles
         */
        detector.prototype.detect = function (image, group, stepSize, roi, canny) {
            if (stepSize === undefined) stepSize = 1;
            if (group === undefined) group = 1;

            var width = this.canvas.width;
            var height = this.canvas.height;

            if (roi)
                this.context.drawImage(image, roi[0], roi[1], roi[2], roi[3], 0, 0, width, height);
            else
                this.context.drawImage(image, 0, 0, width, height);
            var imageData = this.context.getImageData(0, 0, width, height).data;
            this.gray = od.convertRgbaToGrayscale(imageData, this.gray);

            var rects = [];
            var scale = 1;
            for (var i = 0; i < this.numScales; ++i) {
                var scaledWidth = ~~(width / scale);
                var scaledHeight = ~~(height / scale);

                if (scale === 1) {
                    this.scaledGray.set(this.gray);
                } else {
                    this.scaledGray = od.rescaleImage(this.gray, width, height, scale, this.scaledGray);
                }

                if (canny) {
                    this.canny = od.computeCanny(this.scaledGray, scaledWidth, scaledHeight, this.canny);
                    this.cannySat = od.computeSat(this.canny, scaledWidth, scaledHeight, this.cannySat);
                }

                this.sat = od.computeSat(this.scaledGray, scaledWidth, scaledHeight, this.sat);
                this.ssat = od.computeSquaredSat(this.scaledGray, scaledWidth, scaledHeight, this.ssat);
                if (this.tilted) this.rsat = od.computeRsat(this.scaledGray, scaledWidth, scaledHeight, this.rsat);

                var newRects = od.detect(this.sat, this.rsat, this.ssat, this.cannySat, scaledWidth, scaledHeight, stepSize, this.compiledClassifiers[i]);
                for (var j = newRects.length - 1; j >= 0; --j) {
                    var newRect = newRects[j];
                    newRect[0] *= scale;
                    newRect[1] *= scale;
                    newRect[2] *= scale;
                    newRect[3] *= scale;
                }
                rects = rects.concat(newRects);

                scale *= this.scaleFactor;
            }
            return (group ? od.groupRectangles(rects, group) : rects).sort(function (r1, r2) {
                return r2[4] - r1[4];
            });
        };

        return detector;
    })();

    return {
        convertRgbaToGrayscale: convertRgbaToGrayscale,
        rescaleImage: rescaleImage,
        mirrorImage: mirrorImage,
        computeCanny: computeCanny,
        equalizeHistogram: equalizeHistogram,
        computeSat: computeSat,
        computeRsat: computeRsat,
        computeSquaredSat: computeSquaredSat,
        mirrorClassifier: mirrorClassifier,
        compileClassifier: compileClassifier,
        detect: detect,
        groupRectangles: groupRectangles,
        detector: detector
    };
})();

module.exports = od;
},{}]},{},[1])(1)
});