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path: root/Software/Visual_Studio/MachineStudio/Modules/Tango.MachineStudio.Developer/Converters/SegmentLengthToWidthConverter.cs
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using System;
using System.Collections.Generic;
using System.Globalization;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows;
using System.Windows.Data;
using Tango.BL.Entities;

namespace Tango.MachineStudio.Developer.Converters
{
    public class SegmentLengthToWidthConverter : IMultiValueConverter
    {
        public object Convert(object[] values, Type targetType, object parameter, CultureInfo culture)
        {
            try
            {
                if (values.Length == 3)
                {
                    if (values[0] is double)
                    {
                        double jobLength = System.Convert.ToDouble(values[0]);
                        double elementWidth = System.Convert.ToDouble(values[1]);
                        double segmentLength = System.Convert.ToDouble(values[2]);

                        double result = (segmentLength / jobLength) * elementWidth;

                        return double.IsInfinity(result) ? 0d : result;
                    }
                    else
                    {
                        return 0d;
                    }
                }
                else
                {
                    return 0d;
                }
            }
            catch
            {
                return 0d;
            }
        }

        public object[] ConvertBack(object value, Type[] targetTypes, object parameter, CultureInfo culture)
        {
            throw new NotImplementedException();
        }
    }
}
s { color: #aa6600; background-color: #fff0f0 } /* Literal.String.Symbol */ .highlight .bp { color: #003388 } /* Name.Builtin.Pseudo */ .highlight .fm { color: #0066bb; font-weight: bold } /* Name.Function.Magic */ .highlight .vc { color: #336699 } /* Name.Variable.Class */ .highlight .vg { color: #dd7700 } /* Name.Variable.Global */ .highlight .vi { color: #3333bb } /* Name.Variable.Instance */ .highlight .vm { color: #336699 } /* Name.Variable.Magic */ .highlight .il { color: #0000DD; font-weight: bold } /* Literal.Number.Integer.Long */
/*M///////////////////////////////////////////////////////////////////////////////////////
 //
 //  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.
 //
 //
 //                           License Agreement
 //                For Open Source Computer Vision Library
 //
 // Copyright (C) 2013, OpenCV Foundation, 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 the copyright holders 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.
 //
 //M*/

#include "precomp.hpp"
#include "trackerBoostingModel.hpp"

namespace cv
{

class TrackerBoostingImpl : public TrackerBoosting
{
 public:
  TrackerBoostingImpl( const TrackerBoosting::Params &parameters = TrackerBoosting::Params() );
  void read( const FileNode& fn );
  void write( FileStorage& fs ) const;

 protected:

  bool initImpl( const Mat& image, const Rect2d& boundingBox );
  bool updateImpl( const Mat& image, Rect2d& boundingBox );

  TrackerBoosting::Params params;
};

/*
 *  TrackerBoosting
 */

/*
 * Parameters
 */
TrackerBoosting::Params::Params()
{
  numClassifiers = 100;
  samplerOverlap = 0.99f;
  samplerSearchFactor = 1.8f;
  iterationInit = 50;
  featureSetNumFeatures = ( numClassifiers * 10 ) + iterationInit;
}

void TrackerBoosting::Params::read( const cv::FileNode& fn )
{
  numClassifiers = fn["numClassifiers"];
  samplerOverlap = fn["overlap"];
  samplerSearchFactor = fn["samplerSearchFactor"];
  iterationInit = fn["iterationInit"];
  samplerSearchFactor = fn["searchFactor"];
}

void TrackerBoosting::Params::write( cv::FileStorage& fs ) const
{
  fs << "numClassifiers" << numClassifiers;
  fs << "overlap" << samplerOverlap;
  fs << "searchFactor" << samplerSearchFactor;
  fs << "iterationInit" << iterationInit;
  fs << "samplerSearchFactor" << samplerSearchFactor;
}

/*
 * Constructor
 */
Ptr<TrackerBoosting> TrackerBoosting::create(const TrackerBoosting::Params &parameters){
    return Ptr<TrackerBoostingImpl>(new TrackerBoostingImpl(parameters));
}
Ptr<TrackerBoosting> TrackerBoosting::create(){
    return Ptr<TrackerBoostingImpl>(new TrackerBoostingImpl());
}
TrackerBoostingImpl::TrackerBoostingImpl( const TrackerBoostingImpl::Params &parameters ) :
    params( parameters )
{
  isInit = false;
}

void TrackerBoostingImpl::read( const cv::FileNode& fn )
{
  params.read( fn );
}

void TrackerBoostingImpl::write( cv::FileStorage& fs ) const
{
  params.write( fs );
}

bool TrackerBoostingImpl::initImpl( const Mat& image, const Rect2d& boundingBox )
{
  srand (1);
  //sampling
  Mat_<int> intImage;
  Mat_<double> intSqImage;
  Mat image_;
  cvtColor( image, image_, CV_RGB2GRAY );
  integral( image_, intImage, intSqImage, CV_32S );
  TrackerSamplerCS::Params CSparameters;
  CSparameters.overlap = params.samplerOverlap;
  CSparameters.searchFactor = params.samplerSearchFactor;

  Ptr<TrackerSamplerAlgorithm> CSSampler = Ptr<TrackerSamplerCS>( new TrackerSamplerCS( CSparameters ) );

  if( !sampler->addTrackerSamplerAlgorithm( CSSampler ) )
    return false;

  CSSampler.staticCast<TrackerSamplerCS>()->setMode( TrackerSamplerCS::MODE_POSITIVE );
  sampler->sampling( intImage, boundingBox );
  const std::vector<Mat> posSamples = sampler->getSamples();

  CSSampler.staticCast<TrackerSamplerCS>()->setMode( TrackerSamplerCS::MODE_NEGATIVE );
  sampler->sampling( intImage, boundingBox );
  const std::vector<Mat> negSamples = sampler->getSamples();

  if( posSamples.empty() || negSamples.empty() )
    return false;

  Rect ROI = CSSampler.staticCast<TrackerSamplerCS>()->getROI();

  //compute HAAR features
  TrackerFeatureHAAR::Params HAARparameters;
  HAARparameters.numFeatures = params.featureSetNumFeatures;
  HAARparameters.isIntegral = true;
  HAARparameters.rectSize = Size( static_cast<int>(boundingBox.width), static_cast<int>(boundingBox.height) );
  Ptr<TrackerFeature> trackerFeature = Ptr<TrackerFeatureHAAR>( new TrackerFeatureHAAR( HAARparameters ) );
  if( !featureSet->addTrackerFeature( trackerFeature ) )
    return false;

  featureSet->extraction( posSamples );
  const std::vector<Mat> posResponse = featureSet->getResponses();
  featureSet->extraction( negSamples );
  const std::vector<Mat> negResponse = featureSet->getResponses();

  //Model
  model = Ptr<TrackerBoostingModel>( new TrackerBoostingModel( boundingBox ) );
  Ptr<TrackerStateEstimatorAdaBoosting> stateEstimator = Ptr<TrackerStateEstimatorAdaBoosting>(
      new TrackerStateEstimatorAdaBoosting( params.numClassifiers, params.iterationInit, params.featureSetNumFeatures,
                                            Size( static_cast<int>(boundingBox.width), static_cast<int>(boundingBox.height) ), ROI ) );
  model->setTrackerStateEstimator( stateEstimator );

  //Run model estimation and update for iterationInit iterations
  for ( int i = 0; i < params.iterationInit; i++ )
  {
    //compute temp features
    TrackerFeatureHAAR::Params HAARparameters2;
    HAARparameters2.numFeatures = static_cast<int>( posSamples.size() + negSamples.size() );
    HAARparameters2.isIntegral = true;
    HAARparameters2.rectSize = Size( static_cast<int>(boundingBox.width), static_cast<int>(boundingBox.height) );
    Ptr<TrackerFeatureHAAR> trackerFeature2 = Ptr<TrackerFeatureHAAR>( new TrackerFeatureHAAR( HAARparameters2 ) );

    model.staticCast<TrackerBoostingModel>()->setMode( TrackerBoostingModel::MODE_NEGATIVE, negSamples );
    model->modelEstimation( negResponse );
    model.staticCast<TrackerBoostingModel>()->setMode( TrackerBoostingModel::MODE_POSITIVE, posSamples );
    model->modelEstimation( posResponse );
    model->modelUpdate();

    //get replaced classifier and change the features
    std::vector<int> replacedClassifier = stateEstimator->computeReplacedClassifier();
    std::vector<int> swappedClassified = stateEstimator->computeSwappedClassifier();
    for ( size_t j = 0; j < replacedClassifier.size(); j++ )
    {
      if( replacedClassifier[j] != -1 && swappedClassified[j] != -1 )
      {
        trackerFeature.staticCast<TrackerFeatureHAAR>()->swapFeature( replacedClassifier[j], swappedClassified[j] );
        trackerFeature.staticCast<TrackerFeatureHAAR>()->swapFeature( swappedClassified[j], trackerFeature2->getFeatureAt( (int)j ) );
      }
    }
  }

  return true;
}

bool TrackerBoostingImpl::updateImpl( const Mat& image, Rect2d& boundingBox )
{
  Mat_<int> intImage;
  Mat_<double> intSqImage;
  Mat image_;
  cvtColor( image, image_, CV_RGB2GRAY );
  integral( image_, intImage, intSqImage, CV_32S );
  //get the last location [AAM] X(k-1)
  Ptr<TrackerTargetState> lastLocation = model->getLastTargetState();
  Rect lastBoundingBox( (int)lastLocation->getTargetPosition().x, (int)lastLocation->getTargetPosition().y, lastLocation->getTargetWidth(),
                        lastLocation->getTargetHeight() );

  //sampling new frame based on last location
  ( sampler->getSamplers().at( 0 ).second ).staticCast<TrackerSamplerCS>()->setMode( TrackerSamplerCS::MODE_CLASSIFY );
  sampler->sampling( intImage, lastBoundingBox );
  const std::vector<Mat> detectSamples = sampler->getSamples();
  Rect ROI = ( sampler->getSamplers().at( 0 ).second ).staticCast<TrackerSamplerCS>()->getROI();

  if( detectSamples.empty() )
    return false;

  /*//TODO debug samples
   Mat f;
   image.copyTo( f );

   for ( size_t i = 0; i < detectSamples.size(); i = i + 10 )
   {
   Size sz;
   Point off;
   detectSamples.at( i ).locateROI( sz, off );
   rectangle( f, Rect( off.x, off.y, detectSamples.at( i ).cols, detectSamples.at( i ).rows ), Scalar( 255, 0, 0 ), 1 );
   }*/

  std::vector<Mat> responses;
  Mat response;

  std::vector<int> classifiers = model->getTrackerStateEstimator().staticCast<TrackerStateEstimatorAdaBoosting>()->computeSelectedWeakClassifier();
  Ptr<TrackerFeatureHAAR> extractor = featureSet->getTrackerFeature()[0].second.staticCast<TrackerFeatureHAAR>();
  extractor->extractSelected( classifiers, detectSamples, response );
  responses.push_back( response );

  //predict new location
  ConfidenceMap cmap;
  model.staticCast<TrackerBoostingModel>()->setMode( TrackerBoostingModel::MODE_CLASSIFY, detectSamples );
  model.staticCast<TrackerBoostingModel>()->responseToConfidenceMap( responses, cmap );
  model->getTrackerStateEstimator().staticCast<TrackerStateEstimatorAdaBoosting>()->setCurrentConfidenceMap( cmap );
  model->getTrackerStateEstimator().staticCast<TrackerStateEstimatorAdaBoosting>()->setSampleROI( ROI );

  if( !model->runStateEstimator() )
  {
    return false;
  }

  Ptr<TrackerTargetState> currentState = model->getLastTargetState();
  boundingBox = Rect( (int)currentState->getTargetPosition().x, (int)currentState->getTargetPosition().y, currentState->getTargetWidth(),
                      currentState->getTargetHeight() );

  /*//TODO debug
   rectangle( f, lastBoundingBox, Scalar( 0, 255, 0 ), 1 );
   rectangle( f, boundingBox, Scalar( 0, 0, 255 ), 1 );
   imshow( "f", f );
   //waitKey( 0 );*/

  //sampling new frame based on new location
  //Positive sampling
  ( sampler->getSamplers().at( 0 ).second ).staticCast<TrackerSamplerCS>()->setMode( TrackerSamplerCS::MODE_POSITIVE );
  sampler->sampling( intImage, boundingBox );
  const std::vector<Mat> posSamples = sampler->getSamples();

  //Negative sampling
  ( sampler->getSamplers().at( 0 ).second ).staticCast<TrackerSamplerCS>()->setMode( TrackerSamplerCS::MODE_NEGATIVE );
  sampler->sampling( intImage, boundingBox );
  const std::vector<Mat> negSamples = sampler->getSamples();

  if( posSamples.empty() || negSamples.empty() )
    return false;

  //extract features
  featureSet->extraction( posSamples );
  const std::vector<Mat> posResponse = featureSet->getResponses();

  featureSet->extraction( negSamples );
  const std::vector<Mat> negResponse = featureSet->getResponses();

  //compute temp features
  TrackerFeatureHAAR::Params HAARparameters2;
  HAARparameters2.numFeatures = static_cast<int>( posSamples.size() + negSamples.size() );
  HAARparameters2.isIntegral = true;
  HAARparameters2.rectSize = Size( static_cast<int>(boundingBox.width), static_cast<int>(boundingBox.height) );
  Ptr<TrackerFeatureHAAR> trackerFeature2 = Ptr<TrackerFeatureHAAR>( new TrackerFeatureHAAR( HAARparameters2 ) );

  //model estimate
  model.staticCast<TrackerBoostingModel>()->setMode( TrackerBoostingModel::MODE_NEGATIVE, negSamples );
  model->modelEstimation( negResponse );
  model.staticCast<TrackerBoostingModel>()->setMode( TrackerBoostingModel::MODE_POSITIVE, posSamples );
  model->modelEstimation( posResponse );

  //model update
  model->modelUpdate();

  //get replaced classifier and change the features
  std::vector<int> replacedClassifier = model->getTrackerStateEstimator().staticCast<TrackerStateEstimatorAdaBoosting>()->computeReplacedClassifier();
  std::vector<int> swappedClassified = model->getTrackerStateEstimator().staticCast<TrackerStateEstimatorAdaBoosting>()->computeSwappedClassifier();
  for ( size_t j = 0; j < replacedClassifier.size(); j++ )
  {
    if( replacedClassifier[j] != -1 && swappedClassified[j] != -1 )
    {
      featureSet->getTrackerFeature().at( 0 ).second.staticCast<TrackerFeatureHAAR>()->swapFeature( replacedClassifier[j], swappedClassified[j] );
      featureSet->getTrackerFeature().at( 0 ).second.staticCast<TrackerFeatureHAAR>()->swapFeature( swappedClassified[j],
                                                                                                    trackerFeature2->getFeatureAt( (int)j ) );
    }
  }

  return true;
}

} /* namespace cv */