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  //                           License Agreement
  //                For Open Source Computer Vision Library
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  #include "precomp.hpp"
  #include "dpm_cascade.hpp"
  
  using namespace std;
  
  namespace cv
  {
  namespace dpm
  {
  
  class DPMDetectorImpl CV_FINAL : public DPMDetector
  {
  public:
  
      DPMDetectorImpl( const vector<string>& filenames, const vector<string>& classNames=vector<string>() );
      ~DPMDetectorImpl() CV_OVERRIDE;
  
      bool isEmpty() const CV_OVERRIDE;
  
      void detect(Mat &image, CV_OUT vector<ObjectDetection>& objects) CV_OVERRIDE;
  
      const vector<string>& getClassNames() const CV_OVERRIDE;
      size_t getClassCount() const CV_OVERRIDE;
      string extractModelName( const string& filename );
  
  private:
      vector< Ptr<DPMCascade> > detectors;
      vector<string> classNames;
  };
  
  Ptr<DPMDetector> DPMDetector::create(vector<string> const &filenames,
                                       vector<string> const &classNames)
  {
      return makePtr<DPMDetectorImpl>(filenames, classNames);
  }
  
  DPMDetectorImpl::ObjectDetection::ObjectDetection()
      : score(0.f), classID(-1) {}
  
  DPMDetectorImpl::ObjectDetection::ObjectDetection( const Rect& _rect, float _score, int _classID )
      : rect(_rect), score(_score), classID(_classID) {}
  
  
  DPMDetectorImpl::DPMDetectorImpl( const vector<string>& filenames,
          const vector<string>& _classNames )
  {
      for( size_t i = 0; i < filenames.size(); i++ )
      {
          const string filename = filenames[i];
          if( filename.length() < 5 || filename.substr(filename.length()-4, 4) != ".xml" )
              continue;
  
          Ptr<DPMCascade> detector = makePtr<DPMCascade>();
  
          // initialization
          detector->loadCascadeModel( filename.c_str() );
  
          if( detector )
          {
              detectors.push_back( detector );
              if( _classNames.empty() )
              {
                  classNames.push_back( extractModelName(filename));
              }
              else
                  classNames.push_back( _classNames[i] );
          }
      }
  }
  
  DPMDetectorImpl::~DPMDetectorImpl()
  {
  }
  
  bool DPMDetectorImpl::isEmpty() const
  {
      return detectors.empty();
  }
  
  const vector<string>& DPMDetectorImpl::getClassNames() const
  {
      return classNames;
  }
  
  size_t DPMDetectorImpl::getClassCount() const
  {
      return classNames.size();
  }
  
  string DPMDetectorImpl::extractModelName( const string& filename )
  {
      size_t startPos = filename.rfind('/');
      if( startPos == string::npos )
          startPos = filename.rfind('\\');
  
      if( startPos == string::npos )
          startPos = 0;
      else
          startPos++;
  
      const int extentionSize = 4; //.xml
  
      int substrLength = (int)(filename.size() - startPos - extentionSize);
  
      return filename.substr(startPos, substrLength);
  }
  
  void DPMDetectorImpl::detect( Mat &image,
          vector<ObjectDetection> &objectDetections)
  {
      objectDetections.clear();
  
      for( size_t classID = 0; classID < detectors.size(); classID++ )
      {
          // detect objects
          vector< vector<double> > detections;
          detections = detectors[classID]->detect(image);
  
          for (unsigned int i = 0; i < detections.size(); i++)
          {
              ObjectDetection ds = ObjectDetection();
              int s = (int)detections[i].size() - 1;
              ds.score = (float)detections[i][s];
              int x1 = (int)detections[i][0];
              int y1 = (int)detections[i][1];
              int w = (int)detections[i][2] - x1 + 1;
              int h = (int)detections[i][3] - y1 + 1;
              ds.rect = Rect(x1, y1, w, h);
              ds.classID = (int)classID;
  
              objectDetections.push_back(ds);
          }
      }
  }
  
  } // namespace cv
  }
  