opencv_contrib/modules/saliency/samples/computeSaliency.cpp

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#include <opencv2/core/utility.hpp>
#include <opencv2/saliency.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>

using namespace std;
using namespace cv;
using namespace saliency;

static const char* keys =
{ "{@saliency_algorithm | | Saliency algorithm <saliencyAlgorithmType.[saliencyAlgorithmTypeSubType]> }"
    "{@video_name      | | video name            }"
    "{@start_frame     |1| Start frame           }"
    "{@training_path   |ObjectnessTrainedModel| Path of the folder containing the trained files}" };

static void help()
{
  cout << "\nThis example shows the functionality of \"Saliency \""
       "Call:\n"
       "./example_saliency_computeSaliency <saliencyAlgorithmSubType> <video_name> <start_frame> \n"
       << endl;
}

int main( int argc, char** argv )
{

  CommandLineParser parser( argc, argv, keys );

  String saliency_algorithm = parser.get<String>( 0 );
  String video_name = parser.get<String>( 1 );
  int start_frame = parser.get<int>( 2 );
  String training_path = parser.get<String>( 3 );

  if( saliency_algorithm.empty() || video_name.empty() )
  {
    help();
    return -1;
  }

  //open the capture
  VideoCapture cap;
  cap.open( video_name );
  cap.set( CAP_PROP_POS_FRAMES, start_frame );

  if( !cap.isOpened() )
  {
    help();
    cout << "***Could not initialize capturing...***\n";
    cout << "Current parameter's value: \n";
    parser.printMessage();
    return -1;
  }

  Mat frame;

  //instantiates the specific Saliency
  Ptr<Saliency> saliencyAlgorithm;

  Mat binaryMap;
  Mat image;

  cap >> frame;
  if( frame.empty() )
  {
    return 0;
  }

  frame.copyTo( image );

  if( saliency_algorithm.find( "SPECTRAL_RESIDUAL" ) == 0 )
  {
    Mat saliencyMap;
    saliencyAlgorithm = StaticSaliencySpectralResidual::create();
    if( saliencyAlgorithm->computeSaliency( image, saliencyMap ) )
    {
      StaticSaliencySpectralResidual spec;
      spec.computeBinaryMap( saliencyMap, binaryMap );

      imshow( "Saliency Map", saliencyMap );
      imshow( "Original Image", image );
      imshow( "Binary Map", binaryMap );
      waitKey( 0 );
    }

  }
  else if( saliency_algorithm.find( "FINE_GRAINED" ) == 0 )
  {
    Mat saliencyMap;
    saliencyAlgorithm = StaticSaliencyFineGrained::create();
    if( saliencyAlgorithm->computeSaliency( image, saliencyMap ) )
    {
      imshow( "Saliency Map", saliencyMap );
      imshow( "Original Image", image );
      waitKey( 0 );
    }

  }
  else if( saliency_algorithm.find( "BING" ) == 0 )
  {
    if( training_path.empty() )
    {

      cout << "Path of trained files missing! " << endl;
      return -1;
    }

    else
    {
      saliencyAlgorithm = ObjectnessBING::create();
      vector<Vec4i> saliencyMap;
      saliencyAlgorithm.dynamicCast<ObjectnessBING>()->setTrainingPath( training_path );
      saliencyAlgorithm.dynamicCast<ObjectnessBING>()->setBBResDir( "Results" );

      if( saliencyAlgorithm->computeSaliency( image, saliencyMap ) )
      {
        int ndet = int(saliencyMap.size());
        std::cout << "Objectness done " << ndet << std::endl;
        // The result are sorted by objectness. We only use the first maxd boxes here.
        int maxd = 7, step = 255 / maxd, jitter=9; // jitter to seperate single rects
        Mat draw = image.clone();
        for (int i = 0; i < std::min(maxd, ndet); i++) {
          Vec4i bb = saliencyMap[i];
          Scalar col = Scalar(((i*step)%255), 50, 255-((i*step)%255));
          Point off(theRNG().uniform(-jitter,jitter), theRNG().uniform(-jitter,jitter));
          rectangle(draw, Point(bb[0]+off.x, bb[1]+off.y), Point(bb[2]+off.x, bb[3]+off.y), col, 2);
          rectangle(draw, Rect(20, 20+i*10, 10,10), col, -1); // mini temperature scale
        }
        imshow("BING", draw);
        waitKey();
      }
      else
      {
        std::cout << "No saliency found for " << video_name << std::endl;
      }
    }

  }
  else if( saliency_algorithm.find( "BinWangApr2014" ) == 0 )
  {
    saliencyAlgorithm = MotionSaliencyBinWangApr2014::create();
    saliencyAlgorithm.dynamicCast<MotionSaliencyBinWangApr2014>()->setImagesize( image.cols, image.rows );
    saliencyAlgorithm.dynamicCast<MotionSaliencyBinWangApr2014>()->init();

    bool paused = false;
    for ( ;; )
    {
      if( !paused )
      {

        cap >> frame;
        if( frame.empty() )
        {
          return 0;
        }
        cvtColor( frame, frame, COLOR_BGR2GRAY );

        Mat saliencyMap;
        saliencyAlgorithm->computeSaliency( frame, saliencyMap );

        imshow( "image", frame );
        imshow( "saliencyMap", saliencyMap * 255 );
      }

      char c = (char) waitKey( 2 );
      if( c == 'q' )
        break;
      if( c == 'p' )
        paused = !paused;

    }
  }

  return 0;
}