tracking_KL.cpp

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/* +---------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)               |
   |                          http://www.mrpt.org/                             |
   |                                                                           |
   | Copyright (c) 2005-2017, Individual contributors, see AUTHORS file        |
   | See: http://www.mrpt.org/Authors - All rights reserved.                   |
   | Released under BSD License. See details in http://www.mrpt.org/License    |
   +---------------------------------------------------------------------------+ */
 
#include "vision-precomp.h"   // Precompiled headers
 
#include <mrpt/system/memory.h>
#include <mrpt/vision/tracking.h>
#include <mrpt/vision/CFeatureExtraction.h>
 
// Universal include for all versions of OpenCV
#include <mrpt/otherlibs/do_opencv_includes.h>
 
#if HAVE_ALLOCA_H
# include <alloca.h>
#endif
 
 
using namespace mrpt;
using namespace mrpt::vision;
using namespace mrpt::utils;
using namespace std;
 
 
/** Track a set of features from old_img -> new_img using sparse optimal flow (classic KL method)
  *  Optional parameters that can be passed in "extra_params":
  *             - "window_width"  (Default=15)
  *             - "window_height" (Default=15)
  *
  *  \sa OpenCV's method cvCalcOpticalFlowPyrLK
  */
template <typename FEATLIST>
void CFeatureTracker_KL::trackFeatures_impl_templ(
        const CImage &old_img,
        const CImage &new_img,
        FEATLIST &featureList )
{
MRPT_START
 
#if MRPT_HAS_OPENCV
        const unsigned int      window_width = extra_params.getWithDefaultVal("window_width",15);
        const unsigned int      window_height = extra_params.getWithDefaultVal("window_height",15);
 
        const int       LK_levels    = extra_params.getWithDefaultVal("LK_levels",3);
        const int       LK_max_iters = extra_params.getWithDefaultVal("LK_max_iters",10);
        const int       LK_epsilon   = extra_params.getWithDefaultVal("LK_epsilon",0.1);
        const float LK_max_tracking_error = extra_params.getWithDefaultVal("LK_max_tracking_error",150.0f);
 
 
        // Both images must be of the same size
        ASSERT_( old_img.getWidth() == new_img.getWidth() && old_img.getHeight() == new_img.getHeight() );
 
        const size_t  img_width  = old_img.getWidth();
        const size_t  img_height = old_img.getHeight();
 
        const size_t nFeatures  = featureList.size();                                   // Number of features
 
        // Grayscale images
        const CImage prev_gray(old_img, FAST_REF_OR_CONVERT_TO_GRAY);
        const CImage cur_gray(new_img, FAST_REF_OR_CONVERT_TO_GRAY);
 
        // Array conversion MRPT->OpenCV
        if (nFeatures>0)
        {
                CvPoint2D32f *points[2];
 
                points[0] = reinterpret_cast<CvPoint2D32f *>( mrpt_alloca(sizeof(CvPoint2D32f)*nFeatures) );
                points[1] = reinterpret_cast<CvPoint2D32f *>( mrpt_alloca(sizeof(CvPoint2D32f)*nFeatures) );
 
                std::vector<char>       status(nFeatures);
 
                for(size_t i=0;i<nFeatures;++i)
                {
                        points[0][i].x = featureList.getFeatureX(i);
                        points[0][i].y = featureList.getFeatureY(i);
                } // end for
 
                // local scope for auxiliary variables around cvCalcOpticalFlowPyrLK()
                const IplImage *prev_gray_ipl = prev_gray.getAs<IplImage>();
                const IplImage *cur_gray_ipl  = cur_gray.getAs<IplImage>();
 
                // Pyramids
                // JL: It seems that cache'ing the pyramids of previous images doesn't really improve the efficiency (!?!?)
                IplImage* pPyr = NULL;
                IplImage* cPyr = NULL;
 
                int     flags = 0;
 
                float* track_error = reinterpret_cast<float*>( mrpt_alloca(sizeof(float)*nFeatures) );
 
                cvCalcOpticalFlowPyrLK(prev_gray_ipl, cur_gray_ipl, pPyr, cPyr,
                        &points[0][0], &points[1][0], nFeatures, cvSize( window_width, window_height ), LK_levels, &status[0], track_error,
                        cvTermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS,LK_max_iters,LK_epsilon), flags );
 
                cvReleaseImage( &pPyr );
                cvReleaseImage( &cPyr );
 
                for(size_t i=0;i<nFeatures;++i)
                {
                        const bool trck_err_too_large = track_error[i]>LK_max_tracking_error;
 
                        if( status[i] == 1 &&
                                !trck_err_too_large &&
                                points[1][i].x > 0 && points[1][i].y > 0 &&
                                points[1][i].x < img_width && points[1][i].y < img_height )
                        {
                                // Feature could be tracked
                                featureList.setFeatureXf(i, points[1][i].x );
                                featureList.setFeatureYf(i, points[1][i].y );
                                featureList.setTrackStatus(i, status_TRACKED );
                        } // end if
                        else    // Feature could not be tracked
                        {
                                featureList.setFeatureX(i,-1);
                                featureList.setFeatureY(i,-1);
                                featureList.setTrackStatus(i, trck_err_too_large ? status_LOST : status_OOB );
                        } // end else
                } // end for
 
                mrpt_alloca_free( points[0] );
                mrpt_alloca_free( points[1] );
                mrpt_alloca_free( track_error );
 
 
                // In case it needs to rebuild a kd-tree or whatever
                featureList.mark_as_outdated();
        }
 
#else
        THROW_EXCEPTION("The MRPT has been compiled with MRPT_HAS_OPENCV=0 !");
#endif
 
        MRPT_END
} // end trackFeatures
 
 
void CFeatureTracker_KL::trackFeatures_impl(
        const CImage &old_img,
        const CImage &new_img,
        CFeatureList &featureList )
{
        trackFeatures_impl_templ<CFeatureList>(old_img,new_img,featureList);
}
 
void CFeatureTracker_KL::trackFeatures_impl(
        const CImage &old_img,
        const CImage &new_img,
        TSimpleFeatureList &featureList )
{
        trackFeatures_impl_templ<TSimpleFeatureList>(old_img,new_img,featureList);
}
 
void CFeatureTracker_KL::trackFeatures_impl(
        const CImage &old_img,
        const CImage &new_img,
        TSimpleFeaturefList &featureList )
{
        trackFeatures_impl_templ<TSimpleFeaturefList>(old_img,new_img,featureList);
}