CFeatureExtraction_FASTER.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/vision/CFeatureExtraction.h>

// Universal include for all versions of OpenCV
#include <mrpt/otherlibs/do_opencv_includes.h> 


#if MRPT_HAS_OPENCV
#       include "faster/faster_corner_prototypes.h"
#endif

using namespace mrpt;
using namespace mrpt::vision;
using namespace mrpt::system;
using namespace mrpt::utils;
using namespace std;


// ------------  SSE2-optimized implementations of FASTER -------------
void CFeatureExtraction::detectFeatures_SSE2_FASTER9(const CImage &img, TSimpleFeatureList & corners, const int threshold, bool append_to_list, uint8_t octave,std::vector<size_t> * out_feats_index_by_row)
{
#if MRPT_HAS_OPENCV
        const IplImage *IPL = img.getAs<IplImage>();
        ASSERTDEB_(IPL && IPL->nChannels==1)
        if (!append_to_list) corners.clear();

        fast_corner_detect_9 (IPL,corners,threshold,octave,out_feats_index_by_row);
#else
        THROW_EXCEPTION("MRPT built without OpenCV support!")
#endif
}
void CFeatureExtraction::detectFeatures_SSE2_FASTER10(const CImage &img, TSimpleFeatureList & corners, const int threshold, bool append_to_list, uint8_t octave,std::vector<size_t> * out_feats_index_by_row)
{
#if MRPT_HAS_OPENCV
        const IplImage *IPL = img.getAs<IplImage>();
        ASSERTDEB_(IPL && IPL->nChannels==1)
        if (!append_to_list) corners.clear();

        fast_corner_detect_10 (IPL,corners,threshold,octave,out_feats_index_by_row);
#else
        THROW_EXCEPTION("MRPT built without OpenCV support!")
#endif
}
void CFeatureExtraction::detectFeatures_SSE2_FASTER12(const CImage &img, TSimpleFeatureList & corners, const int threshold, bool append_to_list, uint8_t octave,std::vector<size_t> * out_feats_index_by_row)
{
#if MRPT_HAS_OPENCV
        const IplImage *IPL = img.getAs<IplImage>();
        ASSERTDEB_(IPL && IPL->nChannels==1)
        if (!append_to_list) corners.clear();

        fast_corner_detect_12 (IPL,corners,threshold,octave,out_feats_index_by_row);
#else
        THROW_EXCEPTION("MRPT built without OpenCV support!")
#endif
}

/************************************************************************************************
*                                                               extractFeaturesFASTER                                                                                   *
************************************************************************************************/
// N_fast = 9, 10, 12
void  CFeatureExtraction::extractFeaturesFASTER_N(
        const int                                       N_fast,
        const mrpt::utils::CImage       & inImg,
        CFeatureList                        & feats,
        unsigned int                        init_ID,
        unsigned int                        nDesiredFeatures,
        const TImageROI                     & ROI )  const
{
        MRPT_UNUSED_PARAM(ROI);
        MRPT_START

#if MRPT_HAS_OPENCV
        // Make sure we operate on a gray-scale version of the image:
        const CImage inImg_gray( inImg, FAST_REF_OR_CONVERT_TO_GRAY );

        const IplImage *IPL = inImg_gray.getAs<IplImage>();

        TSimpleFeatureList corners;
        TFeatureType type_of_this_feature;

        switch (N_fast)
        {
        case 9:  fast_corner_detect_9 (IPL,corners, options.FASTOptions.threshold, 0, NULL); type_of_this_feature=featFASTER9; break;
        case 10: fast_corner_detect_10(IPL,corners, options.FASTOptions.threshold, 0, NULL); type_of_this_feature=featFASTER10; break;
        case 12: fast_corner_detect_12(IPL,corners, options.FASTOptions.threshold, 0, NULL); type_of_this_feature=featFASTER12; break;
        default:
                THROW_EXCEPTION("Only the 9,10,12 FASTER detectors are implemented.")
                break;
        };

        // *All* the features have been extracted.
        const size_t N = corners.size();

        // Now:
        //  1) Sort them by "response": It's ~100 times faster to sort a list of
        //      indices "sorted_indices" than sorting directly the actual list of features "corners"
        std::vector<size_t> sorted_indices(N);
        for (size_t i=0;i<N;i++)  sorted_indices[i]=i;

        // Use KLT response
        if (options.FASTOptions.use_KLT_response ||
                nDesiredFeatures!=0 // If the user wants us to limit the number of features, we need to do it according to some quality measure
                )
        {
                const int KLT_half_win = 4;
                const int max_x = inImg_gray.getWidth() - 1 - KLT_half_win;
                const int max_y = inImg_gray.getHeight() - 1 - KLT_half_win;

                for (size_t i=0;i<N;i++)
                {
                        const int x = corners[i].pt.x;
                        const int y = corners[i].pt.y;
                        if (x>KLT_half_win && y>KLT_half_win && x<=max_x && y<=max_y)
                                        corners[i].response = inImg_gray.KLT_response(x,y,KLT_half_win);
                        else    corners[i].response = -100;
                }

                std::sort( sorted_indices.begin(), sorted_indices.end(), KeypointResponseSorter<TSimpleFeatureList>(corners) );
        }
        else
        {
                for (size_t i=0;i<N;i++)
                        corners[i].response = 0;
        }

        //  2) Filter by "min-distance" (in options.FASTOptions.min_distance)
        //  3) Convert to MRPT CFeatureList format.
        // Steps 2 & 3 are done together in the while() below.
        // The "min-distance" filter is done by means of a 2D binary matrix where each cell is marked when one
        // feature falls within it. This is not exactly the same than a pure "min-distance" but is pretty close
        // and for large numbers of features is much faster than brute force search of kd-trees.
        // (An intermediate approach would be the creation of a mask image updated for each accepted feature, etc.)

        const bool do_filter_min_dist = options.FASTOptions.min_distance>1;

        // Used half the min-distance since we'll later mark as occupied the ranges [i-1,i+1] for a feature at "i"
        const unsigned int occupied_grid_cell_size = options.FASTOptions.min_distance/2.0;
        const float occupied_grid_cell_size_inv = 1.0f/occupied_grid_cell_size;

        unsigned int grid_lx = !do_filter_min_dist ? 1 : (unsigned int)(1 + inImg.getWidth() * occupied_grid_cell_size_inv);
        unsigned int grid_ly = !do_filter_min_dist ? 1 : (unsigned int)(1 + inImg.getHeight() * occupied_grid_cell_size_inv );

        mrpt::math::CMatrixBool  occupied_sections(grid_lx,grid_ly);  // See the comments above for an explanation.
        occupied_sections.fillAll(false);


        unsigned int    nMax            = (nDesiredFeatures!=0 && N > nDesiredFeatures) ? nDesiredFeatures : N;
        const int               offset          = (int)this->options.patchSize/2 + 1;
        const int               size_2          = options.patchSize/2;
        const size_t    imgH            = inImg.getHeight();
        const size_t    imgW            = inImg.getWidth();
        unsigned int    i                       = 0;
        unsigned int    cont            = 0;
        TFeatureID              nextID          = init_ID;

        if( !options.addNewFeatures )
                feats.clear();

        while( cont != nMax && i!=N )
        {
                // Take the next feature fromt the ordered list of good features:
                const TSimpleFeature &feat = corners[ sorted_indices[i] ];
                i++;

                // Patch out of the image??
                const int xBorderInf =  feat.pt.x - size_2;
                const int xBorderSup =  feat.pt.x + size_2;
                const int yBorderInf =  feat.pt.y - size_2;
                const int yBorderSup =  feat.pt.y + size_2;

                if (!( xBorderSup < (int)imgW && xBorderInf > 0 && yBorderSup < (int)imgH && yBorderInf > 0 ))
                        continue; // nope, skip.

                if (do_filter_min_dist)
                {
                        // Check the min-distance:
                        const size_t section_idx_x = size_t(feat.pt.x * occupied_grid_cell_size_inv);
                        const size_t section_idx_y = size_t(feat.pt.y * occupied_grid_cell_size_inv);

                        if (occupied_sections(section_idx_x,section_idx_y))
                                continue; // Already occupied! skip.

                        // Mark section as occupied
                        occupied_sections.set_unsafe(section_idx_x,section_idx_y, true);
                        if (section_idx_x>0)    occupied_sections.set_unsafe(section_idx_x-1,section_idx_y, true);
                        if (section_idx_y>0)    occupied_sections.set_unsafe(section_idx_x,section_idx_y-1, true);
                        if (section_idx_x<grid_lx-1)    occupied_sections.set_unsafe(section_idx_x+1,section_idx_y, true);
                        if (section_idx_y<grid_ly-1)    occupied_sections.set_unsafe(section_idx_x,section_idx_y+1, true);
                }

                // All tests passed: add new feature:
                CFeaturePtr ft          = CFeature::Create();
                ft->type                        = type_of_this_feature;
                ft->ID                          = nextID++;
                ft->x                           = feat.pt.x;
                ft->y                           = feat.pt.y;
                ft->response            = feat.response;
                ft->orientation         = 0;
                ft->scale                       = 1;
                ft->patchSize           = options.patchSize;            // The size of the feature patch

                if( options.patchSize > 0 )
                {
                        inImg.extract_patch(
                                ft->patch,
                                round( ft->x ) - offset,
                                round( ft->y ) - offset,
                                options.patchSize,
                                options.patchSize );                                            // Image patch surronding the feature
                }
                feats.push_back( ft );
                ++cont;
        }

#endif
        MRPT_END
}