CFeatureExtraction_spinImg.cpp

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2018, 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>
 
using namespace mrpt;
using namespace mrpt::vision;
using namespace mrpt::img;
using namespace mrpt::img;
using namespace mrpt::system;
using namespace mrpt::math;
using namespace std;
 
/************************************************************************************************
*                                                               computeSpinImageDescriptors
**
************************************************************************************************/
void CFeatureExtraction::internal_computeSpinImageDescriptors(
        const CImage& in_img, CFeatureList& in_features) const
{
        MRPT_START
 
        ASSERT_(options.SpinImagesOptions.radius > 1);
 
        // This is a C++ implementation of the descriptor "Intensity-domain spin
        // images" as described
        //  in "A sparse texture representation using affine-invariant regions", S
        //  Lazebnik, C Schmid, J Ponce,
        //  2003 IEEE Computer Society Conference on Computer Vision.
 
        const unsigned int HIST_N_INT =
                options.SpinImagesOptions.hist_size_intensity;
        const unsigned int HIST_N_DIS =
                options.SpinImagesOptions.hist_size_distance;
        const unsigned int R = options.SpinImagesOptions.radius;
        const int img_w = static_cast<int>(in_img.getWidth());
        const int img_h = static_cast<int>(in_img.getHeight());
        const bool img_color = in_img.isColor();
 
        // constant for passing intensity [0,255] to histogram index
        // [0,HIST_N_INT-1]:
        const float k_int2idx = (HIST_N_INT - 1) / 255.0f;
        const float k_idx2int = 1.0f / k_int2idx;
 
        // constant for passing distances in pixels [0,R] to histogram index
        // [0,HIST_N_DIS-1]:
        const float k_dis2idx = (HIST_N_DIS - 1) / static_cast<float>(R);
        const float k_idx2dis = 1.0f / k_dis2idx;
 
        // The Gaussian kernel used below is approximated up to a given distance
        //  in pixels, given by 2 times the appropriate std. deviations:
        const int STD_TIMES = 2;
        const int kernel_size_dist = static_cast<int>(
                ceil(k_dis2idx * STD_TIMES * options.SpinImagesOptions.std_dist));
 
        const float _2var_int =
                -1.0f / (2 * square(options.SpinImagesOptions.std_intensity));
        const float _2var_dist =
                -1.0f / (2 * square(options.SpinImagesOptions.std_dist));
 
        // Create the 2D histogram:
        CMatrixDouble hist2d(HIST_N_INT, HIST_N_DIS);
 
        // Compute intensity-domain spin images
        for (CFeatureList::iterator it = in_features.begin();
                 it != in_features.end(); ++it)
        {
                // Overwrite scale with the descriptor scale:
                (*it)->scale = options.SpinImagesOptions.radius;
 
                // Reset histogram to zeros:
                hist2d.zeros();
 
                // Define the ROI around the interest point which counts for the
                // histogram:
                int px0 = round((*it)->x - R);
                int px1 = round((*it)->x + R);
                int py0 = round((*it)->y - R);
                int py1 = round((*it)->y + R);
 
                // Clip at img borders:
                px0 = max(0, px0);
                px1 = min(img_w - 1, px1);
                py0 = max(0, py0);
                py1 = min(img_h - 1, py1);
 
                uint8_t pix_val;
                uint8_t* aux_pix_ptr;
 
                for (int px = px0; px <= px1; px++)
                {
                        for (int py = py0; py <= py1; py++)
                        {
                                // get the pixel color [0,255]
                                if (!img_color)
                                        pix_val = *in_img.get_unsafe(px, py, 0);
                                else
                                {
                                        aux_pix_ptr = in_img.get_unsafe(px, py, 0);
                                        pix_val =
                                                (aux_pix_ptr[0] + aux_pix_ptr[1] + aux_pix_ptr[2]) / 3;
                                }
 
                                const float pix_dist = hypot((*it)->x - px, (*it)->y - py);
                                const int center_bin_dist = k_dis2idx * pix_dist;
 
// A factor to correct the histogram due to the existence of more pixels at
// larger radius:
//  Obtained as Area = PI ( R1^2 - R2^2 ) for R1,R2 being pix_dist +- Delta/2
// const double density_circular_area = 1.0/ (M_2PI * (center_bin_dist+0.5) *
// square(k_idx2dis) );
 
#if 0
                                // "normal" histogram
                                const int bin_int = k_int2idx * pix_val;
 
                                if (center_bin_dist<static_cast<int>(HIST_N_DIS))  // this accounts for the "square" or "circle" shapes of the area to account for
                                {
                                        hist2d(bin_int,center_bin_dist) +=1; // * density_circular_area;
                                }
#else
                                // Apply a "soft-histogram", so each pixel counts into several
                                // bins,
                                //  weighted by a exponential function to model a Gaussian
                                //  kernel
                                const int bin_int_low = max(
                                        0, static_cast<int>(
                                                   ceil(
                                                           k_int2idx *
                                                           (pix_val -
                                                                STD_TIMES *
                                                                        options.SpinImagesOptions.std_intensity))));
                                const int bin_int_hi = min(
                                        static_cast<int>(HIST_N_INT - 1),
                                        static_cast<int>(
                                                ceil(
                                                        k_int2idx *
                                                        (pix_val +
                                                         STD_TIMES *
                                                                 options.SpinImagesOptions.std_intensity))));
 
                                // cout << "d: " << pix_dist << "v: " << (int)pix_val << "\t";
 
                                if (center_bin_dist <
                                        static_cast<int>(HIST_N_DIS))  // this accounts for the
                                // "square" or "circle"
                                // shapes of the area to
                                // account for
                                {
                                        const int bin_dist_low =
                                                max(0, center_bin_dist - kernel_size_dist);
                                        const int bin_dist_hi =
                                                min(static_cast<int>(HIST_N_DIS - 1),
                                                        center_bin_dist + kernel_size_dist);
 
                                        int bin_dist, bin_int;
                                        float pix_dist_cur_dist =
                                                pix_dist - bin_dist_low * k_idx2dis;
 
                                        for (bin_dist = bin_dist_low; bin_dist <= bin_dist_hi;
                                                 bin_dist++, pix_dist_cur_dist -= k_idx2dis)
                                        {
                                                float pix_val_cur_val =
                                                        pix_val - (bin_int_low * k_idx2int);
 
                                                for (bin_int = bin_int_low; bin_int <= bin_int_hi;
                                                         bin_int++, pix_val_cur_val -= k_idx2int)
                                                {
                                                        // Gaussian kernel:
                                                        double v = _2var_dist * square(pix_dist_cur_dist) +
                                                                           _2var_int * square(pix_val_cur_val);
                                                        //                                                              _2var_dist *
                                                        // square(pix_dist
                                                        //-
                                                        // bin_dist*k_idx2dis ) +
                                                        //                                                              _2var_int  *
                                                        // square(pix_val-(bin_int*k_idx2int));
 
                                                        hist2d.get_unsafe(bin_int, bin_dist) +=
                                                                exp(v);  // * density_circular_area;
                                                }
                                        }
                                        // hist2d(bin_int,bin_dist) *= ;
                                }  // in range
#endif
 
                        }  // end py
                }  // end px
 
                // Normalize:
                hist2d.normalize(0, 1);  // [0,1]
 
#if 0
                {       // Debug
                        static int n=0;
                        CMatrixDouble AA(hist2d);
                        AA.normalize(0,1);
                        CImage  aux_img( AA );
                        aux_img.saveToFile( format("spin_feat_%04i.png",n) );
                        CImage  aux_img2 = in_img;
                        aux_img2.drawCircle((*it)->x,(*it)->y,20,TColor(255,0,0));
                        aux_img2.saveToFile( format("spin_feat_%04i_map.png",n) );
                        n++;
                }
#endif
 
                // Save the histogram as a vector:
                unsigned idx = 0;
                std::vector<float>& ptr_trg = (*it)->descriptors.SpinImg;
                ptr_trg.resize(HIST_N_INT * HIST_N_DIS);
 
                for (unsigned i = 0; i < HIST_N_DIS; i++)
                        for (unsigned j = 0; j < HIST_N_INT; j++)
                                ptr_trg[idx++] = hist2d.get_unsafe(j, i);
 
                (*it)->descriptors.SpinImg_range_rows = HIST_N_DIS;
 
        }  // end for each feature
 
        MRPT_END
}