CFeatureExtraction_LSD_BLD.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 |
   +------------------------------------------------------------------------+ */

/*---------------------------------------------------------------
        CLASS: CFeatureExtraction
        FILE: CFeatureExtraction_LSD_BLD.cpp
        AUTHOR: Raghavender Sahdev <raghavendersahdev@gmail.com>
  ---------------------------------------------------------------*/

#include "vision-precomp.h"  // Precompiled headers
#include <mrpt/system/os.h>
#include <mrpt/vision/CFeatureExtraction.h>  // important import
#include <mrpt/utils/CMemoryStream.h>
#include <mrpt/otherlibs/do_opencv_includes.h>

#ifdef HAVE_OPENCV_XFEATURES2D
#include <opencv2/xfeatures2d.hpp>
#endif
#ifdef HAVE_OPENCV_LINE_DESCRIPTOR
#include <opencv2/line_descriptor.hpp>
using namespace cv::line_descriptor;
#endif

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

#if defined(HAVE_OPENCV_XFEATURES2D) && defined(HAVE_OPENCV_LINE_DESCRIPTOR)
#define HAVE_OPENCV_WITH_LSD 1
#else
#define HAVE_OPENCV_WITH_LSD 0
#endif

void CFeatureExtraction::extractFeaturesLSD(
        const mrpt::utils::CImage& inImg, CFeatureList& feats, unsigned int init_ID,
        unsigned int nDesiredFeatures, const TImageROI& ROI) const
{
        MRPT_START
#if (!HAVE_OPENCV_WITH_LSD)
        THROW_EXCEPTION(
                "This function requires OpenCV modules: xfeatures2d, line_descriptor");
#else
        using namespace cv;

        vector<KeyPoint> cv_feats;  // The opencv keypoint output vector
        vector<KeyLine> cv_line;

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

        const Mat theImg = cvarrToMat(inImg_gray.getAs<IplImage>());
        /* create a random binary mask */
        cv::Mat mask = Mat::ones(theImg.size(), CV_8UC1);

        Ptr<LSDDetector> bd = LSDDetector::createLSDDetector();

        /* extract lines */
        cv::Mat output = theImg.clone();
        bd->detect(
                theImg, cv_line, options.LSDOptions.scale, options.LSDOptions.nOctaves,
                mask);

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

        // Now:
        //  1) Sort them by "response":
        // sort the LSD features by line length
        for (size_t i = 0; i < N; i++)
        {
                for (size_t j = i + 1; j < N; j++)
                {
                        if (cv_line.at(j).lineLength > cv_line.at(i).lineLength)
                        {
                                KeyLine temp_line = cv_line.at(i);
                                cv_line.at(i) = cv_line.at(j);
                                cv_line.at(j) = temp_line;
                        }
                }
        }

        //  2) Filter by "min-distance" (in options.FASTOptions.min_distance)  //
        //  NOT REQUIRED FOR LSD Features
        //  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.)

        unsigned int nMax =
                (nDesiredFeatures != 0 && N > nDesiredFeatures) ? nDesiredFeatures : N;
        const int offset = (int)this->options.patchSize / 2 + 1;
        const size_t 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();

        /* draw lines extracted from octave 0 */
        if (output.channels() == 1) cvtColor(output, output, COLOR_GRAY2BGR);

        while (cont != nMax && i != N)
        {
                KeyLine kl = cv_line[i];
                KeyPoint kp;

                if (kl.octave == 0)
                {
                        Point pt1 = Point2f(kl.startPointX, kl.startPointY);
                        Point pt2 = Point2f(kl.endPointX, kl.endPointY);

                        kp.pt.x = (pt1.x + pt2.x) / 2;
                        kp.pt.y = (pt1.y + pt2.y) / 2;
                        i++;
                        // Patch out of the image??
                        const int xBorderInf = (int)floor(kp.pt.x - size_2);
                        const int xBorderSup = (int)floor(kp.pt.x + size_2);
                        const int yBorderInf = (int)floor(kp.pt.y - size_2);
                        const int yBorderSup = (int)floor(kp.pt.y + size_2);

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

                        // All tests passed: add new feature:
                        CFeature::Ptr ft = std::make_shared<CFeature>();
                        ft->type = featLSD;
                        ft->ID = nextID++;
                        ft->x = kp.pt.x;
                        ft->y = kp.pt.y;
                        ft->x2[0] = pt1.x;
                        ft->x2[1] = pt2.x;
                        ft->y2[0] = pt1.y;
                        ft->y2[1] = pt2.y;
                        ft->response = kl.response;
                        // ft->orientation              = kp.angle;
                        ft->scale = kl.octave;
                        // 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;
                // cout << ft->x << "  " << ft->y << endl;
        }

#endif
        MRPT_END
}

/************************************************************************************************
*                                               internal_computeBLDDescriptors
************************************************************************************************/
void CFeatureExtraction::internal_computeBLDLineDescriptors(
        const mrpt::utils::CImage& in_img, CFeatureList& in_features) const
{
#if (!HAVE_OPENCV_WITH_LSD)
        THROW_EXCEPTION(
                "This function requires OpenCV modules: xfeatures2d, line_descriptor");
#else
        using namespace cv;

        if (in_features.empty()) return;

        const CImage img_grayscale(in_img, FAST_REF_OR_CONVERT_TO_GRAY);
        const Mat img = cvarrToMat(img_grayscale.getAs<IplImage>());

        vector<KeyPoint> cv_feats;  // OpenCV keypoint output vector
        Mat cv_descs;  // OpenCV descriptor output

        cv::Mat mask = Mat::ones(img.size(), CV_8UC1);

        BinaryDescriptor::Params params;
        params.ksize_ = options.BLDOptions.ksize_;
        params.reductionRatio = options.BLDOptions.reductionRatio;
        params.numOfOctave_ = options.BLDOptions.numOfOctave;
        params.widthOfBand_ = options.BLDOptions.widthOfBand;

        Ptr<BinaryDescriptor> bd2 =
                BinaryDescriptor::createBinaryDescriptor(params);
        /* compute lines */
        std::vector<KeyLine> keylines;

        bd2->detect(img, keylines, mask);

        /* compute descriptors */
        bd2->compute(img, keylines, cv_descs);
        keylines.resize(in_features.size());

        // -----------------------------------------------------------------
        // MRPT Wrapping
        // -----------------------------------------------------------------
        CFeatureList::iterator itList;
        int i;
        for (i = 0, itList = in_features.begin(); itList != in_features.end();
                 itList++, i++)
        {
                CFeature::Ptr ft = *itList;

                // Get the BLD descriptor
                ft->descriptors.BLD.resize(cv_descs.cols);
                for (int m = 0; m < cv_descs.cols; ++m)
                        ft->descriptors.BLD[m] =
                                cv_descs.at<int>(i, m);  // Get the SURF descriptor
        }  // end for

#endif  // end of opencv3 version check
}  // end internal_computeBLDDescriptors