CFeatureExtraction_ORB.cpp

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
   |                                                                        |
   | Copyright (c) 2005-2019, Individual contributors, see AUTHORS file     |
   | See: https://www.mrpt.org/Authors - All rights reserved.               |
   | Released under BSD License. See: https://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>

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

void CFeatureExtraction::extractFeaturesORB(
    const mrpt::img::CImage& inImg, CFeatureList& feats,
    const unsigned int init_ID, const unsigned int nDesiredFeatures,
    const TImageROI& ROI)
{
    MRPT_UNUSED_PARAM(ROI);
    MRPT_START

    mrpt::system::CTimeLoggerEntry tle(profiler, "extractFeaturesORB");

#if MRPT_HAS_OPENCV
    using namespace cv;

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

    const bool use_precomputed_feats = feats.size() > 0;

    if (use_precomputed_feats)
    {
        cv_feats.resize(feats.size());
        for (size_t k = 0; k < cv_feats.size(); ++k)
        {
            cv_feats[k].pt.x = feats[k].keypoint.pt.x;
            cv_feats[k].pt.y = feats[k].keypoint.pt.y;
        }
    }

    // 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& cvImg = inImg_gray.asCvMatRef();

    // The detector and descriptor
    profiler.enter("extractFeaturesORB.openCV_detectAndCompute");

#if MRPT_OPENCV_VERSION_NUM < 0x300
    Ptr<Feature2D> orb = Algorithm::create<Feature2D>("Feature2D.ORB");
    orb->operator()(cvImg, Mat(), cv_feats, cv_descs, use_precomputed_feats);
#else
    const size_t n_feats_2_extract =
        nDesiredFeatures == 0 ? 1000 : 3 * nDesiredFeatures;
    Ptr<cv::ORB> orb = cv::ORB::create(
        n_feats_2_extract, options.ORBOptions.scale_factor,
        options.ORBOptions.n_levels);
    orb->detectAndCompute(
        cvImg, Mat(), cv_feats, cv_descs, use_precomputed_feats);
#endif
    profiler.leave("extractFeaturesORB.openCV_detectAndCompute");

    CTimeLoggerEntry tle2(profiler, "extractFeaturesORB.fillFeatsStruct");

    const size_t n_feats = cv_feats.size();

    // if we had input features, just convert cv_feats to CFeatures and return
    const unsigned int patch_size_2 = options.patchSize / 2;
    unsigned int f_id = init_ID;
    if (use_precomputed_feats)
    {
        for (size_t k = 0; k < n_feats; ++k)
        {
            feats[k].descriptors.ORB->resize(cv_descs.cols);
            for (int m = 0; m < cv_descs.cols; ++m)
                (*feats[k].descriptors.ORB)[m] = cv_descs.at<uchar>(k, m);

            /*
            feats[k].response   = cv_feats[k].response;
            feats[k].scale      = cv_feats[k].size;
            feats[k].angle      = cv_feats[k].orientation;
            feats[k].ID         = f_id++;
            */
            feats[k].type = featORB;

            if (options.ORBOptions.extract_patch && options.patchSize > 0)
            {
                inImg.extract_patch(
                    *feats[k].patch,
                    round(feats[k].keypoint.pt.x) - patch_size_2,
                    round(feats[k].keypoint.pt.y) - patch_size_2,
                    options.patchSize, options.patchSize);
            }
        }
        return;
    }

    //  1) Sort the fearues by "response": It's ~100 times faster to sort a list
    //  of
    //      indices "sorted_indices" than sorting directly the actual list of
    //      features "cv_feats"
    std::vector<size_t> sorted_indices(n_feats);
    for (size_t i = 0; i < n_feats; i++) sorted_indices[i] = i;
    std::sort(
        sorted_indices.begin(), sorted_indices.end(),
        KeypointResponseSorter<vector<KeyPoint>>(cv_feats));

    //  2) Filter by "min-distance" (in options.ORBOptions.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.ORBOptions.min_distance > 1;
    const unsigned int occupied_grid_cell_size =
        options.ORBOptions.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.fill(false);

    const size_t n_max_feats = nDesiredFeatures > 0
                                   ? std::min(size_t(nDesiredFeatures), n_feats)
                                   : n_feats;

    if (!options.addNewFeatures) feats.clear();
    // feats.reserve( feats.size() + n_max_feats );

    const size_t imgH = inImg.getHeight();
    const size_t imgW = inImg.getWidth();
    size_t k = 0;
    size_t c_feats = 0;
    while (c_feats < n_max_feats && k < n_feats)
    {
        const size_t idx = sorted_indices[k++];
        const KeyPoint& kp = cv_feats[idx];
        if (options.ORBOptions.extract_patch && options.patchSize > 0)
        {
            // check image boundaries for extracting the patch
            const int xBorderInf = (int)floor(kp.pt.x - patch_size_2);
            const int xBorderSup = (int)floor(kp.pt.x + patch_size_2);
            const int yBorderInf = (int)floor(kp.pt.y - patch_size_2);
            const int yBorderSup = (int)floor(kp.pt.y + patch_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 auto sect_ix = size_t(kp.pt.x * occupied_grid_cell_size_inv);
            const auto sect_iy = size_t(kp.pt.y * occupied_grid_cell_size_inv);

            if (occupied_sections(sect_ix, sect_iy))
                continue;  // Already occupied! skip.

            // Mark section as occupied
            occupied_sections(sect_ix, sect_iy) = true;
            if (sect_ix > 0) occupied_sections(sect_ix - 1, sect_iy) = true;
            if (sect_iy > 0) occupied_sections(sect_ix, sect_iy - 1) = true;
            if (sect_ix < grid_lx - 1)
                occupied_sections(sect_ix + 1, sect_iy) = true;
            if (sect_iy < grid_ly - 1)
                occupied_sections(sect_ix, sect_iy + 1) = true;
        }

        // All tests passed: add new feature:
        CFeature ft;
        ft.type = featORB;
        ft.keypoint.ID = f_id++;
        ft.keypoint.pt.x = kp.pt.x;
        ft.keypoint.pt.y = kp.pt.y;
        ft.response = kp.response;
        ft.orientation = kp.angle;
        ft.keypoint.octave = kp.octave;
        ft.patchSize = 0;

        // descriptor
        ft.descriptors.ORB.emplace();
        ft.descriptors.ORB->resize(cv_descs.cols);
        for (int m = 0; m < cv_descs.cols; ++m)
            (*ft.descriptors.ORB)[m] = cv_descs.at<uchar>(idx, m);

        if (options.ORBOptions.extract_patch && options.patchSize > 0)
        {
            ft.patchSize = options.patchSize;  // The size of the feature patch

            ft.patch.emplace();
            inImg.extract_patch(
                *ft.patch, round(kp.pt.x) - patch_size_2,
                round(kp.pt.y) - patch_size_2, options.patchSize,
                options.patchSize);  // Image patch surronding the feature
        }

        feats.emplace_back(std::move(ft));
        c_feats++;
    }
#endif
    MRPT_END
}

void CFeatureExtraction::internal_computeORBDescriptors(
    const CImage& in_img, CFeatureList& in_features)
{
#if MRPT_HAS_OPENCV
    using namespace cv;
    mrpt::system::CTimeLoggerEntry tle(
        profiler, "internal_computeORBDescriptors");

    const size_t n_feats = in_features.size();
    const CImage inImg_gray(in_img, FAST_REF_OR_CONVERT_TO_GRAY);

    // convert from CFeatureList to vector<KeyPoint>
    vector<KeyPoint> cv_feats(n_feats);
    for (size_t k = 0; k < n_feats; ++k)
    {
        KeyPoint& kp = cv_feats[k];
        kp.pt.x = in_features[k].keypoint.pt.x;
        kp.pt.y = in_features[k].keypoint.pt.y;
        kp.angle = in_features[k].orientation;
        kp.size = in_features[k].keypoint.octave;
    }  // end-for

    const Mat& cvImg = inImg_gray.asCvMatRef();
    Mat cv_descs;

#if MRPT_OPENCV_VERSION_NUM < 0x300
    Ptr<Feature2D> orb = Algorithm::create<Feature2D>("Feature2D.ORB");
    orb->operator()(
        cvImg, cv::noArray(), cv_feats, cv_descs,
        true /* use_precomputed_feats */);
#else
    Ptr<cv::ORB> orb = cv::ORB::create(
        n_feats, options.ORBOptions.scale_factor, options.ORBOptions.n_levels);
    orb->detectAndCompute(
        cvImg, cv::noArray(), cv_feats, cv_descs,
        true /* use_precomputed_feats */);
#endif

    // add descriptor to CFeatureList
    for (size_t k = 0; k < n_feats; ++k)
    {
        in_features[k].descriptors.ORB.emplace();
        auto& orb_desc = *in_features[k].descriptors.ORB;
        orb_desc.resize(cv_descs.cols);
        for (int i = 0; i < cv_descs.cols; ++i)
            orb_desc[i] = cv_descs.at<uchar>(k, i);

    }  // end-for
#endif

}  // end-internal_computeORBImageDescriptors