CFeatureExtraction_FAST.cpp

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
   |                                                                        |
   | Copyright (c) 2005-2020, 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/3rdparty/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 mrpt::math;
using namespace std;

void CFeatureExtraction::extractFeaturesFAST(
    const mrpt::img::CImage& inImg, CFeatureList& feats, unsigned int init_ID,
    unsigned int nDesiredFeatures)
{
    MRPT_START

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

#if MRPT_HAS_OPENCV
    using namespace cv;

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

    // 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 = inImg_gray.asCvMat<cv::Mat>(SHALLOW_COPY);

#if MRPT_OPENCV_VERSION_NUM < 0x300
    FastFeatureDetector fastDetector(
        options.FASTOptions.threshold, options.FASTOptions.nonmax_suppression);
    fastDetector.detect(theImg, cv_feats);
#else
    Ptr<cv::FastFeatureDetector> fastDetector = cv::FastFeatureDetector::create(
        options.FASTOptions.threshold, options.FASTOptions.nonmax_suppression);
    fastDetector->detect(theImg, cv_feats);
#endif

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

    // Use KLT response instead of the OpenCV's original "response" field:
    if (options.FASTOptions.use_KLT_response)
    {
        const unsigned int KLT_half_win = 4;
        const unsigned int max_x = inImg_gray.getWidth() - 1 - KLT_half_win;
        const unsigned int max_y = inImg_gray.getHeight() - 1 - KLT_half_win;
        for (size_t i = 0; i < N; i++)
        {
            const unsigned int x = mrpt::round(cv_feats[i].pt.x);
            const unsigned int y = mrpt::round(cv_feats[i].pt.y);
            if (x > KLT_half_win && y > KLT_half_win && x <= max_x &&
                y <= max_y)
                cv_feats[i].response =
                    inImg_gray.KLT_response(x, y, KLT_half_win);
            else
                cv_feats[i].response = -100;
        }
    }

    // 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 "cv_feats"
    std::vector<size_t> sorted_indices(N);
    for (size_t i = 0; i < N; 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.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 float occupied_grid_cell_size = options.FASTOptions.min_distance / 2;
    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);
    occupied_sections.fill(false);

    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();

    while (cont != nMax && i != N)
    {
        // Take the next feature fromt the ordered list of good features:
        const KeyPoint& kp = cv_feats[sorted_indices[i]];
        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.

        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 = featFAST;
        ft.keypoint.ID = nextID++;
        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 = options.patchSize;  // The size of the feature patch

        if (options.patchSize > 0)
        {
            ft.patch.emplace();
            inImg.extract_patch(
                *ft.patch, round(ft.keypoint.pt.x) - offset,
                round(ft.keypoint.pt.y) - offset, options.patchSize,
                options.patchSize);  // Image patch surronding the feature
        }
        feats.emplace_back(std::move(ft));
        ++cont;
    }

#endif
    MRPT_END
}