surf_flann_based_matcher.cpp

/**
 * @file SURF_FlannMatcher
 * @brief SURF detector + descriptor + FLANN Matcher
 * @author A. Huaman
 */


#include <stdio.h>
#include <iostream>
// these are under /usr/local/include
//#include <opencv2/opencv_modules.hpp>
#include <opencv2/xfeatures2d.hpp>      // since SURF's moved
#include <opencv2/opencv.hpp>
//#include <opencv2/core/core.hpp>
//#include <opencv2/highgui/highgui.hpp>
//#include <opencv2/xfeatures2d/nonfree.hpp>

//#ifndef HAVE_OPENCV_NONFREE
//
//int main(int, char**)
//{
//    printf("The sample requires nonfree module that is not available in your OpenCV distribution.\n");
//    return -1;
//}
//
//#else
//
//# include "opencv2/core/core.hpp"
//# include "opencv2/features2d/features2d.hpp"
//# include "opencv2/highgui/highgui.hpp"
//# include "opencv2/nonfree/features2d.hpp"

using namespace cv;
using namespace cv::xfeatures2d;

void readme();

/**
 * @function main
 * @brief Main function
 */
int main( int argc, char** argv )
{
    if( argc != 3 )
    { readme(); return -1; }

    Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
    Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );

    if( !img_1.data || !img_2.data )
    { printf(" --(!) Error reading images \n"); return -1; }

    //-- Step 1: Detect the keypoints using SURF Detector
    int minHessian = 400;

    //SurfFeatureDetector detector( minHessian );
    Ptr<SURF> surf = SURF::create(minHessian);
    std::vector<KeyPoint> keypoints_1, keypoints_2;

    surf->detect( img_1, keypoints_1 );
    surf->detect( img_2, keypoints_2 );

    //-- Step 2: Calculate descriptors (feature vectors)
    //SurfDescriptorExtractor extractor;

    Mat descriptors_1, descriptors_2;

    surf->compute( img_1, keypoints_1, descriptors_1 );
    surf->compute( img_2, keypoints_2, descriptors_2 );

    //-- Step 3: Matching descriptor vectors using FLANN matcher
    //FlannBasedMatcher matcher;
    //std::vector< DMatch > matches;
    //matcher.match( descriptors_1, descriptors_2, matches );

    //double max_dist = 0; double min_dist = 100;

    ////-- Quick calculation of max and min distances between keypoints
    //for( int i = 0; i < descriptors_1.rows; i++ )
    //{ double dist = matches[i].distance;
    //    if( dist < min_dist ) min_dist = dist;
    //    if( dist > max_dist ) max_dist = dist;
    //}

    //printf("-- Max dist : %f \n", max_dist );
    //printf("-- Min dist : %f \n", min_dist );

    ////-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
    ////-- or a small arbitary value ( 0.02 ) in the event that min_dist is very
    ////-- small)
    ////-- PS.- radiusMatch can also be used here.
    //std::vector< DMatch > good_matches;

    //for( int i = 0; i < descriptors_1.rows; i++ )
    //{ if( matches[i].distance <= max(2*min_dist, 0.02) )
    //    { good_matches.push_back( matches[i]); }
    //}

    ////-- Draw only "good" matches
    //Mat img_matches;
    //drawMatches( img_1, keypoints_1, img_2, keypoints_2,
    //        good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
    //        std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

    ////-- Show detected matches
    //imshow( "Good Matches", img_matches );

    //for( int i = 0; i < (int)good_matches.size(); i++ )
    //{ printf( "-- Good Match [%d] Keypoint 1: %d  -- Keypoint 2: %d  \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); }

    //waitKey(0);

    //return 0;
}

/**
 * @function readme
 */
void readme()
{ printf(" Usage: ./SURF_FlannMatcher <img1> <img2>\n"); }

//#endif