opencv/samples/cpp/descriptor_extractor_matcher.cpp

#include <highgui.h>
#include "opencv2/core/core.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/features2d/features2d.hpp"
#include <iostream>

using namespace cv;
using namespace std;

#define DRAW_RICH_KEYPOINTS_MODE     0
#define DRAW_OUTLIERS_MODE           0

void warpPerspectiveRand( const Mat& src, Mat& dst, Mat& H, RNG& rng )
{
    H.create(3, 3, CV_32FC1);
    H.at<float>(0,0) = rng.uniform( 0.8f, 1.2f);
    H.at<float>(0,1) = rng.uniform(-0.1f, 0.1f);
    H.at<float>(0,2) = rng.uniform(-0.1f, 0.1f)*src.cols;
    H.at<float>(1,0) = rng.uniform(-0.1f, 0.1f);
    H.at<float>(1,1) = rng.uniform( 0.8f, 1.2f);
    H.at<float>(1,2) = rng.uniform(-0.1f, 0.1f)*src.rows;
    H.at<float>(2,0) = rng.uniform( -1e-4f, 1e-4f);
    H.at<float>(2,1) = rng.uniform( -1e-4f, 1e-4f);
    H.at<float>(2,2) = rng.uniform( 0.8f, 1.2f);

    warpPerspective( src, dst, H, src.size() );
}

const string winName = "correspondences";

void doIteration( const Mat& img1, Mat& img2, bool isWarpPerspective,
                  vector<KeyPoint>& keypoints1, const Mat& descriptors1,
                  Ptr<FeatureDetector>& detector, Ptr<DescriptorExtractor>& descriptorExtractor,
                  Ptr<DescriptorMatcher>& descriptorMatcher,
                  double ransacReprojThreshold, RNG& rng )
{
    assert( !img1.empty() );
    Mat H12;
    if( isWarpPerspective )
        warpPerspectiveRand(img1, img2, H12, rng );
    else
        assert( !img2.empty()/* && img2.cols==img1.cols && img2.rows==img1.rows*/ );

    cout << endl << "< Extracting keypoints from second image..." << endl;
    vector<KeyPoint> keypoints2;
    detector->detect( img2, keypoints2 );
    cout << keypoints2.size() << " points" << endl << ">" << endl;

    if( !H12.empty() )
    {
        cout << "< Evaluate feature detector..." << endl;
        float repeatability;
        int correspCount;
        evaluateFeatureDetector( img1, img2, H12, &keypoints1, &keypoints2, repeatability, correspCount );
        cout << "repeatability = " << repeatability << endl;
        cout << "correspCount = " << correspCount << endl;
        cout << ">" << endl;
    }

    cout << "< Computing descriptors for keypoints from second image..." << endl;
    Mat descriptors2;
    descriptorExtractor->compute( img2, keypoints2, descriptors2 );
    cout << ">" << endl;

    cout << "< Matching descriptors..." << endl;
    vector<DMatch> matches;
    descriptorMatcher->match( descriptors1, descriptors2, matches, Mat() );
    cout << ">" << endl;

    if( !H12.empty() )
    {
        cout << "< Evaluate descriptor match..." << endl;
        vector<Point2f> curve;
        Ptr<GenericDescriptorMatch> gdm = new VectorDescriptorMatch( descriptorExtractor, descriptorMatcher );
        evaluateGenericDescriptorMatcher( img1, img2, H12, keypoints1, keypoints2, 0, 0, curve, gdm );
        for( float l_p = 0; l_p < 1 - FLT_EPSILON; l_p+=0.1 )
            cout << "1-precision = " << l_p << "; recall = " << getRecall( curve, l_p ) << endl;
        cout << ">" << endl;
    }

    vector<int> trainIdxs( matches.size() );
    for( size_t i = 0; i < matches.size(); i++ )
        trainIdxs[i] = matches[i].indexTrain;

    if( !isWarpPerspective && ransacReprojThreshold >= 0 )
    {
        cout << "< Computing homography (RANSAC)..." << endl;
        vector<Point2f> points1; KeyPoint::convert(keypoints1, points1);
        vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
        H12 = findHomography( Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold );
        cout << ">" << endl;
    }

    Mat drawImg;
    if( !H12.empty() ) // filter outliers
    {
        vector<char> matchesMask( matches.size(), 0 );
        vector<Point2f> points1; KeyPoint::convert(keypoints1, points1);
        vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
        Mat points1t; perspectiveTransform(Mat(points1), points1t, H12);
        for( size_t i1 = 0; i1 < points1.size(); i1++ )
        {
            if( norm(points2[i1] - points1t.at<Point2f>(i1,0)) < 4 ) // inlier
                matchesMask[i1] = 1;
        }
        // draw inliers
        drawMatches( img1, keypoints1, img2, keypoints2, matches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask
#if DRAW_RICH_KEYPOINTS_MODE
                     , DrawMatchesFlags::DRAW_RICH_KEYPOINTS
#endif
                   );

#if DRAW_OUTLIERS_MODE
        // draw outliers
        for( size_t i1 = 0; i1 < matchesMask.size(); i1++ )
            matchesMask[i1] = !matchesMask[i1];
        drawMatches( img1, keypoints1, img2, keypoints2, matches, drawImg, CV_RGB(0, 0, 255), CV_RGB(255, 0, 0), matchesMask,
                     DrawMatchesFlags::DRAW_OVER_OUTIMG | DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
#endif
    }
    else
        drawMatches( img1, keypoints1, img2, keypoints2, matches, drawImg );

    imshow( winName, drawImg );
}

int main(int argc, char** argv)
{
    if( argc != 4 && argc != 6 )
    {
        cout << "Format:" << endl;
        cout << "case1: second image is obtained from the first (given) image using random generated homography matrix" << endl;
        cout << argv[0] << " [detectorType] [descriptorType] [image1]" << endl;
        cout << "case2: both images are given. If ransacReprojThreshold>=0 then homography matrix are calculated" << endl;
        cout << argv[0] << " [detectorType] [descriptorType] [image1] [image2] [ransacReprojThreshold]" << endl;
        cout << endl << "Mathes are filtered using homography matrix in case1 and case2 (if ransacReprojThreshold>=0)" << endl;
        return -1;
    }
    bool isWarpPerspective = argc == 4;
    double ransacReprojThreshold = -1;
    if( !isWarpPerspective )
        ransacReprojThreshold = atof(argv[5]);

    cout << "< Creating detector, descriptor extractor and descriptor matcher ..." << endl;
    Ptr<FeatureDetector> detector = createFeatureDetector( argv[1] );
    Ptr<DescriptorExtractor> descriptorExtractor = createDescriptorExtractor( argv[2] );
    Ptr<DescriptorMatcher> descriptorMatcher = createDescriptorMatcher( "BruteForce" );
    cout << ">" << endl;
    if( detector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty()  )
    {
        cout << "Can not create detector or descriptor exstractor or descriptor matcher of given types" << endl;
        return -1;
	}
		
    cout << "< Reading the images..." << endl;
    Mat img1 = imread( argv[3] ), img2;
    if( !isWarpPerspective )
        img2 = imread( argv[4] );
    cout << ">" << endl;
    if( img1.empty() || (!isWarpPerspective && img2.empty()) )
    {
        cout << "Can not read images" << endl;
        return -1;
    }

    cout << endl << "< Extracting keypoints from first image..." << endl;
    vector<KeyPoint> keypoints1;
    detector->detect( img1, keypoints1 );
    cout << keypoints1.size() << " points" << endl << ">" << endl;

    cout << "< Computing descriptors for keypoints from first image..." << endl;
    Mat descriptors1;
    descriptorExtractor->compute( img1, keypoints1, descriptors1 );
    cout << ">" << endl;

    namedWindow(winName, 1);
    RNG rng = theRNG();
    doIteration( img1, img2, isWarpPerspective, keypoints1, descriptors1,
                 detector, descriptorExtractor, descriptorMatcher,
                 ransacReprojThreshold, rng );
    for(;;)
    {
        char c = (char)cvWaitKey(0);
        if( c == '\x1b' ) // esc
        {
            cout << "Exiting ..." << endl;
            return 0;
        }
        else if( isWarpPerspective )
        {
            doIteration( img1, img2, isWarpPerspective, keypoints1, descriptors1,
                         detector, descriptorExtractor, descriptorMatcher,
                         ransacReprojThreshold, rng );
        }
    }
    waitKey(0);
    return 0;
}