cvlia's profile - activity

@Vit DRAW_RICH_KEYPOINTS does show more variation in patch size. Thanks for the tip.

Isn't this the dimension of the descriptor, not the size of the keypoint?

I'm using Akaze detectors and descriptors to build a BOW codebook, but the detected keypoints are too small to be discriminative, often containing only a vertical line or a patch of color. I'd like to use larger patches, but I can't figure out how to pass this parameter to the Akaze detector.

For example, using the tutorial code (below), how can I include keypoints which have a radius of at least 15px?

#include <opencv2/features2d.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/opencv.hpp>
#include <vector>
#include <iostream>

using namespace std;
using namespace cv;

const float inlier_threshold = 2.5f; // Distance threshold to identify inliers
const float nn_match_ratio = 0.8f;   // Nearest neighbor matching ratio

int main(void)
{
    Mat img1 = imread("../data/graf1.png", IMREAD_GRAYSCALE);
    Mat img2 = imread("../data/graf3.png", IMREAD_GRAYSCALE);

    Mat homography;
    FileStorage fs("../data/H1to3p.xml", FileStorage::READ);
    fs.getFirstTopLevelNode() >> homography;

    vector<KeyPoint> kpts1, kpts2;
    Mat desc1, desc2;

    Ptr<AKAZE> akaze = AKAZE::create();
    akaze->detectAndCompute(img1, noArray(), kpts1, desc1);
    akaze->detectAndCompute(img2, noArray(), kpts2, desc2);

    BFMatcher matcher(NORM_HAMMING);
    vector< vector<DMatch> > nn_matches;
    matcher.knnMatch(desc1, desc2, nn_matches, 2);

    vector<KeyPoint> matched1, matched2, inliers1, inliers2;
    vector<DMatch> good_matches;
    for(size_t i = 0; i < nn_matches.size(); i++) {
        DMatch first = nn_matches[i][0];
        float dist1 = nn_matches[i][0].distance;
        float dist2 = nn_matches[i][1].distance;

        if(dist1 < nn_match_ratio * dist2) {
            matched1.push_back(kpts1[first.queryIdx]);
            matched2.push_back(kpts2[first.trainIdx]);
        }
    }

    for(unsigned i = 0; i < matched1.size(); i++) {
        Mat col = Mat::ones(3, 1, CV_64F);
        col.at<double>(0) = matched1[i].pt.x;
        col.at<double>(1) = matched1[i].pt.y;

        col = homography * col;
        col /= col.at<double>(2);
        double dist = sqrt( pow(col.at<double>(0) - matched2[i].pt.x, 2) +
                            pow(col.at<double>(1) - matched2[i].pt.y, 2));

        if(dist < inlier_threshold) {
            int new_i = static_cast<int>(inliers1.size());
            inliers1.push_back(matched1[i]);
            inliers2.push_back(matched2[i]);
            good_matches.push_back(DMatch(new_i, new_i, 0));
        }
    }

    Mat res;
    drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
    imwrite("res.png", res);

    double inlier_ratio = inliers1.size() * 1.0 / matched1.size();
    cout << "A-KAZE Matching Results" << endl;
    cout << "*******************************" << endl;
    cout << "# Keypoints 1:                        \t" << kpts1.size() << endl;
    cout << "# Keypoints 2:                        \t" << kpts2.size() << endl;
    cout << "# Matches:                            \t" << matched1.size() << endl;
    cout << "# Inliers:                            \t" << inliers1.size() << endl;
    cout << "# Inliers Ratio:                      \t" << inlier_ratio << endl;
    cout << endl;

    return 0;
}