beblid-demo-code

beblid-demo.cpp

#include <iostream>
#include <opencv2/opencv.hpp>
#include "BEBLID.h"

int main() {

  // Read the input images in grayscale format (CV_8UC1)
  cv::Mat img1 = cv::imread("../imgs/img1.jpg", cv::IMREAD_GRAYSCALE);
  cv::Mat img2 = cv::imread("../imgs/img3.jpg", cv::IMREAD_GRAYSCALE);

  // Create the feature detector, for example ORB
  auto detector = cv::ORB::create();

  // Detect features in both images
  std::vector<cv::KeyPoint> points1, points2;
  detector->detect(img1, points1);
  detector->detect(img2, points2);
  std::cout << "Detected  " << points1.size() << " kps in image1" << std::endl;
  std::cout << "Detected  " << points2.size() << " kps in image2" << std::endl;

  // Use 32 bytes per descriptor and configure the scale factor for ORB detector
  auto descriptor = BEBLID::create(256, 0.75);

  // Describe the detected features i both images
  cv::Mat descriptors1, descriptors2;
  descriptor->compute(img1, points1, descriptors1);
  descriptor->compute(img2, points2, descriptors2);
  std::cout << "Points described" << std::endl;

  // Match the generated descriptors for img1 and img2 using brute force matching
  cv::BFMatcher matcher(cv::NORM_HAMMING, true);
  std::vector<cv::DMatch> matches;
  matcher.match(descriptors1, descriptors2, matches);
  std::cout << "Number of matches: " << matches.size() << std::endl;

  // If there is not enough matches exit
  if (matches.size() < 4) exit(-1);

  // Take only the matched points that will be used to calculate the
  // transformation between both images
  std::vector<cv::Point2d> matched_pts1, matched_pts2;
  for (cv::DMatch match : matches) {
    matched_pts1.push_back(points1[match.queryIdx].pt);
    matched_pts2.push_back(points2[match.trainIdx].pt);
  }

  // Find the homography that transforms a point in the first image to a point in the second image.
  cv::Mat inliers;
  cv::Mat H = cv::findHomography(matched_pts1, matched_pts2, cv::RANSAC, 3, inliers);
  // Print the number of inliers, that is, the number of points correctly
  // mapped by the transformation that we have estimated
  std::cout << "Number of inliers " << cv::sum(inliers)[0]
            << " ( " << (100.0f * cv::sum(inliers)[0] / matches.size()) << "% )" << std::endl;

  // Convert the image to BRG format from grayscale
  cv::cvtColor(img1, img1, cv::COLOR_GRAY2BGR);
  cv::cvtColor(img2, img2, cv::COLOR_GRAY2BGR);

  // Draw all the matched keypoints in red color
  cv::Mat all_matches_img;
  cv::drawMatches(img1, points1, img2, points2, matches,
                  all_matches_img, CV_RGB(255, 0, 0), CV_RGB(255, 0, 0));

  // Show and save the result
  cv::imshow("All matches", all_matches_img);
  cv::waitKey();
  return 0;
}