OpenCV  4.10.0-dev
Open Source Computer Vision
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Detection of planar objects

Prev Tutorial: Features2D + Homography to find a known object
Next Tutorial: AKAZE local features matching

Original author Victor Eruhimov
Compatibility OpenCV >= 3.0

The goal of this tutorial is to learn how to use features2d and calib3d modules for detecting known planar objects in scenes.

Test data: use images in your data folder, for instance, box.png and box_in_scene.png.

  • Create a new console project. Read two input images. :
    Mat img1 = imread(argv[1], IMREAD_GRAYSCALE);
    Mat img2 = imread(argv[2], IMREAD_GRAYSCALE);
    
  • Detect keypoints in both images and compute descriptors for each of the keypoints. :
    // detecting keypoints
    Ptr<Feature2D> surf = SURF::create();
    vector<KeyPoint> keypoints1;
    Mat descriptors1;
    surf->detectAndCompute(img1, Mat(), keypoints1, descriptors1);
    
    ... // do the same for the second image
    
  • Now, find the closest matches between descriptors from the first image to the second: :
    // matching descriptors
    BruteForceMatcher<L2<float> > matcher;
    vector<DMatch> matches;
    matcher.match(descriptors1, descriptors2, matches);
    
  • Visualize the results: :
    // drawing the results
    namedWindow("matches", 1);
    Mat img_matches;
    drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);
    imshow("matches", img_matches);
    waitKey(0);
    
  • Find the homography transformation between two sets of points: :
    vector<Point2f> points1, points2;
    // fill the arrays with the points
    ....
    Mat H = findHomography(Mat(points1), Mat(points2), RANSAC, ransacReprojThreshold);
    
  • Create a set of inlier matches and draw them. Use perspectiveTransform function to map points with homography:

    Mat points1Projected; perspectiveTransform(Mat(points1), points1Projected, H);

  • Use drawMatches for drawing inliers.