Detection of planar objectsΒΆ

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.

  1. Create a new console project. Read two input images.

    Mat img1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
    Mat img2 = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
  2. Detect keypoints in both images.

    // detecting keypoints
    FastFeatureDetector detector(15);
    vector<KeyPoint> keypoints1;
    detector.detect(img1, keypoints1);
    ... // do the same for the second image
  3. Compute descriptors for each of the keypoints.

    // computing descriptors
    SurfDescriptorExtractor extractor;
    Mat descriptors1;
    extractor.compute(img1, keypoints1, descriptors1);
    ... // process keypoints from the second image as well
  4. 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);
  5. Visualize the results:

    // drawing the results
    namedWindow("matches", 1);
    Mat img_matches;
    drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);
    imshow("matches", img_matches);
  6. 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), CV_RANSAC, ransacReprojThreshold);
  7. Create a set of inlier matches and draw them. Use perspectiveTransform function to map points with homography:

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

  8. Use drawMatches for drawing inliers.

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