Detection of planar objects — OpenCV 2.4.10.0 documentation

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# Detection of planar objects

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);
       waitKey(0);

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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