Detection of planar objects

http://docs.opencv.org/doc/tutorials/features2d/detection_of_planar_objects/detection_of_planar_objects.html#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.