Prev Tutorial: Features2D + Homography to find a known object
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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.