Open Source Computer Vision Library https://opencv.org/
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Features2D
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.. highlight:: cpp
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``.
#.
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);
#.
Detect keypoints in both images. ::
// detecting keypoints
FastFeatureDetector detector(15);
vector<KeyPoint> keypoints1;
detector.detect(img1, keypoints1);
... // do the same for the second image
#.
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
#.
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), CV_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.