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75 lines
2.1 KiB
ReStructuredText
75 lines
2.1 KiB
ReStructuredText
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##########
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Features2D
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##########
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.. highlight:: cpp
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Detection of planar objects
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===========================
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The goal of this tutorial is to learn how to use *features2d* and *calib3d* modules for detecting known planar objects in scenes.
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*Test data*: use images in your data folder, for instance, ``box.png`` and ``box_in_scene.png``.
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#.
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Create a new console project. Read two input images. ::
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Mat img1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
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Mat img2 = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
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#.
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Detect keypoints in both images. ::
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// detecting keypoints
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FastFeatureDetector detector(15);
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vector<KeyPoint> keypoints1;
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detector.detect(img1, keypoints1);
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... // do the same for the second image
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#.
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Compute descriptors for each of the keypoints. ::
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// computing descriptors
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SurfDescriptorExtractor extractor;
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Mat descriptors1;
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extractor.compute(img1, keypoints1, descriptors1);
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... // process keypoints from the second image as well
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#.
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Now, find the closest matches between descriptors from the first image to the second: ::
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// matching descriptors
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BruteForceMatcher<L2<float> > matcher;
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vector<DMatch> matches;
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matcher.match(descriptors1, descriptors2, matches);
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#.
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Visualize the results: ::
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// drawing the results
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namedWindow("matches", 1);
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Mat img_matches;
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drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);
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imshow("matches", img_matches);
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waitKey(0);
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#.
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Find the homography transformation between two sets of points: ::
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vector<Point2f> points1, points2;
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// fill the arrays with the points
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....
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Mat H = findHomography(Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold);
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#.
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Create a set of inlier matches and draw them. Use perspectiveTransform function to map points with homography:
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Mat points1Projected;
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perspectiveTransform(Mat(points1), points1Projected, H);
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#.
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Use ``drawMatches`` for drawing inliers.
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