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Open Source Computer Vision Library
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74 lines
2.1 KiB
74 lines
2.1 KiB
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Features2D |
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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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