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111 lines
4.1 KiB
Markdown
111 lines
4.1 KiB
Markdown
Features2d {#tutorial_ug_features2d}
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==========
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Detectors
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---------
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Descriptors
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-----------
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Matching keypoints
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------------------
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### The code
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We will start with a short sample \`opencv/samples/cpp/matcher_simple.cpp\`:
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@code{.cpp}
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Mat img1 = imread(argv[1], IMREAD_GRAYSCALE);
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Mat img2 = imread(argv[2], IMREAD_GRAYSCALE);
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if(img1.empty() || img2.empty())
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{
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printf("Can't read one of the images\n");
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return -1;
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}
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// detecting keypoints
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SurfFeatureDetector detector(400);
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vector<KeyPoint> keypoints1, keypoints2;
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detector.detect(img1, keypoints1);
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detector.detect(img2, keypoints2);
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// computing descriptors
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SurfDescriptorExtractor extractor;
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Mat descriptors1, descriptors2;
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extractor.compute(img1, keypoints1, descriptors1);
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extractor.compute(img2, keypoints2, descriptors2);
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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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// 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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@endcode
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### The code explained
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Let us break the code down.
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@code{.cpp}
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Mat img1 = imread(argv[1], IMREAD_GRAYSCALE);
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Mat img2 = imread(argv[2], IMREAD_GRAYSCALE);
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if(img1.empty() || img2.empty())
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{
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printf("Can't read one of the images\n");
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return -1;
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}
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@endcode
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We load two images and check if they are loaded correctly.
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@code{.cpp}
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// detecting keypoints
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Ptr<FeatureDetector> detector = FastFeatureDetector::create(15);
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vector<KeyPoint> keypoints1, keypoints2;
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detector->detect(img1, keypoints1);
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detector->detect(img2, keypoints2);
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@endcode
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First, we create an instance of a keypoint detector. All detectors inherit the abstract
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FeatureDetector interface, but the constructors are algorithm-dependent. The first argument to each
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detector usually controls the balance between the amount of keypoints and their stability. The range
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of values is different for different detectors (For instance, *FAST* threshold has the meaning of
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pixel intensity difference and usually varies in the region *[0,40]*. *SURF* threshold is applied to
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a Hessian of an image and usually takes on values larger than *100*), so use defaults in case of
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doubt.
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@code{.cpp}
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// computing descriptors
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Ptr<SURF> extractor = SURF::create();
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Mat descriptors1, descriptors2;
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extractor->compute(img1, keypoints1, descriptors1);
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extractor->compute(img2, keypoints2, descriptors2);
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@endcode
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We create an instance of descriptor extractor. The most of OpenCV descriptors inherit
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DescriptorExtractor abstract interface. Then we compute descriptors for each of the keypoints. The
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output Mat of the DescriptorExtractor::compute method contains a descriptor in a row *i* for each
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*i*-th keypoint. Note that the method can modify the keypoints vector by removing the keypoints such
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that a descriptor for them is not defined (usually these are the keypoints near image border). The
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method makes sure that the ouptut keypoints and descriptors are consistent with each other (so that
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the number of keypoints is equal to the descriptors row count). :
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@code{.cpp}
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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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@endcode
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Now that we have descriptors for both images, we can match them. First, we create a matcher that for
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each descriptor from image 2 does exhaustive search for the nearest descriptor in image 1 using
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Euclidean metric. Manhattan distance is also implemented as well as a Hamming distance for Brief
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descriptor. The output vector matches contains pairs of corresponding points indices. :
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@code{.cpp}
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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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@endcode
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The final part of the sample is about visualizing the matching results.
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