mirror of https://github.com/opencv/opencv.git
Merge pull request #4012 from mshabunin:doc-fixes
Vadim Pisarevsky
10 years ago
commit
a87e1c2f90
20 changed files with 172 additions and 165 deletions
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Cascade Classifier Training {#tutorial_ug_traincascade} |
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Cascade Classifier Training {#tutorial_traincascade} |
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=========================== |
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|
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Introduction |
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@ -1,110 +0,0 @@ |
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Features2d {#tutorial_ug_features2d} |
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========== |
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|
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Detectors |
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--------- |
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|
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Descriptors |
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----------- |
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|
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Matching keypoints |
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------------------ |
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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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|
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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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|
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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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|
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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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|
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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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@ -1,8 +0,0 @@ |
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OpenCV User Guide {#tutorial_user_guide} |
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================= |
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|
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- @subpage tutorial_ug_mat |
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- @subpage tutorial_ug_features2d |
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- @subpage tutorial_ug_highgui |
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- @subpage tutorial_ug_traincascade |
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- @subpage tutorial_ug_intelperc |
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