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6f3163f62d
Added the defaultNorm() method to the DescriptorExtractor class. This method returns the default norm type for each descriptor type. The tests and C/C++ samples were updated to get the norm type directly from the DescriptorExtractor inherited classes. This was reported in feature report #2182 (http://code.opencv.org/issues/2182). It will make it possible to get the norm type usually applied matching method for each descriptor, instead of passing it manually.
75 lines
2.1 KiB
C++
75 lines
2.1 KiB
C++
/*
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* shape_context.cpp -- Shape context demo for shape matching
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*/
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#include "opencv2/shape.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/features2d/features2d.hpp"
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#include "opencv2/nonfree/nonfree.hpp"
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#include <opencv2/core/utility.hpp>
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#include <iostream>
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#include <string>
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using namespace std;
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using namespace cv;
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static void help()
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{
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printf("\nThis program demonstrates how to use common interface for shape transformers\n"
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"Call\n"
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"shape_transformation [image1] [image2]\n");
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}
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int main(int argc, char** argv)
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{
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help();
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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() || argc<2)
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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(5000);
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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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BFMatcher matcher(extractor.defaultNorm());
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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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// extract points
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vector<Point2f> pts1, pts2;
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for (size_t ii=0; ii<keypoints1.size(); ii++)
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pts1.push_back( keypoints1[ii].pt );
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for (size_t ii=0; ii<keypoints2.size(); ii++)
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pts2.push_back( keypoints2[ii].pt );
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// Apply TPS
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Ptr<ThinPlateSplineShapeTransformer> mytps = createThinPlateSplineShapeTransformer(25000); //TPS with a relaxed constraint
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mytps->estimateTransformation(pts1, pts2, matches);
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mytps->warpImage(img2, img2);
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imshow("Tranformed", img2);
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waitKey(0);
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return 0;
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}
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