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.
99 lines
2.6 KiB
C++
99 lines
2.6 KiB
C++
#include <iostream>
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#include "opencv2/opencv_modules.hpp"
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#ifdef HAVE_OPENCV_NONFREE
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#include "opencv2/core/core.hpp"
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#include "opencv2/features2d/features2d.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/cudafeatures2d.hpp"
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#include "opencv2/nonfree/cuda.hpp"
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using namespace std;
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using namespace cv;
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using namespace cv::cuda;
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static void help()
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{
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cout << "\nThis program demonstrates using SURF_CUDA features detector, descriptor extractor and BruteForceMatcher_CUDA" << endl;
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cout << "\nUsage:\n\tmatcher_simple_gpu --left <image1> --right <image2>" << endl;
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}
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int main(int argc, char* argv[])
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{
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if (argc != 5)
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{
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help();
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return -1;
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}
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GpuMat img1, img2;
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for (int i = 1; i < argc; ++i)
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{
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if (string(argv[i]) == "--left")
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{
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img1.upload(imread(argv[++i], IMREAD_GRAYSCALE));
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CV_Assert(!img1.empty());
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}
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else if (string(argv[i]) == "--right")
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{
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img2.upload(imread(argv[++i], IMREAD_GRAYSCALE));
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CV_Assert(!img2.empty());
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}
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else if (string(argv[i]) == "--help")
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{
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help();
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return -1;
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}
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}
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cv::cuda::printShortCudaDeviceInfo(cv::cuda::getDevice());
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SURF_CUDA surf;
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// detecting keypoints & computing descriptors
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GpuMat keypoints1GPU, keypoints2GPU;
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GpuMat descriptors1GPU, descriptors2GPU;
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surf(img1, GpuMat(), keypoints1GPU, descriptors1GPU);
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surf(img2, GpuMat(), keypoints2GPU, descriptors2GPU);
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cout << "FOUND " << keypoints1GPU.cols << " keypoints on first image" << endl;
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cout << "FOUND " << keypoints2GPU.cols << " keypoints on second image" << endl;
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// matching descriptors
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BFMatcher_CUDA matcher(surf.defaultNorm());
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GpuMat trainIdx, distance;
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matcher.matchSingle(descriptors1GPU, descriptors2GPU, trainIdx, distance);
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// downloading results
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vector<KeyPoint> keypoints1, keypoints2;
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vector<float> descriptors1, descriptors2;
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vector<DMatch> matches;
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surf.downloadKeypoints(keypoints1GPU, keypoints1);
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surf.downloadKeypoints(keypoints2GPU, keypoints2);
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surf.downloadDescriptors(descriptors1GPU, descriptors1);
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surf.downloadDescriptors(descriptors2GPU, descriptors2);
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BFMatcher_CUDA::matchDownload(trainIdx, distance, matches);
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// drawing the results
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Mat img_matches;
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drawMatches(Mat(img1), keypoints1, Mat(img2), keypoints2, matches, img_matches);
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namedWindow("matches", 0);
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imshow("matches", img_matches);
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waitKey(0);
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return 0;
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}
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#else
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int main()
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{
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std::cerr << "OpenCV was built without nonfree module" << std::endl;
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return 0;
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}
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#endif
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