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@ -46,156 +46,102 @@ |
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#include <iostream> |
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#include <stdio.h> |
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#include "opencv2/core/core.hpp" |
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#include "opencv2/features2d/features2d.hpp" |
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#include "opencv2/core/utility.hpp" |
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#include "opencv2/highgui/highgui.hpp" |
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#include "opencv2/ocl/ocl.hpp" |
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#include "opencv2/nonfree/nonfree.hpp" |
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#include "opencv2/nonfree/ocl.hpp" |
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#include "opencv2/calib3d/calib3d.hpp" |
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#include "opencv2/nonfree/nonfree.hpp" |
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using namespace std; |
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using namespace cv; |
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using namespace cv::ocl; |
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//#define USE_CPU_DESCRIPTOR // use cpu descriptor extractor until ocl descriptor extractor is fixed
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//#define USE_CPU_BFMATCHER
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const int LOOP_NUM = 10; |
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const int GOOD_PTS_MAX = 50; |
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const float GOOD_PORTION = 0.15f; |
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namespace |
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{ |
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void help(); |
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void help() |
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{ |
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cout << "\nThis program demonstrates using SURF_OCL features detector and descriptor extractor" << endl; |
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cout << "\nUsage:\n\tsurf_matcher --left <image1> --right <image2>" << endl; |
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std::cout << "\nThis program demonstrates using SURF_OCL features detector and descriptor extractor" << std::endl; |
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std::cout << "\nUsage:\n\tsurf_matcher --left <image1> --right <image2> [-c]" << std::endl; |
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std::cout << "\nExample:\n\tsurf_matcher --left box.png --right box_in_scene.png" << std::endl; |
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} |
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int64 work_begin = 0; |
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int64 work_end = 0; |
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////////////////////////////////////////////////////
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// This program demonstrates the usage of SURF_OCL.
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// use cpu findHomography interface to calculate the transformation matrix
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int main(int argc, char* argv[]) |
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void workBegin() |
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{ |
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if (argc != 5 && argc != 1) |
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{ |
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help(); |
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return -1; |
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} |
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vector<cv::ocl::Info> info; |
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if(!cv::ocl::getDevice(info)) |
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{ |
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cout << "Error: Did not find a valid OpenCL device!" << endl; |
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return -1; |
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} |
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Mat cpu_img1, cpu_img2, cpu_img1_grey, cpu_img2_grey; |
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oclMat img1, img2; |
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if(argc != 5) |
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work_begin = getTickCount(); |
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} |
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void workEnd() |
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{ |
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work_end = getTickCount() - work_begin; |
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} |
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double getTime(){ |
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return work_end /((double)getTickFrequency() * 1000.); |
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} |
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template<class KPDetector> |
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struct SURFDetector |
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{ |
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KPDetector surf; |
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SURFDetector(double hessian = 800.0) |
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:surf(hessian) |
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{ |
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cpu_img1 = imread("o.png"); |
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cvtColor(cpu_img1, cpu_img1_grey, COLOR_BGR2GRAY); |
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img1 = cpu_img1_grey; |
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CV_Assert(!img1.empty()); |
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cpu_img2 = imread("r2.png"); |
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cvtColor(cpu_img2, cpu_img2_grey, COLOR_BGR2GRAY); |
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img2 = cpu_img2_grey; |
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} |
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else |
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template<class T> |
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void operator()(const T& in, const T& mask, std::vector<cv::KeyPoint>& pts, T& descriptors, bool useProvided = false) |
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{ |
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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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cpu_img1 = imread(argv[++i]); |
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cvtColor(cpu_img1, cpu_img1_grey, COLOR_BGR2GRAY); |
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img1 = cpu_img1_grey; |
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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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cpu_img2 = imread(argv[++i]); |
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cvtColor(cpu_img2, cpu_img2_grey, COLOR_BGR2GRAY); |
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img2 = cpu_img2_grey; |
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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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surf(in, mask, pts, descriptors, useProvided); |
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} |
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}; |
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SURF_OCL surf; |
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//surf.hessianThreshold = 400.f;
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//surf.extended = false;
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// detecting keypoints & computing descriptors
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oclMat keypoints1GPU, keypoints2GPU; |
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oclMat descriptors1GPU, descriptors2GPU; |
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// downloading results
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vector<KeyPoint> keypoints1, keypoints2; |
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vector<DMatch> matches; |
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#ifndef USE_CPU_DESCRIPTOR |
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surf(img1, oclMat(), keypoints1GPU, descriptors1GPU); |
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surf(img2, oclMat(), keypoints2GPU, descriptors2GPU); |
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surf.downloadKeypoints(keypoints1GPU, keypoints1); |
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surf.downloadKeypoints(keypoints2GPU, keypoints2); |
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#ifdef USE_CPU_BFMATCHER |
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//BFMatcher
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BFMatcher matcher(cv::NORM_L2); |
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matcher.match(Mat(descriptors1GPU), Mat(descriptors2GPU), matches); |
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#else |
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BruteForceMatcher_OCL_base matcher(BruteForceMatcher_OCL_base::L2Dist); |
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matcher.match(descriptors1GPU, descriptors2GPU, matches); |
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#endif |
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#else |
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surf(img1, oclMat(), keypoints1GPU); |
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surf(img2, oclMat(), keypoints2GPU); |
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surf.downloadKeypoints(keypoints1GPU, keypoints1); |
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surf.downloadKeypoints(keypoints2GPU, keypoints2); |
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// use SURF_OCL to detect keypoints and use SURF to extract descriptors
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SURF surf_cpu; |
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Mat descriptors1, descriptors2; |
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surf_cpu(cpu_img1, Mat(), keypoints1, descriptors1, true); |
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surf_cpu(cpu_img2, Mat(), keypoints2, descriptors2, true); |
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matcher.match(descriptors1, descriptors2, matches); |
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#endif |
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cout << "OCL: FOUND " << keypoints1GPU.cols << " keypoints on first image" << endl; |
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cout << "OCL: FOUND " << keypoints2GPU.cols << " keypoints on second image" << endl; |
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double max_dist = 0; double min_dist = 100; |
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//-- Quick calculation of max and min distances between keypoints
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for( size_t i = 0; i < keypoints1.size(); i++ ) |
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template<class KPMatcher> |
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struct SURFMatcher |
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{ |
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KPMatcher matcher; |
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template<class T> |
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void match(const T& in1, const T& in2, std::vector<cv::DMatch>& matches) |
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{ |
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double dist = matches[i].distance; |
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if( dist < min_dist ) min_dist = dist; |
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if( dist > max_dist ) max_dist = dist; |
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matcher.match(in1, in2, matches); |
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} |
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}; |
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printf("-- Max dist : %f \n", max_dist ); |
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printf("-- Min dist : %f \n", min_dist ); |
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//-- Draw only "good" matches (i.e. whose distance is less than 2.5*min_dist )
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Mat drawGoodMatches( |
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const Mat& cpu_img1, |
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const Mat& cpu_img2, |
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const std::vector<KeyPoint>& keypoints1, |
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const std::vector<KeyPoint>& keypoints2, |
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std::vector<DMatch>& matches, |
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std::vector<Point2f>& scene_corners_ |
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) |
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{ |
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//-- Sort matches and preserve top 10% matches
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std::sort(matches.begin(), matches.end()); |
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std::vector< DMatch > good_matches; |
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double minDist = matches.front().distance, |
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maxDist = matches.back().distance; |
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for( size_t i = 0; i < keypoints1.size(); i++ ) |
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const int ptsPairs = std::min(GOOD_PTS_MAX, (int)(matches.size() * GOOD_PORTION)); |
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for( int i = 0; i < ptsPairs; i++ ) |
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{ |
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if( matches[i].distance < 3*min_dist ) |
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{ |
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good_matches.push_back( matches[i]); |
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} |
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good_matches.push_back( matches[i] ); |
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} |
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std::cout << "\nMax distance: " << maxDist << std::endl; |
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std::cout << "Min distance: " << minDist << std::endl; |
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std::cout << "Calculating homography using " << ptsPairs << " point pairs." << std::endl; |
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// drawing the results
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Mat img_matches; |
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drawMatches( cpu_img1, keypoints1, cpu_img2, keypoints2, |
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good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), |
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vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); |
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std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); |
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//-- Localize the object
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std::vector<Point2f> obj; |
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@ -207,26 +153,238 @@ int main(int argc, char* argv[]) |
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obj.push_back( keypoints1[ good_matches[i].queryIdx ].pt ); |
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scene.push_back( keypoints2[ good_matches[i].trainIdx ].pt ); |
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} |
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Mat H = findHomography( obj, scene, RANSAC ); |
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//-- Get the corners from the image_1 ( the object to be "detected" )
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std::vector<Point2f> obj_corners(4); |
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obj_corners[0] = Point(0,0); obj_corners[1] = Point( cpu_img1.cols, 0 ); |
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obj_corners[2] = Point( cpu_img1.cols, cpu_img1.rows ); obj_corners[3] = Point( 0, cpu_img1.rows ); |
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std::vector<Point2f> scene_corners(4); |
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Mat H = findHomography( obj, scene, RANSAC ); |
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perspectiveTransform( obj_corners, scene_corners, H); |
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scene_corners_ = scene_corners; |
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//-- Draw lines between the corners (the mapped object in the scene - image_2 )
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line( img_matches, scene_corners[0] + Point2f( (float)cpu_img1.cols, 0), scene_corners[1] + Point2f( (float)cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 ); |
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line( img_matches, scene_corners[1] + Point2f( (float)cpu_img1.cols, 0), scene_corners[2] + Point2f( (float)cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 ); |
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line( img_matches, scene_corners[2] + Point2f( (float)cpu_img1.cols, 0), scene_corners[3] + Point2f( (float)cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 ); |
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line( img_matches, scene_corners[3] + Point2f( (float)cpu_img1.cols, 0), scene_corners[0] + Point2f( (float)cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 ); |
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line( img_matches, |
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scene_corners[0] + Point2f( (float)cpu_img1.cols, 0), scene_corners[1] + Point2f( (float)cpu_img1.cols, 0), |
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Scalar( 0, 255, 0), 2, LINE_AA ); |
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line( img_matches, |
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scene_corners[1] + Point2f( (float)cpu_img1.cols, 0), scene_corners[2] + Point2f( (float)cpu_img1.cols, 0), |
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Scalar( 0, 255, 0), 2, LINE_AA ); |
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line( img_matches, |
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scene_corners[2] + Point2f( (float)cpu_img1.cols, 0), scene_corners[3] + Point2f( (float)cpu_img1.cols, 0), |
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Scalar( 0, 255, 0), 2, LINE_AA ); |
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line( img_matches, |
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scene_corners[3] + Point2f( (float)cpu_img1.cols, 0), scene_corners[0] + Point2f( (float)cpu_img1.cols, 0), |
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Scalar( 0, 255, 0), 2, LINE_AA ); |
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return img_matches; |
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} |
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} |
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////////////////////////////////////////////////////
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// This program demonstrates the usage of SURF_OCL.
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// use cpu findHomography interface to calculate the transformation matrix
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int main(int argc, char* argv[]) |
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{ |
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std::vector<cv::ocl::Info> info; |
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if(cv::ocl::getDevice(info) == 0) |
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{ |
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std::cout << "Error: Did not find a valid OpenCL device!" << std::endl; |
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return -1; |
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} |
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ocl::setDevice(info[0]); |
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Mat cpu_img1, cpu_img2, cpu_img1_grey, cpu_img2_grey; |
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oclMat img1, img2; |
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bool useCPU = false; |
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bool useGPU = false; |
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bool useALL = false; |
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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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cpu_img1 = imread(argv[++i]); |
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CV_Assert(!cpu_img1.empty()); |
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cvtColor(cpu_img1, cpu_img1_grey, COLOR_BGR2GRAY); |
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img1 = cpu_img1_grey; |
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} |
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else if (String(argv[i]) == "--right") |
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{ |
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cpu_img2 = imread(argv[++i]); |
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CV_Assert(!cpu_img2.empty()); |
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cvtColor(cpu_img2, cpu_img2_grey, COLOR_BGR2GRAY); |
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img2 = cpu_img2_grey; |
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} |
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else if (String(argv[i]) == "-c") |
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{ |
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useCPU = true; |
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useGPU = false; |
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useALL = false; |
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}else if(String(argv[i]) == "-g") |
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{ |
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useGPU = true; |
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useCPU = false; |
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useALL = false; |
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}else if(String(argv[i]) == "-a") |
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{ |
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useALL = true; |
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useCPU = false; |
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useGPU = false; |
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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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if(!useCPU) |
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{ |
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std::cout |
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<< "Device name:" |
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<< info[0].DeviceName[0] |
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<< std::endl; |
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} |
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double surf_time = 0.; |
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//declare input/output
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std::vector<KeyPoint> keypoints1, keypoints2; |
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std::vector<DMatch> matches; |
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std::vector<KeyPoint> gpu_keypoints1; |
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std::vector<KeyPoint> gpu_keypoints2; |
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std::vector<DMatch> gpu_matches; |
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Mat descriptors1CPU, descriptors2CPU; |
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oclMat keypoints1GPU, keypoints2GPU; |
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oclMat descriptors1GPU, descriptors2GPU; |
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//instantiate detectors/matchers
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SURFDetector<SURF> cpp_surf; |
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SURFDetector<SURF_OCL> ocl_surf; |
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SURFMatcher<BFMatcher> cpp_matcher; |
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SURFMatcher<BFMatcher_OCL> ocl_matcher; |
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//-- start of timing section
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if (useCPU) |
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{ |
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for (int i = 0; i <= LOOP_NUM; i++) |
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{ |
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if(i == 1) workBegin(); |
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cpp_surf(cpu_img1_grey, Mat(), keypoints1, descriptors1CPU); |
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cpp_surf(cpu_img2_grey, Mat(), keypoints2, descriptors2CPU); |
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cpp_matcher.match(descriptors1CPU, descriptors2CPU, matches); |
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} |
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workEnd(); |
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std::cout << "CPP: FOUND " << keypoints1.size() << " keypoints on first image" << std::endl; |
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std::cout << "CPP: FOUND " << keypoints2.size() << " keypoints on second image" << std::endl; |
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surf_time = getTime(); |
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std::cout << "SURF run time: " << surf_time / LOOP_NUM << " ms" << std::endl<<"\n"; |
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} |
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else if(useGPU) |
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{ |
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for (int i = 0; i <= LOOP_NUM; i++) |
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{ |
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if(i == 1) workBegin(); |
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ocl_surf(img1, oclMat(), keypoints1, descriptors1GPU); |
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ocl_surf(img2, oclMat(), keypoints2, descriptors2GPU); |
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ocl_matcher.match(descriptors1GPU, descriptors2GPU, matches); |
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} |
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workEnd(); |
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std::cout << "OCL: FOUND " << keypoints1.size() << " keypoints on first image" << std::endl; |
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std::cout << "OCL: FOUND " << keypoints2.size() << " keypoints on second image" << std::endl; |
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surf_time = getTime(); |
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std::cout << "SURF run time: " << surf_time / LOOP_NUM << " ms" << std::endl<<"\n"; |
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}else |
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{ |
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//cpu runs
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for (int i = 0; i <= LOOP_NUM; i++) |
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{ |
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if(i == 1) workBegin(); |
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cpp_surf(cpu_img1_grey, Mat(), keypoints1, descriptors1CPU); |
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cpp_surf(cpu_img2_grey, Mat(), keypoints2, descriptors2CPU); |
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cpp_matcher.match(descriptors1CPU, descriptors2CPU, matches); |
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} |
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workEnd(); |
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std::cout << "\nCPP: FOUND " << keypoints1.size() << " keypoints on first image" << std::endl; |
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std::cout << "CPP: FOUND " << keypoints2.size() << " keypoints on second image" << std::endl; |
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surf_time = getTime(); |
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std::cout << "(CPP)SURF run time: " << surf_time / LOOP_NUM << " ms" << std::endl; |
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//gpu runs
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for (int i = 0; i <= LOOP_NUM; i++) |
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{ |
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if(i == 1) workBegin(); |
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ocl_surf(img1, oclMat(), gpu_keypoints1, descriptors1GPU); |
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ocl_surf(img2, oclMat(), gpu_keypoints2, descriptors2GPU); |
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ocl_matcher.match(descriptors1GPU, descriptors2GPU, gpu_matches); |
|
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} |
|
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|
workEnd(); |
|
|
|
|
std::cout << "\nOCL: FOUND " << keypoints1.size() << " keypoints on first image" << std::endl; |
|
|
|
|
std::cout << "OCL: FOUND " << keypoints2.size() << " keypoints on second image" << std::endl; |
|
|
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|
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|
|
surf_time = getTime(); |
|
|
|
|
std::cout << "(OCL)SURF run time: " << surf_time / LOOP_NUM << " ms" << std::endl<<"\n"; |
|
|
|
|
|
|
|
|
|
} |
|
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|
//--------------------------------------------------------------------------
|
|
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|
|
std::vector<Point2f> cpu_corner; |
|
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|
|
Mat img_matches = drawGoodMatches(cpu_img1, cpu_img2, keypoints1, keypoints2, matches, cpu_corner); |
|
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|
|
std::vector<Point2f> gpu_corner; |
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|
|
Mat ocl_img_matches; |
|
|
|
|
if(useALL || (!useCPU&&!useGPU)) |
|
|
|
|
{ |
|
|
|
|
ocl_img_matches = drawGoodMatches(cpu_img1, cpu_img2, gpu_keypoints1, gpu_keypoints2, gpu_matches, gpu_corner); |
|
|
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|
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|
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|
|
//check accuracy
|
|
|
|
|
std::cout<<"\nCheck accuracy:\n"; |
|
|
|
|
|
|
|
|
|
if(cpu_corner.size()!=gpu_corner.size()) |
|
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|
|
std::cout<<"Failed\n"; |
|
|
|
|
else |
|
|
|
|
{ |
|
|
|
|
bool result = false; |
|
|
|
|
for(size_t i = 0; i < cpu_corner.size(); i++) |
|
|
|
|
{ |
|
|
|
|
if((std::abs(cpu_corner[i].x - gpu_corner[i].x) > 10) |
|
|
|
|
||(std::abs(cpu_corner[i].y - gpu_corner[i].y) > 10)) |
|
|
|
|
{ |
|
|
|
|
std::cout<<"Failed\n"; |
|
|
|
|
result = false; |
|
|
|
|
break; |
|
|
|
|
} |
|
|
|
|
result = true; |
|
|
|
|
} |
|
|
|
|
if(result) |
|
|
|
|
std::cout<<"Passed\n"; |
|
|
|
|
} |
|
|
|
|
} |
|
|
|
|
|
|
|
|
|
//-- Show detected matches
|
|
|
|
|
namedWindow("ocl surf matches", 0); |
|
|
|
|
imshow("ocl surf matches", img_matches); |
|
|
|
|
waitKey(0); |
|
|
|
|
if (useCPU) |
|
|
|
|
{ |
|
|
|
|
namedWindow("cpu surf matches", 0); |
|
|
|
|
imshow("cpu surf matches", img_matches); |
|
|
|
|
} |
|
|
|
|
else if(useGPU) |
|
|
|
|
{ |
|
|
|
|
namedWindow("ocl surf matches", 0); |
|
|
|
|
imshow("ocl surf matches", img_matches); |
|
|
|
|
}else |
|
|
|
|
{ |
|
|
|
|
namedWindow("cpu surf matches", 0); |
|
|
|
|
imshow("cpu surf matches", img_matches); |
|
|
|
|
|
|
|
|
|
namedWindow("ocl surf matches", 0); |
|
|
|
|
imshow("ocl surf matches", ocl_img_matches); |
|
|
|
|
} |
|
|
|
|
waitKey(0); |
|
|
|
|
return 0; |
|
|
|
|
} |
|
|
|
|
|
Roman Donchenko
12 years ago
committed by
OpenCV Buildbot