#include <iostream>
#include <stdio.h>
#include "opencv2/core/core.hpp"
-#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/ocl/ocl.hpp"
-#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/nonfree/ocl.hpp"
#include "opencv2/calib3d/calib3d.hpp"
+#include "opencv2/nonfree/nonfree.hpp"
-using namespace std;
using namespace cv;
using namespace cv::ocl;
-//#define USE_CPU_DESCRIPTOR // use cpu descriptor extractor until ocl descriptor extractor is fixed
-//#define USE_CPU_BFMATCHER
+const int LOOP_NUM = 10;
+const int GOOD_PTS_MAX = 50;
+const float GOOD_PORTION = 0.15f;
+
+namespace
+{
void help();
void help()
{
- cout << "\nThis program demonstrates using SURF_OCL features detector and descriptor extractor" << endl;
- cout << "\nUsage:\n\tsurf_matcher --left <image1> --right <image2>" << endl;
+ std::cout << "\nThis program demonstrates using SURF_OCL features detector and descriptor extractor" << std::endl;
+ std::cout << "\nUsage:\n\tsurf_matcher --left <image1> --right <image2> [-c]" << std::endl;
+ std::cout << "\nExample:\n\tsurf_matcher --left box.png --right box_in_scene.png" << std::endl;
}
+int64 work_begin = 0;
+int64 work_end = 0;
-////////////////////////////////////////////////////
-// This program demonstrates the usage of SURF_OCL.
-// use cpu findHomography interface to calculate the transformation matrix
-int main(int argc, char* argv[])
+void workBegin()
+{
+ work_begin = getTickCount();
+}
+void workEnd()
{
- if (argc != 5 && argc != 1)
- {
- help();
- return -1;
- }
- vector<cv::ocl::Info> info;
- if(!cv::ocl::getDevice(info))
- {
- cout << "Error: Did not find a valid OpenCL device!" << endl;
- return -1;
- }
- Mat cpu_img1, cpu_img2, cpu_img1_grey, cpu_img2_grey;
- oclMat img1, img2;
- if(argc != 5)
+ work_end = getTickCount() - work_begin;
+}
+double getTime(){
+ return work_end /((double)cvGetTickFrequency() * 1000.);
+}
+
+template<class KPDetector>
+struct SURFDetector
+{
+ KPDetector surf;
+ SURFDetector(double hessian = 800.0)
+ :surf(hessian)
{
- cpu_img1 = imread("o.png");
- cvtColor(cpu_img1, cpu_img1_grey, CV_BGR2GRAY);
- img1 = cpu_img1_grey;
- CV_Assert(!img1.empty());
-
- cpu_img2 = imread("r2.png");
- cvtColor(cpu_img2, cpu_img2_grey, CV_BGR2GRAY);
- img2 = cpu_img2_grey;
}
- else
+ template<class T>
+ void operator()(const T& in, const T& mask, vector<cv::KeyPoint>& pts, T& descriptors, bool useProvided = false)
{
- for (int i = 1; i < argc; ++i)
- {
- if (string(argv[i]) == "--left")
- {
- cpu_img1 = imread(argv[++i]);
- cvtColor(cpu_img1, cpu_img1_grey, CV_BGR2GRAY);
- img1 = cpu_img1_grey;
- CV_Assert(!img1.empty());
- }
- else if (string(argv[i]) == "--right")
- {
- cpu_img2 = imread(argv[++i]);
- cvtColor(cpu_img2, cpu_img2_grey, CV_BGR2GRAY);
- img2 = cpu_img2_grey;
- }
- else if (string(argv[i]) == "--help")
- {
- help();
- return -1;
- }
- }
+ surf(in, mask, pts, descriptors, useProvided);
}
+};
- SURF_OCL surf;
- //surf.hessianThreshold = 400.f;
- //surf.extended = false;
-
- // detecting keypoints & computing descriptors
- oclMat keypoints1GPU, keypoints2GPU;
- oclMat descriptors1GPU, descriptors2GPU;
-
- // downloading results
- vector<KeyPoint> keypoints1, keypoints2;
- vector<DMatch> matches;
-
-
-#ifndef USE_CPU_DESCRIPTOR
- surf(img1, oclMat(), keypoints1GPU, descriptors1GPU);
- surf(img2, oclMat(), keypoints2GPU, descriptors2GPU);
-
- surf.downloadKeypoints(keypoints1GPU, keypoints1);
- surf.downloadKeypoints(keypoints2GPU, keypoints2);
-
-
-#ifdef USE_CPU_BFMATCHER
- //BFMatcher
- BFMatcher matcher(cv::NORM_L2);
- matcher.match(Mat(descriptors1GPU), Mat(descriptors2GPU), matches);
-#else
- BruteForceMatcher_OCL_base matcher(BruteForceMatcher_OCL_base::L2Dist);
- matcher.match(descriptors1GPU, descriptors2GPU, matches);
-#endif
-
-#else
- surf(img1, oclMat(), keypoints1GPU);
- surf(img2, oclMat(), keypoints2GPU);
- surf.downloadKeypoints(keypoints1GPU, keypoints1);
- surf.downloadKeypoints(keypoints2GPU, keypoints2);
-
- // use SURF_OCL to detect keypoints and use SURF to extract descriptors
- SURF surf_cpu;
- Mat descriptors1, descriptors2;
- surf_cpu(cpu_img1, Mat(), keypoints1, descriptors1, true);
- surf_cpu(cpu_img2, Mat(), keypoints2, descriptors2, true);
- matcher.match(descriptors1, descriptors2, matches);
-#endif
- cout << "OCL: FOUND " << keypoints1GPU.cols << " keypoints on first image" << endl;
- cout << "OCL: FOUND " << keypoints2GPU.cols << " keypoints on second image" << endl;
-
- double max_dist = 0; double min_dist = 100;
- //-- Quick calculation of max and min distances between keypoints
- for( size_t i = 0; i < keypoints1.size(); i++ )
+template<class KPMatcher>
+struct SURFMatcher
+{
+ KPMatcher matcher;
+ template<class T>
+ void match(const T& in1, const T& in2, vector<cv::DMatch>& matches)
{
- double dist = matches[i].distance;
- if( dist < min_dist ) min_dist = dist;
- if( dist > max_dist ) max_dist = dist;
+ matcher.match(in1, in2, matches);
}
+};
- printf("-- Max dist : %f \n", max_dist );
- printf("-- Min dist : %f \n", min_dist );
-
- //-- Draw only "good" matches (i.e. whose distance is less than 2.5*min_dist )
+Mat drawGoodMatches(
+ const Mat& cpu_img1,
+ const Mat& cpu_img2,
+ const vector<KeyPoint>& keypoints1,
+ const vector<KeyPoint>& keypoints2,
+ vector<DMatch>& matches,
+ vector<Point2f>& scene_corners_
+ )
+{
+ //-- Sort matches and preserve top 10% matches
+ std::sort(matches.begin(), matches.end());
std::vector< DMatch > good_matches;
+ double minDist = matches.front().distance,
+ maxDist = matches.back().distance;
- for( size_t i = 0; i < keypoints1.size(); i++ )
+ const int ptsPairs = std::min(GOOD_PTS_MAX, (int)(matches.size() * GOOD_PORTION));
+ for( int i = 0; i < ptsPairs; i++ )
{
- if( matches[i].distance < 3*min_dist )
- {
- good_matches.push_back( matches[i]);
- }
+ good_matches.push_back( matches[i] );
}
+ std::cout << "\nMax distance: " << maxDist << std::endl;
+ std::cout << "Min distance: " << minDist << std::endl;
+
+ std::cout << "Calculating homography using " << ptsPairs << " point pairs." << std::endl;
// drawing the results
Mat img_matches;
drawMatches( cpu_img1, keypoints1, cpu_img2, keypoints2,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
- vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
+ vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
//-- Localize the object
std::vector<Point2f> obj;
obj.push_back( keypoints1[ good_matches[i].queryIdx ].pt );
scene.push_back( keypoints2[ good_matches[i].trainIdx ].pt );
}
- Mat H = findHomography( obj, scene, CV_RANSAC );
-
//-- Get the corners from the image_1 ( the object to be "detected" )
std::vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( cpu_img1.cols, 0 );
obj_corners[2] = cvPoint( cpu_img1.cols, cpu_img1.rows ); obj_corners[3] = cvPoint( 0, cpu_img1.rows );
std::vector<Point2f> scene_corners(4);
-
+
+ Mat H = findHomography( obj, scene, CV_RANSAC );
perspectiveTransform( obj_corners, scene_corners, H);
+ scene_corners_ = scene_corners;
+
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
- 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 );
- 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 );
- 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 );
- 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 );
+ 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), 2, CV_AA );
+ 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), 2, CV_AA );
+ 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), 2, CV_AA );
+ 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), 2, CV_AA );
+ return img_matches;
+}
+
+}
+////////////////////////////////////////////////////
+// This program demonstrates the usage of SURF_OCL.
+// use cpu findHomography interface to calculate the transformation matrix
+int main(int argc, char* argv[])
+{
+ vector<cv::ocl::Info> info;
+ if(cv::ocl::getDevice(info) == 0)
+ {
+ std::cout << "Error: Did not find a valid OpenCL device!" << std::endl;
+ return -1;
+ }
+ ocl::setDevice(info[0]);
+
+ Mat cpu_img1, cpu_img2, cpu_img1_grey, cpu_img2_grey;
+ oclMat img1, img2;
+ bool useCPU = false;
+ bool useGPU = false;
+ bool useALL = false;
+
+ for (int i = 1; i < argc; ++i)
+ {
+ if (string(argv[i]) == "--left")
+ {
+ cpu_img1 = imread(argv[++i]);
+ CV_Assert(!cpu_img1.empty());
+ cvtColor(cpu_img1, cpu_img1_grey, CV_BGR2GRAY);
+ img1 = cpu_img1_grey;
+ }
+ else if (string(argv[i]) == "--right")
+ {
+ cpu_img2 = imread(argv[++i]);
+ CV_Assert(!cpu_img2.empty());
+ cvtColor(cpu_img2, cpu_img2_grey, CV_BGR2GRAY);
+ img2 = cpu_img2_grey;
+ }
+ else if (string(argv[i]) == "-c")
+ {
+ useCPU = true;
+ useGPU = false;
+ useALL = false;
+ }else if(string(argv[i]) == "-g")
+ {
+ useGPU = true;
+ useCPU = false;
+ useALL = false;
+ }else if(string(argv[i]) == "-a")
+ {
+ useALL = true;
+ useCPU = false;
+ useGPU = false;
+ }
+ else if (string(argv[i]) == "--help")
+ {
+ help();
+ return -1;
+ }
+ }
+ if(!useCPU)
+ {
+ std::cout
+ << "Device name:"
+ << info[0].DeviceName[0]
+ << std::endl;
+ }
+ double surf_time = 0.;
+
+ //declare input/output
+ vector<KeyPoint> keypoints1, keypoints2;
+ vector<DMatch> matches;
+
+ vector<KeyPoint> gpu_keypoints1;
+ vector<KeyPoint> gpu_keypoints2;
+ vector<DMatch> gpu_matches;
+
+ Mat descriptors1CPU, descriptors2CPU;
+
+ oclMat keypoints1GPU, keypoints2GPU;
+ oclMat descriptors1GPU, descriptors2GPU;
+
+ //instantiate detectors/matchers
+ SURFDetector<SURF> cpp_surf;
+ SURFDetector<SURF_OCL> ocl_surf;
+
+ SURFMatcher<BFMatcher> cpp_matcher;
+ SURFMatcher<BFMatcher_OCL> ocl_matcher;
+
+ //-- start of timing section
+ if (useCPU)
+ {
+ for (int i = 0; i <= LOOP_NUM; i++)
+ {
+ if(i == 1) workBegin();
+ cpp_surf(cpu_img1_grey, Mat(), keypoints1, descriptors1CPU);
+ cpp_surf(cpu_img2_grey, Mat(), keypoints2, descriptors2CPU);
+ cpp_matcher.match(descriptors1CPU, descriptors2CPU, matches);
+ }
+ workEnd();
+ std::cout << "CPP: FOUND " << keypoints1.size() << " keypoints on first image" << std::endl;
+ std::cout << "CPP: FOUND " << keypoints2.size() << " keypoints on second image" << std::endl;
+
+ surf_time = getTime();
+ std::cout << "SURF run time: " << surf_time / LOOP_NUM << " ms" << std::endl<<"\n";
+ }
+ else if(useGPU)
+ {
+ for (int i = 0; i <= LOOP_NUM; i++)
+ {
+ if(i == 1) workBegin();
+ ocl_surf(img1, oclMat(), keypoints1, descriptors1GPU);
+ ocl_surf(img2, oclMat(), keypoints2, descriptors2GPU);
+ ocl_matcher.match(descriptors1GPU, descriptors2GPU, matches);
+ }
+ workEnd();
+ std::cout << "OCL: FOUND " << keypoints1.size() << " keypoints on first image" << std::endl;
+ std::cout << "OCL: FOUND " << keypoints2.size() << " keypoints on second image" << std::endl;
+
+ surf_time = getTime();
+ std::cout << "SURF run time: " << surf_time / LOOP_NUM << " ms" << std::endl<<"\n";
+ }else
+ {
+ //cpu runs
+ for (int i = 0; i <= LOOP_NUM; i++)
+ {
+ if(i == 1) workBegin();
+ cpp_surf(cpu_img1_grey, Mat(), keypoints1, descriptors1CPU);
+ cpp_surf(cpu_img2_grey, Mat(), keypoints2, descriptors2CPU);
+ cpp_matcher.match(descriptors1CPU, descriptors2CPU, matches);
+ }
+ workEnd();
+ std::cout << "\nCPP: FOUND " << keypoints1.size() << " keypoints on first image" << std::endl;
+ std::cout << "CPP: FOUND " << keypoints2.size() << " keypoints on second image" << std::endl;
+
+ surf_time = getTime();
+ std::cout << "(CPP)SURF run time: " << surf_time / LOOP_NUM << " ms" << std::endl;
+
+ //gpu runs
+ for (int i = 0; i <= LOOP_NUM; i++)
+ {
+ if(i == 1) workBegin();
+ ocl_surf(img1, oclMat(), gpu_keypoints1, descriptors1GPU);
+ ocl_surf(img2, oclMat(), gpu_keypoints2, descriptors2GPU);
+ ocl_matcher.match(descriptors1GPU, descriptors2GPU, gpu_matches);
+ }
+ 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;
+
+ surf_time = getTime();
+ std::cout << "(OCL)SURF run time: " << surf_time / LOOP_NUM << " ms" << std::endl<<"\n";
+
+ }
+
+ //--------------------------------------------------------------------------
+ std::vector<Point2f> cpu_corner;
+ Mat img_matches = drawGoodMatches(cpu_img1, cpu_img2, keypoints1, keypoints2, matches, cpu_corner);
+
+ std::vector<Point2f> gpu_corner;
+ Mat ocl_img_matches;
+ if(useALL || (!useCPU&&!useGPU))
+ {
+ ocl_img_matches = drawGoodMatches(cpu_img1, cpu_img2, gpu_keypoints1, gpu_keypoints2, gpu_matches, gpu_corner);
+
+ //check accuracy
+ std::cout<<"\nCheck accuracy:\n";
+
+ if(cpu_corner.size()!=gpu_corner.size())
+ std::cout<<"Failed\n";
+ else
+ {
+ bool result = false;
+ for(int 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;
}
projects / platform / upstream / opencv.git / commitdiff