232 lines
8.4 KiB
C++
232 lines
8.4 KiB
C++
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
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// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// @Authors
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// Peng Xiao, pengxiao@multicorewareinc.com
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other oclMaterials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors as is and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include <iostream>
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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/ocl/ocl.hpp"
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#include "opencv2/nonfree/nonfree.hpp"
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#include "opencv2/calib3d/calib3d.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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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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}
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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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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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{
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cpu_img1 = imread("o.png");
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cvtColor(cpu_img1, cpu_img1_grey, CV_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, CV_BGR2GRAY);
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img2 = cpu_img2_grey;
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}
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else
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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, CV_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, CV_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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}
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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( int i = 0; i < keypoints1.size(); i++ )
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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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}
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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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std::vector< DMatch > good_matches;
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for( int i = 0; i < keypoints1.size(); 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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}
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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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//-- Localize the object
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std::vector<Point2f> obj;
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std::vector<Point2f> scene;
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for( int i = 0; i < good_matches.size(); i++ )
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{
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//-- Get the keypoints from the good matches
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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, CV_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] = cvPoint(0,0); obj_corners[1] = cvPoint( cpu_img1.cols, 0 );
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obj_corners[2] = cvPoint( cpu_img1.cols, cpu_img1.rows ); obj_corners[3] = cvPoint( 0, cpu_img1.rows );
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std::vector<Point2f> scene_corners(4);
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perspectiveTransform( obj_corners, scene_corners, H);
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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( cpu_img1.cols, 0), scene_corners[1] + Point2f( cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 );
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line( img_matches, scene_corners[1] + Point2f( cpu_img1.cols, 0), scene_corners[2] + Point2f( cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 );
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line( img_matches, scene_corners[2] + Point2f( cpu_img1.cols, 0), scene_corners[3] + Point2f( cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 );
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line( img_matches, scene_corners[3] + Point2f( cpu_img1.cols, 0), scene_corners[0] + Point2f( cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 );
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//-- Show detected matches
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namedWindow("ocl surf matches", 0);
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imshow("ocl surf matches", img_matches);
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waitKey(0);
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return 0;
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}
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