d85f27b537
The new sample adjust some parameters thus it should always be able to calculate valid homography matrix when input is box.png and box_in_scene.png. Pure cpp surf and bfmatcher implementation is also added to show the user its accuracy and performance.
390 lines
13 KiB
C++
390 lines
13 KiB
C++
/*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 <stdio.h>
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#include "opencv2/core/core.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/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 cv;
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using namespace cv::ocl;
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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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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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void workBegin()
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{
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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)cvGetTickFrequency() * 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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}
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template<class T>
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void operator()(const T& in, const T& mask, vector<cv::KeyPoint>& pts, T& descriptors, bool useProvided = false)
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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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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, vector<cv::DMatch>& matches)
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{
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matcher.match(in1, in2, matches);
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}
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};
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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 vector<KeyPoint>& keypoints1,
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const vector<KeyPoint>& keypoints2,
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vector<DMatch>& matches,
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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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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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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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//-- Localize the object
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std::vector<Point2f> obj;
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std::vector<Point2f> scene;
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for( size_t 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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//-- 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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Mat H = findHomography( obj, scene, CV_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,
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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, CV_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, CV_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, CV_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, CV_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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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, CV_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, CV_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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vector<KeyPoint> keypoints1, keypoints2;
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vector<DMatch> matches;
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vector<KeyPoint> gpu_keypoints1;
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vector<KeyPoint> gpu_keypoints2;
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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();
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std::cout << "\nOCL: 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 << "(OCL)SURF run time: " << surf_time / LOOP_NUM << " ms" << std::endl<<"\n";
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}
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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;
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if(useALL || (!useCPU&&!useGPU))
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{
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ocl_img_matches = drawGoodMatches(cpu_img1, cpu_img2, gpu_keypoints1, gpu_keypoints2, gpu_matches, gpu_corner);
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//check accuracy
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std::cout<<"\nCheck accuracy:\n";
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if(cpu_corner.size()!=gpu_corner.size())
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std::cout<<"Failed\n";
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else
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{
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bool result = false;
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for(int i = 0; i < cpu_corner.size(); i++)
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{
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if((std::abs(cpu_corner[i].x - gpu_corner[i].x) > 10)
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||(std::abs(cpu_corner[i].y - gpu_corner[i].y) > 10))
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{
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std::cout<<"Failed\n";
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result = false;
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break;
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}
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result = true;
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}
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if(result)
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std::cout<<"Passed\n";
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}
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}
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//-- Show detected matches
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if (useCPU)
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{
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namedWindow("cpu surf matches", 0);
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imshow("cpu surf matches", img_matches);
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}
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else if(useGPU)
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{
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namedWindow("ocl surf matches", 0);
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imshow("ocl surf matches", img_matches);
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}else
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{
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namedWindow("cpu surf matches", 0);
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imshow("cpu surf matches", img_matches);
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namedWindow("ocl surf matches", 0);
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imshow("ocl surf matches", ocl_img_matches);
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}
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waitKey(0);
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return 0;
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}
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