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opencv/samples/ocl/surf_matcher.cpp
OpenCV Buildbot 81f826db2b Normalize line endings and whitespace
2012-10-17 15:57:49 +04:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Peng Xiao, pengxiao@multicorewareinc.com
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other oclMaterials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors as is and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#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/calib3d/calib3d.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
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;
}
////////////////////////////////////////////////////
// This program demonstrates the usage of SURF_OCL.
// use cpu findHomography interface to calculate the transformation matrix
int main(int argc, char* argv[])
{
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)
{
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
{
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_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++ )
{
double dist = matches[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}
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 )
std::vector< DMatch > good_matches;
for( size_t i = 0; i < keypoints1.size(); i++ )
{
if( matches[i].distance < 3*min_dist )
{
good_matches.push_back( matches[i]);
}
}
// 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 );
//-- Localize the object
std::vector<Point2f> obj;
std::vector<Point2f> scene;
for( size_t i = 0; i < good_matches.size(); i++ )
{
//-- Get the keypoints from the good matches
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);
perspectiveTransform( obj_corners, scene_corners, H);
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
line( img_matches, scene_corners[0] + Point2f( cpu_img1.cols, 0), scene_corners[1] + Point2f( cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( cpu_img1.cols, 0), scene_corners[2] + Point2f( cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( cpu_img1.cols, 0), scene_corners[3] + Point2f( cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( cpu_img1.cols, 0), scene_corners[0] + Point2f( cpu_img1.cols, 0), Scalar( 0, 255, 0), 4 );
//-- Show detected matches
namedWindow("ocl surf matches", 0);
imshow("ocl surf matches", img_matches);
waitKey(0);
return 0;
}
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