利用OpenCV进行图像匹配

涉及软件：VS2012,OpenCV2.4.9

参考网页：http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/features2d/feature_homography/feature_homography.html  但有改动。

1)新建一个Win32控制台工程，我将其命名为sewerCV

2)输入如下代码：

#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/legacy/legacy.hpp"

#pragma comment(lib, "E:\\cv\\opencv\\build\\x86\\vc11\\lib\\opencv_core249d.lib")
#pragma comment(lib, "E:\\cv\\opencv\\build\\x86\\vc11\\lib\\opencv_highgui249d.lib")
#pragma comment(lib, "E:\\cv\\opencv\\build\\x86\\vc11\\lib\\opencv_features2d249d.lib")
#pragma comment(lib, "E:\\cv\\opencv\\build\\x86\\vc11\\lib\\opencv_calib3d249d.lib")
#pragma comment(lib, "E:\\cv\\opencv\\build\\x86\\vc11\\lib\\opencv_nonfree249d.lib")
#pragma comment(lib, "E:\\cv\\opencv\\build\\x86\\vc11\\lib\\opencv_legacy249d.lib")
#pragma comment(lib, "E:\\cv\\opencv\\build\\x86\\vc11\\lib\\opencv_flann249d.lib")

using namespace cv;

void readme();

/** @function main */
int main( int argc, char** argv )
{
if( argc != 3 )
{ readme(); return -1; }

Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );

if( !img_object.data || !img_scene.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }

//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 400;

SurfFeatureDetector detector( minHessian );

std::vector<KeyPoint> keypoints_object, keypoints_scene;

detector.detect( img_object, keypoints_object );
detector.detect( img_scene, keypoints_scene );

//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;

Mat descriptors_object, descriptors_scene;

extractor.compute( img_object, keypoints_object, descriptors_object );
extractor.compute( img_scene, keypoints_scene, descriptors_scene );

//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
std::vector< DMatch > matches;
matcher.match( descriptors_object, descriptors_scene, matches );

double max_dist = 0; double min_dist = 100;

//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < descriptors_object.rows; 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 3*min_dist )
std::vector< DMatch > good_matches;

for( int i = 0; i < descriptors_object.rows; i++ )
{ if( matches[i].distance < 3*min_dist )
{ good_matches.push_back( matches[i]); }
}

Mat img_matches;
drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
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( int i = 0; i < good_matches.size(); i++ )
{
//-- Get the keypoints from the good matches
obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
scene.push_back( keypoints_scene[ 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( img_object.cols, 0 );
obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.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( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );

//-- Show detected matches
imshow( "Good Matches & Object detection", img_matches );

waitKey(0);
return 0;
}

/** @function readme */
void readme()
{ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl;
std::cin.get();
}

下面提供两个图片，一个是莱娜图，另一个是包含莱娜图的一个截屏。程序将把两张图中的几个对应点找到并连线
原图1<<lena.jpg>>：



原图2<<hehe.png>>：



我在命令行运行如下指令：



运行结果：