/**
* @file SURF_Homography
* @brief SURF detector + descriptor + FLANN Matcher + FindHomography
* @author A. Huaman
*/
#include
#include
#include
#include "opencv2/core/core.hpp"
#include
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include
#include
using namespace cv;
using namespace std;
#ifdef _DEBUG
#pragma comment (lib, "opencv_calib3d246d.lib")
#pragma comment (lib, "opencv_contrib246d.lib")
#pragma comment (lib,"opencv_imgproc246d.lib")
#pragma comment (lib, "opencv_core246d.lib")
#pragma comment (lib, "opencv_features2d246d.lib")
#pragma comment (lib, "opencv_flann246d.lib")
#pragma comment (lib, "opencv_gpu246d.lib")
#pragma comment (lib, "opencv_highgui246d.lib")
#pragma comment (lib, "opencv_legacy246d.lib")
#pragma comment (lib, "opencv_ml246d.lib")
#pragma comment (lib, "opencv_objdetect246d.lib")
#pragma comment (lib, "opencv_ts246d.lib")
#pragma comment (lib, "opencv_video246d.lib")
#pragma comment (lib, "opencv_nonfree246d.lib")
#else
#pragma comment (lib, "opencv_calib3d246.lib")
#pragma comment (lib, "opencv_contrib246.lib")
#pragma comment (lib, "opencv_imgproc246.lib")
#pragma comment (lib, "opencv_core246.lib")
#pragma comment (lib, "opencv_features2d246.lib")
#pragma comment (lib, "opencv_flann246.lib")
#pragma comment (lib, "opencv_gpu246.lib")
#pragma comment (lib, "opencv_highgui246.lib")
#pragma comment (lib, "opencv_legacy246.lib")
#pragma comment (lib, "opencv_ml246.lib")
#pragma comment (lib, "opencv_objdetect246.lib")
#pragma comment (lib, "opencv_ts246.lib")
#pragma comment (lib, "opencv_video246.lib")
#pragma comment (lib, "opencv_nonfree246.lib")
#endif
int main()
{
initModule_nonfree();//初始化模块，使用SIFT或SURF时用到
Ptr detector = FeatureDetector::create( "SURF" );//创建SIFT特征检测器，可改成SURF/ORB
Ptr descriptor_extractor = DescriptorExtractor::create( "SURF" );//创建特征向量生成器，可改成SURF/ORB
Ptr descriptor_matcher = DescriptorMatcher::create( "BruteForce" );//创建特征匹配器
if( detector.empty() || descriptor_extractor.empty() )
cout<<"fail to create detector!";
//读入图像
Mat img1 = imread("1.jpg");
Mat img2 = imread("2.jpg");
//特征点检测
double t = getTickCount();//当前滴答数
vector m_LeftKey,m_RightKey;
detector->detect( img1, m_LeftKey );//检测img1中的SIFT特征点，存储到m_LeftKey中
detector->detect( img2, m_RightKey );
cout<<"图像1特征点个数:"<compute( img1, m_LeftKey, descriptors1 );
descriptor_extractor->compute( img2, m_RightKey, descriptors2 );
t = ((double)getTickCount() - t)/getTickFrequency();
cout<<"SIFT算法用时："< matches;//匹配结果
descriptor_matcher->match( descriptors1, descriptors2, matches );//匹配两个图像的特征矩阵
cout<<"Match个数："< max_dist) max_dist = dist;
}
cout<<"最大距离："< goodMatches;
for(int i=0; i m_Matches=goodMatches;
// 分配空间
int ptCount = (int)m_Matches.size();
Mat p1(ptCount, 2, CV_32F);
Mat p2(ptCount, 2, CV_32F);
// 把Keypoint转换为Mat
Point2f pt;
for (int i=0; i(i, 0) = pt.x;
p1.at(i, 1) = pt.y;
pt = m_RightKey[m_Matches[i].trainIdx].pt;
p2.at(i, 0) = pt.x;
p2.at(i, 1) = pt.y;
}
// 用RANSAC方法计算F
Mat m_Fundamental;
vector m_RANSACStatus; // 这个变量用于存储RANSAC后每个点的状态
findFundamentalMat(p1, p2, m_RANSACStatus, FM_RANSAC);
// 计算野点个数
int OutlinerCount = 0;
for (int i=0; i m_LeftInlier;
vector m_RightInlier;
vector m_InlierMatches;
m_InlierMatches.resize(InlinerCount);
m_LeftInlier.resize(InlinerCount);
m_RightInlier.resize(InlinerCount);
InlinerCount=0;
float inlier_minRx=img1.cols; //用于存储内点中右图最小横坐标，以便后续融合
for (int i=0; i(i, 0);
m_LeftInlier[InlinerCount].y = p1.at(i, 1);
m_RightInlier[InlinerCount].x = p2.at(i, 0);
m_RightInlier[InlinerCount].y = p2.at(i, 1);
m_InlierMatches[InlinerCount].queryIdx = InlinerCount;
m_InlierMatches[InlinerCount].trainIdx = InlinerCount;
if(m_RightInlier[InlinerCount].x key1(InlinerCount);
vector key2(InlinerCount);
KeyPoint::convert(m_LeftInlier, key1);
KeyPoint::convert(m_RightInlier, key2);
// 显示计算F过后的内点匹配
Mat OutImage;
drawMatches(img1, key1, img2, key2, m_InlierMatches, OutImage);
cvNamedWindow( "Match features", 1);
cvShowImage("Match features", &IplImage(OutImage));
waitKey(0);
cvDestroyAllWindows();
//矩阵H用以存储RANSAC得到的单应矩阵
Mat H = findHomography( m_LeftInlier, m_RightInlier, RANSAC );
//存储左图四角，及其变换到右图位置
std::vector obj_corners(4);
obj_corners[0] = Point(0,0); obj_corners[1] = Point( img1.cols, 0 );
obj_corners[2] = Point( img1.cols, img1.rows ); obj_corners[3] = Point( 0, img1.rows );
std::vector scene_corners(4);
perspectiveTransform( obj_corners, scene_corners, H);
//画出变换后图像位置
Point2f offset( (float)img1.cols, 0);
line( OutImage, scene_corners[0]+offset, scene_corners[1]+offset, Scalar( 0, 255, 0), 4 );
line( OutImage, scene_corners[1]+offset, scene_corners[2]+offset, Scalar( 0, 255, 0), 4 );
line( OutImage, scene_corners[2]+offset, scene_corners[3]+offset, Scalar( 0, 255, 0), 4 );
line( OutImage, scene_corners[3]+offset, scene_corners[0]+offset, Scalar( 0, 255, 0), 4 );
imshow( "Good Matches & Object detection", OutImage );
waitKey(0);
imwrite("warp_position.jpg",OutImage);
int drift = scene_corners[1].x; //储存偏移量
//新建一个矩阵存储配准后四角的位置
int width = int(max(abs(scene_corners[1].x), abs(scene_corners[2].x)));
int height= img1.rows; //或者：int height = int(max(abs(scene_corners[2].y), abs(scene_corners[3].y)));
float origin_x=0,origin_y=0;
if(scene_corners[0].x<0) {
if (scene_corners[3].x<0) origin_x+=min(scene_corners[0].x,scene_corners[3].x);
else origin_x+=scene_corners[0].x;}
width-=int(origin_x);
if(scene_corners[0].y<0) {
if (scene_corners[1].y) origin_y+=min(scene_corners[0].y,scene_corners[1].y);
else origin_y+=scene_corners[0].y;}
//可选：height-=int(origin_y);
Mat imageturn=Mat::zeros(width,height,img1.type());
//获取新的变换矩阵，使图像完整显示
for (int i=0;i<4;i++) {scene_corners[i].x -= origin_x; } //可选：scene_corners[i].y -= (float)origin_y; }
Mat H1=getPerspectiveTransform(obj_corners, scene_corners);
//进行图像变换，显示效果
warpPerspective(img1,imageturn,H1,Size(width,height));
imshow("image_Perspective", imageturn);
waitKey(0);
//图像融合
int width_ol=width-int(inlier_minRx-origin_x);
int start_x=int(inlier_minRx-origin_x);
cout<<"width: "<
