参考教程
依赖opencv扩展库，使用sifi匹配
保存配准信息
"./config/calibratedPara.yaml"
#include <iostream>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include <opencv2/opencv.hpp>
#include<opencv2/xfeatures2d.hpp>
#include<opencv2/core/core.hpp>
#define PATH_XMAL "./config/calibratedPara.yaml"
#define IMG_WIDTH 2592//2592
#define IMG_HEIGHT 1944//2048
using namespace cv;
using namespace std;
using namespace cv::xfeatures2d;//只有加上这句命名空间，SiftFeatureDetector and SiftFeatureExtractor才可以使用
/******************************************************
*name :Rect CalcCorners(const Mat& H, const Mat& src)
*function :通过H计算图片角点位置，返回左上和宽高
*time :2019-4-28
********************************************************/
Rect CalcCorners(const Mat& H, const Mat& src)
{
double v1[3];
Mat _V1 = Mat(3, 1, CV_64FC1, v1);
//左上角(0,0,1)
Mat _V2 = (Mat_<double>(3, 1) << 0, 0, 1);
_V1 = H * _V2;
Point _left_top;
_left_top.x = v1[0] / v1[2];
_left_top.y = v1[1] / v1[2];
//左下角(0,src.rows,1)
_V2 = (Mat_<double>(3, 1) << 0, src.rows, 1);
_V1 = H * _V2;
Point _left_bottom;
_left_bottom.x = v1[0] / v1[2];
_left_bottom.y = v1[1] / v1[2];
//右上角(src.cols,0,1)
_V2 = (Mat_<double>(3, 1) << src.cols, 0, 1);
_V1 = H * _V2;
Point _right_top;
_right_top.x = v1[0] / v1[2];
_right_top.y = v1[1] / v1[2];
//右下角(src.cols,src.rows,1)
_V2 = (Mat_<double>(3, 1) << src.cols, src.rows, 1);
_V1 = H * _V2;
Point _right_bottom;
_right_bottom.x = v1[0] / v1[2];
_right_bottom.y = v1[1] / v1[2];
int _x1 = (int)max(_left_bottom.x, _left_top.x);
int _y1 = (int)max(_left_top.y, _right_top.y);
int _x2 = (int)min(_right_top.x, _right_bottom.x);
int _y2 = (int)min(_left_bottom.y, _right_bottom.y);
cout << "point is " << _x1 << " " << _y1 << " " << _x2 << " " << _y2 << endl;
if (_x2 > IMG_WIDTH) _x2 = IMG_WIDTH - 1;
if (_y2 > IMG_HEIGHT) _y2 = IMG_HEIGHT - 1;
if (_x1 < 0) _x1 = 0;
if (_y1 < 0) _y1 = 0;
cout << "point is " << _x1 << " " << _y1 << " " << _x2 << " " << _y2 << endl;
return Rect(Point(_x1, _y1), Point(_x2, _y2)); //表示左上点和右下点
}
Rect inscrRect;
cv::Mat warpedPic;
cv::Mat Homography;
Mat compicCalibrate;
int main()
{
//Create SIFT class pointer
Ptr<Feature2D> f2d = xfeatures2d::SIFT::create();
//SiftFeatureDetector siftDetector;
//Loading images
Mat img_1 = imread("1.bmp");
Mat img_2 = imread("2.bmp");
if (!img_1.data || !img_2.data)
{
cout << "Reading picture error！" << endl;
return false;
}
//Detect the keypoints
double t0 = getTickCount();//当前
vector<KeyPoint> keypoints_1, keypoints_2;
f2d->detect(img_1, keypoints_1);
f2d->detect(img_2, keypoints_2);
cout << "The keypoints number of img1 is:" << keypoints_1.size() << endl;
cout << "The keypoints number of img2 is:" << keypoints_2.size() << endl;
//Calculate descriptors (feature vectors)
Mat descriptors_1, descriptors_2;
f2d->compute(img_1, keypoints_1, descriptors_1);
f2d->compute(img_2, keypoints_2, descriptors_2);
double freq = getTickFrequency();
double tt = ((double)getTickCount() - t0) / freq;
cout << "Extract SIFT Time:" << tt << "ms" << endl;
//画关键点
Mat img_keypoints_1, img_keypoints_2;
drawKeypoints(img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), 0);
drawKeypoints(img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), 0);
//imshow("img_keypoints_1",img_keypoints_1);
//imshow("img_keypoints_2",img_keypoints_2);
//Matching descriptor vector using BFMatcher
BFMatcher matcher;
vector<DMatch> matches;
matcher.match(descriptors_1, descriptors_2, matches);
cout << "The number of match:" << matches.size() << endl;
//绘制匹配出的关键点
Mat img_matches;
drawMatches(img_1, keypoints_1, img_2, keypoints_2, matches, img_matches);
//imshow("Match image",img_matches);
//计算匹配结果中距离最大和距离最小值
double min_dist = matches[0].distance, max_dist = matches[0].distance;
for (int m = 0; m < matches.size(); m++)
{
if (matches[m].distance<min_dist)
{
min_dist = matches[m].distance;
}
if (matches[m].distance>max_dist)
{
max_dist = matches[m].distance;
}
}
cout << "min dist=" << min_dist << endl;
cout << "max dist=" << max_dist << endl;
//筛选出较好的匹配点
vector<DMatch> goodMatches;
for (int m = 0; m < matches.size(); m++)
{
if (matches[m].distance < 0.6*max_dist)
{
goodMatches.push_back(matches[m]);
}
}
cout << "The number of good matches:" << goodMatches.size() << endl;
//画出匹配结果
Mat img_out;
//红色连接的是匹配的特征点数，绿色连接的是未匹配的特征点数
//matchColor – Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1) , the color is generated randomly.
//singlePointColor – Color of single keypoints(circles), which means that keypoints do not have the matches.If singlePointColor == Scalar::all(-1), the color is generated randomly.
//CV_RGB(0, 255, 0)存储顺序为R-G-B,表示绿色
drawMatches(img_1, keypoints_1, img_2, keypoints_2, goodMatches, img_out, Scalar::all(-1), CV_RGB(0, 0, 255), Mat(), 2);
namedWindow("good Matches", 0);
imshow("good Matches", img_out);
//RANSAC匹配过程
vector<DMatch> m_Matches;
m_Matches = goodMatches;
int ptCount = goodMatches.size();
if (ptCount < 100)
{
cout << "Don't find enough match points" << endl;
return 0;
}
//坐标转换为float类型
vector <KeyPoint> RAN_KP1, RAN_KP2;
//size_t是标准C库中定义的，应为unsigned int，在64位系统中为long unsigned int,在C++中为了适应不同的平台，增加可移植性。
for (size_t i = 0; i < m_Matches.size(); i++)
{
RAN_KP1.push_back(keypoints_1[goodMatches[i].queryIdx]);
RAN_KP2.push_back(keypoints_2[goodMatches[i].trainIdx]);
//RAN_KP1是要存储img01中能与img02匹配的点
//goodMatches存储了这些匹配点对的img01和img02的索引值
}
//坐标变换
vector <Point2f> p01, p02;
for (size_t i = 0; i < m_Matches.size(); i++)
{
p01.push_back(RAN_KP1[i].pt);
p02.push_back(RAN_KP2[i].pt);
}
/*vector <Point2f> img1_corners(4);
img1_corners[0] = Point(0,0);
img1_corners[1] = Point(img_1.cols,0);
img1_corners[2] = Point(img_1.cols, img_1.rows);
img1_corners[3] = Point(0, img_1.rows);
vector <Point2f> img2_corners(4);*/
////求转换矩阵
//Mat m_homography;
//vector<uchar> m;
//m_homography = findHomography(p01, p02, RANSAC);//寻找匹配图像
//求基础矩阵 Fundamental,3*3的基础矩阵
vector<uchar> RansacStatus;
Mat Fundamental = findFundamentalMat(p01, p02, RansacStatus, FM_RANSAC);
//重新定义关键点RR_KP和RR_matches来存储新的关键点和基础矩阵，通过RansacStatus来删除误匹配点
vector <KeyPoint> RR_KP1, RR_KP2;
vector <DMatch> RR_matches;
int index = 0;
for (size_t i = 0; i < m_Matches.size(); i++)
{
if (RansacStatus[i] != 0)
{
RR_KP1.push_back(RAN_KP1[i]);
RR_KP2.push_back(RAN_KP2[i]);
m_Matches[i].queryIdx = index;
m_Matches[i].trainIdx = index;
RR_matches.push_back(m_Matches[i]);
index++;
}
}
cout << "RANSAC后匹配点数" << RR_matches.size() << endl;
Mat img_RR_matches;
drawMatches(img_1, RR_KP1, img_2, RR_KP2, RR_matches, img_RR_matches);
namedWindow("After RANSAC", 0);
imshow("After RANSAC", img_RR_matches);
//等待任意按键按下
waitKey(1);
vector<cv::Point2f> Pic1Point, Pic2Point;
for (int i = 0; i < RR_matches.size(); i++)
{
Pic1Point.push_back(RR_KP1[RR_matches[i].queryIdx].pt);
Pic2Point.push_back(RR_KP2[RR_matches[i].trainIdx].pt);
}
Homography = cv::findHomography(Pic1Point, Pic2Point, CV_RANSAC); //计算将p2投影到p1上的单映性矩阵
FileStorage fs(PATH_XMAL, FileStorage::WRITE); //单应矩阵保存
fs << "Homography" << Homography;
warpPerspective(img_1, warpedPic, Homography, cv::Size(img_2.cols, img_2.rows));//第一路图像根据参数Homography变换映射到warpedPic图
inscrRect = CalcCorners(Homography, img_1);// 第一路图像根据参数Homography计算本土映射区域的起始点和宽高
fs << "inscrRect" << inscrRect;//保存在xml
fs.release();
Rect cutRoi(inscrRect.x, inscrRect.y, inscrRect.width, inscrRect.height);// 定义一个抠图区域
Mat Pic1Roi = warpedPic(cutRoi).clone();//第一张变换图扣出对应区域
compicCalibrate.create(inscrRect.height, inscrRect.width * 2, CV_8UC3);
// Mat Pic1Roi = warpedPic(inscrRect);
Mat Pic2Roi = img_2(inscrRect);
Pic1Roi.copyTo(compicCalibrate(Rect(0, 0, Pic1Roi.cols, Pic1Roi.rows)));
Pic2Roi.copyTo(compicCalibrate(Rect(Pic1Roi.cols, 0, Pic2Roi.cols, Pic2Roi.rows)));
namedWindow("martch", 0);
imshow("martch", compicCalibrate);
waitKey(0);
}