//// ConsoleApplication40.cpp : 定义控制台应用程序的入口点。
////
//
//#include "stdafx.h"
//#include <iostream>
//#include <stdio.h>
//#include "opencv2/core.hpp"
//#include "opencv2/core/utility.hpp"
//#include "opencv2/core/ocl.hpp"
//#include "opencv2/imgcodecs.hpp"
//#include "opencv2/highgui.hpp"
//#include "opencv2/features2d.hpp"
//#include "opencv2/calib3d.hpp"
//#include "opencv2/imgproc.hpp"
//#include"opencv2/flann.hpp"
//#include"opencv2/xfeatures2d.hpp"
//#include"opencv2/ml.hpp"
//
//using namespace cv;
//using namespace std;
//using namespace cv::xfeatures2d;
//using namespace cv::ml;
//
//int main()
//{
// Mat a = imread("1.bmp", IMREAD_GRAYSCALE); //读取灰度图像
// Mat b = imread("2.bmp", IMREAD_GRAYSCALE);
//
// Ptr<SURF> surf; //创建方式和2中的不一样
// surf = SURF::create(800);
//
// BFMatcher matcher;
// Mat c, d;
// vector<KeyPoint>key1, key2;
// vector<DMatch> matches;
//
// surf->detectAndCompute(a, Mat(), key1, c);
// surf->detectAndCompute(b, Mat(), key2, d);
//
// matcher.match(c, d, matches); //匹配
//
// sort(matches.begin(), matches.end()); //筛选匹配点
// vector< DMatch > good_matches;
// int ptsPairs = std::min(50, (int)(matches.size() * 0.15));
// cout << ptsPairs << endl;
// for (int i = 0; i < ptsPairs; i++)
// {
// good_matches.push_back(matches[i]);
// }
// Mat outimg;
// drawMatches(a, key1, b, key2, good_matches, outimg, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS); //绘制匹配点
//
// std::vector<Point2f> obj;
// std::vector<Point2f> scene;
//
// for (size_t i = 0; i < good_matches.size(); i++)
// {
// obj.push_back(key1[good_matches[i].queryIdx].pt);
// scene.push_back(key2[good_matches[i].trainIdx].pt);
// }
//
// std::vector<Point2f> obj_corners(4);
// obj_corners[0] = Point(0, 0);
// obj_corners[1] = Point(a.cols, 0);
// obj_corners[2] = Point(a.cols, a.rows);
// obj_corners[3] = Point(0, a.rows);
// std::vector<Point2f> scene_corners(4);
//
// Mat H = findHomography(obj, scene, RANSAC); //寻找匹配的图像
// perspectiveTransform(obj_corners, scene_corners, H);
//
// line(outimg, scene_corners[0] + Point2f((float)a.cols, 0), scene_corners[1] + Point2f((float)a.cols, 0), Scalar(0, 255, 0), 2, LINE_AA); //绘制
// line(outimg, scene_corners[1] + Point2f((float)a.cols, 0), scene_corners[2] + Point2f((float)a.cols, 0), Scalar(0, 255, 0), 2, LINE_AA);
// line(outimg, scene_corners[2] + Point2f((float)a.cols, 0), scene_corners[3] + Point2f((float)a.cols, 0), Scalar(0, 255, 0), 2, LINE_AA);
// line(outimg, scene_corners[3] + Point2f((float)a.cols, 0), scene_corners[0] + Point2f((float)a.cols, 0), Scalar(0, 255, 0), 2, LINE_AA);
// imshow("aaaa", outimg);
// cvWaitKey(0);
//}
#include "stdafx.h"
#include <iostream>
#include <stdio.h>
#include "opencv2/core.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/ocl.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/calib3d.hpp"
#include "opencv2/imgproc.hpp"
#include"opencv2/flann.hpp"
#include"opencv2/xfeatures2d.hpp"
#include"opencv2/ml.hpp"
using namespace cv;
using namespace std;
using namespace cv::xfeatures2d;
using namespace cv::ml;
void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst);
typedef struct
{
Point2f left_top;
Point2f left_bottom;
Point2f right_top;
Point2f right_bottom;
}four_corners_t;
four_corners_t corners;
void CalcCorners(const Mat& H, const Mat& src)
{
double v2[] = { 0, 0, 1 };//左上角
double v1[3];//变换后的坐标值
Mat V2 = Mat(3, 1, CV_64FC1, v2); //列向量
Mat V1 = Mat(3, 1, CV_64FC1, v1); //列向量
V1 = H * V2;
//左上角(0,0,1)
cout << "V2: " << V2 << endl;
cout << "V1: " << V1 << endl;
corners.left_top.x = v1[0] / v1[2];
corners.left_top.y = v1[1] / v1[2];
//左下角(0,src.rows,1)
v2[0] = 0;
v2[1] = src.rows;
v2[2] = 1;
V2 = Mat(3, 1, CV_64FC1, v2); //列向量
V1 = Mat(3, 1, CV_64FC1, v1); //列向量
V1 = H * V2;
corners.left_bottom.x = v1[0] / v1[2];
corners.left_bottom.y = v1[1] / v1[2];
//右上角(src.cols,0,1)
v2[0] = src.cols;
v2[1] = 0;
v2[2] = 1;
V2 = Mat(3, 1, CV_64FC1, v2); //列向量
V1 = Mat(3, 1, CV_64FC1, v1); //列向量
V1 = H * V2;
corners.right_top.x = v1[0] / v1[2];
corners.right_top.y = v1[1] / v1[2];
//右下角(src.cols,src.rows,1)
v2[0] = src.cols;
v2[1] = src.rows;
v2[2] = 1;
V2 = Mat(3, 1, CV_64FC1, v2); //列向量
V1 = Mat(3, 1, CV_64FC1, v1); //列向量
V1 = H * V2;
corners.right_bottom.x = v1[0] / v1[2];
corners.right_bottom.y = v1[1] / v1[2];
}
int main()
{
/*VideoCapture cap1(0);
VideoCapture cap2(1);
bool stop(false);
Mat frame1;
Mat frame2;
namedWindow("cam1", CV_WINDOW_AUTOSIZE);
namedWindow("cam2", CV_WINDOW_AUTOSIZE);
if (cap1.isOpened() && cap2.isOpened())
{
cout << "*** ***" << endl;
cout << "摄像头已启动！" << endl;
}
else
{
cout << "*** ***" << endl;
cout << "警告：请检查摄像头是否安装好!" << endl;
cout << "程序结束！" << endl << "*** ***" << endl;
return -1;
}
cap1.set(CV_CAP_PROP_FOCUS, 0);
cap2.set(CV_CAP_PROP_FOCUS, 0);
while (1)
{
Mat a, b;
cap2 >> b;
cap1 >> a;*/
Mat a = imread("4.jpg", 1);//右图
Mat b = imread("3.jpg", 1);//左图
resize(a, a, Size(a.cols / 2,a.rows / 2), (0, 0), (0, 0), 3);
resize(b, b, Size(b.cols / 2,b.rows / 2), (0, 0), (0, 0), 3);
Ptr<SURF> surf; //创建方式和OpenCV2中的不一样,并且要加上命名空间xfreatures2d
//否则即使配置好了还是显示SURF为未声明的标识符
surf = SURF::create(5000);
BFMatcher matcher; //实例化一个暴力匹配器
Mat c, d;
vector<KeyPoint>key1, key2;
vector<DMatch> matches; //DMatch是用来描述匹配好的一对特征点的类，包含这两个点之间的相关信息
//比如左图有个特征m，它和右图的特征点n最匹配，这个DMatch就记录它俩最匹配，并且还记录m和n的
//特征向量的距离和其他信息，这个距离在后面用来做筛选
surf->detectAndCompute(a, Mat(), key1, c);//输入图像，输入掩码，输入特征点，输出Mat，存放所有特征点的描述向量
surf->detectAndCompute(b, Mat(), key2, d);//这个Mat行数为特征点的个数，列数为每个特征向量的尺寸，SURF是64（维）
matcher.match(d, c, matches); //匹配，数据来源是特征向量，结果存放在DMatch类型里面
//sort函数对数据进行升序排列
sort(matches.begin(), matches.end()); //筛选匹配点，根据match里面特征对的距离从小到大排序
vector< DMatch > good_matches;
int ptsPairs = std::min(50, (int)(matches.size() * 0.15));
cout << ptsPairs << endl;
for (int i = 0; i < ptsPairs; i++)
{
good_matches.push_back(matches[i]);//距离最小的50个压入新的DMatch
}
Mat outimg; //drawMatches这个函数直接画出摆在一起的图
drawMatches(b, key2, a, key1, good_matches, outimg, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS); //绘制匹配点
imshow("桌面", outimg);
///////////////////////图像配准及融合////////////////////////
vector<Point2f> imagePoints1, imagePoints2;
for (int i = 0; i < good_matches.size(); i++)
{
imagePoints2.push_back(key2[good_matches[i].queryIdx].pt);
imagePoints1.push_back(key1[good_matches[i].trainIdx].pt);
}
//获取图像1到图像2的投影映射矩阵 尺寸为3*3
Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
////也可以使用getPerspectiveTransform方法获得透视变换矩阵，不过要求只能有4个点，效果稍差
//Mat homo=getPerspectiveTransform(imagePoints1,imagePoints2);
cout << "变换矩阵为：\n" << homo << endl << endl; //输出映射矩阵
//计算配准图的四个顶点坐标
CalcCorners(homo, a);
cout << "left_top:" << corners.left_top << endl;
cout << "left_bottom:" << corners.left_bottom << endl;
cout << "right_top:" << corners.right_top << endl;
cout << "right_bottom:" << corners.right_bottom << endl;
//图像配准
Mat imageTransform1, imageTransform2;
warpPerspective(a, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x), b.rows));
//warpPerspective(a, imageTransform2, adjustMat*homo, Size(b.cols*1.3, b.rows*1.8));
imshow("直接经过透视矩阵变换", imageTransform1);
imwrite("trans1.jpg", imageTransform1);
//创建拼接后的图,需提前计算图的大小
int dst_width = imageTransform1.cols; //取最右点的长度为拼接图的长度
int dst_height = b.rows;
Mat dst(dst_height, dst_width, CV_8UC3);
dst.setTo(0);
imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows)));
b.copyTo(dst(Rect(0, 0, b.cols, b.rows)));
imshow("b_dst", dst);
OptimizeSeam(b, imageTransform1, dst);
imshow("dst", dst);
//imwrite("dst.jpg", dst);
waitKey();
//}
return 0;
}
//优化两图的连接处，使得拼接自然
void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)
{
int start = MIN(corners.left_top.x, corners.left_bottom.x);//开始位置，即重叠区域的左边界
double processWidth = img1.cols - start;//重叠区域的宽度
int rows = dst.rows;
int cols = img1.cols; //注意，是列数*通道数
double alpha = 1;//img1中像素的权重
for (int i = 0; i < rows; i++)
{
uchar* p = img1.ptr<uchar>(i); //获取第i行的首地址
uchar* t = trans.ptr<uchar>(i);
uchar* d = dst.ptr<uchar>(i);
for (int j = start; j < cols; j++)
{
//如果遇到图像trans中无像素的黑点，则完全拷贝img1中的数据
if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0)
{
alpha = 1;
}
else
{
//img1中像素的权重，与当前处理点距重叠区域左边界的距离成正比，实验证明，这种方法确实好
alpha = (processWidth - (j - start)) / processWidth;
}
d[j * 3] = p[j * 3] * alpha + t[j * 3] * (1 - alpha);
d[j * 3 + 1] = p[j * 3 + 1] * alpha + t[j * 3 + 1] * (1 - alpha);
d[j * 3 + 2] = p[j * 3 + 2] * alpha + t[j * 3 + 2] * (1 - alpha);
}
}
}