利用OpenCV（2.4.13）的SURF/SIFT + RANSAC + 加权融合实现图像拼接（vs17）

利用OpenCV-2.4.13和vs2017实现SURF/SIFT + RANSAC + 线性加权融合来实现图像的左右拼接，上下也可以拼接，改一下adjustMat就可以(代码中是x偏移量，你换成y的偏移量，把值换成rows就好，同时在计算透视变换的时候Size参数改为(image02.cols, image02.rows + image01.rows))，代码均有注释，同时将每一步结构都显示出来了，附有示例图片，直接就可以运行。

// ImageMosaic.cpp : 此文件包含 “main“ 函数。程序执行将在此处开始并结束。
// source code : https://blog.csdn.net/dcrmg/article/details/52629856
// explanation : https://blog.csdn.net/u012384044/article/details/73162675#commentBox
//

#include “pch.h“
#include 
#include 

#include “opencv2/highgui/highgui.hpp“  
#include “opencv2/nonfree/nonfree.hpp“  
#include “opencv2/legacy/legacy.hpp“ 

using namespace cv;
using namespace std;

//计算原始图像点位在经过矩阵变换后在目标图像上对应位置
Point2f getTransformPoint（const Point2f originalPoint const Mat &transformMaxtri）;

int main（int argc char *argv[]）
{
	string filename1 = “100-0038_img.jpg“;
	string filename2 = “100-0039_img.jpg“;
	Mat image01 = imread（filename1）;
	Mat image02 = imread（filename2）;
	imshow（“拼接图像1“ image01）;
	imshow（“拼接图像2“ image02）;

	//灰度图转换
	Mat image1 image2;
	cvtColor（image01 image1 CV_RGB2GRAY）;
	cvtColor（image02 image2 CV_RGB2GRAY）;

	//提取特征点  
	SurfFeatureDetector detector（800）;    // surf. hessianThreshold
	//SiftFeatureDetector detector（800）;  // sift. nfeatures

	vector keyPoint1 keyPoint2;
	detector.detect（image1 keyPoint1）;
	detector.detect（image2 keyPoint2）;
	
	// 显示特征点
	Mat image1KeyPoint image2KeyPoint;
	drawKeypoints（image01 keyPoint1 image1KeyPoint
		Scalar::all（-1） DrawMatchesFlags::DRAW_RICH_KEYPOINTS）;
	drawKeypoints（image02 keyPoint2 image2KeyPoint
		Scalar::all（-1） DrawMatchesFlags::DRAW_RICH_KEYPOINTS）;
	imshow（“image1Keypoint“ image1KeyPoint）;
	imshow（“image2Keypoint“ image2KeyPoint）;
	//waitKey（0）;

	//特征点描述，为下边的特征点匹配做准备  
	SurfDescriptorExtractor descriptor;    //surf
	//SiftDescriptorExtractor descriptor;  //sift

	Mat imageDesc1 imageDesc2;
	descriptor.compute（image1 keyPoint1 imageDesc1）;
	descriptor.compute（image2 keyPoint2 imageDesc2）;

	//获得匹配特征点，并提取最优配对  	
	FlannbasedMatcher matcher;
	vector matchePoints;
	matcher.match（imageDesc1 imageDesc2 matchePoints Mat（））;

	// 粗匹配图像
	Mat outImg;
	drawMatches（image01 keyPoint1 image02 keyPoint2 matchePoints outImg
		Scalar::all（-1） Scalar::all（-1） vector（） DrawMatchesFlags::DRAW_RICH_KEYPOINTS）;
	imshow（“粗匹配结果“ outImg）;
	//waitKey（0）;

	sort（matchePoints.begin（） matchePoints.end（））; //特征点排序	
	//获取排在前N个的最优匹配特征点
	vector imagePoints1 imagePoints2;
	for （int i = 0; i < 10; i++）
	{
		imagePoints1.push_back（keyPoint1[matchePoints[i].queryIdx].pt）;
		imagePoints2.push_back（keyPoint2[matchePoints[i].trainIdx].pt）;
	}

	//获取图像1到图像2的投影映射矩阵，尺寸为3*3
	vector inliersMask（imagePoints1.size（））;
	Mat homo = findHomography（imagePoints1 imagePoints2 CV_RANSAC 3.0 inliersMask）;
	vectormatches_ransac;
	// 手动的保留RANSAC过滤后的匹配点对 
	// 这一步做的没必要，因为最后确定匹配区域的时候只用到了第一个点。
	for （int i = 0; i < inliersMask.size（）; i++）
	{
		cout << int（inliersMask[i]） << endl;
		if （inliersMask[i]）
		{
			matches_ransac.push_back（matchePoints[i]）;
		}
	}
	drawMatches（image01 keyPoint1 image02 keyPoint

 属性            大小     日期    时间   名称
----------- ---------  ---------- -----  ----
     目录           0  2018-10-15 17:30  ImageMosaic\
     目录           0  2018-10-16 15:06  ImageMosaic\ImageMosaic\
     目录           0  2018-10-15 17:30  ImageMosaic\ImageMosaic\.vs\
     目录           0  2018-10-15 17:30  ImageMosaic\ImageMosaic\.vs\ImageMosaic\
     目录           0  2018-10-16 15:06  ImageMosaic\ImageMosaic\.vs\ImageMosaic\v15\
     文件       51712  2018-10-16 15:06  ImageMosaic\ImageMosaic\.vs\ImageMosaic\v15\.suo
     文件    19685376  2018-10-16 15:06  ImageMosaic\ImageMosaic\.vs\ImageMosaic\v15\Browse.VC.db
     目录           0  2018-10-16 09:19  ImageMosaic\ImageMosaic\.vs\ImageMosaic\v15\ipch\
     目录           0  2018-10-16 15:06  ImageMosaic\ImageMosaic\.vs\ImageMosaic\v15\ipch\AutoPCH\
     文件      327680  2018-10-16 09:19  ImageMosaic\ImageMosaic\.vs\ImageMosaic\v15\ipch\beec6b604aac47a8.ipch
     文件      327680  2018-10-16 15:04  ImageMosaic\ImageMosaic\.vs\ImageMosaic\v15\ipch\e7d55abe53aad59d.ipch
     目录           0  2018-10-16 15:05  ImageMosaic\ImageMosaic\ImageMosaic\
     文件      109215  2004-10-22 08:25  ImageMosaic\ImageMosaic\ImageMosaic\100-0023_img.jpg
     文件      118280  2004-10-22 08:25  ImageMosaic\ImageMosaic\ImageMosaic\100-0024_img.jpg
     文件      133387  2004-10-22 08:25  ImageMosaic\ImageMosaic\ImageMosaic\100-0025_img.jpg
     文件       61019  2004-10-22 08:25  ImageMosaic\ImageMosaic\ImageMosaic\100-0038_img.jpg
     文件       87303  2004-10-22 08:25  ImageMosaic\ImageMosaic\ImageMosaic\100-0039_img.jpg
     文件       60433  2004-10-22 08:25  ImageMosaic\ImageMosaic\ImageMosaic\100-0040_img.jpg
     文件      126555  2004-10-22 08:25  ImageMosaic\ImageMosaic\ImageMosaic\101-0104_img.jpg
     文件       13412  2018-10-16 15:04  ImageMosaic\ImageMosaic\ImageMosaic\ImageMosaic.cpp
     文件        9531  2018-10-16 09:19  ImageMosaic\ImageMosaic\ImageMosaic\ImageMosaic.vcxproj
     文件        1159  2018-10-15 17:30  ImageMosaic\ImageMosaic\ImageMosaic\ImageMosaic.vcxproj.filters
     文件         165  2018-10-15 17:30  ImageMosaic\ImageMosaic\ImageMosaic\ImageMosaic.vcxproj.user
     文件         188  2018-10-15 17:30  ImageMosaic\ImageMosaic\ImageMosaic\pch.cpp
     文件         614  2018-10-15 17:30  ImageMosaic\ImageMosaic\ImageMosaic\pch.h
     文件      215891  2018-10-16 15:04  ImageMosaic\ImageMosaic\ImageMosaic\拼接结果.jpg
     文件        1447  2018-10-15 17:30  ImageMosaic\ImageMosaic\ImageMosaic.sln