Opencv拼接多张图片

Posted 2023-02-21

【中文标题】Opencv拼接多张图片

【英文标题】：Opencv stitching multiple images

【发布时间】：2015-04-28 07:55:58

【问题描述】：

我一直致力于在 opencv 中拼接多个图像以创建马赛克。 我在opencv上关注了这个链接： Stitch multiple images using OpenCV (Python) 这是我到目前为止得到的代码：

// imgstch.cpp :
// imgstch.cpp :
//#include "stdafx.h"
#include<opencv/cv.h>
#include<opencv/highgui.h>
#include<iostream>
#include<stdio.h>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#define _CRT_SECURE_NO_WARNINGS
using namespace std;
using namespace cv;
int main()

//-- Input the two images
cv::Mat img1;
std::vector<cv::KeyPoint> img1_keypoints;
cv::Mat img1_descriptors;
cv::Mat img2;
std::vector<cv::KeyPoint> img2_keypoints;
cv::Mat img2_descriptors;
img1 = cv::imread("/home/ishita/Downloads/ishita/Downloads/Mosaic/b2.JPG");
img2 = cv::imread("/home/ishita/Downloads/ishita/Downloads/Mosaic/b1.JPG");
//-- ORB feature detector, extractor and descriptor
int minHessian = 1800;
OrbFeatureDetector detector( minHessian );
OrbDescriptorExtractor extractor;
detector.detect(img1, img1_keypoints);
detector.detect(img2, img2_keypoints);
extractor.compute(img1, img1_keypoints, img1_descriptors);
extractor.compute(img2, img2_keypoints, img2_descriptors);
//-- Matching descriptor vectors with a brute force matcher
BFMatcher matcher(NORM_HAMMING);
std::vector< DMatch > matches;
matcher.match( img1_descriptors, img2_descriptors, matches );
imshow("image1", img1);
imshow("image2",img2);
//-- Draw matches
Mat img_matches;
drawMatches( img1, img1_keypoints, img2, img2_keypoints, matches, img_matches );
//-- Show detected matches
imshow("Matches", img_matches );imwrite("/home/ishita/img_stitch/img_matches.jpg",img_matches);
double max_dist = 0; double min_dist = 10;
//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < matches.size(); i++ )

double dist = matches[i].distance;
if( dist < min_dist && dist >3)

min_dist = dist;

if( dist > max_dist) max_dist = 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 < matches.size(); i++ )

//cout<<matches[i].distance<<endl;
if( matches[i].distance < 3*min_dist && matches[i].distance > 3)

good_matches.push_back( matches[i]); 

//calculate the Homography
vector<Point2f> p1, p2;
for (unsigned int i = 0; i < matches.size(); i++) 
p1.push_back(img1_keypoints[matches[i].queryIdx].pt);
p2.push_back(img2_keypoints[matches[i].trainIdx].pt);

// Homografía
vector<unsigned char> match_mask;
Mat H = findHomography(p1,p2,CV_RANSAC);
cout << "H = "<< endl << " " << H << endl << endl;
// Use the Homography Matrix to warp the images

cv::Mat result;
result=img1.clone();
warpPerspective(img1,result,H,cv::Size(img1.cols+img2.cols,img1.rows));
cv::Mat half(result,cv::Rect(0,0,img2.cols,img2.rows));
img2.copyTo(half);
imwrite("/home/ishita/img_stitch/result.jpg",result);
imshow( "Result", result );



//for images 2 and 3


cv::Mat img3;
std::vector<cv::KeyPoint> img3_keypoints;
cv::Mat img3_descriptors;

img3 = cv::imread("/home/ishita/Downloads/ishita/Downloads/Mosaic/b3.JPG");

//detector.detect(img2, img2_keypoints);
detector.detect(img3, img3_keypoints);
//extractor.compute(img2, img2_keypoints, img2_descriptors);
extractor.compute(img3, img3_keypoints, img3_descriptors);

matcher.match( img1_descriptors, img3_descriptors, matches );
//imshow("image2", img1);
imshow("image3",img3);
//-- Draw matches
Mat img_matches2;
drawMatches( img1, img1_keypoints, img3, img3_keypoints, matches, img_matches2 );
//-- Show detected matches
imshow("Matches2", img_matches2 );imwrite("/home/ishita/img_stitch/img_matches.jpg",img_matches2);
max_dist = 0; min_dist = 10;
//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < matches.size(); i++ )

double dist = matches[i].distance;
if( dist < min_dist && dist >3)

min_dist = dist;

if( dist > max_dist) max_dist = dist;

//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
std::vector< DMatch > good_matches2;
for( int i = 0; i < matches.size(); i++ )

//cout<<matches[i].distance<<endl;
if( matches[i].distance < 3*min_dist && matches[i].distance > 3)

good_matches2.push_back( matches[i]); 

//calculate the Homography
vector<Point2f> p3, p4;
for (unsigned int i = 0; i < matches.size(); i++) 
p3.push_back(img1_keypoints[matches[i].queryIdx].pt);
p4.push_back(img3_keypoints[matches[i].trainIdx].pt);

// Homografía
vector<unsigned char> match_mask2;
Mat H2 = findHomography(p3,p4,CV_RANSAC);
Mat H3 = H * H2;
cout << "H2= "<< endl << " " << H2 << endl << endl;
// Use the Homography Matrix to warp the images
cv::Mat result2;
result2 = result.clone();
warpPerspective(result,result2,H3,cv::Size(img3.cols+result.cols,result.rows));
 cv::Mat half2(result,cv::Rect(0,0,img3.cols,img3.rows));
 img3.copyTo(half2);
 imwrite("/home/ishita/img_stitch/result.jpg",result2);
 imshow( "Result2", result2 );




waitKey(0);
return 0;

前两张图拼接结果符合要求，但第三张图拼接结果不合适。

这背后的逻辑或方法的实现可能有什么问题？

图片和结果可以在这里找到： https://drive.google.com/folderview?id=0BxXVoeIUgVW7fnFMbExhTzN4QnRueXZpQmpILTZCWFZoTlZEckxfWV83VjkxMmFNSXdLVWM&usp=sharing

【问题讨论】：

你能分享结果图像吗？

输入和结果图片可以在最后给出的链接中找到。

链接失效了，请把它变成可访问的

【参考方案1】：

这个答案为时已晚，但可能对其他人有所帮助。

在某些算法中，要拼接的图像尺寸必须相同。 （如果问题是这个测试下面的解决方案）

1- you could run program once for (img1 , img2)=>result1. 

2- then for (img2 , img3)=>result2.

3- (result1 , result2) => final

这是一次获取所有图像的另一个示例：

https://www.geeksforgeeks.org/stitching-input-images-panorama-using-opencv-c/