Opencv stitching multiple images

Question

I have been working on stitching multiple images in opencv in order to create a mosaic.
I followed this link on opencv: Stitch multiple images using OpenCV (Python)
Here's the code that I have got so far :

// 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;
}

The result of stitching first two images is as required but the result of stitching the third image is not appropriate.

What could be possibly wrong with the logic behind this or in the method's implementation?

The images and the result can be found here : https://drive.google.com/folderview?id=0BxXVoeIUgVW7fnFMbExhTzN4QnRueXZpQmpILTZCWFZoTlZEckxfWV83VjkxMmFNSXdLVWM&usp=sharing

The input and result images can be found at the link given in the end. — ishita vishnoi, Apr 29 '15 at 22:14

score 0 · Answer 1 · answered May 01 '18 at 12:15

this answer is too late but may be helpful for other.

in some algorithms dimension of image to be stitching must be same. (if problem is this test below solution)

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

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

3- (result1 , result2) => final

here is other example that get all images at once:

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

Opencv stitching multiple images

1 Answers1