Encontré un ejemplo muy útil de la costura de imágenes, pero mi problema fue que ese tipo de imágenes aquí es un ejemplo

y aquí hay otra imagen

cuando uso el grabador de OpenCV, las imágenes resultantes se vuelven más pequeñas como esta

¿Hay algún método para aplicar una transformación en las imágenes de entrada para que sean como esta?

aquí está el código

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include<opencv2/opencv.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/stitching/stitcher.hpp>
#include<vector>
using namespace cv;
using namespace std;
cv::vector<cv::Mat> ImagesList;
string result_name ="/TopViewsHorizantale/1.bmp";
int main()
{
      // Load the images

 Mat image1= imread("current_00000.bmp" );
 Mat image2= imread("current_00001.bmp" );
 cv::resize(image1, image1, image2.size());
 Mat gray_image1;
 Mat gray_image2;
 Mat Matrix = Mat(3,3,CV_32FC1);

 // Convert to Grayscale
 cvtColor( image1, gray_image1, CV_RGB2GRAY );
 cvtColor( image2, gray_image2, CV_RGB2GRAY );
 namedWindow("first image",WINDOW_AUTOSIZE);
 namedWindow("second image",WINDOW_AUTOSIZE);
 imshow("first image",image2);
 imshow("second image",image1);

if( !gray_image1.data || !gray_image2.data )
 { std::cout<< " --(!) Error reading images " << std::endl; return -1; }

//-- Step 1: Detect the keypoints using SURF Detector
 int minHessian = 400;

SurfFeatureDetector detector( minHessian );

std::vector< KeyPoint > keypoints_object, keypoints_scene;

detector.detect( gray_image1, keypoints_object );
detector.detect( gray_image2, keypoints_scene );

//-- Step 2: Calculate descriptors (feature vectors)
 SurfDescriptorExtractor extractor;

Mat descriptors_object, descriptors_scene;

extractor.compute( gray_image1, keypoints_object, descriptors_object );
extractor.compute( gray_image2, keypoints_scene, descriptors_scene );

//-- Step 3: Matching descriptor vectors using FLANN matcher
 FlannBasedMatcher matcher;
 std::vector< DMatch > matches;
 matcher.match( descriptors_object, descriptors_scene, matches );

double max_dist = 0; double min_dist = 100;

//-- Quick calculation of max and min distances between keypoints
 for( int i = 0; i < descriptors_object.rows; i++ )
 { double dist = matches[i].distance;
 if( dist < min_dist ) min_dist = dist;
 if( dist > max_dist ) max_dist = dist;
 }

printf("-- Max dist : %f \n", max_dist );
printf("-- Min dist : %f \n", min_dist );

//-- Use only "good" matches (i.e. whose distance is less than 3*min_dist )
 std::vector< DMatch > good_matches;

for( int i = 0; i < descriptors_object.rows; i++ )
 { if( matches[i].distance < 3*min_dist )
 { good_matches.push_back( matches[i]); }
 }
 std::vector< Point2f > obj;
 std::vector< Point2f > scene;

for( int i = 0; i < good_matches.size(); i++ )
 {
 //-- Get the keypoints from the good matches
 obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
 scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
 }

// Find the Homography Matrix
 Mat H = findHomography( obj, scene, CV_RANSAC );
 // Use the Homography Matrix to warp the images
 cv::Mat result;
      int N = image1.rows + image2.rows;
 int M = image1.cols+image2.cols;
 warpPerspective(image1,result,H,cv::Size(N,M));
 cv::Mat half(result,cv::Rect(0,0,image2.rows,image2.cols));
 result.copyTo(half);
 namedWindow("Result",WINDOW_AUTOSIZE);
 imshow( "Result", result);

 imwrite(result_name, result);

 waitKey(0);
 return 0;
}

También aquí hay un enlace para algunas imágenes ::https://www.dropbox.com/sh/ovzkqomxvzw8rww/AAB2DDCrCF6NlCFre7V1Gb6La?dl=0 Muchas gracias Lafi