Ich fand ein sehr nützliches Beispiel für das Zusammenfügen von Bildern, aber mein Problem war, dass diese Art von Bildern hier ein Beispiel ist.

und hier ist ein anderes Bild

wenn ich opencv stitcher benutze, werden die Ergebnisse immer kleiner

Gibt es eine Methode, um eine Transformation auf die Eingabebilder anzuwenden, sodass sie wie folgt aussehen?

hier ist der Code

#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;
}

Auch hier ist ein Link für einige Bilder ::https: //www.dropbox.com/sh/ovzkqomxvzw8rww/AAB2DDCrCF6NlCFre7V1Gb6La? dl = 0 Vielen Dank Lafi