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First you should read this and this.

#include <stdio.h>
#include <iostream>

#include <opencv2/opencv.hpp> 
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/xfeatures2d.hpp>
#include <opencv2/xfeatures2d/nonfree.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

using namespace cv;

void readme();

/** @function main */
int main( int argc, char** argv )
{
 if( argc != 3 )
 { readme(); return -1; }

// Load the images
// Mat image1= imread( argv[2] );
// Mat image2= imread( argv[1] );
 Mat image1= imread( "F:/Images/pano/pano1.jpg" );
 Mat image2= imread( "F:/Images/pano/pano2.jpg"  );

 Mat gray_image1;
 Mat gray_image2;
 // Convert to Grayscale
 cvtColor( image1, gray_image1, CV_RGB2GRAY );
 cvtColor( image2, gray_image2, CV_RGB2GRAY );

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;


 Ptr<Feature2D> b= cv::xfeatures2d::SurfFeatureDetector::create( minHessian );
Mat descriptors_object, descriptors_scene;

std::vector< KeyPoint > keypoints_object, keypoints_scene;

b->detectAndCompute( gray_image1,Mat(), keypoints_object,descriptors_object );
b->detectAndCompute( gray_image2,Mat(), 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 = cv::findHomography( obj, scene, CV_RANSAC );
 // Use the Homography Matrix to warp the images
 cv::Mat result;
 warpPerspective(image1,result,H,cv::Size(image1.cols+image2.cols,image1.rows));
 cv::Mat half(result,cv::Rect(0,0,image2.cols,image2.rows));
 image2.copyTo(half);
 imshow( "Result", result );

 waitKey(0);
 return 0;
 }

/** @function readme */
 void readme()
 { std::cout << " Usage: Panorama < img1 > < img2 >" << std::endl; }

First you should read this and this.

#include <stdio.h>
#include <iostream>

#include <opencv2/opencv.hpp> 
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/xfeatures2d.hpp>
#include <opencv2/xfeatures2d/nonfree.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

using namespace cv;

void readme();

/** @function main */
int main( int argc, char** argv )
{
 if( argc != 3 )
 { readme(); return -1; }

// Load the images
// Mat image1= imread( argv[2] );
// Mat image2= imread( argv[1] );
 Mat image1= imread( "F:/Images/pano/pano1.jpg" );
 Mat image2= imread( "F:/Images/pano/pano2.jpg"  );

 Mat gray_image1;
 Mat gray_image2;
 // Convert to Grayscale
 cvtColor( image1, gray_image1, CV_RGB2GRAY );
 cvtColor( image2, gray_image2, CV_RGB2GRAY );

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;


 Ptr<Feature2D> b= cv::xfeatures2d::SurfFeatureDetector::create( minHessian );
Mat descriptors_object, descriptors_scene;

std::vector< KeyPoint > keypoints_object, keypoints_scene;

b->detectAndCompute( gray_image1,Mat(), keypoints_object,descriptors_object );
b->detectAndCompute( gray_image2,Mat(), 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 = cv::findHomography( obj, scene, CV_RANSAC );
 // Use the Homography Matrix to warp the images
 cv::Mat result;
 warpPerspective(image1,result,H,cv::Size(image1.cols+image2.cols,image1.rows));
 cv::Mat half(result,cv::Rect(0,0,image2.cols,image2.rows));
 image2.copyTo(half);
 imshow( "Result", result );

 waitKey(0);
 return 0;
 }

/** @function readme */
 void readme()
 { std::cout << " Usage: Panorama < img1 > < img2 >" << std::endl; }