#include "stdafx.h"
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;
using namespace std;
/** @function main */
int main( )
{
// Load the images
IplImage *img1;
img1 = cvLoadImage("panorama_image2.jpg");
Mat image1(img1);
Mat gray_image1(img1);
IplImage *img2;
img2 = cvLoadImage("panorama_image1.jpg");
Mat image2(img2);
Mat gray_image2(img2);
/*Mat image1= imread( "1.bmp" );
Mat image2= imread( "2.bmp" );
Mat gray_image1=imread( "1.bmp" );
Mat gray_image2=imread( "2.bmp" ); */
// 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;
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
Mat result;
warpPerspective(image1,result,H,cv::Size(image1.cols+image2.cols,image1.rows));
Mat half(result,cv::Rect(0,0,image2.cols,image2.rows));
image2.copyTo(half);
imshow( "Result", result);
waitKey(0);
return 0;
}
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