Panorama – Image Stitching in OpenCV

translationstitching.hpp

#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;
 
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 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;
 
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;
 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; }

Does the openCV have this possiblity to make a mosaic images ?

some research info;

http://docs.opencv.org/2.4/doc/tutorials/features2d/feature_homography/feature_homography.html
http://stackoverflow.com/questions/10256802/how-to-merge-two-images-in-opencv
https://ramsrigoutham.com/2012/11/22/panorama-image-stitching-in-opencv/

sntitching
http://docs.opencv.org/2.4/modules/stitching/doc/stitching.html

experiment 0
https://cg2010studio.com/2012/05/19/opencv-%E5%BD%B1%E5%83%8F%E6%8B%BC%E6%8E%A5-%E5%AF%AC%E6%99%AF%E5%92%8C%E7%92%B0%E6%99%AF-image-stitching-wide-view-and-round-view/

experiment 1
https://cg2010studio.com/2012/04/25/opencv-%E5%BD%B1%E5%83%8F%E6%8B%BC%E6%8E%A5%E5%AF%A6%E9%A9%972-image-stitching-experiment/

google camera stitching source:
https://android.googlesource.com/platform/packages/apps/Camera/+/android-4.4.2_r2/jni/feature_mos/src/mosaic/

process flow:

multiple files comparison.