Align images based on a detected features in Opencv

Hi I've a base image and other images that I'd like to rotate with the same angle as the base image.

this is my base image.

this is an example image that I'd like to rotate.

here my full code.

  #include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/imgproc/imgproc.hpp"

#define PI 3.14159265

using namespace cv;
using namespace std;


void rotate(cv::Mat& src, double angle, cv::Mat& dst)
{
    int len = std::max(src.cols, src.rows);
     cv::Point2f pt(len/2., len/2.);
     cv::Mat r = cv::getRotationMatrix2D(pt, angle, 1.0);

     cv::warpAffine(src, dst, r, cv::Size(len, len));
}



float angleBetween(const Point &v1, const Point &v2)
{
    float len1 = sqrt(v1.x * v1.x + v1.y * v1.y);
    float len2 = sqrt(v2.x * v2.x + v2.y * v2.y);

    float dot = v1.x * v2.x + v1.y * v2.y;

    float a = dot / (len1 * len2);

    if (a >= 1.0)
        return 0.0;
    else if (a <= -1.0)
        return PI;
    else{
        int degree;
        degree = acos(a)*180/PI;
        return degree;
        };
}



int main()
{

    Mat char1 = imread( "/Users/Rodrane/Documents/XCODE/OpenCV/mkedenemeleri/anarev/rotated.jpg",CV_LOAD_IMAGE_GRAYSCALE );

    Mat image = imread("/Users/Rodrane/Documents/XCODE/OpenCV/mkedenemeleri/anarev/gain2000_crop.jpg", CV_LOAD_IMAGE_GRAYSCALE );




    if( !char1.data )
    {
        std::cout<< "Error reading object " << std::endl;
        return -1;
    }

    GaussianBlur( char1, char1, Size(3, 3), 2, 2 );
    GaussianBlur( image, image, Size(3, 3), 2, 2 );
    adaptiveThreshold(char1,char1,255,CV_ADAPTIVE_THRESH_MEAN_C,CV_THRESH_BINARY,9,14);
    adaptiveThreshold(image,image,255,CV_ADAPTIVE_THRESH_MEAN_C,CV_THRESH_BINARY,9,14);

    //Detect the keypoints using SURF Detector
    int minHessian = 200;

    SurfFeatureDetector detector( minHessian );
    std::vector<KeyPoint> kp_object;

    detector.detect( char1, kp_object );

    //Calculate descriptors (feature vectors)
    SurfDescriptorExtractor extractor;
    Mat des_object;

    extractor.compute( char1, kp_object, des_object );

    FlannBasedMatcher matcher;


    namedWindow("Good Matches");

    std::vector<Point2f> obj_corners(4);

    //Get the corners from the object
    obj_corners[0] = cvPoint(0,0);
    obj_corners[1] = cvPoint( char1.cols, 0 );
    obj_corners[2] = cvPoint( char1.cols, char1.rows );
    obj_corners[3] = cvPoint( 0, char1.rows );



    Mat frame;




    Mat des_image, img_matches;
    std::vector<KeyPoint> kp_image;
    std::vector<vector<DMatch > > matches;
    std::vector<DMatch > good_matches;
    std::vector<Point2f> obj;
    std::vector<Point2f> scene;
    std::vector<Point2f> scene_corners(4);
    Mat H;


    detector.detect( image, kp_image );
    extractor.compute( image, kp_image, des_image );

    matcher.knnMatch(des_object, des_image, matches, 2);

    for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
    {
        if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
        {
            good_matches.push_back(matches[i][0]);
        }
    }



    //Draw only "good" matches


    drawMatches( char1, kp_object, image, kp_image, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

    if (good_matches.size() >= 4)
    {
        for( int i = 0; i < good_matches.size(); i++ )
        {
            //Get the keypoints from the good matches
            obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
            scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
            cout<<angleBetween(obj[i],scene[i])<<endl; //angles between images

        }

        H = findHomography( obj, scene, CV_RANSAC );


        perspectiveTransform( obj_corners, scene_corners, H);

       // cout<<angleBetween(obj[0], scene[0])<<endl;


        //Draw lines between the corners (the mapped object in the scene image )

    }

    //Show detected matches
    // resize(img_matches, img_matches, Size(img_matches.cols/2, img_matches.rows/2));

    imshow( "Good Matches", img_matches );
    waitKey();

    return 0;
}

what actually my code doing is;

I do detect features of both images
Calculate degrees between point of my base image and example image

since all degrees between points are different how can I rotate my image depending on a features?

also for example lets say features of character M is detected and the angle is 30 in some conditions rotating image for degree 30 will give me horizontally aligned but vertically wrong.

the problem is even the first features are in the same line this doesn't mean example image rotated correctly (it might need rotate 180 degrees more for example)

I remade your function without using the angles:

void rotate(cv::Mat& originalImage,cv::Mat& rotatedImage,cv::InputArray rotated,
cv::Mat& dst) {
    std::vector<cv::Point2f> original(4);
    original[0] = cv::Point( 0, 0);
    original[1] = cv::Point( originalImage.cols, 0 );
    original[2] = cv::Point( originalImage.cols, originalImage.rows );
    original[3] = cv::Point( 0, originalImage.rows );

    dst = cv::Mat::zeros(originalImage.rows, originalImage.cols, CV_8UC3);
    cv::Mat transform = cv::getPerspectiveTransform(rotated, original);
    cv::warpPerspective(rotatedImage, dst, transform, dst.size() );
}

Note that the input 'rotated' is in your case 'scene_corners' and 'dst' is the resulting image.