### Navigation

- index
- next |
- previous |
- OpenCV 2.4.12.3 documentation »
- OpenCV Tutorials »
- feature2d module. 2D Features framework »

# Features2D + Homography to find a known object

## Goal

In this tutorial you will learn how to:

- Use the function findHomography to find the transform between matched keypoints.
- Use the function perspectiveTransform to map the points.

## Theory

## Code

This tutorial code’s is shown lines below. You can also download it from here

    #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"

    using namespace cv;

    void readme();

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

      Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
      Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );

      if( !img_object.data || !img_scene.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( img_object, keypoints_object );
      detector.detect( img_scene, keypoints_scene );

      //-- Step 2: Calculate descriptors (feature vectors)
      SurfDescriptorExtractor extractor;

      Mat descriptors_object, descriptors_scene;

      extractor.compute( img_object, keypoints_object, descriptors_object );
      extractor.compute( img_scene, 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 );

      //-- Draw 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]); }
      }

      Mat img_matches;
      drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
                   good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
                   vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

      //-- Localize the object
      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 );
      }

      Mat H = findHomography( obj, scene, CV_RANSAC );

      //-- Get the corners from the image_1 ( the object to be "detected" )
      std::vector<Point2f> obj_corners(4);
      obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
      obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
      std::vector<Point2f> scene_corners(4);

      perspectiveTransform( obj_corners, scene_corners, H);

      //-- Draw lines between the corners (the mapped object in the scene - image_2 )
      line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
      line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
      line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
      line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );

      //-- Show detected matches
      imshow( "Good Matches & Object detection", img_matches );

      waitKey(0);
      return 0;
      }

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

## Explanation

## Result

1. And here is the result for the detected object (highlighted in green)

## Help and Feedback

You did not find what you were looking for?

- Ask a question on the Q&A forum.
- If you think something is missing or wrong in the documentation, please file a bug report.

### Navigation

- index
- next |
- previous |
- OpenCV 2.4.12.3 documentation »
- OpenCV Tutorials »
- feature2d module. 2D Features framework »