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findHomography problems

asked 2013-05-20 11:26:05 -0500

nqchanh gravatar image

i've tried to detect an object from a picture using FeatureDetection interface, but i've got problems with my input images. Here is my code:

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

using namespace cv;

void readme();

/** @function main */
int main( int argc, char** argv )
{

  //Mat img_object = imread( "facerot.png", CV_LOAD_IMAGE_COLOR );
  //Mat img_scene = imread( "BKDEC.png", CV_LOAD_IMAGE_COLOR );

Mat img_object = imread("img_2.png", CV_LOAD_IMAGE_COLOR  );
Mat img_scene = imread( "img_1.png", CV_LOAD_IMAGE_COLOR  );

//Mat img_object = imread( "black white image 2.jpg", CV_LOAD_IMAGE_COLOR);
//Mat img_scene = imread( "black white image 1.jpg", CV_LOAD_IMAGE_COLOR);

  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 ...
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answered 2013-05-20 15:36:18 -0500

mkr gravatar image

Quickly running through your program - the debugger shows, that the size of good_matches and hence also the obj and scene is zero. Originally, 9 matches are found, but they are discarded when you apply the max_dist and min_dist constraints. I strongly recommend you to make friends with the debugger :) Good luck with fixing the code!

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answered 2013-05-20 16:39:51 -0500

DutchMark gravatar image

updated 2013-05-20 16:40:06 -0500

I found that getting this all to work is more of an art than a science. One thing that stands out to me is that you don't check how many items you have in good_matches. findHomography() will cause an error if you pass less than 4 points. So you should at least put an if (good_matches.size()>=4) somewhere in there.

In my opinion there's also a bug in FlannBasedMatcher, as the call matcher.match( descriptors_object, descriptors_scene, matches ); should actually be matcher.match( descriptors_scene, descriptors_object, matches ); if you trust the documentation. Except that it would give terrible results.

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Asked: 2013-05-20 11:26:05 -0500

Seen: 1,993 times

Last updated: May 20 '13