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answered 2013-10-03 02:09:45 -0600

Moster gravatar image

I quickly tested it with 2 test images. The 2nd smaller one showed up within the bigger one.

cv::Size t = smallImage.size();
Mat roi(img_matches,cv::Rect(0,0, t.width, t.height) );
smallImage.copyTo(roi);

I quickly tested it with 2 test images. The 2nd smaller one showed up within the bigger one.

cv::Size t = smallImage.size();
Mat roi(img_matches,cv::Rect(0,0, t.width, t.height) );
smallImage.copyTo(roi);

Edit:

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

using namespace cv;
using namespace std;

void readme();

/** @function main */
int main( int argc, char** argv )
{
  if( argc != 4 )
  { readme(); return -1; }

  cv::initModule_nonfree();

  Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
  Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
  Mat img_replacement = imread(argv[3], 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 = 300;

  SurfFeatureDetector detector( minHessian, 4, 2, true, false );

  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;


  //-- Localize the object
  std::vector<Point2f> obj;
  std::vector<Point2f> scene;

  for( unsigned 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);

  Mat help;
  cv::resize(img_replacement, help, img_object.size());
  warpPerspective(help, img_replacement, H, help.size());

  Mat mask = cv::Mat::ones(img_object.size(), CV_8U);
  Mat mask2;
  warpPerspective(mask, mask2, H, mask.size());

  img_replacement.copyTo(img_scene, mask2);

  //-- Draw lines between the corners (the mapped object in the scene - image_2 )
/*  line( img_scene, scene_corners[0] , scene_corners[1], Scalar(0, 255, 0), 4 );
  line( img_scene, scene_corners[1] , scene_corners[2], Scalar( 0, 255, 0), 4 );
  line( img_scene, scene_corners[2] , scene_corners[3], Scalar( 0, 255, 0), 4 );
  line( img_scene, scene_corners[3] , scene_corners[0], Scalar( 0, 255, 0), 4 );

  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 );*/

  //-- Show detected matches
  imshow( "Good Matches & Object detection", img_scene );
  //imshow(" Derp", img_replacement);
  waitKey(0);
  return 0;
  }

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

I quickly tested it with 2 test images. The 2nd smaller one showed up within the bigger one.

cv::Size t = smallImage.size();
Mat roi(img_matches,cv::Rect(0,0, t.width, t.height) );
smallImage.copyTo(roi);

Edit:

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

using namespace cv;
using namespace std;

void readme();

/** @function main */
int main( int argc, char** argv )
{
  if( argc != 4 )
  { readme(); return -1; }

  cv::initModule_nonfree();

  Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
  Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
  Mat img_replacement = imread(argv[3], 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 = 300;

  SurfFeatureDetector detector( minHessian, 4, 2, true, false );

  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;


  //-- Localize the object
  std::vector<Point2f> obj;
  std::vector<Point2f> scene;

  for( unsigned 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);

  Mat help;
  cv::resize(img_replacement, help, img_object.size());
  warpPerspective(help, img_replacement, H, help.size());
img_scene.size());

  Mat mask = cv::Mat::ones(img_object.size(), CV_8U);
  Mat mask2;
  warpPerspective(mask, mask2, H, mask.size());

  img_replacement.copyTo(img_scene, mask2);

  //-- Draw lines between the corners (the mapped object in the scene - image_2 )
/*  line( img_scene, scene_corners[0] , scene_corners[1], Scalar(0, 255, 0), 4 );
  line( img_scene, scene_corners[1] , scene_corners[2], Scalar( 0, 255, 0), 4 );
  line( img_scene, scene_corners[2] , scene_corners[3], Scalar( 0, 255, 0), 4 );
  line( img_scene, scene_corners[3] , scene_corners[0], Scalar( 0, 255, 0), 4 );

  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 );*/

  //-- Show detected matches
  imshow( "Good Matches & Object detection", img_scene );
  //imshow(" Derp", img_replacement);
  waitKey(0);
  return 0;
  }

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

I quickly tested it with 2 test images. The 2nd smaller one showed up within the bigger one.

cv::Size t = smallImage.size();
Mat roi(img_matches,cv::Rect(0,0, t.width, t.height) );
smallImage.copyTo(roi);

Edit:

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

using namespace cv;
using namespace std;

void readme();

/** @function main */
int main( int argc, char** argv )
{
  if( argc != 4 )
  { readme(); return -1; }

  cv::initModule_nonfree();

  Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
  Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
  Mat img_replacement = imread(argv[3], 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 = 300;

  SurfFeatureDetector detector( minHessian, 4, 2, true, false );

  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;


  //-- Localize the object
  std::vector<Point2f> obj;
  std::vector<Point2f> scene;

  for( unsigned 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);

  Mat help;
  cv::resize(img_replacement, help, img_object.size());
  warpPerspective(help, img_replacement, H, img_scene.size());

  Mat mask = cv::Mat::ones(img_object.size(), CV_8U);
  Mat mask2;
  warpPerspective(mask, mask2, H, mask.size());
img_scene.size());

  img_replacement.copyTo(img_scene, mask2);

  //-- Draw lines between the corners (the mapped object in the scene - image_2 )
/*  line( img_scene, scene_corners[0] , scene_corners[1], Scalar(0, 255, 0), 4 );
  line( img_scene, scene_corners[1] , scene_corners[2], Scalar( 0, 255, 0), 4 );
  line( img_scene, scene_corners[2] , scene_corners[3], Scalar( 0, 255, 0), 4 );
  line( img_scene, scene_corners[3] , scene_corners[0], Scalar( 0, 255, 0), 4 );

  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 );*/

  //-- Show detected matches
  imshow( "Good Matches & Object detection", img_scene );
  //imshow(" Derp", img_replacement);
  waitKey(0);
  return 0;
  }

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