OpenCV 使用二维特征点(Features2D)和单映射(Homography)寻找已知物体

 #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"

 using namespace cv;

 void readme();

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

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

   if( !img_object.data || !img_scene.data )
   { std::cout<< " --(!) Error reading images " << std::endl; return -; }

   //-- Step 1: Detect the keypoints using SURF Detector
   int minHessian = ;

   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 = ; double min_dist = ;

   //-- Quick calculation of max and min distances between keypoints
   for( int i = ; 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 = ; i < descriptors_object.rows; i++ )
   { if( matches[i].distance < *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(-), Scalar::all(-),
                vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

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

   for( int i = ; 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();
   obj_corners[] = cvPoint(,); obj_corners[] = cvPoint( img_object.cols,  );
   obj_corners[] = cvPoint( img_object.cols, img_object.rows ); obj_corners[] = cvPoint( , img_object.rows );
   std::vector<Point2f> scene_corners();

   perspectiveTransform( obj_corners, scene_corners, H);

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

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

   waitKey();
   return ;
   }

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