全景拼接配准后如何融合？？求解

小村长 2013-10-29 08:09:06

刚入门opencv 图像处理，最近做全景拼接，看了tutorials 特征检测初步写了以下代码，但不知道后面怎么融合，求大神指教!! 已经琢磨了好几天了。

#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/features2d.hpp"



using namespace std;

using namespace cv;





int main( int argc, char** argv )

{

  if( argc != 3 )

  { readme(); return -1; }



  Mat img_object = imread( argv[1], IMREAD_GRAYSCALE );

  Mat img_scene = imread( argv[2], IMREAD_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 from img_1 in img_2

  std::vector<Point2f> obj;

  std::vector<Point2f> scene;



  for( size_t 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, RANSAC );



  //-- Get the corners from the image_1 ( the object to be "detected" )

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

  obj_corners[0] = Point(0,0); obj_corners[1] = Point( img_object.cols, 0 );

  obj_corners[2] = Point( img_object.cols, img_object.rows ); obj_corners[3] = Point( 0, img_object.rows );

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



  perspectiveTransform( obj_corners, scene_corners, H);



Mat imageturn=Mat::zeros(max(scene_corners[1].x,scene_corners[2].x),

        max(scene_corners[2].y,scene_corners[3].y),img1.type());

    warpPerspective(img1,imageturn,H,cv::Size(imageturn.cols,imageturn.rows));







  //-- Draw lines between the corners (the mapped object in the scene - image_2 )

  Point2f offset( (float)img_object.cols, 0);

  line( img_matches, scene_corners[0] + offset, scene_corners[1] + offset, Scalar(0, 255, 0), 4 );

  line( img_matches, scene_corners[1] + offset, scene_corners[2] + offset, Scalar( 0, 255, 0), 4 );

  line( img_matches, scene_corners[2] + offset, scene_corners[3] + offset, Scalar( 0, 255, 0), 4 );

  line( img_matches, scene_corners[3] + offset, scene_corners[0] + offset, Scalar( 0, 255, 0), 4 );



  //-- Show detected matches

  imshow( "Good Matches & Object detection", img_matches );



  waitKey(0);



  return 0;

}