Question

由于我没有在代码示例和文档中找到答案（也许是因为我使用SIFT nonfree）我对OpenCV功能有疑问。

我有一小组图像（50）。对于每个图像，我计算KeyPoint（SIFT检测器）并提取描述符值。然后选择所有200（唯一）来创建描述符字典。

目标是仅使用这200个描述符描述每个图像（图像中的每个原始描述符必须用字典中最近的（欧几里德）描述符替换）。为了保持清晰 - ＆gt;我正在寻找机制，可以为图像中的每个OriginalImageDescriptor创建这样的地图（DictionaryDescriptor，OriginalImageDescriptor）。

我考虑过使用DescriptorMatcher，但我不确定这正是我想要的，因为在this示例中我可以看到KeyPoint与其他KeyPoint无关 - DescriptorMatcher是否匹配trainImage中的每个描述符与queryImage中的某些描述符？

我正在使用OpenCV 2.4.8

谢谢！

Answer 1

请仔细阅读以下示例，以帮助您更好地理解：

我从opencv的 SURF_Homography 中取了这个例子并进行了一些修改，以便您可以轻松使用它。在这里，您可以看到匹配器具有 good_matches [i] .queryIdx 和 good_matches [i] .trainIdx 。这两个是相应的匹配对。这里使用了FlannMatcher。您可以选择任何其他首选匹配器。

#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 cv;

int main( int argc, char** argv )
{

  Mat img_object = imread( "image1.jpg", CV_LOAD_IMAGE_GRAYSCALE );
  Mat img_scene = imread( "image2.jpg", 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 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, CV_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);


  //-- 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;
}

OpenCV功能

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