体育赛事期间的 OpenCV 横幅匹配

Posted 2023-02-22

【中文标题】体育赛事期间的 OpenCV 横幅匹配

【英文标题】：OpenCV banner matching during sports events

【发布时间】：2014-09-16 15:20:19

【问题描述】：

我想在体育场墙壁上的体育赛事期间在图像上匹配商业横幅。我用this example。如果图像足够大，它的工作就很好（scene_1）。但是如果有某种失真，或者图片小于某个限制（scene_2），那么它就会发疯，此外 OpenCV 会匹配完全不相关的点。

成功和失败的匹配示例：

有没有办法只匹配靠近在一起的点，形成一个矩形？或者有没有更好的方法来做这样的匹配？我现在最大的问题是如何让它更精确，因为它可以从 10 次出现中得到 3 次很好的匹配（这个项目的目的是处理整个视频以获得某种统计信息）。

这是我的代码：

#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/nonfree.hpp"

using namespace cv;

void readme();

int main( int argc, char** argv )

  if( argc != 3 )
   readme(); return -1; 

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

  void readme()
   std::cout << " Usage: ./SURF_descriptor <img1 - object> <img2 - scene>" << std::endl; 

【问题讨论】：

看看这个***.com/questions/10168686/…

来吧 OP，请给我一些甜蜜的支持。我什至会把我的源代码邮寄给你！

【参考方案1】：

尝试 SIFT 而不是 SURF。有人声称 SURF 更健壮，但根据我的个人经验和 CV 社区最近的趋势，SIFT 仍然是要走的路。

另外，假设您只是想在目标图像中找到一个对象，您可以使用一些密度聚类方法来进一步过滤掉一些异常值。使用 DBSCAN 提高检测质量的一个很好的例子是

Le、Viet Phuong 等人。 “通过基于单应性的后过滤器改进文档分类的徽标识别和匹配。”文档分析与识别 (ICDAR)，2013 年第 12 届国际会议。 IEEE，2013 年。

他们的方法非常简单易行，我自己实现了，发现它对某些任务非常有用。就是一两百行代码。