OpenCV 通过 Features2D 和 Homography 查找已知对象

目标

本文中你将学会：

• 使用 findHomography 函数来查找匹配关键点之间的转换
• 使用 perspectiveTransform 来映射点

代码

完整的代码可从这里 下载

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

结果

下面是检测对象的结果（绿色框内）