OpenCV學習筆記（九）——2維特徵Feature2D

基於特徵點的映像匹配是影像處理中經常會遇到的問題，手動選取特徵點太麻煩了。比較經典常用的特徵點自動提取的辦法有Harris特徵、SIFT特徵、SURF特徵。

先介紹利用SURF特徵的特徵描述辦法，其操作封裝在類SurfFeatureDetector中，利用類內的detect函數可以檢測出SURF特徵的關鍵點，儲存在vector容器中。第二部利用SurfDescriptorExtractor類進行特徵向量的相關計算。將之前的vector變數變成向量矩陣形式儲存在Mat中。最後強行匹配兩幅映像的特徵向量，利用了類BruteForceMatcher中的函數match。代碼如下：

/** * @file SURF_descriptor * @brief SURF detector + descritpor + BruteForce Matcher + drawing matches with OpenCV functions * @author A. Huaman */#include <stdio.h>#include <iostream>#include "opencv2/core/core.hpp"#include "opencv2/features2d/features2d.hpp"#include "opencv2/highgui/highgui.hpp"using namespace cv;void readme();/** * @function main * @brief Main function */int main( int argc, char** argv ){  if( argc != 3 )  { return -1; }  Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );  Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );    if( !img_1.data || !img_2.data )  { return -1; }  //-- Step 1: Detect the keypoints using SURF Detector  int minHessian = 400;  SurfFeatureDetector detector( minHessian );  std::vector<KeyPoint> keypoints_1, keypoints_2;  detector.detect( img_1, keypoints_1 );  detector.detect( img_2, keypoints_2 );  //-- Step 2: Calculate descriptors (feature vectors)  SurfDescriptorExtractor extractor;  Mat descriptors_1, descriptors_2;  extractor.compute( img_1, keypoints_1, descriptors_1 );  extractor.compute( img_2, keypoints_2, descriptors_2 );  //-- Step 3: Matching descriptor vectors with a brute force matcher  BruteForceMatcher< L2<float> > matcher;  std::vector< DMatch > matches;  matcher.match( descriptors_1, descriptors_2, matches );  //-- Draw matches  Mat img_matches;  drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );   //-- Show detected matches  imshow("Matches", img_matches );  waitKey(0);  return 0;}/** * @function readme */void readme(){ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }

當然，進行強匹配的效果不夠理想，這裡再介紹一種FLANN特徵匹配演算法。前兩步與上述代碼相同，第三步利用FlannBasedMatcher類進行特徵匹配，並只保留好的特徵匹配點，代碼如下：

  //-- Step 3: Matching descriptor vectors using FLANN matcher  FlannBasedMatcher matcher;  std::vector< DMatch > matches;  matcher.match( descriptors_1, descriptors_2, matches );  double max_dist = 0; double min_dist = 100;  //-- Quick calculation of max and min distances between keypoints  for( int i = 0; i < descriptors_1.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 2*min_dist )  //-- PS.- radiusMatch can also be used here.  std::vector< DMatch > good_matches;  for( int i = 0; i < descriptors_1.rows; i++ )  { if( matches[i].distance < 2*min_dist )    { good_matches.push_back( matches[i]); }  }    //-- Draw only "good" matches  Mat img_matches;  drawMatches( img_1, keypoints_1, img_2, keypoints_2,                good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),                vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );   //-- Show detected matches  imshow( "Good Matches", img_matches );

在FLANN特徵匹配的基礎上，還可以進一步利用Homography映射找出已知物體。具體來說就是利用findHomography函數利用匹配的關鍵點找出相應的變換，再利用perspectiveTransform函數映射點群。具體代碼如下：

  //-- Localize the object from img_1 in img_2   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_1[ good_matches[i].queryIdx ].pt );    scene.push_back( keypoints_2[ good_matches[i].trainIdx ].pt );   }  Mat H = findHomography( obj, scene, CV_RANSAC );  //-- Get the corners from the image_1 ( the object to be "detected" )  Point2f obj_corners[4] = { cvPoint(0,0), cvPoint( img_1.cols, 0 ), cvPoint( img_1.cols, img_1.rows ), cvPoint( 0, img_1.rows ) };  Point scene_corners[4];  //-- Map these corners in the scene ( image_2)  for( int i = 0; i < 4; i++ )  {    double x = obj_corners[i].x;     double y = obj_corners[i].y;    double Z = 1./( H.at<double>(2,0)*x + H.at<double>(2,1)*y + H.at<double>(2,2) );    double X = ( H.at<double>(0,0)*x + H.at<double>(0,1)*y + H.at<double>(0,2) )*Z;    double Y = ( H.at<double>(1,0)*x + H.at<double>(1,1)*y + H.at<double>(1,2) )*Z;    scene_corners[i] = cvPoint( cvRound(X) + img_1.cols, cvRound(Y) );  }       //-- Draw lines between the corners (the mapped object in the scene - image_2 )  line( img_matches, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 2 );  line( img_matches, scene_corners[1], scene_corners[2], Scalar( 0, 255, 0), 2 );  line( img_matches, scene_corners[2], scene_corners[3], Scalar( 0, 255, 0), 2 );  line( img_matches, scene_corners[3], scene_corners[0], Scalar( 0, 255, 0), 2 );  //-- Show detected matches  imshow( "Good Matches & Object detection", img_matches );

然後再看一下Harris特徵檢測，在電腦視覺中，通常需要找出兩幀映像的匹配點，如果能找到兩幅映像如何相關，就能提取出兩幅映像的資訊。我們說的特徵的最大特點就是它具有唯一可識別這一特點，映像特徵的類型通常指邊界、角點（興趣點）、斑點（興趣地區）。角點就是映像的一個局部特徵，應用廣泛。harris角點檢測是一種直接基於灰階映像的角點提取演算法，穩定性高，尤其對L型角點檢測精度高，但由於採用了高斯濾波，運算速度相對較慢，角點資訊有丟失和位置位移的現象，而且角點提取有聚簇現象。具體實現就是使用函數cornerHarris實現。

除了利用Harris進行角點檢測，還可以利用Shi-Tomasi方法進行角點檢測。使用函數goodFeaturesToTrack對角點進行檢測，效果也不錯。也可以自己製作角點檢測的函數，需要用到cornerMinEigenVal函數和minMaxLoc函數，最後的特徵點選取，判斷條件要根據自己的情況編輯。如果對特徵點，角點的精度要求更高，可以用cornerSubPix函數將角點定位到子像素。