如何使用OpenCV的特徵匹配檢測複製僞造移動

Question

在我的OpenCV的項目，我想檢測的圖像複製僞造的舉動。我知道如何使用opencv FLANN在2個不同的圖像中進行特徵匹配，但是我對如何使用FLANN檢測圖像中的複製移動僞造變得非常困惑。

P.S1：我得到的篩關鍵點和形象的描述，並卡在使用特徵匹配類。

P.S2：特徵匹配的類型不是對我很重要。

在此先感謝。

更新：

這些圖片是什麼，我需要

一個例子，有一個相匹配兩幅圖像的特徵並做一些喜歡它的代碼兩個圖像（沒有一個），在機器人的OpenCV本地格式的代碼是象下面這樣：

vector<KeyPoint> keypoints; 
     Mat descriptors; 

     // Create a SIFT keypoint detector. 
     SiftFeatureDetector detector; 
     detector.detect(image_gray, keypoints); 
     LOGI("Detected %d Keypoints ...", (int) keypoints.size()); 

     // Compute feature description. 
     detector.compute(image, keypoints, descriptors); 
     LOGI("Compute Feature ..."); 


     FlannBasedMatcher matcher; 
     std::vector<DMatch> matches; 
     matcher.match(descriptors, descriptors, matches); 

     double max_dist = 0; double min_dist = 100; 

     //-- Quick calculation of max and min distances between keypoints 
      for(int i = 0; i < descriptors.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, 
      //-- or a small arbitary value (0.02) in the event that min_dist is very 
      //-- small) 
      //-- PS.- radiusMatch can also be used here. 
      std::vector<DMatch> good_matches; 

      for(int i = 0; i < descriptors.rows; i++) 
      { if(matches[i].distance <= max(2*min_dist, 0.02)) 
      { good_matches.push_back(matches[i]); } 
      } 

      //-- Draw only "good" matches 
      Mat img_matches; 
      drawMatches(image, keypoints, image, keypoints, 
         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); 
      imwrite(imgOutFile, img_matches); 

Answer 1

我不知道這是否是使用關鍵點這個問題的好辦法。我寧願測試template matching（使用圖像上的滑動窗口作爲補丁）。與關鍵點相比，這種方法的缺點是對旋轉和縮放比較敏感。

如果你想使用的關鍵點，您可以：

• 找出一組關鍵點（SURF，過篩，或任何你想要的）的，
• 計算匹配分數與其他所有關鍵點，與knnMatch功能的蠻力部隊匹配（cv::BFMatcher），

• 保持區別點之間的匹配，即距離大於零（或閾值）的點。

  int nknn = 10; // max number of matches for each keypoint 
  double minDist = 0.5; // distance threshold 
  
  // Match each keypoint with every other keypoints 
  cv::BFMatcher matcher(cv::NORM_L2, false); 
  std::vector< std::vector<cv::DMatch> > matches; 
  matcher.knnMatch(descriptors, descriptors, matches, nknn); 
  
  double max_dist = 0; double min_dist = 100; 
  
  //-- Quick calculation of max and min distances between keypoints 
  for(int i = 0; i < descriptors.rows; i++) 
  { 
      double dist = matches[i].distance; 
      if(dist < min_dist) min_dist = dist; 
      if(dist > max_dist) max_dist = dist; 
  } 
  
  // Compute distance and store distant matches 
  std::vector<cv::DMatch> good_matches; 
  for (int i = 0; i < matches.size(); i++) 
  { 
      for (int j = 0; j < matches[i].size(); j++) 
      { 
       // The METRIC distance 
       if(matches[i][j].distance> max(2*min_dist, 0.02)) 
        continue; 
  
       // The PIXELIC distance 
       Point2f pt1 = keypoints[queryIdx].pt; 
       Point2f pt2 = keypoints[trainIdx].pt; 
  
       double dist = cv::norm(pt1 - pt2); 
       if (dist > minDist) 
        good_matches.push_back(matches[i][j]); 
      } 
  } 
  
  Mat img_matches; 
  drawMatches(image_gray, keypoints, image_gray, keypoints, good_matches, img_matches);