Merge pull request #12908 from alexevans:Issue11855

modules/core/src/batch_distance.cpp

@@ -297,19 +297,21 @@ void cv::batchDistance( InputArray _src1, InputArray _src2,
         nidx = Scalar::all(-1);
     }
 
+
     if( crosscheck )
     {
         CV_Assert( K == 1 && update == 0 && mask.empty() );
         CV_Assert(!nidx.empty());
-        Mat tdist, tidx;
+        Mat tdist, tidx, sdist, sidx;
         batchDistance(src2, src1, tdist, dtype, tidx, normType, K, mask, 0, false);
+        batchDistance(src1, src2, sdist, dtype, sidx, normType, K, mask, 0, false);
 
         // if an idx-th element from src1 appeared to be the nearest to i-th element of src2,
         // we update the minimum mutual distance between idx-th element of src1 and the whole src2 set.
         // As a result, if nidx[idx] = i*, it means that idx-th element of src1 is the nearest
         // to i*-th element of src2 and i*-th element of src2 is the closest to idx-th element of src1.
         // If nidx[idx] = -1, it means that there is no such ideal couple for it in src2.
-        // This O(N) procedure is called cross-check and it helps to eliminate some false matches.
+        // This O(2N) procedure is called cross-check and it helps to eliminate some false matches.
         if( dtype == CV_32S )
         {
             for( int i = 0; i < tdist.rows; i++ )
@@ -336,6 +338,13 @@ void cv::batchDistance( InputArray _src1, InputArray _src2,
                 }
             }
         }
+        for( int i = 0; i < sdist.rows; i++ )
+        {
+            if( tidx.at<int>(sidx.at<int>(i)) != i )
+            {
+                nidx.at<int>(i) = -1;
+            }
+        }
         return;
     }
 

modules/features2d/test/test_matchers_algorithmic.cpp

@@ -558,4 +558,23 @@ TEST( Features2d_DMatch, read_write )
     ASSERT_NE( strstr(str.c_str(), "4.5"), (char*)0 );
 }
 
+
+TEST(Features2d_DMatch, issue_11855)
+{
+    Mat sources = (Mat_<uchar>(2, 3) << 1, 1, 0,
+                                        1, 1, 1);
+    Mat targets = (Mat_<uchar>(2, 3) << 1, 1, 1,
+                                        0, 0, 0);
+
+    Ptr<BFMatcher> bf = BFMatcher::create(NORM_HAMMING, true);
+    vector<vector<DMatch> > match;
+    bf->knnMatch(sources, targets, match, 1, noArray(), true);
+
+    ASSERT_EQ((size_t)1, match.size());
+    ASSERT_EQ((size_t)1, match[0].size());
+    EXPECT_EQ(1, match[0][0].queryIdx);
+    EXPECT_EQ(0, match[0][0].trainIdx);
+    EXPECT_EQ(0.0f, match[0][0].distance);
+}
+
 }} // namespace