remove duplicate matches

REQUIRE

@@ -5,5 +5,4 @@ Distributions 0.12
 FixedPointNumbers 0.3
 Compat 0.17
 FLANN
-NearestNeighbors
 Distances

src/ImageFeatures.jl

@@ -6,7 +6,6 @@ module ImageFeatures
 using Images, ColorTypes, FixedPointNumbers, Distributions
 using Compat
 using FLANN, Distances
-using NearestNeighbors
 
 include("core.jl")
 include("const.jl")

src/core.jl

@@ -87,6 +87,7 @@ function match_keypoints(keypoints_1::Keypoints, keypoints_2::Keypoints, desc_1,
     s_key = keypoints_1
     l_key = keypoints_2
     order = false
+
     if length(desc_1) > length(desc_2)
         smaller = desc_2
         larger = desc_1
@@ -97,38 +98,41 @@ function match_keypoints(keypoints_1::Keypoints, keypoints_2::Keypoints, desc_1,
 
     matches = Keypoints[]
 
-    ndims=length(larger[1])
-    n_large=length(larger)
-    n_small=length(smaller)
-
-    data=Matrix{Float64}(ndims, n_large);
-    for i in 1:ndims
-        for j in 1:n_large
-            data[i,j]=larger[j][i]?1:0
-        end
-    end
-
     if is_windows() && Sys.WORD_SIZE==32
-        tree = KDTree(data, Cityblock())
-
-        for i in 1:n_small
-            idx, dist = NearestNeighbors.knn(tree, smaller[i], 1)
-            if dist[1]/ndims < threshold
-                id_min = idx[1]
+        hamming_distances = [hamming_distance(s, l) for s in smaller, l in larger]
+        for i in 1:length(smaller)
+            if any(hamming_distances[i, :] .< threshold)
+                id_min = indmin(hamming_distances[i, :])
                 push!(matches, order ? [l_key[id_min], s_key[i]] : [s_key[i], l_key[id_min]])
+                hamming_distances[:, id_min] = 1.0
             end
-        end   
+        end
     else
-        tree = flann(data, FLANNParameters(), Cityblock())
+        ndims=length(larger[1])
+        n_large=length(larger)
+        n_small=length(smaller)
+        data=Matrix{Float64}(ndims, n_large);
+        for i in 1:ndims
+            for j in 1:n_large
+                data[i,j]=larger[j][i]?1:0
+            end
+        end 
 
+        tree = flann(data, FLANNParameters(), Cityblock())
+        matched = zeros(Bool, n_large)
         for i in 1:n_small
-            idx, dist = FLANN.knn(tree, Vector{Float64}(smaller[i]), 1)
-            if dist[1]/ndims < threshold
-                id_min = idx[1]
-                push!(matches, order ? [l_key[id_min], s_key[i]] : [s_key[i], l_key[id_min]])
+            idx = inrange(tree, Vector{Float64}(smaller[i]), 0.1)
+            for j in idx[1]
+                if !matched[j]
+                    id_min = j
+                    push!(matches, order ? [l_key[id_min], s_key[i]] : [s_key[i], l_key[id_min]])
+                    matched[j] = true
+                    break
+                end
             end
         end
     end
+
     matches
 end