Merge pull request #786 from alicevision/fix/voctree/unsigned

Fix/voctree/unsigned
alicevision · May 10, 2020 · 0998635 · 0998635
2 parents e6b6f9a + bfc0e19
commit 0998635
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Showing 2 changed files with 35 additions and 30 deletions.
diff --git a/src/aliceVision/voctree/Database.cpp b/src/aliceVision/voctree/Database.cpp
@@ -119,7 +119,7 @@ void Database::find( const SparseHistogram& query, std::size_t N, std::vector<Do
   {
     // for each document/image in the database compute the distance between the 
     // histograms of the query image and the others
-    float distance = sparseDistance(query, document.second, distanceMethod, word_weights_);
+    const float distance = sparseDistance(query, document.second, distanceMethod, word_weights_);
     acc(DocMatch(document.first, distance));
   }
 

diff --git a/src/aliceVision/voctree/VocabularyTree.cpp b/src/aliceVision/voctree/VocabularyTree.cpp
@@ -6,19 +6,23 @@
 
 #include "VocabularyTree.hpp"
 
+#include <algorithm>
+
 namespace aliceVision {
 namespace voctree {
 
 float sparseDistance(const SparseHistogram& v1, const SparseHistogram& v2, const std::string &distanceMethod, const std::vector<float>& word_weights)
 {
 
-  float distance = 0.0f;
-  float epsilon = 0.001;
+  float distance{0.0f};
+  const float epsilon{0.001f};
 
-  SparseHistogram::const_iterator i1 = v1.begin(), i1e = v1.end();
-  SparseHistogram::const_iterator i2 = v2.begin(), i2e = v2.end();
+  auto i1 = v1.cbegin();
+  auto i1e = v1.cend();
+  auto i2 = v2.cbegin();
+  auto i2e = v2.cend();
 
-  if(distanceMethod.compare("classic") == 0) 
+  if(distanceMethod == "classic")
   {      
     while(i1 != i1e && i2 != i2e)
     {
@@ -34,7 +38,8 @@ float sparseDistance(const SparseHistogram& v1, const SparseHistogram& v2, const
       }
       else
       {
-        distance += fabs(i1->second.size() - i2->second.size());
+        const auto val = std::minmax(i1->second.size(), i2->second.size());
+        distance += static_cast<float>(val.second - val.first);
         ++i1;
         ++i2;
       }
@@ -53,11 +58,11 @@ float sparseDistance(const SparseHistogram& v1, const SparseHistogram& v2, const
     }
   }
 
-  else if(distanceMethod.compare("commonPoints") == 0) 
+  else if(distanceMethod == "commonPoints")
   {
-    double score = 0.0;
-    double N1 = 0.0;
-    double N2 = 0.0;
+    float score{0.f};
+    float N1{0.f};
+    float N2{0.f};
 
     while(i1 != i1e && i2 != i2e)
     {
@@ -96,11 +101,11 @@ float sparseDistance(const SparseHistogram& v1, const SparseHistogram& v2, const
     distance = - score;
   }
 
-  else if(distanceMethod.compare("strongCommonPoints") == 0) 
+  else if(distanceMethod == "strongCommonPoints")
   {
-    double score = 0.0;
-    double N1 = 0.0;
-    double N2 = 0.0;
+    float score{0.f};
+    float N1{0.f};
+    float N2{0.f};
 
     while(i1 != i1e && i2 != i2e)
     {
@@ -116,7 +121,7 @@ float sparseDistance(const SparseHistogram& v1, const SparseHistogram& v2, const
       }
       else
       {
-        if( ( fabs(i1->second.size() - 1.0) < epsilon ) && ( fabs(i2->second.size() - 1.0) < epsilon) )
+        if( ( fabs(i1->second.size() - 1.f) < epsilon ) && ( fabs(i2->second.size() - 1.f) < epsilon) )
         {
           score += 1;
           N1 += 1;
@@ -142,11 +147,11 @@ float sparseDistance(const SparseHistogram& v1, const SparseHistogram& v2, const
     distance = - score;
   }
 
-  else if(distanceMethod.compare("weightedStrongCommonPoints") == 0) 
+  else if(distanceMethod == "weightedStrongCommonPoints")
   {
-    double score = 0.0;
-    double N1 = 0.0;
-    double N2 = 0.0;
+    float score{0.f};
+    float N1{0.f};
+    float N2{0.f};
 
     while(i1 != i1e && i2 != i2e)
     {
@@ -160,7 +165,7 @@ float sparseDistance(const SparseHistogram& v1, const SparseHistogram& v2, const
         N1 += i1->second.size()*word_weights[i1->first];
          ++i1;
       }
-        if( ( fabs(i1->second.size() - 1.0) < epsilon ) && ( fabs(i2->second.size() - 1.0) < epsilon) )
+        if( ( fabs(i1->second.size() - 1.f) < epsilon ) && ( fabs(i2->second.size() - 1.f) < epsilon) )
         {
           score += word_weights[i1->first];
           N1 += word_weights[i1->first];
@@ -185,12 +190,12 @@ float sparseDistance(const SparseHistogram& v1, const SparseHistogram& v2, const
     distance = - score;
   }
 
-  else if(distanceMethod.compare("inversedWeightedCommonPoints") == 0)
+  else if(distanceMethod == "inversedWeightedCommonPoints")
   {
-    double score = 0.0;
-    double N1 = 0.0;
-    double N2 = 0.0;
-    std::map<int,int> compteur;
+    float score{0.f};
+    float N1{0.f};
+    float N2{0.f};
+    std::map<int,int> counter;
 
     while(i1 != i1e && i2 != i2e)
     {
@@ -206,7 +211,7 @@ float sparseDistance(const SparseHistogram& v1, const SparseHistogram& v2, const
       }
       else
       {
-        compteur[i1->first] += std::min(i1->second.size(), i2->second.size());
+        counter[i1->first] += std::min(i1->second.size(), i2->second.size());
         N1 += i1->second.size() / word_weights[i1->first];
         N2 += i2->second.size() / word_weights[i2->first];
         ++i1;
@@ -222,12 +227,12 @@ float sparseDistance(const SparseHistogram& v1, const SparseHistogram& v2, const
 
     while(i2 != i2e)
     {
-      N2 += i2->second.size() / word_weights[i2->first];;
+      N2 += i2->second.size() / word_weights[i2->first];
       ++i2;
     }
 
-    for(auto iCompteur = compteur.begin(); iCompteur != compteur.end(); iCompteur++)
-      score += (1.0/iCompteur->second) * word_weights[iCompteur->first];
+    for(const auto elem : counter)
+      score += (1.f/ elem.second) * word_weights[elem.first];
 
     distance = - score;
   }