From af58e08ee22bb7618d7275c994c6b64fdea4a77d Mon Sep 17 00:00:00 2001
From: Alexander Alekhin <alexander.alekhin@intel.com>
Date: Thu, 8 Feb 2018 16:00:41 +0300
Subject: [PATCH] build: fix C++17 build

---
 modules/optflow/src/sparse_matching_gpc.cpp            | 10 +++++++++-
 .../xfeatures2d/samples/bagofwords_classification.cpp  | 10 ++++++++++
 2 files changed, 19 insertions(+), 1 deletion(-)

diff --git a/modules/optflow/src/sparse_matching_gpc.cpp b/modules/optflow/src/sparse_matching_gpc.cpp
index 343b11604..6c485e160 100644
--- a/modules/optflow/src/sparse_matching_gpc.cpp
+++ b/modules/optflow/src/sparse_matching_gpc.cpp
@@ -40,13 +40,16 @@
  //
  //M*/
 
+#include "precomp.hpp"
 #include "opencv2/core/core_c.h"
 #include "opencv2/core/private.hpp"
 #include "opencv2/flann/miniflann.hpp"
 #include "opencv2/imgcodecs.hpp"
-#include "precomp.hpp"
 #include "opencl_kernels_optflow.hpp"
 #include "opencv2/core/hal/intrin.hpp"
+#ifdef CV_CXX11
+#include <random>  // std::mt19937
+#endif
 
 /* Disable "from double to float" and "from size_t to int" warnings.
  * Fixing these would make the code look ugly by introducing explicit cast all around.
@@ -402,7 +405,12 @@ void getTrainingSamples( const Mat &from, const Mat &to, const Mat &gt, GPCSampl
                                                             // with a small displacement and train to better distinguish hard pairs.
   std::nth_element( mag.begin(), mag.begin() + n, mag.end() );
   mag.resize( n );
+#ifdef CV_CXX11
+  std::mt19937 std_rng(cv::theRNG()());
+  std::shuffle(mag.begin(), mag.end(), std_rng);
+#else
   std::random_shuffle( mag.begin(), mag.end() );
+#endif
   n /= patchRadius;
   mag.resize( n );
 
diff --git a/modules/xfeatures2d/samples/bagofwords_classification.cpp b/modules/xfeatures2d/samples/bagofwords_classification.cpp
index 8ef7497eb..13a19a316 100644
--- a/modules/xfeatures2d/samples/bagofwords_classification.cpp
+++ b/modules/xfeatures2d/samples/bagofwords_classification.cpp
@@ -864,7 +864,12 @@ void VocData::calcPrecRecall_impl(const vector<char>& ground_truth, const vector
     {
         recall_norm = recall_normalization;
     } else {
+#ifdef CV_CXX11
+        recall_norm = (int)std::count_if(ground_truth.begin(),ground_truth.end(),
+                [](const char a) { return a == (char)1; });
+#else
         recall_norm = (int)std::count_if(ground_truth.begin(),ground_truth.end(),std::bind2nd(std::equal_to<char>(),(char)1));
+#endif
     }
 
     ap = 0;
@@ -994,7 +999,12 @@ void VocData::calcClassifierConfMatRow(const string& obj_class, const vector<Obd
         /* in order to calculate the total number of relevant images for normalization of recall
             it's necessary to extract the ground truth for the images under consideration */
         getClassifierGroundTruth(obj_class, images, ground_truth);
+#ifdef CV_CXX11
+        total_relevant = (int)std::count_if(ground_truth.begin(),ground_truth.end(),
+                [](const char a) { return a == (char)1; });
+#else
         total_relevant = (int)std::count_if(ground_truth.begin(),ground_truth.end(),std::bind2nd(std::equal_to<char>(),(char)1));
+#endif
     }
 
     /* iterate through images */