remove boost from kmeans.h

Now boost went from our code

.travis.yml

@@ -7,10 +7,8 @@ install:
   - sudo add-apt-repository --yes ppa:lttng/daily
   - sudo add-apt-repository ppa:amarburg/opencv3 -y # opencv3 ppa
   - sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y
-  - sudo add-apt-repository ppa:boost-latest -y
   - sudo apt-get update -qq
   - sudo apt-get install -qq libopencv3-dev 
-  - sudo apt-get install -qq libboost-all-dev
   - sudo apt-get install g++-4.8 -y
   - export CXX="g++-4.8"
 script:

CMakeLists.txt

@@ -16,7 +16,7 @@ include_directories(..)
 include_directories(${PROJECT_SOURCE_DIR}/sse)
 
 link_directories(/usr/local/lib)
-set(REQUIRED_LIB opencv_core opencv_imgproc opencv_imgcodecs opencv_highgui opencv_features2d opencv_ml boost_thread boost_system)
+set(REQUIRED_LIB opencv_core opencv_imgproc opencv_imgcodecs opencv_highgui opencv_features2d opencv_ml)
 
 add_subdirectory(sse)
 

Dockerfile

@@ -7,8 +7,7 @@ MAINTAINER zdd <zddhub@gmail.com>
 
 RUN apt-get update \
 	&& apt-get install g++ -y \
-	&& apt-get install cmake -y \
-	&& apt-get install libboost-all-dev -y
+	&& apt-get install cmake -y
 
 RUN mkdir opensse
 COPY . opensse

README.md

@@ -33,7 +33,7 @@ Recognize input sketch image, you can try it via [web version](http://online.ope
 How to install
 ==============
 
-OpenSSE library uses [OpenCV 3.0.0+](http://opencv.org/) and [boost 1.55.0](http://www.boost.org/), it's a cross-platform library.
+OpenSSE core library only depends on [OpenCV 3.0.0+](http://opencv.org/), it's a cross-platform library.
 
 You can compile and install it like below:
 
@@ -53,8 +53,8 @@ More detail info, go to [OpenSSE Wiki](https://github.com/zddhub/opensse/wiki)
 Todo List
 =========
 - [x] Docker support
+- [x] Remove boost for core library, done
 - [ ] Speed up train steps
-- [ ] Remove boost for core library, 83% done
 - [ ] Recognize sketch
 - [ ] Support big image set
 

opensse.h

@@ -10,6 +10,6 @@
 #include "sse/io/json_parser.h"
 #include "sse/quantize/quantizer.h"
 #include "sse/vocabulary/kmeans_init.h"
-// #include "sse/vocabulary/kmeans.h"
+#include "sse/vocabulary/kmeans.h"
 
 #endif

opensse.pri

@@ -21,8 +21,7 @@ INCLUDEPATH += $$PWD \
     /usr/local/include
 
 LIBS += -L/usr/local/lib/ \
-            -lopencv_core -lopencv_imgproc -lopencv_imgcodecs -lopencv_highgui -lopencv_features2d -lopencv_ml \
-            -lboost_thread -lboost_system
+            -lopencv_core -lopencv_imgproc -lopencv_imgcodecs -lopencv_highgui -lopencv_features2d -lopencv_ml
 }
 
 macx: {

sse/opensse.h

@@ -10,6 +10,6 @@
 #include "opensse/io/json_parser.h"
 #include "opensse/quantize/quantizer.h"
 #include "opensse/vocabulary/kmeans_init.h"
-// #include "opensse/vocabulary/kmeans.h"
+#include "opensse/vocabulary/kmeans.h"
 
 #endif

sse/vocabulary/kmeans.h

@@ -21,15 +21,12 @@
 #include <algorithm>
 #include <iostream>
 #include <set>
+#include <thread>
+#include <mutex>
 
 #include "../common/distance.h"
 #include "../common/types.h"
 
-#include <boost/random.hpp>
-#include <boost/thread/thread.hpp>
-#include <boost/thread/mutex.hpp>
-#include <boost/thread/locks.hpp>
-
 #include "kmeans_init.h"
 
 namespace sse {
@@ -40,8 +37,8 @@ namespace sse {
 template <class collection_t, class dist_fn>
 class Kmeans
 {
-    typedef boost::mutex               mutex_t;
-    typedef boost::lock_guard<mutex_t> locker_t;
+    typedef std::mutex               mutex_t;
+    typedef std::lock_guard<mutex_t> locker_t;
 
     typedef typename collection_t::value_type sample_t;
 
@@ -83,8 +80,6 @@ class Kmeans
      */
     void run(std::size_t maxiteration, double minchangesfraction)
     {
-        using namespace boost;
-
         // main iteration
         std::size_t iteration = 0;
         for (;;)
@@ -94,14 +89,20 @@ class Kmeans
             std::size_t changes = 0;
 
             // distribute items on clusters in parallel
-            thread_group pool;
+            // thread_group pool;
+            std::vector<std::thread> pools(std::thread::hardware_concurrency());
+            // pools.emplace_back(functor); // pass in the argument of std::thread()
+
             std::size_t idx = 0;
             mutex_t mtx;
-            for (std::size_t i = 0; i < thread::hardware_concurrency(); i++)
+            for (std::size_t i = 0; i < std::thread::hardware_concurrency(); i++)
+            {
+                pools[i] = std::thread(&Kmeans::distribute_samples, this, std::ref(idx), std::ref(changes), std::ref(mtx));
+            }
+            for (std::size_t i = 0; i < std::thread::hardware_concurrency(); i++)
             {
-                pool.create_thread(bind(&Kmeans::distribute_samples, this, boost::ref(idx), boost::ref(changes), boost::ref(mtx)));
+                pools[i].join();
             }
-            pool.join_all();
 
             iteration++;
 
@@ -135,14 +136,13 @@ class Kmeans
                 {
                     invalid.push_back(i);
                 }
-                //std::cout << "clustersize " << i << ": " << clustersize[i] << std::endl;
             }
 
             // fix invalid centers, i.e. those with no members
             while (!invalid.empty() && !valid.empty())
             {
                 std::size_t current = invalid.back();
-std::cout << "handle invalid clusters: " << invalid.size() << std::endl;
+                std::cout << "handle invalid clusters: " << invalid.size() << std::endl;
                 // compute for each valid cluster the variance
                 // of distances to all members and get the
                 // most distant member
@@ -176,16 +176,16 @@ std::cout << "handle invalid clusters: " << invalid.size() << std::endl;
                 _centers[current] = _collection[farthest[c]];
                 _clusters[farthest[c]] = current;
 
-std::cout << "reassign " << current << " to sample " << farthest[c] << " of cluster " << c << std::endl;
+                std::cout << "reassign " << current << " to sample " << farthest[c] << " of cluster " << c << std::endl;
 
                 valid.pop_back();
                 invalid.pop_back();
             }
 
-std::cout << "iteration " << iteration << std::endl;
+            std::cout << "iteration " << iteration << std::endl;
         }
 
-std::cout << "kmeans iterations: " << iteration << std::endl;
+        std::cout << "kmeans iterations: " << iteration << std::endl;
     }
 
 
@@ -231,7 +231,7 @@ std::cout << "kmeans iterations: " << iteration << std::endl;
         for (std::size_t i = 0; i < lhs.size(); i++) lhs[i] /= rhs;
     }
 
-    private:
+private:
 
     void distribute_samples(std::size_t& index, std::size_t& changes, mutex_t& mutex)
     {
@@ -249,7 +249,9 @@ std::cout << "kmeans iterations: " << iteration << std::endl;
             }
 
             // compute distance of current point to every center
-            std::transform(_centers.begin(), _centers.end(), dists.begin(), boost::bind(_distfn, boost::ref(_collection[i]), boost::arg<1>()));
+            // std::cout <<endl << "collection[" << i << "]=" << _collection[i].size() << " " << _centers.size() <<endl;
+            std::vector<Vec_f32_t> collections(_centers.size(), _collection[i]);
+            std::transform(_centers.begin(), _centers.end(), collections.begin(), dists.begin(), _distfn);
 
             // find the minimum distance, i.e. the nearest center
             std::size_t c = std::distance(dists.begin(), std::min_element(dists.begin(), dists.end()));
@@ -263,7 +265,6 @@ std::cout << "kmeans iterations: " << iteration << std::endl;
                     _clusters[i] = c;
                     currentchanges++;
                 }
-//if (i % 1000 == 0) std::cout << "kmeans distribute: " << i << std::endl;
             }
         }
 
@@ -279,7 +280,7 @@ std::cout << "kmeans iterations: " << iteration << std::endl;
     std::vector<sample_t>    _centers;
     std::vector<std::size_t> _clusters;
 
-    boost::mutex        _mutex;
+    mutex_t        _mutex;
 };
 
 } //namespace sse

tools/vocabulary.cpp

@@ -19,7 +19,6 @@
 using namespace std;
 
 #include "opensse/opensse.h"
-#include "vocabulary/kmeans.h"
 
 using namespace sse;