Shape predictor trainer optimizations (#126)

* Shape predictor trainer optimizations

* Fixed performance leak in single thread mode & made VS2010 support

dlib/image_processing/shape_predictor_abstract.h

@@ -148,6 +148,7 @@ namespace dlib
                 - #get_num_test_splits() == 20
                 - #get_feature_pool_region_padding() == 0
                 - #get_random_seed() == ""
+                - #get_num_threads() == 0
                 - This object will not be verbose
         !*/
 
@@ -367,6 +368,26 @@ namespace dlib
                 - #get_num_test_splits() == num
         !*/
 
+        unsigned long get_num_threads (
+        ) const;
+        /*!
+            ensures
+                - When running training process, it is possible to make some parts of it parallel
+                  using CPU threads with #parallel_for() extension and creating #thread_pool internally
+                  When get_num_threads() == 0, trainer will not create threads and all processing will
+                  be done in the calling thread
+        !*/
+
+        void set_num_threads (
+            unsigned long num
+        );
+        /*!
+            requires
+                - num >= 0
+            ensures
+                - #get_num_threads() == num
+        !*/
+
         void be_verbose (
         );
         /*!

examples/train_shape_predictor_ex.cpp

@@ -39,7 +39,7 @@ std::vector<std::vector<double> > get_interocular_distances (
 // ----------------------------------------------------------------------------------------
 
 int main(int argc, char** argv)
-{  
+{
     try
     {
         // In this example we are going to train a shape_predictor based on the
@@ -108,6 +108,9 @@ int main(int argc, char** argv)
         trainer.set_nu(0.05);
         trainer.set_tree_depth(2);
 
+        // some parts of training process can be parellelized.
+        // Trainer will use this count of threads when possible
+        trainer.set_num_threads(2);
 
         // Tell the trainer to print status messages to the console so we can
         // see how long the training will take.