improved comments

examples/train_object_detector.cpp

@@ -2,7 +2,7 @@
 /*
 
     This is an example showing how you might use dlib to create a reasonably 
-    function command line tool for object detection.  This example assumes 
+    functional command line tool for object detection.  This example assumes 
     you are familiar with the contents of at least the following example 
     programs:
         - object_detector_ex.cpp
@@ -13,17 +13,15 @@
 
     This program is a command line tool for learning to detect objects in images.  
     Therefore, to create an object detector it requires a set of annotated training 
-    images.  To create this annotated data you will need to compile the imglab tool 
-    included with dlib.  To do this, go to the tools/imglab folder and type the 
-    following:
+    images.  To create this annotated data you will need to use the imglab tool 
+    included with dlib.  It is located in the tools/imglab folder and can be compiled
+    using the following commands.  
+        cd tools/imglab
         mkdir build
         cd build
         cmake ..
-        make
-    Note that you may need to install CMake (www.cmake.org) for this to work.  Also, 
-    if you are using visual studio then you should use the following command to 
-    compile instead of "make"
         cmake --build . --config Release
+    Note that you may need to install CMake (www.cmake.org) for this to work.  
 
     Next, lets assume you have a folder of images called /tmp/images.  These images 
     should contain examples of the objects you want to learn to detect.  You will 
@@ -39,13 +37,14 @@
     with boxes.  Note that it is important to label all the objects since any object 
     not labeled is implicitly assumed to be not an object we should detect.
 
-    Once you finish labeling objects go to the file menu, click save, and close the
-    program. This will save the object boxes back to mydataset.xml.  You can verify 
+    Once you finish labeling objects go to the file menu, click save, and then close 
+    the program. This will save the object boxes back to mydataset.xml.  You can verify 
     this by opening the tool again with
         ./imglab mydataset.xml
     and observing that the boxes are present.
 
-    Returning to the present example program, we can now issue the command 
+    Returning to the present example program, we can compile it using cmake just as we 
+    did with the imglab tool.  Once compiled, we can issue the command 
         ./train_object_detector -tv mydataset.xml
     which will train an object detection model based on our labeled data.  The model 
     will be saved to the file object_detector.svm.  Once this has finished we can use 
@@ -55,8 +54,8 @@
     be indicated by a red box.
 
 
-    There are also a number of other useful command line options in the current
-    example program which you can explore below. 
+    There are a number of other useful command line options in the current example 
+    program which you can explore below. 
 */