Deleted all trailing whitespace.

modules/features2d/doc/object_categorization.rst

@@ -40,7 +40,7 @@ Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. ::
 
 BOWTrainer::add
 -------------------
-Adds descriptors to a training set. 
+Adds descriptors to a training set.
 
 .. ocv:function:: void BOWTrainer::add( const Mat& descriptors )
 
@@ -66,7 +66,7 @@ Returns the count of all descriptors stored in the training set.
 
 BOWTrainer::cluster
 -----------------------
-Clusters train descriptors. 
+Clusters train descriptors.
 
 .. ocv:function:: Mat BOWTrainer::cluster() const
 
@@ -116,7 +116,7 @@ Class to compute an image descriptor using the *bag of visual words*. Such a com
     #. Compute descriptors for a given image and its keypoints set.
     #. Find the nearest visual words from the vocabulary for each keypoint descriptor.
     #. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words encountered in the image. The ``i``-th bin of the histogram is a frequency of ``i``-th word of the vocabulary in the given image.
-    
+
 The class declaration is the following: ::
 
         class BOWImgDescriptorExtractor

modules/features2d/src/bagofwords.cpp

@@ -178,10 +178,10 @@ void BOWImgDescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& key
 
     // Normalize image descriptor.
     imgDescriptor /= descriptors.rows;
-	
+
     // Add the descriptors of image keypoints
     if (_descriptors) {
-        *_descriptors = descriptors.clone(); 
+        *_descriptors = descriptors.clone();
     }
 }
 

modules/features2d/src/evaluation.cpp

@@ -258,7 +258,7 @@ struct IntersectAreaCounter
     {
         CV_Assert( miny < maxy );
         CV_Assert( dr > FLT_EPSILON );
-        
+
         int temp_bua = bua, temp_bna = bna;
         for( int i = range.begin(); i != range.end(); i++ )
         {