Normalize whitespace in documentation and text files

modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst

@@ -41,7 +41,7 @@ Abstract base class for computing descriptors for image keypoints. ::
 
 
 In this interface, a keypoint descriptor can be represented as a
-dense, fixed-dimension vector of a basic type. Most descriptors 
+dense, fixed-dimension vector of a basic type. Most descriptors
 follow this pattern as it simplifies computing
 distances between descriptors. Therefore, a collection of
 descriptors is represented as

modules/features2d/doc/object_categorization.rst

@@ -34,7 +34,7 @@ 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 )
 
@@ -60,7 +60,7 @@ 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
 
@@ -110,7 +110,7 @@ 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