Replaced code in .rst file with literalinclude as suggested

doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.rst

@@ -21,97 +21,8 @@ Code
 
 This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/features2D/SURF_FlannMatcher.cpp>`_
 
-.. code-block:: cpp
+.. literalinclude:: ../../../../samples/cpp/tutorial_code/features2D/SURF_FlannMatcher.cpp
-
+   :language: cpp
-   #include <stdio.h>
-   #include <iostream>
-   #include "opencv2/core/core.hpp"
-   #include "opencv2/features2d/features2d.hpp"
-   #include "opencv2/highgui/highgui.hpp"
-   #include "opencv2/nonfree/nonfree.hpp"
-
-   using namespace cv;
-
-   void readme();
-
-   /** @function main */
-   int main( int argc, char** argv )
-   {
-     if( argc != 3 )
-     { readme(); return -1; }
-
-     Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
-     Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
-
-     if( !img_1.data || !img_2.data )
-     { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
-
-     //-- Step 1: Detect the keypoints using SURF Detector
-     int minHessian = 400;
-
-     SurfFeatureDetector detector( minHessian );
-
-     std::vector<KeyPoint> keypoints_1, keypoints_2;
-
-     detector.detect( img_1, keypoints_1 );
-     detector.detect( img_2, keypoints_2 );
-
-     //-- Step 2: Calculate descriptors (feature vectors)
-     SurfDescriptorExtractor extractor;
-
-     Mat descriptors_1, descriptors_2;
-
-     extractor.compute( img_1, keypoints_1, descriptors_1 );
-     extractor.compute( img_2, keypoints_2, descriptors_2 );
-
-     //-- Step 3: Matching descriptor vectors using FLANN matcher
-     FlannBasedMatcher matcher;
-     std::vector< DMatch > matches;
-     matcher.match( descriptors_1, descriptors_2, matches );
-
-     double max_dist = 0; double min_dist = 100;
-
-     //-- Quick calculation of max and min distances between keypoints
-     for( int i = 0; i < descriptors_1.rows; i++ )
-     { double dist = matches[i].distance;
-       if( dist < min_dist ) min_dist = dist;
-       if( dist > max_dist ) max_dist = dist;
-     }
-
-     printf("-- Max dist : %f \n", max_dist );
-     printf("-- Min dist : %f \n", min_dist );
-
-     //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
-     //-- or a small arbitary value ( 0.02 ) in the event that min_dist is very
-     //-- small)
-     //-- PS.- radiusMatch can also be used here.
-     std::vector< DMatch > good_matches;
-
-     for( int i = 0; i < descriptors_1.rows; i++ )
-     { if( matches[i].distance <= max(2*min_dist, 0.02) )
-       { good_matches.push_back( matches[i]); }
-     }
-
-     //-- Draw only "good" matches
-     Mat img_matches;
-     drawMatches( img_1, keypoints_1, img_2, keypoints_2,
-                  good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
-                  vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
-
-     //-- Show detected matches
-     imshow( "Good Matches", img_matches );
-
-     for( int i = 0; i < good_matches.size(); i++ )
-     { printf( "-- Good Match [%d] Keypoint 1: %d  -- Keypoint 2: %d  \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); }
-
-     waitKey(0);
-
-     return 0;
-    }
-
-    /** @function readme */
-    void readme()
-    { std::cout << " Usage: ./SURF_FlannMatcher <img1> <img2>" << std::endl; }
 
 Explanation
 ============