Merge remote-tracking branch 'upstream/3.4' into merge-3.4

Revert "documentation: avoid links to 'master' branch from 3.4 maintenance branch"
This reverts commit 9ba9358ecb.

Revert "documentation: avoid links to 'master' branch from 3.4 maintenance branch (2)"
This reverts commit f185802489.

doc/tutorials/features2d/feature_homography/feature_homography.markdown

@@ -9,125 +9,40 @@ In this tutorial you will learn how to:
 -   Use the function @ref cv::findHomography to find the transform between matched keypoints.
 -   Use the function @ref cv::perspectiveTransform to map the points.
 
+\warning You need the <a href="https://github.com/opencv/opencv_contrib">OpenCV contrib modules</a> to be able to use the SURF features
+(alternatives are ORB, KAZE, ... features).
+
 Theory
 ------
 
 Code
 ----
 
-This tutorial code's is shown lines below.
-@code{.cpp}
-#include <stdio.h>
-#include <iostream>
-#include "opencv2/core.hpp"
-#include "opencv2/imgproc.hpp"
-#include "opencv2/features2d.hpp"
-#include "opencv2/highgui.hpp"
-#include "opencv2/calib3d.hpp"
-#include "opencv2/xfeatures2d.hpp"
+@add_toggle_cpp
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.cpp)
+@include samples/cpp/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.cpp
+@end_toggle
 
-using namespace cv;
-using namespace cv::xfeatures2d;
+@add_toggle_java
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/features2D/feature_homography/SURFFLANNMatchingHomographyDemo.java)
+@include samples/java/tutorial_code/features2D/feature_homography/SURFFLANNMatchingHomographyDemo.java
+@end_toggle
 
-void readme();
+@add_toggle_python
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.py)
+@include samples/python/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.py
+@end_toggle
 
-/* @function main */
-int main( int argc, char** argv )
-{
-  if( argc != 3 )
-  { readme(); return -1; }
-
-  Mat img_object = imread( argv[1], IMREAD_GRAYSCALE );
-  Mat img_scene = imread( argv[2], IMREAD_GRAYSCALE );
-
-  if( !img_object.data || !img_scene.data )
-  { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
-
-  //-- Step 1: Detect the keypoints and extract descriptors using SURF
-  int minHessian = 400;
-
-  Ptr<SURF> detector = SURF::create( minHessian );
-
-  std::vector<KeyPoint> keypoints_object, keypoints_scene;
-  Mat descriptors_object, descriptors_scene;
-
-  detector->detectAndCompute( img_object, Mat(), keypoints_object, descriptors_object );
-  detector->detectAndCompute( img_scene, Mat(), keypoints_scene, descriptors_scene );
-
-  //-- Step 2: Matching descriptor vectors using FLANN matcher
-  FlannBasedMatcher matcher;
-  std::vector< DMatch > matches;
-  matcher.match( descriptors_object, descriptors_scene, matches );
-
-  double max_dist = 0; double min_dist = 100;
-
-  //-- Quick calculation of max and min distances between keypoints
-  for( int i = 0; i < descriptors_object.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 3*min_dist )
-  std::vector< DMatch > good_matches;
-
-  for( int i = 0; i < descriptors_object.rows; i++ )
-  { if( matches[i].distance <= 3*min_dist )
-     { good_matches.push_back( matches[i]); }
-  }
-
-  Mat img_matches;
-  drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
-               good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
-               std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
-
-  //-- Localize the object
-  std::vector<Point2f> obj;
-  std::vector<Point2f> scene;
-
-  for( size_t i = 0; i < good_matches.size(); i++ )
-  {
-    //-- Get the keypoints from the good matches
-    obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
-    scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
-  }
-
-  Mat H = findHomography( obj, scene, RANSAC );
-
-  //-- Get the corners from the image_1 ( the object to be "detected" )
-  std::vector<Point2f> obj_corners(4);
-  obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
-  obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
-  std::vector<Point2f> scene_corners(4);
-
-  perspectiveTransform( obj_corners, scene_corners, H);
-
-  //-- Draw lines between the corners (the mapped object in the scene - image_2 )
-  line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
-  line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
-  line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
-  line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
-
-  //-- Show detected matches
-  imshow( "Good Matches & Object detection", img_matches );
-
-  waitKey(0);
-  return 0;
-  }
-
-  /* @function readme */
-  void readme()
-  { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
-@endcode
 Explanation
 -----------
 
 Result
 ------
 
--#  And here is the result for the detected object (highlighted in green)
+-   And here is the result for the detected object (highlighted in green). Note that since the homography is estimated with a RANSAC approach,
+    detected false matches will not impact the homography calculation.
 
     ![](images/Feature_Homography_Result.jpg)