Merge pull request #25292 from kaingwade:features2d_parts_to_contrib

Features2d cleanup: Move several feature detectors and descriptors to opencv_contrib #25292

features2d cleanup: #24999

The PR moves KAZE, AKAZE, AgastFeatureDetector, BRISK and BOW to opencv_contrib/xfeatures2d.

Related PR: opencv/opencv_contrib#3709

doc/opencv.bib

@@ -1,12 +1,3 @@
-@incollection{ABD12,
-  author = {Alcantarilla, Pablo Fern{\'a}ndez and Bartoli, Adrien and Davison, Andrew J},
-  title = {KAZE features},
-  booktitle = {Computer Vision--ECCV 2012},
-  year = {2012},
-  pages = {214--227},
-  publisher = {Springer},
-  url = {https://www.doc.ic.ac.uk/~ajd/Publications/alcantarilla_etal_eccv2012.pdf}
-}
 @article{ANB13,
   author = {Pablo Fern{\'{a}}ndez Alcantarilla and Jes{\'{u}}s Nuevo and Adrien Bartoli},
   editor = {Tilo Burghardt and Dima Damen and Walterio W. Mayol{-}Cuevas and Majid Mirmehdi},
@@ -597,14 +588,6 @@
   doi = {10.5201/ipol.2011.my-asift},
   url = {http://www.ipol.im/pub/algo/my_affine_sift/}
 }
-@inproceedings{LCS11,
-  author = {Leutenegger, Stefan and Chli, Margarita and Siegwart, Roland Yves},
-  title = {BRISK: Binary robust invariant scalable keypoints},
-  booktitle = {Computer Vision (ICCV), 2011 IEEE International Conference on},
-  year = {2011},
-  pages = {2548--2555},
-  publisher = {IEEE}
-}
 @article{Louhichi07,
   author = {Louhichi, H. and Fournel, T. and Lavest, J. M. and Ben Aissia, H.},
   title = {Self-calibration of Scheimpflug cameras: an easy protocol},

doc/py_tutorials/py_feature2d/py_brief/py_brief.markdown

@@ -1,92 +0,0 @@
-BRIEF (Binary Robust Independent Elementary Features) {#tutorial_py_brief}
-=====================================================
-
-Goal
-----
-
-In this chapter
-    -   We will see the basics of BRIEF algorithm
-
-Theory
-------
-
-We know SIFT uses 128-dim vector for descriptors. Since it is using floating point numbers, it takes
-basically 512 bytes. Similarly SURF also takes minimum of 256 bytes (for 64-dim). Creating such a
-vector for thousands of features takes a lot of memory which are not feasible for resource-constraint
-applications especially for embedded systems. Larger the memory, longer the time it takes for
-matching.
-
-But all these dimensions may not be needed for actual matching. We can compress it using several
-methods like PCA, LDA etc. Even other methods like hashing using LSH (Locality Sensitive Hashing) is
-used to convert these SIFT descriptors in floating point numbers to binary strings. These binary
-strings are used to match features using Hamming distance. This provides better speed-up because
-finding hamming distance is just applying XOR and bit count, which are very fast in modern CPUs with
-SSE instructions. But here, we need to find the descriptors first, then only we can apply hashing,
-which doesn't solve our initial problem on memory.
-
-BRIEF comes into picture at this moment. It provides a shortcut to find the binary strings directly
-without finding descriptors. It takes smoothened image patch and selects a set of \f$n_d\f$ (x,y)
-location pairs in an unique way (explained in paper). Then some pixel intensity comparisons are done
-on these location pairs. For eg, let first location pairs be \f$p\f$ and \f$q\f$. If \f$I(p) < I(q)\f$, then its
-result is 1, else it is 0. This is applied for all the \f$n_d\f$ location pairs to get a
-\f$n_d\f$-dimensional bitstring.
-
-This \f$n_d\f$ can be 128, 256 or 512. OpenCV supports all of these, but by default, it would be 256
-(OpenCV represents it in bytes. So the values will be 16, 32 and 64). So once you get this, you can
-use Hamming Distance to match these descriptors.
-
-One important point is that BRIEF is a feature descriptor, it doesn't provide any method to find the
-features. So you will have to use any other feature detectors like SIFT, SURF etc. The paper
-recommends to use CenSurE which is a fast detector and BRIEF works even slightly better for CenSurE
-points than for SURF points.
-
-In short, BRIEF is a faster method feature descriptor calculation and matching. It also provides
-high recognition rate unless there is large in-plane rotation.
-
-STAR(CenSurE) in OpenCV
-------
-STAR is a feature detector derived from CenSurE.
-Unlike CenSurE however, which uses polygons like squares, hexagons and octagons to approach a circle,
-Star emulates a circle with 2 overlapping squares: 1 upright and 1 45-degree rotated. These polygons are bi-level.
-They can be seen as polygons with thick borders. The borders and the enclosed area have weights of opposing signs.
-This has better computational characteristics than other scale-space detectors and it is capable of real-time implementation.
-In contrast to SIFT and SURF, which find extrema at sub-sampled pixels that compromises accuracy at larger scales,
-CenSurE creates a feature vector using full spatial resolution at all scales in the pyramid.
-BRIEF in OpenCV
----------------
-
-Below code shows the computation of BRIEF descriptors with the help of CenSurE detector.
-
-note, that you need [opencv contrib](https://github.com/opencv/opencv_contrib)) to use this.
-@code{.py}
-import numpy as np
-import cv2 as cv
-from matplotlib import pyplot as plt
-
-img = cv.imread('simple.jpg', cv.IMREAD_GRAYSCALE)
-
-# Initiate FAST detector
-star = cv.xfeatures2d.StarDetector_create()
-
-# Initiate BRIEF extractor
-brief = cv.xfeatures2d.BriefDescriptorExtractor_create()
-
-# find the keypoints with STAR
-kp = star.detect(img,None)
-
-# compute the descriptors with BRIEF
-kp, des = brief.compute(img, kp)
-
-print( brief.descriptorSize() )
-print( des.shape )
-@endcode
-The function brief.getDescriptorSize() gives the \f$n_d\f$ size used in bytes. By default it is 32. Next one
-is matching, which will be done in another chapter.
-
-Additional Resources
---------------------
-
--#  Michael Calonder, Vincent Lepetit, Christoph Strecha, and Pascal Fua, "BRIEF: Binary Robust
-    Independent Elementary Features", 11th European Conference on Computer Vision (ECCV), Heraklion,
-    Crete. LNCS Springer, September 2010.
-2.  [LSH (Locality Sensitive Hashing)](https://en.wikipedia.org/wiki/Locality-sensitive_hashing) at wikipedia.

doc/py_tutorials/py_feature2d/py_surf_intro/py_surf_intro.markdown

@@ -1,157 +0,0 @@
-Introduction to SURF (Speeded-Up Robust Features) {#tutorial_py_surf_intro}
-=================================================
-
-Goal
-----
-
-In this chapter,
-    -   We will see the basics of SURF
-    -   We will see SURF functionalities in OpenCV
-
-Theory
-------
-
-In last chapter, we saw SIFT for keypoint detection and description. But it was comparatively slow
-and people needed more speeded-up version. In 2006, three people, Bay, H., Tuytelaars, T. and Van
-Gool, L, published another paper, "SURF: Speeded Up Robust Features" which introduced a new
-algorithm called SURF. As name suggests, it is a speeded-up version of SIFT.
-
-In SIFT, Lowe approximated Laplacian of Gaussian with Difference of Gaussian for finding
-scale-space. SURF goes a little further and approximates LoG with Box Filter. Below image shows a
-demonstration of such an approximation. One big advantage of this approximation is that, convolution
-with box filter can be easily calculated with the help of integral images. And it can be done in
-parallel for different scales. Also the SURF rely on determinant of Hessian matrix for both scale
-and location.
-
-![image](images/surf_boxfilter.jpg)
-
-For orientation assignment, SURF uses wavelet responses in horizontal and vertical direction for a
-neighbourhood of size 6s. Adequate gaussian weights are also applied to it. Then they are plotted in
-a space as given in below image. The dominant orientation is estimated by calculating the sum of all
-responses within a sliding orientation window of angle 60 degrees. Interesting thing is that,
-wavelet response can be found out using integral images very easily at any scale. For many
-applications, rotation invariance is not required, so no need of finding this orientation, which
-speeds up the process. SURF provides such a functionality called Upright-SURF or U-SURF. It improves
-speed and is robust upto \f$\pm 15^{\circ}\f$. OpenCV supports both, depending upon the flag,
-**upright**. If it is 0, orientation is calculated. If it is 1, orientation is not calculated and it
-is faster.
-
-![image](images/surf_orientation.jpg)
-
-For feature description, SURF uses Wavelet responses in horizontal and vertical direction (again,
-use of integral images makes things easier). A neighbourhood of size 20sX20s is taken around the
-keypoint where s is the size. It is divided into 4x4 subregions. For each subregion, horizontal and
-vertical wavelet responses are taken and a vector is formed like this,
-\f$v=( \sum{d_x}, \sum{d_y}, \sum{|d_x|}, \sum{|d_y|})\f$. This when represented as a vector gives SURF
-feature descriptor with total 64 dimensions. Lower the dimension, higher the speed of computation
-and matching, but provide better distinctiveness of features.
-
-For more distinctiveness, SURF feature descriptor has an extended 128 dimension version. The sums of
-\f$d_x\f$ and \f$|d_x|\f$ are computed separately for \f$d_y < 0\f$ and \f$d_y \geq 0\f$. Similarly, the sums of
-\f$d_y\f$ and \f$|d_y|\f$ are split up according to the sign of \f$d_x\f$ , thereby doubling the number of
-features. It doesn't add much computation complexity. OpenCV supports both by setting the value of
-flag **extended** with 0 and 1 for 64-dim and 128-dim respectively (default is 128-dim)
-
-Another important improvement is the use of sign of Laplacian (trace of Hessian Matrix) for
-underlying interest point. It adds no computation cost since it is already computed during
-detection. The sign of the Laplacian distinguishes bright blobs on dark backgrounds from the reverse
-situation. In the matching stage, we only compare features if they have the same type of contrast
-(as shown in image below). This minimal information allows for faster matching, without reducing the
-descriptor's performance.
-
-![image](images/surf_matching.jpg)
-
-In short, SURF adds a lot of features to improve the speed in every step. Analysis shows it is 3
-times faster than SIFT while performance is comparable to SIFT. SURF is good at handling images with
-blurring and rotation, but not good at handling viewpoint change and illumination change.
-
-SURF in OpenCV
---------------
-
-OpenCV provides SURF functionalities just like SIFT. You initiate a SURF object with some optional
-conditions like 64/128-dim descriptors, Upright/Normal SURF etc. All the details are well explained
-in docs. Then as we did in SIFT, we can use SURF.detect(), SURF.compute() etc for finding keypoints
-and descriptors.
-
-First we will see a simple demo on how to find SURF keypoints and descriptors and draw it. All
-examples are shown in Python terminal since it is just same as SIFT only.
-@code{.py}
->>> img = cv.imread('fly.png', cv.IMREAD_GRAYSCALE)
-
-# Create SURF object. You can specify params here or later.
-# Here I set Hessian Threshold to 400
->>> surf = cv.xfeatures2d.SURF_create(400)
-
-# Find keypoints and descriptors directly
->>> kp, des = surf.detectAndCompute(img,None)
-
->>> len(kp)
- 699
-@endcode
-1199 keypoints is too much to show in a picture. We reduce it to some 50 to draw it on an image.
-While matching, we may need all those features, but not now. So we increase the Hessian Threshold.
-@code{.py}
-# Check present Hessian threshold
->>> print( surf.getHessianThreshold() )
-400.0
-
-# We set it to some 50000. Remember, it is just for representing in picture.
-# In actual cases, it is better to have a value 300-500
->>> surf.setHessianThreshold(50000)
-
-# Again compute keypoints and check its number.
->>> kp, des = surf.detectAndCompute(img,None)
-
->>> print( len(kp) )
-47
-@endcode
-It is less than 50. Let's draw it on the image.
-@code{.py}
->>> img2 = cv.drawKeypoints(img,kp,None,(255,0,0),4)
-
->>> plt.imshow(img2),plt.show()
-@endcode
-See the result below. You can see that SURF is more like a blob detector. It detects the white blobs
-on wings of butterfly. You can test it with other images.
-
-![image](images/surf_kp1.jpg)
-
-Now I want to apply U-SURF, so that it won't find the orientation.
-@code{.py}
-# Check upright flag, if it False, set it to True
->>> print( surf.getUpright() )
-False
-
->>> surf.setUpright(True)
-
-# Recompute the feature points and draw it
->>> kp = surf.detect(img,None)
->>> img2 = cv.drawKeypoints(img,kp,None,(255,0,0),4)
-
->>> plt.imshow(img2),plt.show()
-@endcode
-See the results below. All the orientations are shown in same direction. It is faster than
-previous. If you are working on cases where orientation is not a problem (like panorama stitching)
-etc, this is better.
-
-![image](images/surf_kp2.jpg)
-
-Finally we check the descriptor size and change it to 128 if it is only 64-dim.
-@code{.py}
-# Find size of descriptor
->>> print( surf.descriptorSize() )
-64
-
-# That means flag, "extended" is False.
->>> surf.getExtended()
- False
-
-# So we make it to True to get 128-dim descriptors.
->>> surf.setExtended(True)
->>> kp, des = surf.detectAndCompute(img,None)
->>> print( surf.descriptorSize() )
-128
->>> print( des.shape )
-(47, 128)
-@endcode
-Remaining part is matching which we will do in another chapter.

doc/py_tutorials/py_feature2d/py_table_of_contents_feature2d.markdown

@@ -22,28 +22,15 @@ Feature Detection and Description {#tutorial_py_table_of_contents_feature2d}
     is not good enough when scale of image changes. Lowe developed a breakthrough method to find
     scale-invariant features and it is called SIFT
 
--   @subpage tutorial_py_surf_intro
-
-    SIFT is really good,
-    but not fast enough, so people came up with a speeded-up version called SURF.
-
 -   @subpage tutorial_py_fast
 
     All the above feature
     detection methods are good in some way. But they are not fast enough to work in real-time
     applications like SLAM. There comes the FAST algorithm, which is really "FAST".
 
--   @subpage tutorial_py_brief
-
-    SIFT uses a feature
-    descriptor with 128 floating point numbers. Consider thousands of such features. It takes lots of
-    memory and more time for matching. We can compress it to make it faster. But still we have to
-    calculate it first. There comes BRIEF which gives the shortcut to find binary descriptors with
-    less memory, faster matching, still higher recognition rate.
-
 -   @subpage tutorial_py_orb
 
-    SIFT and SURF are good in what they do, but what if you have to pay a few dollars every year to use them in your applications? Yeah, they are patented!!! To solve that problem, OpenCV devs came up with a new "FREE" alternative to SIFT & SURF, and that is ORB.
+    SURF is good in what it does, but what if you have to pay a few dollars every year to use it in your applications? Yeah, it is patented!!! To solve that problem, OpenCV devs came up with a new "FREE" alternative to SIFT & SURF, and that is ORB.
 
 -   @subpage tutorial_py_matcher
 

doc/tutorials/features2d/akaze_matching/akaze_matching.markdown

@@ -22,6 +22,8 @@ number of inliers (i.e. matches that fit in the given homography).
 You can find expanded version of this example here:
 <https://github.com/pablofdezalc/test_kaze_akaze_opencv>
 
+\warning You need the [OpenCV contrib module *xfeatures2d*](https://github.com/opencv/opencv_contrib/tree/5.x/modules/xfeatures2d) to be able to use the AKAZE features.
+
 Data
 ----
 
@@ -42,7 +44,7 @@ You can find the images (*graf1.png*, *graf3.png*) and homography (*H1to3p.xml*)
 
 @add_toggle_cpp
 -   **Downloadable code**: Click
-    [here](https://raw.githubusercontent.com/opencv/opencv/5.x/samples/cpp/tutorial_code/features2D/AKAZE_match.cpp)
+    [here](https://github.com/opencv/opencv/5.x/samples/cpp/tutorial_code/features2D/AKAZE_match.cpp)
 
 -   **Code at glance:**
     @include samples/cpp/tutorial_code/features2D/AKAZE_match.cpp
@@ -50,7 +52,7 @@ You can find the images (*graf1.png*, *graf3.png*) and homography (*H1to3p.xml*)
 
 @add_toggle_java
 -   **Downloadable code**: Click
-    [here](https://raw.githubusercontent.com/opencv/opencv/5.x/samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java)
+    [here](https://github.com/opencv/opencv/5.x/samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java)
 
 -   **Code at glance:**
     @include samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java
@@ -58,7 +60,7 @@ You can find the images (*graf1.png*, *graf3.png*) and homography (*H1to3p.xml*)
 
 @add_toggle_python
 -   **Downloadable code**: Click
-    [here](https://raw.githubusercontent.com/opencv/opencv/5.x/samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py)
+    [here](https://github.com/opencv/opencv/5.x/samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py)
 
 -   **Code at glance:**
     @include samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py

doc/tutorials/features2d/akaze_tracking/akaze_tracking.markdown

@@ -17,6 +17,8 @@ Introduction
 In this tutorial we will compare *AKAZE* and *ORB* local features using them to find matches between
 video frames and track object movements.
 
+\warning You need the [OpenCV contrib module *xfeatures2d*](https://github.com/opencv/opencv_contrib/tree/5.x/modules/xfeatures2d) to be able to use the AKAZE features.
+
 The algorithm is as follows:
 
 -   Detect and describe keypoints on the first frame, manually set object boundaries

modules/features2d/doc/read_file_nondiff32.pl

@@ -1,284 +0,0 @@
-#!/usr/bin/perl
-use strict;
-use warnings;
-use autodie; # die if problem reading or writing a file
-
-my $filein = "./agast.txt";
-my $fileout = "./agast_new.txt";
-my $i1=1;
-my $i2=1;
-my $i3=1;
-my $tmp;
-my $ifcount0=0;
-my $ifcount1=0;
-my $ifcount2=0;
-my $ifcount3=0;
-my $ifcount4=0;
-my $elsecount;
-my $myfirstline = $ARGV[0];
-my $mylastline  = $ARGV[1];
-my $tablename  = $ARGV[2];
-my @array0 = ();
-my @array1 = ();
-my @array2 = ();
-my @array3 = ();
-my $homogeneous;
-my $success_homogeneous;
-my $structured;
-my $success_structured;
-
- open(my $in1,  "<",  $filein)  or die "Can't open $filein: $!";
- open(my $out, ">",  $fileout) or die "Can't open $fileout: $!";
-
-
-    $array0[0] = 0;
-    $i1=1;
-    while (my $line1 = <$in1>)
-    {
-        chomp $line1;
-    $array0[$i1] = 0;
-    if (($i1>=$myfirstline)&&($i1<=$mylastline))
-    {
-                if($line1=~/if\(ptr\[offset(\d+)/)
-                {
-                  if($line1=~/if\(ptr\[offset(\d+).*\>.*cb/)
-          {
-            $tmp=$1;
-                  }
-          else
-                  {
-                   if($line1=~/if\(ptr\[offset(\d+).*\<.*c\_b/)
-           {
-            $tmp=$1+128;
-                   }
-                   else
-           {
-                        die "invalid array index!"
-           }
-          }
-          $array1[$ifcount1] = $tmp;
-          $array0[$ifcount1] = $i1;
-          $ifcount1++;
-        }
-        else
-        {
-        }
-    }
-        $i1++;
-    }
-   $homogeneous=$ifcount1;
-   $success_homogeneous=$ifcount1+1;
-   $structured=$ifcount1+2;
-   $success_structured=$ifcount1+3;
-
-  close $in1 or die "Can't close $filein: $!";
-
-  open($in1,  "<",  $filein)  or die "Can't open $filein: $!";
-
-
-    $i1=1;
-    while (my $line1 = <$in1>)
-    {
-        chomp $line1;
-    if (($i1>=$myfirstline)&&($i1<=$mylastline))
-    {
-           if ($array0[$ifcount2] == $i1)
-       {
-        $array2[$ifcount2]=0;
-        $array3[$ifcount2]=0;
-                if ($array0[$ifcount2+1] == ($i1+1))
-            {
-          $array2[$ifcount2]=($ifcount2+1);
-            }
-        else
-            {
-                  open(my $in2,  "<",  $filein)  or die "Can't open $filein: $!";
-          $i2=1;
-                  while (my $line2 = <$in2>)
-                  {
-                      chomp $line2;
-                      if ($i2 == $i1)
-                      {
-                          last;
-                      }
-                      $i2++;
-                  }
-                  my $line2 = <$in2>;
-                  chomp $line2;
-                  if ($line2=~/goto (\w+)/)
-                  {
-                               $tmp=$1;
-                               if ($tmp eq "homogeneous")
-                               {
-                                   $array2[$ifcount2]=$homogeneous;
-                               }
-                               if ($tmp eq "success_homogeneous")
-                               {
-                                   $array2[$ifcount2]=$success_homogeneous;
-                               }
-                               if ($tmp eq "structured")
-                               {
-                                   $array2[$ifcount2]=$structured;
-                               }
-                               if ($tmp eq "success_structured")
-                               {
-                                   $array2[$ifcount2]=$success_structured;
-                               }
-                  }
-                  else
-                  {
-                      die "goto expected: $!";
-                  }
-                  close $in2 or die "Can't close $filein: $!";
-                }
-                #find next else and interpret it
-                open(my $in3,  "<",  $filein)  or die "Can't open $filein: $!";
-        $i3=1;
-        $ifcount3=0;
-                $elsecount=0;
-                while (my $line3 = <$in3>)
-                {
-                      chomp $line3;
-                      $i3++;
-                      if ($i3 == $i1)
-                      {
-                          last;
-                      }
-                }
-                while (my $line3 = <$in3>)
-                {
-                      chomp $line3;
-                      $ifcount3++;
-                      if (($elsecount==0)&&($i3>$i1))
-                      {
-                            if ($line3=~/goto (\w+)/)
-                            {
-                               $tmp=$1;
-                               if ($tmp eq "homogeneous")
-                               {
-                                   $array3[$ifcount2]=$homogeneous;
-                               }
-                               if ($tmp eq "success_homogeneous")
-                               {
-                                   $array3[$ifcount2]=$success_homogeneous;
-                               }
-                               if ($tmp eq "structured")
-                               {
-                                   $array3[$ifcount2]=$structured;
-                               }
-                               if ($tmp eq "success_structured")
-                               {
-                                   $array3[$ifcount2]=$success_structured;
-                               }
-                            }
-                            else
-                            {
-                                if ($line3=~/if\(ptr\[offset/)
-                                {
-                                        $ifcount4=0;
-                                        while ($array0[$ifcount4]!=$i3)
-                                        {
-                                            $ifcount4++;
-                                            if ($ifcount4==$ifcount1)
-                                            {
-                                          die "if else match expected: $!";
-                                            }
-                                            $array3[$ifcount2]=$ifcount4;
-                                        }
-                                }
-                                else
-                                {
-                                        die "elseif or elsegoto match expected: $!";
-                                }
-                            }
-                            last;
-                      }
-                      else
-                      {
-                            if ($line3=~/if\(ptr\[offset/)
-                            {
-                              $elsecount++;
-                            }
-                            else
-                            {
-                                if ($line3=~/else/)
-                                {
-                                  $elsecount--;
-                                }
-                            }
-                      }
-                      $i3++;
-                }
-                printf("%3d [%3d][0x%08x]\n", $array0[$ifcount2], $ifcount2, (($array1[$ifcount2]&15)<<28)|($array2[$ifcount2]<<16)|(($array1[$ifcount2]&128)<<5)|($array3[$ifcount2]));
-                close $in3 or die "Can't close $filein: $!";
-        $ifcount2++;
-       }
-       else
-       {
-       }
-    }
-        $i1++;
-    }
-
-  printf("    [%3d][0x%08x]\n", $homogeneous, 252);
-  printf("    [%3d][0x%08x]\n", $success_homogeneous, 253);
-  printf("    [%3d][0x%08x]\n", $structured, 254);
-  printf("    [%3d][0x%08x]\n", $success_structured, 255);
-
-  close $in1 or die "Can't close $filein: $!";
-
-  $ifcount0=0;
-  $ifcount2=0;
-  printf $out "    static const unsigned long %s[] = {\n        ", $tablename;
-  while ($ifcount0 < $ifcount1)
-  {
-      printf $out "0x%08x, ", (($array1[$ifcount0]&15)<<28)|($array2[$ifcount0]<<16)|(($array1[$ifcount0]&128)<<5)|($array3[$ifcount0]);
-
-     $ifcount0++;
-     $ifcount2++;
-     if ($ifcount2==8)
-     {
-      $ifcount2=0;
-      printf $out "\n";
-      printf $out "        ";
-     }
-
-  }
-  printf $out "0x%08x, ", 252;
-     $ifcount0++;
-     $ifcount2++;
-     if ($ifcount2==8)
-     {
-      $ifcount2=0;
-      printf $out "\n";
-      printf $out "        ";
-     }
-  printf $out "0x%08x, ", 253;
-     $ifcount0++;
-     $ifcount2++;
-     if ($ifcount2==8)
-     {
-      $ifcount2=0;
-      printf $out "\n";
-      printf $out "        ";
-     }
-  printf $out "0x%08x, ", 254;
-     $ifcount0++;
-     $ifcount2++;
-     if ($ifcount2==8)
-     {
-      $ifcount2=0;
-      printf $out "\n";
-      printf $out "        ";
-     }
-  printf $out "0x%08x\n", 255;
-     $ifcount0++;
-     $ifcount2++;
-     printf $out "    };\n\n";
-
-  $#array0 = -1;
-  $#array1 = -1;
-  $#array2 = -1;
-  $#array3 = -1;
-
- close $out or die "Can't close $fileout: $!";

modules/features2d/doc/read_file_score32.pl

@@ -1,244 +0,0 @@
-#!/usr/bin/perl
-use strict;
-use warnings;
-use autodie; # die if problem reading or writing a file
-
-my $filein = "./agast_score.txt";
-my $fileout = "./agast_new.txt";
-my $i1=1;
-my $i2=1;
-my $i3=1;
-my $tmp;
-my $ifcount0=0;
-my $ifcount1=0;
-my $ifcount2=0;
-my $ifcount3=0;
-my $ifcount4=0;
-my $elsecount;
-my $myfirstline = $ARGV[0];
-my $mylastline  = $ARGV[1];
-my $tablename  = $ARGV[2];
-my @array0 = ();
-my @array1 = ();
-my @array2 = ();
-my @array3 = ();
-my $is_not_a_corner;
-my $is_a_corner;
-
- open(my $in1,  "<",  $filein)  or die "Can't open $filein: $!";
- open(my $out, ">",  $fileout) or die "Can't open $fileout: $!";
-
-
-    $array0[0] = 0;
-    $i1=1;
-    while (my $line1 = <$in1>)
-    {
-        chomp $line1;
-    $array0[$i1] = 0;
-    if (($i1>=$myfirstline)&&($i1<=$mylastline))
-    {
-                if($line1=~/if\(ptr\[offset(\d+)/)
-                {
-                  if($line1=~/if\(ptr\[offset(\d+).*\>.*cb/)
-          {
-            $tmp=$1;
-                  }
-          else
-                  {
-                   if($line1=~/if\(ptr\[offset(\d+).*\<.*c\_b/)
-           {
-            $tmp=$1+128;
-                   }
-                   else
-           {
-                        die "invalid array index!"
-           }
-          }
-          $array1[$ifcount1] = $tmp;
-          $array0[$ifcount1] = $i1;
-          $ifcount1++;
-        }
-        else
-        {
-        }
-    }
-        $i1++;
-    }
-   $is_not_a_corner=$ifcount1;
-   $is_a_corner=$ifcount1+1;
-
-  close $in1 or die "Can't close $filein: $!";
-
-  open($in1,  "<",  $filein)  or die "Can't open $filein: $!";
-
-
-    $i1=1;
-    while (my $line1 = <$in1>)
-    {
-        chomp $line1;
-    if (($i1>=$myfirstline)&&($i1<=$mylastline))
-    {
-           if ($array0[$ifcount2] == $i1)
-       {
-        $array2[$ifcount2]=0;
-        $array3[$ifcount2]=0;
-                if ($array0[$ifcount2+1] == ($i1+1))
-            {
-          $array2[$ifcount2]=($ifcount2+1);
-            }
-        else
-            {
-                  open(my $in2,  "<",  $filein)  or die "Can't open $filein: $!";
-          $i2=1;
-                  while (my $line2 = <$in2>)
-                  {
-                      chomp $line2;
-                      if ($i2 == $i1)
-                      {
-                          last;
-                      }
-                      $i2++;
-                  }
-                  my $line2 = <$in2>;
-                  chomp $line2;
-                  if ($line2=~/goto (\w+)/)
-                  {
-                               $tmp=$1;
-                               if ($tmp eq "is_not_a_corner")
-                               {
-                                   $array2[$ifcount2]=$is_not_a_corner;
-                               }
-                               if ($tmp eq "is_a_corner")
-                               {
-                                   $array2[$ifcount2]=$is_a_corner;
-                               }
-                  }
-                  else
-                  {
-                      die "goto expected: $!";
-                  }
-                  close $in2 or die "Can't close $filein: $!";
-                }
-                #find next else and interpret it
-                open(my $in3,  "<",  $filein)  or die "Can't open $filein: $!";
-        $i3=1;
-        $ifcount3=0;
-                $elsecount=0;
-                while (my $line3 = <$in3>)
-                {
-                      chomp $line3;
-                      $i3++;
-                      if ($i3 == $i1)
-                      {
-                          last;
-                      }
-                }
-                while (my $line3 = <$in3>)
-                {
-                      chomp $line3;
-                      $ifcount3++;
-                      if (($elsecount==0)&&($i3>$i1))
-                      {
-                            if ($line3=~/goto (\w+)/)
-                            {
-                               $tmp=$1;
-                               if ($tmp eq "is_not_a_corner")
-                               {
-                                   $array3[$ifcount2]=$is_not_a_corner;
-                               }
-                               if ($tmp eq "is_a_corner")
-                               {
-                                   $array3[$ifcount2]=$is_a_corner;
-                               }
-                            }
-                            else
-                            {
-                                if ($line3=~/if\(ptr\[offset/)
-                                {
-                                        $ifcount4=0;
-                                        while ($array0[$ifcount4]!=$i3)
-                                        {
-                                            $ifcount4++;
-                                            if ($ifcount4==$ifcount1)
-                                            {
-                                          die "if else match expected: $!";
-                                            }
-                                            $array3[$ifcount2]=$ifcount4;
-                                        }
-                                }
-                                else
-                                {
-                                        die "elseif or elsegoto match expected: $!";
-                                }
-                            }
-                            last;
-                      }
-                      else
-                      {
-                            if ($line3=~/if\(ptr\[offset/)
-                            {
-                              $elsecount++;
-                            }
-                            else
-                            {
-                                if ($line3=~/else/)
-                                {
-                                  $elsecount--;
-                                }
-                            }
-                      }
-                      $i3++;
-                }
-                printf("%3d [%3d][0x%08x]\n", $array0[$ifcount2], $ifcount2, (($array1[$ifcount2]&15)<<28)|($array2[$ifcount2]<<16)|(($array1[$ifcount2]&128)<<5)|($array3[$ifcount2]));
-                close $in3 or die "Can't close $filein: $!";
-        $ifcount2++;
-       }
-       else
-       {
-       }
-    }
-        $i1++;
-    }
-
-  printf("    [%3d][0x%08x]\n", $is_not_a_corner, 254);
-  printf("    [%3d][0x%08x]\n", $is_a_corner, 255);
-
-  close $in1 or die "Can't close $filein: $!";
-
-  $ifcount0=0;
-  $ifcount2=0;
-  printf $out "    static const unsigned long %s[] = {\n        ", $tablename;
-  while ($ifcount0 < $ifcount1)
-  {
-      printf $out "0x%08x, ", (($array1[$ifcount0]&15)<<28)|($array2[$ifcount0]<<16)|(($array1[$ifcount0]&128)<<5)|($array3[$ifcount0]);
-
-     $ifcount0++;
-     $ifcount2++;
-     if ($ifcount2==8)
-     {
-      $ifcount2=0;
-      printf $out "\n";
-      printf $out "        ";
-     }
-
-  }
-  printf $out "0x%08x, ", 254;
-     $ifcount0++;
-     $ifcount2++;
-     if ($ifcount2==8)
-     {
-      $ifcount2=0;
-      printf $out "\n";
-      printf $out "        ";
-     }
-  printf $out "0x%08x\n", 255;
-     $ifcount0++;
-     $ifcount2++;
-      printf $out "    };\n\n";
-
-  $#array0 = -1;
-  $#array1 = -1;
-  $#array2 = -1;
-  $#array3 = -1;
-
- close $out or die "Can't close $fileout: $!";

modules/features2d/doc/run_agast_tables.bat

@@ -1,32 +0,0 @@
-perl read_file_score32.pl 9059 9385 table_5_8_corner_struct
-move agast_new.txt agast_score_table.txt
-perl read_file_score32.pl 2215 3387 table_7_12d_corner_struct
-copy /A agast_score_table.txt + agast_new.txt agast_score_table.txt
-del agast_new.txt
-perl read_file_score32.pl 3428 9022 table_7_12s_corner_struct
-copy /A agast_score_table.txt + agast_new.txt agast_score_table.txt
-del agast_new.txt
-perl read_file_score32.pl 118 2174 table_9_16_corner_struct
-copy /A agast_score_table.txt + agast_new.txt agast_score_table.txt
-del agast_new.txt
-
-perl read_file_nondiff32.pl 103 430 table_5_8_struct1
-move agast_new.txt agast_table.txt
-perl read_file_nondiff32.pl 440 779 table_5_8_struct2
-copy /A agast_table.txt + agast_new.txt agast_table.txt
-del agast_new.txt
-perl read_file_nondiff32.pl 869 2042 table_7_12d_struct1
-copy /A agast_table.txt + agast_new.txt agast_table.txt
-del agast_new.txt
-perl read_file_nondiff32.pl 2052 3225 table_7_12d_struct2
-copy /A agast_table.txt + agast_new.txt agast_table.txt
-del agast_new.txt
-perl read_file_nondiff32.pl 3315 4344 table_7_12s_struct1
-copy /A agast_table.txt + agast_new.txt agast_table.txt
-del agast_new.txt
-perl read_file_nondiff32.pl 4354 5308 table_7_12s_struct2
-copy /A agast_table.txt + agast_new.txt agast_table.txt
-del agast_new.txt
-perl read_file_nondiff32.pl 5400 7454 table_9_16_struct
-copy /A agast_table.txt + agast_new.txt agast_table.txt
-del agast_new.txt

modules/features2d/include/opencv2/features2d.hpp

@@ -62,9 +62,6 @@
     All objects that implement vector descriptor matchers inherit the DescriptorMatcher interface.
 
     @defgroup features2d_draw Drawing Function of Keypoints and Matches
-    @defgroup features2d_category Object Categorization
-
-    This section describes approaches based on local 2D features and used to categorize objects.
 
     @defgroup feature2d_hal Hardware Acceleration Layer
     @{
@@ -341,71 +338,6 @@ typedef SIFT SiftFeatureDetector;
 typedef SIFT SiftDescriptorExtractor;
 
 
-/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 .
- */
-class CV_EXPORTS_W BRISK : public Feature2D
-{
-public:
-    /** @brief The BRISK constructor
-
-    @param thresh AGAST detection threshold score.
-    @param octaves detection octaves. Use 0 to do single scale.
-    @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
-    keypoint.
-     */
-    CV_WRAP static Ptr<BRISK> create(int thresh=30, int octaves=3, float patternScale=1.0f);
-
-    /** @brief The BRISK constructor for a custom pattern
-
-    @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
-    keypoint scale 1).
-    @param numberList defines the number of sampling points on the sampling circle. Must be the same
-    size as radiusList..
-    @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
-    scale 1).
-    @param dMin threshold for the long pairings used for orientation determination (in pixels for
-    keypoint scale 1).
-    @param indexChange index remapping of the bits. */
-    CV_WRAP static Ptr<BRISK> create(const std::vector<float> &radiusList, const std::vector<int> &numberList,
-        float dMax=5.85f, float dMin=8.2f, const std::vector<int>& indexChange=std::vector<int>());
-
-    /** @brief The BRISK constructor for a custom pattern, detection threshold and octaves
-
-    @param thresh AGAST detection threshold score.
-    @param octaves detection octaves. Use 0 to do single scale.
-    @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
-    keypoint scale 1).
-    @param numberList defines the number of sampling points on the sampling circle. Must be the same
-    size as radiusList..
-    @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
-    scale 1).
-    @param dMin threshold for the long pairings used for orientation determination (in pixels for
-    keypoint scale 1).
-    @param indexChange index remapping of the bits. */
-    CV_WRAP static Ptr<BRISK> create(int thresh, int octaves, const std::vector<float> &radiusList,
-        const std::vector<int> &numberList, float dMax=5.85f, float dMin=8.2f,
-        const std::vector<int>& indexChange=std::vector<int>());
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-
-    /** @brief Set detection threshold.
-    @param threshold AGAST detection threshold score.
-    */
-    CV_WRAP virtual void setThreshold(int threshold) = 0;
-    CV_WRAP virtual int getThreshold() const = 0;
-
-    /** @brief Set detection octaves.
-    @param octaves detection octaves. Use 0 to do single scale.
-    */
-    CV_WRAP virtual void setOctaves(int octaves) = 0;
-    CV_WRAP virtual int getOctaves() const = 0;
-    /** @brief Set detection patternScale.
-    @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
-    keypoint.
-    */
-    CV_WRAP virtual void setPatternScale(float patternScale) = 0;
-    CV_WRAP virtual float getPatternScale() const = 0;
-};
-
 /** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor
 
 described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects
@@ -616,56 +548,6 @@ CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
                       int threshold, bool nonmaxSuppression=true, FastFeatureDetector::DetectorType type=FastFeatureDetector::TYPE_9_16 );
 
 
-/** @brief Wrapping class for feature detection using the AGAST method. :
- */
-class CV_EXPORTS_W AgastFeatureDetector : public Feature2D
-{
-public:
-    enum DetectorType
-    {
-        AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3,
-    };
-
-    enum
-    {
-        THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001,
-    };
-
-    CV_WRAP static Ptr<AgastFeatureDetector> create( int threshold=10,
-                                                     bool nonmaxSuppression=true,
-                                                     AgastFeatureDetector::DetectorType type = AgastFeatureDetector::OAST_9_16);
-
-    CV_WRAP virtual void setThreshold(int threshold) = 0;
-    CV_WRAP virtual int getThreshold() const = 0;
-
-    CV_WRAP virtual void setNonmaxSuppression(bool f) = 0;
-    CV_WRAP virtual bool getNonmaxSuppression() const = 0;
-
-    CV_WRAP virtual void setType(AgastFeatureDetector::DetectorType type) = 0;
-    CV_WRAP virtual AgastFeatureDetector::DetectorType getType() const = 0;
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-};
-
-/** @brief Detects corners using the AGAST algorithm
-
-@param image grayscale image where keypoints (corners) are detected.
-@param keypoints keypoints detected on the image.
-@param threshold threshold on difference between intensity of the central pixel and pixels of a
-circle around this pixel.
-@param nonmaxSuppression if true, non-maximum suppression is applied to detected keypoints (corners).
-@param type one of the four neighborhoods as defined in the paper:
-AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d,
-AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16
-
-For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results.
-The 32-bit binary tree tables were generated automatically from original code using perl script.
-The perl script and examples of tree generation are placed in features2d/doc folder.
-Detects corners using the AGAST algorithm by @cite mair2010_agast .
-
- */
-CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
-                      int threshold, bool nonmaxSuppression=true, AgastFeatureDetector::DetectorType type=AgastFeatureDetector::OAST_9_16 );
-
 /** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. :
  */
 class CV_EXPORTS_W GFTTDetector : public Feature2D
@@ -773,134 +655,6 @@ public:
 };
 
 
-/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 .
-
-@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo
-F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision
-(ECCV), Fiorenze, Italy, October 2012.
-*/
-class CV_EXPORTS_W KAZE : public Feature2D
-{
-public:
-    enum DiffusivityType
-    {
-        DIFF_PM_G1 = 0,
-        DIFF_PM_G2 = 1,
-        DIFF_WEICKERT = 2,
-        DIFF_CHARBONNIER = 3
-    };
-
-    /** @brief The KAZE constructor
-
-    @param extended Set to enable extraction of extended (128-byte) descriptor.
-    @param upright Set to enable use of upright descriptors (non rotation-invariant).
-    @param threshold Detector response threshold to accept point
-    @param nOctaves Maximum octave evolution of the image
-    @param nOctaveLayers Default number of sublevels per scale level
-    @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
-    DIFF_CHARBONNIER
-     */
-    CV_WRAP static Ptr<KAZE> create(bool extended=false, bool upright=false,
-                                    float threshold = 0.001f,
-                                    int nOctaves = 4, int nOctaveLayers = 4,
-                                    KAZE::DiffusivityType diffusivity = KAZE::DIFF_PM_G2);
-
-    CV_WRAP virtual void setExtended(bool extended) = 0;
-    CV_WRAP virtual bool getExtended() const = 0;
-
-    CV_WRAP virtual void setUpright(bool upright) = 0;
-    CV_WRAP virtual bool getUpright() const = 0;
-
-    CV_WRAP virtual void setThreshold(double threshold) = 0;
-    CV_WRAP virtual double getThreshold() const = 0;
-
-    CV_WRAP virtual void setNOctaves(int octaves) = 0;
-    CV_WRAP virtual int getNOctaves() const = 0;
-
-    CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0;
-    CV_WRAP virtual int getNOctaveLayers() const = 0;
-
-    CV_WRAP virtual void setDiffusivity(KAZE::DiffusivityType diff) = 0;
-    CV_WRAP virtual KAZE::DiffusivityType getDiffusivity() const = 0;
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-};
-
-/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13.
-
-@details AKAZE descriptors can only be used with KAZE or AKAZE keypoints. This class is thread-safe.
-
-@note When you need descriptors use Feature2D::detectAndCompute, which
-provides better performance. When using Feature2D::detect followed by
-Feature2D::compute scale space pyramid is computed twice.
-
-@note AKAZE implements T-API. When image is passed as UMat some parts of the algorithm
-will use OpenCL.
-
-@note [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear
-Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In
-British Machine Vision Conference (BMVC), Bristol, UK, September 2013.
-
-*/
-class CV_EXPORTS_W AKAZE : public Feature2D
-{
-public:
-    // AKAZE descriptor type
-    enum DescriptorType
-    {
-        DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation
-        DESCRIPTOR_KAZE = 3,
-        DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation
-        DESCRIPTOR_MLDB = 5
-    };
-
-    /** @brief The AKAZE constructor
-
-    @param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE,
-    DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.
-    @param descriptor_size Size of the descriptor in bits. 0 -\> Full size
-    @param descriptor_channels Number of channels in the descriptor (1, 2, 3)
-    @param threshold Detector response threshold to accept point
-    @param nOctaves Maximum octave evolution of the image
-    @param nOctaveLayers Default number of sublevels per scale level
-    @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
-    DIFF_CHARBONNIER
-    @param max_points Maximum amount of returned points. In case if image contains
-    more features, then the features with highest response are returned.
-    Negative value means no limitation.
-     */
-    CV_WRAP static Ptr<AKAZE> create(AKAZE::DescriptorType descriptor_type = AKAZE::DESCRIPTOR_MLDB,
-                                     int descriptor_size = 0, int descriptor_channels = 3,
-                                     float threshold = 0.001f, int nOctaves = 4,
-                                     int nOctaveLayers = 4, KAZE::DiffusivityType diffusivity = KAZE::DIFF_PM_G2,
-                                     int max_points = -1);
-
-    CV_WRAP virtual void setDescriptorType(AKAZE::DescriptorType dtype) = 0;
-    CV_WRAP virtual AKAZE::DescriptorType getDescriptorType() const = 0;
-
-    CV_WRAP virtual void setDescriptorSize(int dsize) = 0;
-    CV_WRAP virtual int getDescriptorSize() const = 0;
-
-    CV_WRAP virtual void setDescriptorChannels(int dch) = 0;
-    CV_WRAP virtual int getDescriptorChannels() const = 0;
-
-    CV_WRAP virtual void setThreshold(double threshold) = 0;
-    CV_WRAP virtual double getThreshold() const = 0;
-
-    CV_WRAP virtual void setNOctaves(int octaves) = 0;
-    CV_WRAP virtual int getNOctaves() const = 0;
-
-    CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0;
-    CV_WRAP virtual int getNOctaveLayers() const = 0;
-
-    CV_WRAP virtual void setDiffusivity(KAZE::DiffusivityType diff) = 0;
-    CV_WRAP virtual KAZE::DiffusivityType getDiffusivity() const = 0;
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-
-    CV_WRAP virtual void setMaxPoints(int max_points) = 0;
-    CV_WRAP virtual int getMaxPoints() const = 0;
-};
-
-
 /****************************************************************************************\
 *                                      Distance                                          *
 \****************************************************************************************/
@@ -1424,165 +1178,6 @@ CV_EXPORTS int getNearestPoint( const std::vector<Point2f>& recallPrecisionCurve
 
 //! @}
 
-/****************************************************************************************\
-*                                     Bag of visual words                                *
-\****************************************************************************************/
-
-//! @addtogroup features2d_category
-//! @{
-
-/** @brief Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors.
-
-For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka,
-Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. :
- */
-class CV_EXPORTS_W BOWTrainer
-{
-public:
-    BOWTrainer();
-    virtual ~BOWTrainer();
-
-    /** @brief Adds descriptors to a training set.
-
-    @param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a
-    descriptor.
-
-    The training set is clustered using clustermethod to construct the vocabulary.
-     */
-    CV_WRAP void add( const Mat& descriptors );
-
-    /** @brief Returns a training set of descriptors.
-    */
-    CV_WRAP const std::vector<Mat>& getDescriptors() const;
-
-    /** @brief Returns the count of all descriptors stored in the training set.
-    */
-    CV_WRAP int descriptorsCount() const;
-
-    CV_WRAP virtual void clear();
-
-    /** @overload */
-    CV_WRAP virtual Mat cluster() const = 0;
-
-    /** @brief Clusters train descriptors.
-
-    @param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor.
-    Descriptors are not added to the inner train descriptor set.
-
-    The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first
-    variant of the method, train descriptors stored in the object are clustered. In the second variant,
-    input descriptors are clustered.
-     */
-    CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0;
-
-protected:
-    std::vector<Mat> descriptors;
-    int size;
-};
-
-/** @brief kmeans -based class to train visual vocabulary using the *bag of visual words* approach. :
- */
-class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer
-{
-public:
-    /** @brief The constructor.
-
-    @see cv::kmeans
-    */
-    CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
-                      int attempts=3, int flags=KMEANS_PP_CENTERS );
-    virtual ~BOWKMeansTrainer();
-
-    // Returns trained vocabulary (i.e. cluster centers).
-    CV_WRAP virtual Mat cluster() const CV_OVERRIDE;
-    CV_WRAP virtual Mat cluster( const Mat& descriptors ) const CV_OVERRIDE;
-
-protected:
-
-    int clusterCount;
-    TermCriteria termcrit;
-    int attempts;
-    int flags;
-};
-
-/** @brief Class to compute an image descriptor using the *bag of visual words*.
-
-Such a computation consists of the following steps:
-
-1.  Compute descriptors for a given image and its keypoints set.
-2.  Find the nearest visual words from the vocabulary for each keypoint descriptor.
-3.  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.
- */
-class CV_EXPORTS_W BOWImgDescriptorExtractor
-{
-public:
-    /** @brief The constructor.
-
-    @param dextractor Descriptor extractor that is used to compute descriptors for an input image and
-    its keypoints.
-    @param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary
-    for each keypoint descriptor of the image.
-     */
-    CV_WRAP BOWImgDescriptorExtractor( const Ptr<Feature2D>& dextractor,
-                                       const Ptr<DescriptorMatcher>& dmatcher );
-    /** @overload */
-    BOWImgDescriptorExtractor( const Ptr<DescriptorMatcher>& dmatcher );
-    virtual ~BOWImgDescriptorExtractor();
-
-    /** @brief Sets a visual vocabulary.
-
-    @param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the
-    vocabulary is a visual word (cluster center).
-     */
-    CV_WRAP void setVocabulary( const Mat& vocabulary );
-
-    /** @brief Returns the set vocabulary.
-    */
-    CV_WRAP const Mat& getVocabulary() const;
-
-    /** @brief Computes an image descriptor using the set visual vocabulary.
-
-    @param image Image, for which the descriptor is computed.
-    @param keypoints Keypoints detected in the input image.
-    @param imgDescriptor Computed output image descriptor.
-    @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
-    pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
-    returned if it is non-zero.
-    @param descriptors Descriptors of the image keypoints that are returned if they are non-zero.
-     */
-    void compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray imgDescriptor,
-                  std::vector<std::vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 );
-    /** @overload
-    @param keypointDescriptors Computed descriptors to match with vocabulary.
-    @param imgDescriptor Computed output image descriptor.
-    @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
-    pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
-    returned if it is non-zero.
-    */
-    void compute( InputArray keypointDescriptors, OutputArray imgDescriptor,
-                  std::vector<std::vector<int> >* pointIdxsOfClusters=0 );
-    // compute() is not constant because DescriptorMatcher::match is not constant
-
-    CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector<KeyPoint>& keypoints, CV_OUT Mat& imgDescriptor )
-    { compute(image,keypoints,imgDescriptor); }
-
-    /** @brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0.
-    */
-    CV_WRAP int descriptorSize() const;
-
-    /** @brief Returns an image descriptor type.
-     */
-    CV_WRAP int descriptorType() const;
-
-protected:
-    Mat vocabulary;
-    Ptr<DescriptorExtractor> dextractor;
-    Ptr<DescriptorMatcher> dmatcher;
-};
-
-//! @} features2d_category
 
 } /* namespace cv */
 

modules/features2d/misc/java/test/AGASTFeatureDetectorTest.java

@@ -1,85 +0,0 @@
-package org.opencv.test.features2d;
-
-import org.opencv.test.OpenCVTestCase;
-import org.opencv.test.OpenCVTestRunner;
-import org.opencv.features2d.AgastFeatureDetector;
-
-public class AGASTFeatureDetectorTest extends OpenCVTestCase {
-
-    AgastFeatureDetector detector;
-
-    @Override
-    protected void setUp() throws Exception {
-        super.setUp();
-        detector = AgastFeatureDetector.create(); // default (10,true,3)
-    }
-
-    public void testCreate() {
-        assertNotNull(detector);
-    }
-
-    public void testDetectListOfMatListOfListOfKeyPoint() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectMatListOfKeyPoint() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectMatListOfKeyPointMat() {
-        fail("Not yet implemented");
-    }
-
-    public void testEmpty() {
-        fail("Not yet implemented");
-    }
-
-    public void testRead() {
-        String filename = OpenCVTestRunner.getTempFileName("xml");
-        writeFile(filename, "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.AgastFeatureDetector</name>\n<threshold>11</threshold>\n<nonmaxSuppression>0</nonmaxSuppression>\n<type>2</type>\n</opencv_storage>\n");
-
-        detector.read(filename);
-
-        assertEquals(11, detector.getThreshold());
-        assertEquals(false, detector.getNonmaxSuppression());
-        assertEquals(2, detector.getType());
-    }
-
-    public void testReadYml() {
-        String filename = OpenCVTestRunner.getTempFileName("yml");
-        writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.AgastFeatureDetector\"\nthreshold: 11\nnonmaxSuppression: 0\ntype: 2\n");
-
-        detector.read(filename);
-
-        assertEquals(11, detector.getThreshold());
-        assertEquals(false, detector.getNonmaxSuppression());
-        assertEquals(2, detector.getType());
-    }
-
-    public void testWrite() {
-        String filename = OpenCVTestRunner.getTempFileName("xml");
-
-        detector.write(filename);
-
-        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.AgastFeatureDetector</name>\n<threshold>10</threshold>\n<nonmaxSuppression>1</nonmaxSuppression>\n<type>3</type>\n</opencv_storage>\n";
-        String actual = readFile(filename);
-        actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
-        assertEquals(truth, actual);
-    }
-
-    public void testWriteYml() {
-        String filename = OpenCVTestRunner.getTempFileName("yml");
-
-        detector.write(filename);
-
-        String truth = "%YAML:1.0\n---\nname: \"Feature2D.AgastFeatureDetector\"\nthreshold: 10\nnonmaxSuppression: 1\ntype: 3\n";
-        String actual = readFile(filename);
-        actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
-        assertEquals(truth, actual);
-    }
-
-}

modules/features2d/misc/java/test/AKAZEDescriptorExtractorTest.java

@@ -1,67 +0,0 @@
-package org.opencv.test.features2d;
-
-import org.opencv.test.OpenCVTestCase;
-import org.opencv.test.OpenCVTestRunner;
-import org.opencv.features2d.AKAZE;
-
-public class AKAZEDescriptorExtractorTest extends OpenCVTestCase {
-
-    AKAZE extractor;
-
-    @Override
-    protected void setUp() throws Exception {
-        super.setUp();
-        extractor = AKAZE.create(); // default (5,0,3,0.001f,4,4,1)
-    }
-
-    public void testCreate() {
-        assertNotNull(extractor);
-    }
-
-    public void testDetectListOfMatListOfListOfKeyPoint() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectMatListOfKeyPoint() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectMatListOfKeyPointMat() {
-        fail("Not yet implemented");
-    }
-
-    public void testEmpty() {
-        fail("Not yet implemented");
-    }
-
-    public void testReadYml() {
-        String filename = OpenCVTestRunner.getTempFileName("yml");
-        writeFile(filename, "%YAML:1.0\n---\nformat: 3\nname: \"Feature2D.AKAZE\"\ndescriptor: 4\ndescriptor_channels: 2\ndescriptor_size: 32\nthreshold: 0.125\noctaves: 3\nsublevels: 5\ndiffusivity: 2\n");
-
-        extractor.read(filename);
-
-        assertEquals(4, extractor.getDescriptorType());
-        assertEquals(2, extractor.getDescriptorChannels());
-        assertEquals(32, extractor.getDescriptorSize());
-        assertEquals(0.125, extractor.getThreshold());
-        assertEquals(3, extractor.getNOctaves());
-        assertEquals(5, extractor.getNOctaveLayers());
-        assertEquals(2, extractor.getDiffusivity());
-    }
-
-    public void testWriteYml() {
-        String filename = OpenCVTestRunner.getTempFileName("yml");
-
-        extractor.write(filename);
-
-        String truth = "%YAML:1.0\n---\nformat: 3\nname: \"Feature2D.AKAZE\"\ndescriptor: 5\ndescriptor_channels: 3\ndescriptor_size: 0\nthreshold: 0.0010000000474974513\noctaves: 4\nsublevels: 4\ndiffusivity: 1\nmax_points: -1\n";
-        String actual = readFile(filename);
-        actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
-        assertEquals(truth, actual);
-    }
-
-}

modules/features2d/misc/java/test/BOWImgDescriptorExtractorTest.java

@@ -1,48 +0,0 @@
-package org.opencv.test.features2d;
-
-import org.opencv.core.Core;
-import org.opencv.core.CvType;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfKeyPoint;
-import org.opencv.core.Point;
-import org.opencv.core.Scalar;
-import org.opencv.core.KeyPoint;
-import org.opencv.features2d.ORB;
-import org.opencv.features2d.DescriptorMatcher;
-import org.opencv.features2d.BOWImgDescriptorExtractor;
-import org.opencv.test.OpenCVTestCase;
-import org.opencv.test.OpenCVTestRunner;
-import org.opencv.imgproc.Imgproc;
-
-public class BOWImgDescriptorExtractorTest extends OpenCVTestCase {
-
-    ORB extractor;
-    DescriptorMatcher matcher;
-    int matSize;
-
-    public static void assertDescriptorsClose(Mat expected, Mat actual, int allowedDistance) {
-        double distance = Core.norm(expected, actual, Core.NORM_HAMMING);
-        assertTrue("expected:<" + allowedDistance + "> but was:<" + distance + ">", distance <= allowedDistance);
-    }
-
-    private Mat getTestImg() {
-        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
-        Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
-        Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
-
-        return cross;
-    }
-
-    @Override
-    protected void setUp() throws Exception {
-        super.setUp();
-        extractor = ORB.create();
-        matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
-        matSize = 100;
-    }
-
-    public void testCreate() {
-        BOWImgDescriptorExtractor bow = new BOWImgDescriptorExtractor(extractor, matcher);
-    }
-
-}

modules/features2d/misc/java/test/BRIEFDescriptorExtractorTest.java

@@ -1,102 +0,0 @@
-package org.opencv.test.features2d;
-
-import org.opencv.core.CvType;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfKeyPoint;
-import org.opencv.core.Point;
-import org.opencv.core.Scalar;
-import org.opencv.core.KeyPoint;
-import org.opencv.test.OpenCVTestCase;
-import org.opencv.test.OpenCVTestRunner;
-import org.opencv.imgproc.Imgproc;
-import org.opencv.features2d.Feature2D;
-
-public class BRIEFDescriptorExtractorTest extends OpenCVTestCase {
-
-    Feature2D extractor;
-    int matSize;
-
-    private Mat getTestImg() {
-        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
-        Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
-        Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
-
-        return cross;
-    }
-
-    @Override
-    protected void setUp() throws Exception {
-        super.setUp();
-        extractor = createClassInstance(XFEATURES2D+"BriefDescriptorExtractor", DEFAULT_FACTORY, null, null);
-        matSize = 100;
-    }
-
-    public void testComputeListOfMatListOfListOfKeyPointListOfMat() {
-        fail("Not yet implemented");
-    }
-
-    public void testComputeMatListOfKeyPointMat() {
-        KeyPoint point = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
-        MatOfKeyPoint keypoints = new MatOfKeyPoint(point);
-        Mat img = getTestImg();
-        Mat descriptors = new Mat();
-
-        extractor.compute(img, keypoints, descriptors);
-
-        Mat truth = new Mat(1, 32, CvType.CV_8UC1) {
-            {
-                put(0, 0, 96, 0, 76, 24, 47, 182, 68, 137,
-                          149, 195, 67, 16, 187, 224, 74, 8,
-                          82, 169, 87, 70, 44, 4, 192, 56,
-                          13, 128, 44, 106, 146, 72, 194, 245);
-            }
-        };
-
-        assertMatEqual(truth, descriptors);
-    }
-
-    public void testCreate() {
-        assertNotNull(extractor);
-    }
-
-    public void testDescriptorSize() {
-        assertEquals(32, extractor.descriptorSize());
-    }
-
-    public void testDescriptorType() {
-        assertEquals(CvType.CV_8U, extractor.descriptorType());
-    }
-
-    public void testEmpty() {
-//        assertFalse(extractor.empty());
-        fail("Not yet implemented"); // BRIEF does not override empty() method
-    }
-
-    public void testRead() {
-        String filename = OpenCVTestRunner.getTempFileName("yml");
-        writeFile(filename, "%YAML:1.0\n---\ndescriptorSize: 64\n");
-
-        extractor.read(filename);
-
-        assertEquals(64, extractor.descriptorSize());
-    }
-
-    public void testWrite() {
-        String filename = OpenCVTestRunner.getTempFileName("xml");
-
-        extractor.write(filename);
-
-        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.BRIEF</name>\n<descriptorSize>32</descriptorSize>\n<use_orientation>0</use_orientation>\n</opencv_storage>\n";
-        assertEquals(truth, readFile(filename));
-    }
-
-    public void testWriteYml() {
-        String filename = OpenCVTestRunner.getTempFileName("yml");
-
-        extractor.write(filename);
-
-        String truth = "%YAML:1.0\n---\nname: \"Feature2D.BRIEF\"\ndescriptorSize: 32\nuse_orientation: 0\n";
-        assertEquals(truth, readFile(filename));
-    }
-
-}

modules/features2d/misc/java/test/BRISKDescriptorExtractorTest.java

@@ -1,63 +0,0 @@
-package org.opencv.test.features2d;
-
-import org.opencv.test.OpenCVTestCase;
-import org.opencv.test.OpenCVTestRunner;
-import org.opencv.features2d.BRISK;
-
-public class BRISKDescriptorExtractorTest extends OpenCVTestCase {
-
-    BRISK extractor;
-
-    @Override
-    protected void setUp() throws Exception {
-        super.setUp();
-        extractor = BRISK.create(); // default (30,3,1)
-    }
-
-    public void testCreate() {
-        assertNotNull(extractor);
-    }
-
-    public void testDetectListOfMatListOfListOfKeyPoint() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectMatListOfKeyPoint() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectMatListOfKeyPointMat() {
-        fail("Not yet implemented");
-    }
-
-    public void testEmpty() {
-        fail("Not yet implemented");
-    }
-
-    public void testReadYml() {
-        String filename = OpenCVTestRunner.getTempFileName("yml");
-        writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.BRISK\"\nthreshold: 31\noctaves: 4\npatternScale: 1.1\n");
-
-        extractor.read(filename);
-
-        assertEquals(31, extractor.getThreshold());
-        assertEquals(4, extractor.getOctaves());
-        assertEquals(1.1f, extractor.getPatternScale());
-    }
-
-    public void testWriteYml() {
-        String filename = OpenCVTestRunner.getTempFileName("yml");
-
-        extractor.write(filename);
-
-        String truth = "%YAML:1.0\n---\nname: \"Feature2D.BRISK\"\nthreshold: 30\noctaves: 3\npatternScale: 1.\n";
-        String actual = readFile(filename);
-        actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
-        assertEquals(truth, actual);
-    }
-
-}

modules/features2d/misc/java/test/KAZEDescriptorExtractorTest.java

@@ -1,66 +0,0 @@
-package org.opencv.test.features2d;
-
-import org.opencv.test.OpenCVTestCase;
-import org.opencv.test.OpenCVTestRunner;
-import org.opencv.features2d.KAZE;
-
-public class KAZEDescriptorExtractorTest extends OpenCVTestCase {
-
-    KAZE extractor;
-
-    @Override
-    protected void setUp() throws Exception {
-        super.setUp();
-        extractor = KAZE.create(); // default (false,false,0.001f,4,4,1)
-    }
-
-    public void testCreate() {
-        assertNotNull(extractor);
-    }
-
-    public void testDetectListOfMatListOfListOfKeyPoint() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectMatListOfKeyPoint() {
-        fail("Not yet implemented");
-    }
-
-    public void testDetectMatListOfKeyPointMat() {
-        fail("Not yet implemented");
-    }
-
-    public void testEmpty() {
-        fail("Not yet implemented");
-    }
-
-    public void testReadYml() {
-        String filename = OpenCVTestRunner.getTempFileName("yml");
-        writeFile(filename, "%YAML:1.0\n---\nformat: 3\nname: \"Feature2D.KAZE\"\nextended: 1\nupright: 1\nthreshold: 0.125\noctaves: 3\nsublevels: 5\ndiffusivity: 2\n");
-
-        extractor.read(filename);
-
-        assertEquals(true, extractor.getExtended());
-        assertEquals(true, extractor.getUpright());
-        assertEquals(0.125, extractor.getThreshold());
-        assertEquals(3, extractor.getNOctaves());
-        assertEquals(5, extractor.getNOctaveLayers());
-        assertEquals(2, extractor.getDiffusivity());
-    }
-
-    public void testWriteYml() {
-        String filename = OpenCVTestRunner.getTempFileName("yml");
-
-        extractor.write(filename);
-
-        String truth = "%YAML:1.0\n---\nformat: 3\nname: \"Feature2D.KAZE\"\nextended: 0\nupright: 0\nthreshold: 0.0010000000474974513\noctaves: 4\nsublevels: 4\ndiffusivity: 1\n";
-        String actual = readFile(filename);
-        actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
-        assertEquals(truth, actual);
-    }
-
-}

modules/features2d/misc/js/gen_dict.json

@@ -2,16 +2,12 @@
     "whitelist":
     {
         "Feature2D": ["detect", "compute", "detectAndCompute", "descriptorSize", "descriptorType", "defaultNorm", "empty", "getDefaultName"],
-        "BRISK": ["create", "getDefaultName"],
         "ORB": ["create", "setMaxFeatures", "setScaleFactor", "setNLevels", "setEdgeThreshold", "setFastThreshold", "setFirstLevel", "setWTA_K", "setScoreType", "setPatchSize", "getFastThreshold", "getDefaultName"],
         "MSER": ["create", "detectRegions", "setDelta", "getDelta", "setMinArea", "getMinArea", "setMaxArea", "getMaxArea", "setPass2Only", "getPass2Only", "getDefaultName"],
         "FastFeatureDetector": ["create", "setThreshold", "getThreshold", "setNonmaxSuppression", "getNonmaxSuppression", "setType", "getType", "getDefaultName"],
-        "AgastFeatureDetector": ["create", "setThreshold", "getThreshold", "setNonmaxSuppression", "getNonmaxSuppression", "setType", "getType", "getDefaultName"],
         "GFTTDetector": ["create", "setMaxFeatures", "getMaxFeatures", "setQualityLevel", "getQualityLevel", "setMinDistance", "getMinDistance", "setBlockSize", "getBlockSize", "setHarrisDetector", "getHarrisDetector", "setK", "getK", "getDefaultName"],
         "SimpleBlobDetector": ["create", "setParams", "getParams", "getDefaultName"],
         "SimpleBlobDetector_Params": [],
-        "KAZE": ["create", "setExtended", "getExtended", "setUpright", "getUpright", "setThreshold", "getThreshold", "setNOctaves", "getNOctaves", "setNOctaveLayers", "getNOctaveLayers", "setDiffusivity", "getDiffusivity", "getDefaultName"],
-        "AKAZE": ["create", "setDescriptorType", "getDescriptorType", "setDescriptorSize", "getDescriptorSize", "setDescriptorChannels", "getDescriptorChannels", "setThreshold", "getThreshold", "setNOctaves", "getNOctaves", "setNOctaveLayers", "getNOctaveLayers", "setDiffusivity", "getDiffusivity", "getDefaultName"],
         "DescriptorMatcher": ["add", "clear", "empty", "isMaskSupported", "train", "match", "knnMatch", "radiusMatch", "clone", "create"],
         "BFMatcher": ["isMaskSupported", "create"],
         "": ["drawKeypoints", "drawMatches", "drawMatchesKnn"]

modules/features2d/misc/objc/gen_dict.json

@@ -6,8 +6,7 @@
         }
     },
     "enum_fix" : {
-        "FastFeatureDetector" : { "DetectorType": "FastDetectorType" },
-        "AgastFeatureDetector" : { "DetectorType": "AgastDetectorType" }
+        "FastFeatureDetector" : { "DetectorType": "FastDetectorType" }
     },
     "func_arg_fix" : {
         "Feature2D": {

modules/features2d/misc/python/pyopencv_features2d.hpp

@@ -1,9 +1,6 @@
 #ifdef HAVE_OPENCV_FEATURES2D
 typedef SimpleBlobDetector::Params SimpleBlobDetector_Params;
-typedef AKAZE::DescriptorType AKAZE_DescriptorType;
-typedef AgastFeatureDetector::DetectorType AgastFeatureDetector_DetectorType;
 typedef FastFeatureDetector::DetectorType FastFeatureDetector_DetectorType;
 typedef DescriptorMatcher::MatcherType DescriptorMatcher_MatcherType;
-typedef KAZE::DiffusivityType KAZE_DiffusivityType;
 typedef ORB::ScoreType ORB_ScoreType;
 #endif

modules/features2d/misc/python/test/test_feature_homography.py

@@ -92,7 +92,7 @@ TrackedTarget = namedtuple('TrackedTarget', 'target, p0, p1, H, quad')
 
 class PlaneTracker:
     def __init__(self):
-        self.detector = cv.AKAZE_create(threshold = 0.003)
+        self.detector = cv.ORB_create( nfeatures = 1000 )
         self.matcher = cv.FlannBasedMatcher(flann_params, {})  # bug : need to pass empty dict (#1329)
         self.targets = []
         self.frame_points = []

modules/features2d/perf/opencl/perf_feature2d.cpp

@@ -29,7 +29,7 @@ OCL_PERF_TEST_P(feature2d, detect, testing::Combine(Feature2DType::all(), TEST_I
 
 OCL_PERF_TEST_P(feature2d, extract, testing::Combine(testing::Values(DETECTORS_EXTRACTORS), TEST_IMAGES))
 {
-    Ptr<Feature2D> detector = AKAZE::create();
+    Ptr<Feature2D> detector = ORB::create();
     Ptr<Feature2D> extractor = getFeature2D(get<0>(GetParam()));
     std::string filename = getDataPath(get<1>(GetParam()));
     Mat mimg = imread(filename, IMREAD_GRAYSCALE);

modules/features2d/perf/perf_feature2d.cpp

@@ -25,7 +25,7 @@ PERF_TEST_P(feature2d, detect, testing::Combine(Feature2DType::all(), TEST_IMAGE
 
 PERF_TEST_P(feature2d, extract, testing::Combine(testing::Values(DETECTORS_EXTRACTORS), TEST_IMAGES))
 {
-    Ptr<Feature2D> detector = AKAZE::create();
+    Ptr<Feature2D> detector = ORB::create();
     Ptr<Feature2D> extractor = getFeature2D(get<0>(GetParam()));
     std::string filename = getDataPath(get<1>(GetParam()));
     Mat img = imread(filename, IMREAD_GRAYSCALE);

modules/features2d/perf/perf_feature2d.hpp

@@ -13,15 +13,10 @@ namespace opencv_test
     FAST_DEFAULT, FAST_20_TRUE_TYPE5_8, FAST_20_TRUE_TYPE7_12, FAST_20_TRUE_TYPE9_16,   \
     FAST_20_FALSE_TYPE5_8, FAST_20_FALSE_TYPE7_12, FAST_20_FALSE_TYPE9_16,              \
                                                                                         \
-    AGAST_DEFAULT, AGAST_5_8, AGAST_7_12d, AGAST_7_12s, AGAST_OAST_9_16,                \
-                                                                                        \
     MSER_DEFAULT
 
 #define DETECTORS_EXTRACTORS                                                            \
     ORB_DEFAULT, ORB_1500_13_1,                                                         \
-    AKAZE_DEFAULT, AKAZE_DESCRIPTOR_KAZE,                                               \
-    BRISK_DEFAULT,                                                                      \
-    KAZE_DEFAULT,                                                                       \
     SIFT_DEFAULT
 
 #define CV_ENUM_EXPAND(name, ...) CV_ENUM(name, __VA_ARGS__)
@@ -58,24 +53,6 @@ static inline Ptr<Feature2D> getFeature2D(Feature2DType type)
         return FastFeatureDetector::create(20, false, FastFeatureDetector::TYPE_7_12);
     case FAST_20_FALSE_TYPE9_16:
         return FastFeatureDetector::create(20, false, FastFeatureDetector::TYPE_9_16);
-    case AGAST_DEFAULT:
-        return AgastFeatureDetector::create();
-    case AGAST_5_8:
-        return AgastFeatureDetector::create(70, true, AgastFeatureDetector::AGAST_5_8);
-    case AGAST_7_12d:
-        return AgastFeatureDetector::create(70, true, AgastFeatureDetector::AGAST_7_12d);
-    case AGAST_7_12s:
-        return AgastFeatureDetector::create(70, true, AgastFeatureDetector::AGAST_7_12s);
-    case AGAST_OAST_9_16:
-        return AgastFeatureDetector::create(70, true, AgastFeatureDetector::OAST_9_16);
-    case AKAZE_DEFAULT:
-        return AKAZE::create();
-    case AKAZE_DESCRIPTOR_KAZE:
-        return AKAZE::create(AKAZE::DESCRIPTOR_KAZE);
-    case BRISK_DEFAULT:
-        return BRISK::create();
-    case KAZE_DEFAULT:
-        return KAZE::create();
     case MSER_DEFAULT:
         return MSER::create();
     case SIFT_DEFAULT:

modules/features2d/src/agast_score.hpp

@@ -1,69 +0,0 @@
-/* This is AGAST and OAST, an optimal and accelerated corner detector
-              based on the accelerated segment tests
-   Below is the original copyright and the references */
-
-/*
-Copyright (C) 2010  Elmar Mair
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions
-are met:
-
-    *Redistributions of source code must retain the above copyright
-     notice, this list of conditions and the following disclaimer.
-
-    *Redistributions in binary form must reproduce the above copyright
-     notice, this list of conditions and the following disclaimer in the
-     documentation and/or other materials provided with the distribution.
-
-    *Neither the name of the University of Cambridge nor the names of
-     its contributors may be used to endorse or promote products derived
-     from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
-CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
-PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
-LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
-NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-*/
-
-/*
-The references are:
- * Adaptive and Generic Corner Detection Based on the Accelerated Segment Test,
-   Elmar Mair and Gregory D. Hager and Darius Burschka
-   and Michael Suppa and Gerhard Hirzinger ECCV 2010
-   URL: http://www6.in.tum.de/Main/ResearchAgast
-*/
-
-
-#ifndef __OPENCV_FEATURES_2D_AGAST_HPP__
-#define __OPENCV_FEATURES_2D_AGAST_HPP__
-
-#ifdef __cplusplus
-
-#include "precomp.hpp"
-namespace cv
-{
-
-#if !(defined __i386__ || defined(_M_IX86) || defined __x86_64__ || defined(_M_X64))
-int agast_tree_search(const uint32_t table_struct32[], int pixel_[], const unsigned char* const ptr, int threshold);
-int AGAST_ALL_SCORE(const uchar* ptr, const int pixel[], int threshold, AgastFeatureDetector::DetectorType agasttype);
-#endif //!(defined __i386__ || defined(_M_IX86) || defined __x86_64__ || defined(_M_X64))
-
-
-void makeAgastOffsets(int pixel[16], int row_stride, AgastFeatureDetector::DetectorType type);
-
-template<AgastFeatureDetector::DetectorType type>
-int agast_cornerScore(const uchar* ptr, const int pixel[], int threshold);
-
-
-}
-#endif
-#endif

modules/features2d/src/akaze.cpp

@@ -1,276 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2008, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of Intel Corporation may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-/*
-OpenCV wrapper of reference implementation of
-[1] Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces.
-Pablo F. Alcantarilla, J. Nuevo and Adrien Bartoli.
-In British Machine Vision Conference (BMVC), Bristol, UK, September 2013
-http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla13bmvc.pdf
-@author Eugene Khvedchenya <ekhvedchenya@gmail.com>
-*/
-
-#include "precomp.hpp"
-#include "kaze/AKAZEFeatures.h"
-
-#include <iostream>
-
-namespace cv
-{
-    using namespace std;
-
-    class AKAZE_Impl : public AKAZE
-    {
-    public:
-        AKAZE_Impl(DescriptorType _descriptor_type, int _descriptor_size, int _descriptor_channels,
-                 float _threshold, int _octaves, int _sublevels, KAZE::DiffusivityType _diffusivity, int _max_points)
-        : descriptor(_descriptor_type)
-        , descriptor_channels(_descriptor_channels)
-        , descriptor_size(_descriptor_size)
-        , threshold(_threshold)
-        , octaves(_octaves)
-        , sublevels(_sublevels)
-        , diffusivity(_diffusivity)
-        , max_points(_max_points)
-        {
-        }
-
-        virtual ~AKAZE_Impl() CV_OVERRIDE
-        {
-
-        }
-
-        void setDescriptorType(DescriptorType dtype) CV_OVERRIDE{ descriptor = dtype; }
-        DescriptorType getDescriptorType() const CV_OVERRIDE{ return descriptor; }
-
-        void setDescriptorSize(int dsize) CV_OVERRIDE { descriptor_size = dsize; }
-        int getDescriptorSize() const CV_OVERRIDE { return descriptor_size; }
-
-        void setDescriptorChannels(int dch) CV_OVERRIDE { descriptor_channels = dch; }
-        int getDescriptorChannels() const CV_OVERRIDE { return descriptor_channels; }
-
-        void setThreshold(double threshold_) CV_OVERRIDE { threshold = (float)threshold_; }
-        double getThreshold() const CV_OVERRIDE { return threshold; }
-
-        void setNOctaves(int octaves_) CV_OVERRIDE { octaves = octaves_; }
-        int getNOctaves() const CV_OVERRIDE { return octaves; }
-
-        void setNOctaveLayers(int octaveLayers_) CV_OVERRIDE { sublevels = octaveLayers_; }
-        int getNOctaveLayers() const CV_OVERRIDE { return sublevels; }
-
-        void setDiffusivity(KAZE::DiffusivityType diff_) CV_OVERRIDE{ diffusivity = diff_; }
-        KAZE::DiffusivityType getDiffusivity() const CV_OVERRIDE{ return diffusivity; }
-
-        void setMaxPoints(int max_points_) CV_OVERRIDE { max_points = max_points_; }
-        int getMaxPoints() const CV_OVERRIDE { return max_points; }
-
-        // returns the descriptor size in bytes
-        int descriptorSize() const CV_OVERRIDE
-        {
-            switch (descriptor)
-            {
-            case DESCRIPTOR_KAZE:
-            case DESCRIPTOR_KAZE_UPRIGHT:
-                return 64;
-
-            case DESCRIPTOR_MLDB:
-            case DESCRIPTOR_MLDB_UPRIGHT:
-                // We use the full length binary descriptor -> 486 bits
-                if (descriptor_size == 0)
-                {
-                    int t = (6 + 36 + 120) * descriptor_channels;
-                    return divUp(t, 8);
-                }
-                else
-                {
-                    // We use the random bit selection length binary descriptor
-                    return divUp(descriptor_size, 8);
-                }
-
-            default:
-                return -1;
-            }
-        }
-
-        // returns the descriptor type
-        int descriptorType() const CV_OVERRIDE
-        {
-            switch (descriptor)
-            {
-            case DESCRIPTOR_KAZE:
-            case DESCRIPTOR_KAZE_UPRIGHT:
-                    return CV_32F;
-
-            case DESCRIPTOR_MLDB:
-            case DESCRIPTOR_MLDB_UPRIGHT:
-                    return CV_8U;
-
-                default:
-                    return -1;
-            }
-        }
-
-        // returns the default norm type
-        int defaultNorm() const CV_OVERRIDE
-        {
-            switch (descriptor)
-            {
-            case DESCRIPTOR_KAZE:
-            case DESCRIPTOR_KAZE_UPRIGHT:
-                return NORM_L2;
-
-            case DESCRIPTOR_MLDB:
-            case DESCRIPTOR_MLDB_UPRIGHT:
-                return NORM_HAMMING;
-
-            default:
-                return -1;
-            }
-        }
-
-        void detectAndCompute(InputArray image, InputArray mask,
-                              std::vector<KeyPoint>& keypoints,
-                              OutputArray descriptors,
-                              bool useProvidedKeypoints) CV_OVERRIDE
-        {
-            CV_INSTRUMENT_REGION();
-
-            CV_Assert( ! image.empty() );
-
-            AKAZEOptions options;
-            options.descriptor = descriptor;
-            options.descriptor_channels = descriptor_channels;
-            options.descriptor_size = descriptor_size;
-            options.img_width = image.cols();
-            options.img_height = image.rows();
-            options.dthreshold = threshold;
-            options.omax = octaves;
-            options.nsublevels = sublevels;
-            options.diffusivity = diffusivity;
-
-            AKAZEFeatures impl(options);
-            impl.Create_Nonlinear_Scale_Space(image);
-
-            if (!useProvidedKeypoints)
-            {
-                impl.Feature_Detection(keypoints);
-            }
-
-            if (!mask.empty())
-            {
-                KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
-            }
-
-            if (max_points > 0 && (int)keypoints.size() > max_points) {
-                std::partial_sort(keypoints.begin(), keypoints.begin() + max_points, keypoints.end(),
-                    [](const cv::KeyPoint& k1, const cv::KeyPoint& k2) {return k1.response > k2.response;});
-                keypoints.erase(keypoints.begin() + max_points, keypoints.end());
-            }
-
-            if(descriptors.needed())
-            {
-                impl.Compute_Descriptors(keypoints, descriptors);
-
-                CV_Assert((descriptors.empty() || descriptors.cols() == descriptorSize()));
-                CV_Assert((descriptors.empty() || (descriptors.type() == descriptorType())));
-            }
-        }
-
-        void write(FileStorage& fs) const CV_OVERRIDE
-        {
-            writeFormat(fs);
-            fs << "name" << getDefaultName();
-            fs << "descriptor" << descriptor;
-            fs << "descriptor_channels" << descriptor_channels;
-            fs << "descriptor_size" << descriptor_size;
-            fs << "threshold" << threshold;
-            fs << "octaves" << octaves;
-            fs << "sublevels" << sublevels;
-            fs << "diffusivity" << diffusivity;
-            fs << "max_points" << max_points;
-        }
-
-        void read(const FileNode& fn) CV_OVERRIDE
-        {
-            // if node is empty, keep previous value
-            if (!fn["descriptor"].empty())
-                descriptor = static_cast<DescriptorType>((int)fn["descriptor"]);
-            if (!fn["descriptor_channels"].empty())
-                descriptor_channels = (int)fn["descriptor_channels"];
-            if (!fn["descriptor_size"].empty())
-                descriptor_size = (int)fn["descriptor_size"];
-            if (!fn["threshold"].empty())
-                threshold = (float)fn["threshold"];
-            if (!fn["octaves"].empty())
-                octaves = (int)fn["octaves"];
-            if (!fn["sublevels"].empty())
-                sublevels = (int)fn["sublevels"];
-            if (!fn["diffusivity"].empty())
-                diffusivity = static_cast<KAZE::DiffusivityType>((int)fn["diffusivity"]);
-            if (!fn["max_points"].empty())
-                max_points = (int)fn["max_points"];
-        }
-
-        DescriptorType descriptor;
-        int descriptor_channels;
-        int descriptor_size;
-        float threshold;
-        int octaves;
-        int sublevels;
-        KAZE::DiffusivityType diffusivity;
-        int max_points;
-    };
-
-    Ptr<AKAZE> AKAZE::create(DescriptorType descriptor_type,
-                             int descriptor_size, int descriptor_channels,
-                             float threshold, int octaves,
-                             int sublevels, KAZE::DiffusivityType diffusivity, int max_points)
-    {
-        return makePtr<AKAZE_Impl>(descriptor_type, descriptor_size, descriptor_channels,
-                                   threshold, octaves, sublevels, diffusivity, max_points);
-    }
-
-    String AKAZE::getDefaultName() const
-    {
-        return (Feature2D::getDefaultName() + ".AKAZE");
-    }
-
-}

modules/features2d/src/bagofwords.cpp

@@ -1,216 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                        Intel License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of Intel Corporation may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "precomp.hpp"
-
-namespace cv
-{
-
-BOWTrainer::BOWTrainer() : size(0)
-{}
-
-BOWTrainer::~BOWTrainer()
-{}
-
-void BOWTrainer::add( const Mat& _descriptors )
-{
-    CV_Assert( !_descriptors.empty() );
-    if( !descriptors.empty() )
-    {
-        CV_Assert( descriptors[0].cols == _descriptors.cols );
-        CV_Assert( descriptors[0].type() == _descriptors.type() );
-        size += _descriptors.rows;
-    }
-    else
-    {
-        size = _descriptors.rows;
-    }
-
-    descriptors.push_back(_descriptors);
-}
-
-const std::vector<Mat>& BOWTrainer::getDescriptors() const
-{
-    return descriptors;
-}
-
-int BOWTrainer::descriptorsCount() const
-{
-    return descriptors.empty() ? 0 : size;
-}
-
-void BOWTrainer::clear()
-{
-    descriptors.clear();
-}
-
-BOWKMeansTrainer::BOWKMeansTrainer( int _clusterCount, const TermCriteria& _termcrit,
-                                    int _attempts, int _flags ) :
-    clusterCount(_clusterCount), termcrit(_termcrit), attempts(_attempts), flags(_flags)
-{}
-
-Mat BOWKMeansTrainer::cluster() const
-{
-    CV_INSTRUMENT_REGION();
-
-    CV_Assert( !descriptors.empty() );
-
-    Mat mergedDescriptors( descriptorsCount(), descriptors[0].cols, descriptors[0].type() );
-    for( size_t i = 0, start = 0; i < descriptors.size(); i++ )
-    {
-        Mat submut = mergedDescriptors.rowRange((int)start, (int)(start + descriptors[i].rows));
-        descriptors[i].copyTo(submut);
-        start += descriptors[i].rows;
-    }
-    return cluster( mergedDescriptors );
-}
-
-BOWKMeansTrainer::~BOWKMeansTrainer()
-{}
-
-Mat BOWKMeansTrainer::cluster( const Mat& _descriptors ) const
-{
-    CV_INSTRUMENT_REGION();
-
-    Mat labels, vocabulary;
-    kmeans( _descriptors, clusterCount, labels, termcrit, attempts, flags, vocabulary );
-    return vocabulary;
-}
-
-
-BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& _dextractor,
-                                                      const Ptr<DescriptorMatcher>& _dmatcher ) :
-    dextractor(_dextractor), dmatcher(_dmatcher)
-{}
-
-BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorMatcher>& _dmatcher ) :
-    dmatcher(_dmatcher)
-{}
-
-BOWImgDescriptorExtractor::~BOWImgDescriptorExtractor()
-{}
-
-void BOWImgDescriptorExtractor::setVocabulary( const Mat& _vocabulary )
-{
-    dmatcher->clear();
-    vocabulary = _vocabulary;
-    dmatcher->add( std::vector<Mat>(1, vocabulary) );
-}
-
-const Mat& BOWImgDescriptorExtractor::getVocabulary() const
-{
-    return vocabulary;
-}
-
-void BOWImgDescriptorExtractor::compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray imgDescriptor,
-                                         std::vector<std::vector<int> >* pointIdxsOfClusters, Mat* descriptors )
-{
-    CV_INSTRUMENT_REGION();
-
-    imgDescriptor.release();
-
-    if( keypoints.empty() )
-        return;
-
-    // Compute descriptors for the image.
-    Mat _descriptors;
-    dextractor->compute( image, keypoints, _descriptors );
-
-    compute( _descriptors, imgDescriptor, pointIdxsOfClusters );
-
-    // Add the descriptors of image keypoints
-    if (descriptors) {
-        *descriptors = _descriptors.clone();
-    }
-}
-
-int BOWImgDescriptorExtractor::descriptorSize() const
-{
-    return vocabulary.empty() ? 0 : vocabulary.rows;
-}
-
-int BOWImgDescriptorExtractor::descriptorType() const
-{
-    return CV_32FC1;
-}
-
-void BOWImgDescriptorExtractor::compute( InputArray keypointDescriptors, OutputArray _imgDescriptor, std::vector<std::vector<int> >* pointIdxsOfClusters )
-{
-    CV_INSTRUMENT_REGION();
-
-    CV_Assert( !vocabulary.empty() );
-    CV_Assert(!keypointDescriptors.empty());
-
-    int clusterCount = descriptorSize(); // = vocabulary.rows
-
-    // Match keypoint descriptors to cluster center (to vocabulary)
-    std::vector<DMatch> matches;
-    dmatcher->match( keypointDescriptors, matches );
-
-    // Compute image descriptor
-    if( pointIdxsOfClusters )
-    {
-        pointIdxsOfClusters->clear();
-        pointIdxsOfClusters->resize(clusterCount);
-    }
-
-    _imgDescriptor.create(1, clusterCount, descriptorType());
-    _imgDescriptor.setTo(Scalar::all(0));
-
-    Mat imgDescriptor = _imgDescriptor.getMat();
-
-    float *dptr = imgDescriptor.ptr<float>();
-    for( size_t i = 0; i < matches.size(); i++ )
-    {
-        int queryIdx = matches[i].queryIdx;
-        int trainIdx = matches[i].trainIdx; // cluster index
-        CV_Assert( queryIdx == (int)i );
-
-        dptr[trainIdx] = dptr[trainIdx] + 1.f;
-        if( pointIdxsOfClusters )
-            (*pointIdxsOfClusters)[trainIdx].push_back( queryIdx );
-    }
-
-    // Normalize image descriptor.
-    imgDescriptor /= keypointDescriptors.size().height;
-}
-
-}

modules/features2d/src/kaze.cpp

@@ -1,213 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2008, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of Intel Corporation may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-/*
-OpenCV wrapper of reference implementation of
-[1] KAZE Features. Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison.
-In European Conference on Computer Vision (ECCV), Fiorenze, Italy, October 2012
-http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla12eccv.pdf
-@author Eugene Khvedchenya <ekhvedchenya@gmail.com>
-*/
-
-#include "precomp.hpp"
-#include "kaze/KAZEFeatures.h"
-
-namespace cv
-{
-
-    class KAZE_Impl CV_FINAL : public KAZE
-    {
-    public:
-        KAZE_Impl(bool _extended, bool _upright, float _threshold, int _octaves,
-                   int _sublevels, KAZE::DiffusivityType _diffusivity)
-        : extended(_extended)
-        , upright(_upright)
-        , threshold(_threshold)
-        , octaves(_octaves)
-        , sublevels(_sublevels)
-        , diffusivity(_diffusivity)
-        {
-        }
-
-        virtual ~KAZE_Impl() CV_OVERRIDE {}
-
-        void setExtended(bool extended_) CV_OVERRIDE { extended = extended_; }
-        bool getExtended() const CV_OVERRIDE { return extended; }
-
-        void setUpright(bool upright_) CV_OVERRIDE { upright = upright_; }
-        bool getUpright() const CV_OVERRIDE { return upright; }
-
-        void setThreshold(double threshold_) CV_OVERRIDE { threshold = (float)threshold_; }
-        double getThreshold() const CV_OVERRIDE { return threshold; }
-
-        void setNOctaves(int octaves_) CV_OVERRIDE { octaves = octaves_; }
-        int getNOctaves() const CV_OVERRIDE { return octaves; }
-
-        void setNOctaveLayers(int octaveLayers_) CV_OVERRIDE { sublevels = octaveLayers_; }
-        int getNOctaveLayers() const CV_OVERRIDE { return sublevels; }
-
-        void setDiffusivity(KAZE::DiffusivityType diff_) CV_OVERRIDE{ diffusivity = diff_; }
-        KAZE::DiffusivityType getDiffusivity() const CV_OVERRIDE{ return diffusivity; }
-
-        // returns the descriptor size in bytes
-        int descriptorSize() const CV_OVERRIDE
-        {
-            return extended ? 128 : 64;
-        }
-
-        // returns the descriptor type
-        int descriptorType() const CV_OVERRIDE
-        {
-            return CV_32F;
-        }
-
-        // returns the default norm type
-        int defaultNorm() const CV_OVERRIDE
-        {
-            return NORM_L2;
-        }
-
-        void detectAndCompute(InputArray image, InputArray mask,
-                              std::vector<KeyPoint>& keypoints,
-                              OutputArray descriptors,
-                              bool useProvidedKeypoints) CV_OVERRIDE
-        {
-            CV_INSTRUMENT_REGION();
-
-            cv::Mat img = image.getMat();
-            if (img.channels() > 1)
-                cvtColor(image, img, COLOR_BGR2GRAY);
-
-            Mat img1_32;
-            if ( img.depth() == CV_32F )
-                img1_32 = img;
-            else if ( img.depth() == CV_8U )
-                img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
-            else if ( img.depth() == CV_16U )
-                img.convertTo(img1_32, CV_32F, 1.0 / 65535.0, 0);
-
-            CV_Assert( ! img1_32.empty() );
-
-            KAZEOptions options;
-            options.img_width = img.cols;
-            options.img_height = img.rows;
-            options.extended = extended;
-            options.upright = upright;
-            options.dthreshold = threshold;
-            options.omax = octaves;
-            options.nsublevels = sublevels;
-            options.diffusivity = diffusivity;
-
-            KAZEFeatures impl(options);
-            impl.Create_Nonlinear_Scale_Space(img1_32);
-
-            if (!useProvidedKeypoints)
-            {
-                impl.Feature_Detection(keypoints);
-            }
-
-            if (!mask.empty())
-            {
-                cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
-            }
-
-            if( descriptors.needed() )
-            {
-                Mat desc;
-                impl.Feature_Description(keypoints, desc);
-                desc.copyTo(descriptors);
-
-                CV_Assert((!desc.rows || desc.cols == descriptorSize()));
-                CV_Assert((!desc.rows || (desc.type() == descriptorType())));
-            }
-        }
-
-        void write(FileStorage& fs) const CV_OVERRIDE
-        {
-            writeFormat(fs);
-            fs << "name" << getDefaultName();
-            fs << "extended" << (int)extended;
-            fs << "upright" << (int)upright;
-            fs << "threshold" << threshold;
-            fs << "octaves" << octaves;
-            fs << "sublevels" << sublevels;
-            fs << "diffusivity" << diffusivity;
-        }
-
-        void read(const FileNode& fn) CV_OVERRIDE
-        {
-            // if node is empty, keep previous value
-            if (!fn["extended"].empty())
-                extended = (int)fn["extended"] != 0;
-            if (!fn["upright"].empty())
-                upright = (int)fn["upright"] != 0;
-            if (!fn["threshold"].empty())
-                threshold = (float)fn["threshold"];
-            if (!fn["octaves"].empty())
-                octaves = (int)fn["octaves"];
-            if (!fn["sublevels"].empty())
-                sublevels = (int)fn["sublevels"];
-            if (!fn["diffusivity"].empty())
-                diffusivity = static_cast<KAZE::DiffusivityType>((int)fn["diffusivity"]);
-        }
-
-        bool extended;
-        bool upright;
-        float threshold;
-        int octaves;
-        int sublevels;
-        KAZE::DiffusivityType diffusivity;
-    };
-
-    Ptr<KAZE> KAZE::create(bool extended, bool upright,
-                            float threshold,
-                            int octaves, int sublevels,
-                            KAZE::DiffusivityType diffusivity)
-    {
-        return makePtr<KAZE_Impl>(extended, upright, threshold, octaves, sublevels, diffusivity);
-    }
-
-    String KAZE::getDefaultName() const
-    {
-        return (Feature2D::getDefaultName() + ".KAZE");
-    }
-
-}

modules/features2d/src/kaze/AKAZEConfig.h

@@ -1,65 +0,0 @@
-/**
- * @file AKAZEConfig.h
- * @brief AKAZE configuration file
- * @date Feb 23, 2014
- * @author Pablo F. Alcantarilla, Jesus Nuevo
- */
-
-#ifndef __OPENCV_FEATURES_2D_AKAZE_CONFIG_H__
-#define __OPENCV_FEATURES_2D_AKAZE_CONFIG_H__
-
-namespace cv
-{
-/* ************************************************************************* */
-/// AKAZE configuration options structure
-struct AKAZEOptions {
-
-    AKAZEOptions()
-        : omax(4)
-        , nsublevels(4)
-        , img_width(0)
-        , img_height(0)
-        , soffset(1.6f)
-        , derivative_factor(1.5f)
-        , sderivatives(1.0)
-        , diffusivity(KAZE::DIFF_PM_G2)
-
-        , dthreshold(0.001f)
-        , min_dthreshold(0.00001f)
-
-        , descriptor(AKAZE::DESCRIPTOR_MLDB)
-        , descriptor_size(0)
-        , descriptor_channels(3)
-        , descriptor_pattern_size(10)
-
-        , kcontrast(0.001f)
-        , kcontrast_percentile(0.7f)
-        , kcontrast_nbins(300)
-    {
-    }
-
-    int omax;                       ///< Maximum octave evolution of the image 2^sigma (coarsest scale sigma units)
-    int nsublevels;                 ///< Default number of sublevels per scale level
-    int img_width;                  ///< Width of the input image
-    int img_height;                 ///< Height of the input image
-    float soffset;                  ///< Base scale offset (sigma units)
-    float derivative_factor;        ///< Factor for the multiscale derivatives
-    float sderivatives;             ///< Smoothing factor for the derivatives
-    KAZE::DiffusivityType diffusivity;   ///< Diffusivity type
-
-    float dthreshold;               ///< Detector response threshold to accept point
-    float min_dthreshold;           ///< Minimum detector threshold to accept a point
-
-    AKAZE::DescriptorType descriptor;     ///< Type of descriptor
-    int descriptor_size;            ///< Size of the descriptor in bits. 0->Full size
-    int descriptor_channels;        ///< Number of channels in the descriptor (1, 2, 3)
-    int descriptor_pattern_size;    ///< Actual patch size is 2*pattern_size*point.scale
-
-    float kcontrast;                ///< The contrast factor parameter
-    float kcontrast_percentile;     ///< Percentile level for the contrast factor
-    int kcontrast_nbins;            ///< Number of bins for the contrast factor histogram
-};
-
-}
-
-#endif

modules/features2d/src/kaze/AKAZEFeatures.h

@@ -1,115 +0,0 @@
-/**
- * @file AKAZE.h
- * @brief Main class for detecting and computing binary descriptors in an
- * accelerated nonlinear scale space
- * @date Mar 27, 2013
- * @author Pablo F. Alcantarilla, Jesus Nuevo
- */
-
-#ifndef __OPENCV_FEATURES_2D_AKAZE_FEATURES_H__
-#define __OPENCV_FEATURES_2D_AKAZE_FEATURES_H__
-
-/* ************************************************************************* */
-// Includes
-#include "AKAZEConfig.h"
-
-namespace cv
-{
-
-/// A-KAZE nonlinear diffusion filtering evolution
-template <typename MatType>
-struct Evolution
-{
-  Evolution() {
-    etime = 0.0f;
-    esigma = 0.0f;
-    octave = 0;
-    sublevel = 0;
-    sigma_size = 0;
-    octave_ratio = 0.0f;
-    border = 0;
-  }
-
-  template <typename T>
-  explicit Evolution(const Evolution<T> &other) {
-    size = other.size;
-    etime = other.etime;
-    esigma = other.esigma;
-    octave = other.octave;
-    sublevel = other.sublevel;
-    sigma_size = other.sigma_size;
-    octave_ratio = other.octave_ratio;
-    border = other.border;
-
-    other.Lx.copyTo(Lx);
-    other.Ly.copyTo(Ly);
-    other.Lt.copyTo(Lt);
-    other.Lsmooth.copyTo(Lsmooth);
-    other.Ldet.copyTo(Ldet);
-  }
-
-  MatType Lx, Ly;           ///< First order spatial derivatives
-  MatType Lt;               ///< Evolution image
-  MatType Lsmooth;          ///< Smoothed image, used only for computing determinant, released afterwards
-  MatType Ldet;             ///< Detector response
-
-  Size size;                ///< Size of the layer
-  float etime;              ///< Evolution time
-  float esigma;             ///< Evolution sigma. For linear diffusion t = sigma^2 / 2
-  int octave;               ///< Image octave
-  int sublevel;             ///< Image sublevel in each octave
-  int sigma_size;           ///< Integer esigma. For computing the feature detector responses
-  float octave_ratio;       ///< Scaling ratio of this octave. ratio = 2^octave
-  int border;               ///< Width of border where descriptors cannot be computed
-};
-
-typedef Evolution<Mat> MEvolution;
-typedef Evolution<UMat> UEvolution;
-typedef std::vector<MEvolution> Pyramid;
-typedef std::vector<UEvolution> UMatPyramid;
-
-/* ************************************************************************* */
-// AKAZE Class Declaration
-class AKAZEFeatures {
-
-private:
-
-  AKAZEOptions options_;                ///< Configuration options for AKAZE
-  Pyramid evolution_;        ///< Vector of nonlinear diffusion evolution
-
-  /// FED parameters
-  int ncycles_;                  ///< Number of cycles
-  bool reordering_;              ///< Flag for reordering time steps
-  std::vector<std::vector<float > > tsteps_;  ///< Vector of FED dynamic time steps
-  std::vector<int> nsteps_;      ///< Vector of number of steps per cycle
-
-  /// Matrices for the M-LDB descriptor computation
-  cv::Mat descriptorSamples_;  // List of positions in the grids to sample LDB bits from.
-  cv::Mat descriptorBits_;
-  cv::Mat bitMask_;
-
-  /// Scale Space methods
-  void Allocate_Memory_Evolution();
-  void Find_Scale_Space_Extrema(std::vector<Mat>& keypoints_by_layers);
-  void Do_Subpixel_Refinement(std::vector<Mat>& keypoints_by_layers,
-    std::vector<KeyPoint>& kpts);
-
-  /// Feature description methods
-  void Compute_Keypoints_Orientation(std::vector<cv::KeyPoint>& kpts) const;
-
-public:
-  /// Constructor with input arguments
-  AKAZEFeatures(const AKAZEOptions& options);
-  void Create_Nonlinear_Scale_Space(InputArray img);
-  void Feature_Detection(std::vector<cv::KeyPoint>& kpts);
-  void Compute_Descriptors(std::vector<cv::KeyPoint>& kpts, OutputArray desc);
-};
-
-/* ************************************************************************* */
-/// Inline functions
-void generateDescriptorSubsample(cv::Mat& sampleList, cv::Mat& comparisons,
-                                 int nbits, int pattern_size, int nchannels);
-
-}
-
-#endif

modules/features2d/src/kaze/KAZEConfig.h

@@ -1,56 +0,0 @@
-/**
- * @file KAZEConfig.h
- * @brief Configuration file
- * @date Dec 27, 2011
- * @author Pablo F. Alcantarilla
- */
-
-#ifndef __OPENCV_FEATURES_2D_KAZE_CONFIG_H__
-#define __OPENCV_FEATURES_2D_KAZE_CONFIG_H__
-
-// OpenCV Includes
-#include "../precomp.hpp"
-#include <opencv2/features2d.hpp>
-
-namespace cv
-{
-//*************************************************************************************
-
-struct KAZEOptions {
-
-    KAZEOptions()
-        : diffusivity(KAZE::DIFF_PM_G2)
-
-        , soffset(1.60f)
-        , omax(4)
-        , nsublevels(4)
-        , img_width(0)
-        , img_height(0)
-        , sderivatives(1.0f)
-        , dthreshold(0.001f)
-        , kcontrast(0.01f)
-        , kcontrast_percentille(0.7f)
-                , kcontrast_bins(300)
-        , upright(false)
-        , extended(false)
-    {
-    }
-
-    KAZE::DiffusivityType diffusivity;
-    float soffset;
-    int omax;
-    int nsublevels;
-    int img_width;
-    int img_height;
-    float sderivatives;
-    float dthreshold;
-    float kcontrast;
-    float kcontrast_percentille;
-    int  kcontrast_bins;
-    bool upright;
-    bool extended;
-};
-
-}
-
-#endif

modules/features2d/src/kaze/KAZEFeatures.h

@@ -1,64 +0,0 @@
-
-/**
- * @file KAZE.h
- * @brief Main program for detecting and computing descriptors in a nonlinear
- * scale space
- * @date Jan 21, 2012
- * @author Pablo F. Alcantarilla
- */
-
-#ifndef __OPENCV_FEATURES_2D_KAZE_FEATURES_H__
-#define __OPENCV_FEATURES_2D_KAZE_FEATURES_H__
-
-/* ************************************************************************* */
-// Includes
-#include "KAZEConfig.h"
-#include "nldiffusion_functions.h"
-#include "fed.h"
-#include "TEvolution.h"
-
-namespace cv
-{
-
-/* ************************************************************************* */
-// KAZE Class Declaration
-class KAZEFeatures
-{
-private:
-
-    /// Parameters of the Nonlinear diffusion class
-    KAZEOptions options_;               ///< Configuration options for KAZE
-    std::vector<TEvolution> evolution_;    ///< Vector of nonlinear diffusion evolution
-
-    /// Vector of keypoint vectors for finding extrema in multiple threads
-    std::vector<std::vector<cv::KeyPoint> > kpts_par_;
-
-    /// FED parameters
-    int ncycles_;                  ///< Number of cycles
-    bool reordering_;              ///< Flag for reordering time steps
-    std::vector<std::vector<float > > tsteps_;  ///< Vector of FED dynamic time steps
-    std::vector<int> nsteps_;      ///< Vector of number of steps per cycle
-
-public:
-
-    /// Constructor
-    KAZEFeatures(KAZEOptions& options);
-
-    /// Public methods for KAZE interface
-    void Allocate_Memory_Evolution(void);
-    int Create_Nonlinear_Scale_Space(const cv::Mat& img);
-    void Feature_Detection(std::vector<cv::KeyPoint>& kpts);
-    void Feature_Description(std::vector<cv::KeyPoint>& kpts, cv::Mat& desc);
-    static void Compute_Main_Orientation(cv::KeyPoint& kpt, const std::vector<TEvolution>& evolution_, const KAZEOptions& options);
-
-    /// Feature Detection Methods
-    void Compute_KContrast(const cv::Mat& img, const float& kper);
-    void Compute_Multiscale_Derivatives(void);
-    void Compute_Detector_Response(void);
-    void Determinant_Hessian(std::vector<cv::KeyPoint>& kpts);
-    void Do_Subpixel_Refinement(std::vector<cv::KeyPoint>& kpts);
-};
-
-}
-
-#endif

modules/features2d/src/kaze/TEvolution.h

@@ -1,41 +0,0 @@
-/**
- * @file TEvolution.h
- * @brief Header file with the declaration of the TEvolution struct
- * @date Jun 02, 2014
- * @author Pablo F. Alcantarilla
- */
-
-#ifndef __OPENCV_FEATURES_2D_TEVOLUTION_H__
-#define __OPENCV_FEATURES_2D_TEVOLUTION_H__
-
-namespace cv
-{
-
-/* ************************************************************************* */
-/// KAZE/A-KAZE nonlinear diffusion filtering evolution
-struct TEvolution
-{
-  TEvolution() {
-    etime = 0.0f;
-    esigma = 0.0f;
-    octave = 0;
-    sublevel = 0;
-    sigma_size = 0;
-  }
-
-  Mat Lx, Ly;           ///< First order spatial derivatives
-  Mat Lxx, Lxy, Lyy;    ///< Second order spatial derivatives
-  Mat Lt;               ///< Evolution image
-  Mat Lsmooth;          ///< Smoothed image
-  Mat Ldet;             ///< Detector response
-
-  float etime;              ///< Evolution time
-  float esigma;             ///< Evolution sigma. For linear diffusion t = sigma^2 / 2
-  int octave;               ///< Image octave
-  int sublevel;             ///< Image sublevel in each octave
-  int sigma_size;           ///< Integer esigma. For computing the feature detector responses
-};
-
-}
-
-#endif

modules/features2d/src/kaze/fed.cpp

@@ -1,192 +0,0 @@
-//=============================================================================
-//
-// fed.cpp
-// Authors: Pablo F. Alcantarilla (1), Jesus Nuevo (2)
-// Institutions: Georgia Institute of Technology (1)
-//               TrueVision Solutions (2)
-// Date: 15/09/2013
-// Email: pablofdezalc@gmail.com
-//
-// AKAZE Features Copyright 2013, Pablo F. Alcantarilla, Jesus Nuevo
-// All Rights Reserved
-// See LICENSE for the license information
-//=============================================================================
-
-/**
- * @file fed.cpp
- * @brief Functions for performing Fast Explicit Diffusion and building the
- * nonlinear scale space
- * @date Sep 15, 2013
- * @author Pablo F. Alcantarilla, Jesus Nuevo
- * @note This code is derived from FED/FJ library from Grewenig et al.,
- * The FED/FJ library allows solving more advanced problems
- * Please look at the following papers for more information about FED:
- * [1] S. Grewenig, J. Weickert, C. Schroers, A. Bruhn. Cyclic Schemes for
- * PDE-Based Image Analysis. Technical Report No. 327, Department of Mathematics,
- * Saarland University, Saarbrücken, Germany, March 2013
- * [2] S. Grewenig, J. Weickert, A. Bruhn. From box filtering to fast explicit diffusion.
- * DAGM, 2010
- *
-*/
-#include "../precomp.hpp"
-#include "fed.h"
-
-using namespace std;
-
-//*************************************************************************************
-//*************************************************************************************
-
-/**
- * @brief This function allocates an array of the least number of time steps such
- * that a certain stopping time for the whole process can be obtained and fills
- * it with the respective FED time step sizes for one cycle
- * The function returns the number of time steps per cycle or 0 on failure
- * @param T Desired process stopping time
- * @param M Desired number of cycles
- * @param tau_max Stability limit for the explicit scheme
- * @param reordering Reordering flag
- * @param tau The vector with the dynamic step sizes
- */
-int fed_tau_by_process_time(const float& T, const int& M, const float& tau_max,
-                            const bool& reordering, std::vector<float>& tau) {
-  // All cycles have the same fraction of the stopping time
-  return fed_tau_by_cycle_time(T/(float)M,tau_max,reordering,tau);
-}
-
-//*************************************************************************************
-//*************************************************************************************
-
-/**
- * @brief This function allocates an array of the least number of time steps such
- * that a certain stopping time for the whole process can be obtained and fills it
- * it with the respective FED time step sizes for one cycle
- * The function returns the number of time steps per cycle or 0 on failure
- * @param t Desired cycle stopping time
- * @param tau_max Stability limit for the explicit scheme
- * @param reordering Reordering flag
- * @param tau The vector with the dynamic step sizes
- */
-int fed_tau_by_cycle_time(const float& t, const float& tau_max,
-                          const bool& reordering, std::vector<float> &tau) {
-  int n = 0;          // Number of time steps
-  float scale = 0.0;  // Ratio of t we search to maximal t
-
-  // Compute necessary number of time steps
-  n = cvCeil(sqrtf(3.0f*t/tau_max+0.25f)-0.5f-1.0e-8f);
-  scale = 3.0f*t/(tau_max*(float)(n*(n+1)));
-
-  // Call internal FED time step creation routine
-  return fed_tau_internal(n,scale,tau_max,reordering,tau);
-}
-
-//*************************************************************************************
-//*************************************************************************************
-
-/**
- * @brief This function allocates an array of time steps and fills it with FED
- * time step sizes
- * The function returns the number of time steps per cycle or 0 on failure
- * @param n Number of internal steps
- * @param scale Ratio of t we search to maximal t
- * @param tau_max Stability limit for the explicit scheme
- * @param reordering Reordering flag
- * @param tau The vector with the dynamic step sizes
- */
-int fed_tau_internal(const int& n, const float& scale, const float& tau_max,
-                     const bool& reordering, std::vector<float> &tau) {
-  float c = 0.0, d = 0.0;     // Time savers
-  vector<float> tauh;    // Helper vector for unsorted taus
-
-  if (n <= 0) {
-    return 0;
-  }
-
-  // Allocate memory for the time step size
-  tau = vector<float>(n);
-
-  if (reordering) {
-    tauh = vector<float>(n);
-  }
-
-  // Compute time saver
-  c = 1.0f / (4.0f * (float)n + 2.0f);
-  d = scale * tau_max / 2.0f;
-
-  // Set up originally ordered tau vector
-  for (int k = 0; k < n; ++k) {
-    float h = cosf((float)CV_PI * (2.0f * (float)k + 1.0f) * c);
-
-    if (reordering) {
-      tauh[k] = d / (h * h);
-    }
-    else {
-      tau[k] = d / (h * h);
-    }
-  }
-
-  // Permute list of time steps according to chosen reordering function
-  int kappa = 0, prime = 0;
-
-  if (reordering == true) {
-    // Choose kappa cycle with k = n/2
-    // This is a heuristic. We can use Leja ordering instead!!
-    kappa = n / 2;
-
-    // Get modulus for permutation
-    prime = n + 1;
-
-    while (!fed_is_prime_internal(prime)) {
-      prime++;
-    }
-
-    // Perform permutation
-    for (int k = 0, l = 0; l < n; ++k, ++l) {
-      int index = 0;
-      while ((index = ((k+1)*kappa) % prime - 1) >= n) {
-        k++;
-      }
-
-      tau[l] = tauh[index];
-    }
-  }
-
-  return n;
-}
-
-//*************************************************************************************
-//*************************************************************************************
-
-/**
- * @brief This function checks if a number is prime or not
- * @param number Number to check if it is prime or not
- * @return true if the number is prime
- */
-bool fed_is_prime_internal(const int& number) {
-  bool is_prime = false;
-
-  if (number <= 1) {
-    return false;
-  }
-  else if (number == 1 || number == 2 || number == 3 || number == 5 || number == 7) {
-    return true;
-  }
-  else if ((number % 2) == 0 || (number % 3) == 0 || (number % 5) == 0 || (number % 7) == 0) {
-    return false;
-  }
-  else {
-    is_prime = true;
-    int upperLimit = (int)sqrt(1.0f + number);
-    int divisor = 11;
-
-    while (divisor <= upperLimit ) {
-      if (number % divisor == 0)
-      {
-        is_prime = false;
-      }
-
-      divisor +=2;
-    }
-
-    return is_prime;
-  }
-}

modules/features2d/src/kaze/fed.h

@@ -1,25 +0,0 @@
-#ifndef __OPENCV_FEATURES_2D_FED_H__
-#define __OPENCV_FEATURES_2D_FED_H__
-
-//******************************************************************************
-//******************************************************************************
-
-// Includes
-#include <vector>
-
-//*************************************************************************************
-//*************************************************************************************
-
-// Declaration of functions
-int fed_tau_by_process_time(const float& T, const int& M, const float& tau_max,
-                            const bool& reordering, std::vector<float>& tau);
-int fed_tau_by_cycle_time(const float& t, const float& tau_max,
-                          const bool& reordering, std::vector<float> &tau) ;
-int fed_tau_internal(const int& n, const float& scale, const float& tau_max,
-                     const bool& reordering, std::vector<float> &tau);
-bool fed_is_prime_internal(const int& number);
-
-//*************************************************************************************
-//*************************************************************************************
-
-#endif // __OPENCV_FEATURES_2D_FED_H__

modules/features2d/src/kaze/nldiffusion_functions.cpp

@@ -1,542 +0,0 @@
-//=============================================================================
-//
-// nldiffusion_functions.cpp
-// Author: Pablo F. Alcantarilla
-// Institution: University d'Auvergne
-// Address: Clermont Ferrand, France
-// Date: 27/12/2011
-// Email: pablofdezalc@gmail.com
-//
-// KAZE Features Copyright 2012, Pablo F. Alcantarilla
-// All Rights Reserved
-// See LICENSE for the license information
-//=============================================================================
-
-/**
- * @file nldiffusion_functions.cpp
- * @brief Functions for non-linear diffusion applications:
- * 2D Gaussian Derivatives
- * Perona and Malik conductivity equations
- * Perona and Malik evolution
- * @date Dec 27, 2011
- * @author Pablo F. Alcantarilla
- */
-
-#include "../precomp.hpp"
-#include "nldiffusion_functions.h"
-#include <iostream>
-
-// Namespaces
-
-/* ************************************************************************* */
-
-namespace cv
-{
-using namespace std;
-
-/* ************************************************************************* */
-/**
- * @brief This function smoothes an image with a Gaussian kernel
- * @param src Input image
- * @param dst Output image
- * @param ksize_x Kernel size in X-direction (horizontal)
- * @param ksize_y Kernel size in Y-direction (vertical)
- * @param sigma Kernel standard deviation
- */
-void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int ksize_y, float sigma) {
-
-    int ksize_x_ = 0, ksize_y_ = 0;
-
-    // Compute an appropriate kernel size according to the specified sigma
-    if (sigma > ksize_x || sigma > ksize_y || ksize_x == 0 || ksize_y == 0) {
-        ksize_x_ = cvCeil(2.0f*(1.0f + (sigma - 0.8f) / (0.3f)));
-        ksize_y_ = ksize_x_;
-    }
-
-    // The kernel size must be and odd number
-    if ((ksize_x_ % 2) == 0) {
-        ksize_x_ += 1;
-    }
-
-    if ((ksize_y_ % 2) == 0) {
-        ksize_y_ += 1;
-    }
-
-    // Perform the Gaussian Smoothing with border replication
-    GaussianBlur(src, dst, Size(ksize_x_, ksize_y_), sigma, sigma, BORDER_REPLICATE);
-}
-
-/* ************************************************************************* */
-/**
- * @brief This function computes image derivatives with Scharr kernel
- * @param src Input image
- * @param dst Output image
- * @param xorder Derivative order in X-direction (horizontal)
- * @param yorder Derivative order in Y-direction (vertical)
- * @note Scharr operator approximates better rotation invariance than
- * other stencils such as Sobel. See Weickert and Scharr,
- * A Scheme for Coherence-Enhancing Diffusion Filtering with Optimized Rotation Invariance,
- * Journal of Visual Communication and Image Representation 2002
- */
-void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder) {
-    Scharr(src, dst, CV_32F, xorder, yorder, 1.0, 0, BORDER_DEFAULT);
-}
-
-/* ************************************************************************* */
-/**
- * @brief This function computes the Perona and Malik conductivity coefficient g1
- * g1 = exp(-|dL|^2/k^2)
- * @param _Lx First order image derivative in X-direction (horizontal)
- * @param _Ly First order image derivative in Y-direction (vertical)
- * @param _dst Output image
- * @param k Contrast factor parameter
- */
-void pm_g1(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
-  _dst.create(_Lx.size(), _Lx.type());
-  Mat Lx = _Lx.getMat();
-  Mat Ly = _Ly.getMat();
-  Mat dst = _dst.getMat();
-
-  Size sz = Lx.size();
-  float inv_k = 1.0f / (k*k);
-  for (int y = 0; y < sz.height; y++) {
-
-    const float* Lx_row = Lx.ptr<float>(y);
-    const float* Ly_row = Ly.ptr<float>(y);
-    float* dst_row = dst.ptr<float>(y);
-
-    for (int x = 0; x < sz.width; x++) {
-      dst_row[x] = (-inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]));
-    }
-  }
-
-  exp(dst, dst);
-}
-
-/* ************************************************************************* */
-/**
- * @brief This function computes the Perona and Malik conductivity coefficient g2
- * g2 = 1 / (1 + dL^2 / k^2)
- * @param _Lx First order image derivative in X-direction (horizontal)
- * @param _Ly First order image derivative in Y-direction (vertical)
- * @param _dst Output image
- * @param k Contrast factor parameter
- */
-void pm_g2(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
-    CV_INSTRUMENT_REGION();
-
-    _dst.create(_Lx.size(), _Lx.type());
-    Mat Lx = _Lx.getMat();
-    Mat Ly = _Ly.getMat();
-    Mat dst = _dst.getMat();
-
-    Size sz = Lx.size();
-    dst.create(sz, Lx.type());
-    float k2inv = 1.0f / (k * k);
-
-    for(int y = 0; y < sz.height; y++) {
-        const float *Lx_row = Lx.ptr<float>(y);
-        const float *Ly_row = Ly.ptr<float>(y);
-        float* dst_row = dst.ptr<float>(y);
-        for(int x = 0; x < sz.width; x++) {
-            dst_row[x] = 1.0f / (1.0f + ((Lx_row[x] * Lx_row[x] + Ly_row[x] * Ly_row[x]) * k2inv));
-        }
-    }
-}
-/* ************************************************************************* */
-/**
- * @brief This function computes Weickert conductivity coefficient gw
- * @param _Lx First order image derivative in X-direction (horizontal)
- * @param _Ly First order image derivative in Y-direction (vertical)
- * @param _dst Output image
- * @param k Contrast factor parameter
- * @note For more information check the following paper: J. Weickert
- * Applications of nonlinear diffusion in image processing and computer vision,
- * Proceedings of Algorithmy 2000
- */
-void weickert_diffusivity(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
-  _dst.create(_Lx.size(), _Lx.type());
-  Mat Lx = _Lx.getMat();
-  Mat Ly = _Ly.getMat();
-  Mat dst = _dst.getMat();
-
-  Size sz = Lx.size();
-  float inv_k = 1.0f / (k*k);
-  for (int y = 0; y < sz.height; y++) {
-
-    const float* Lx_row = Lx.ptr<float>(y);
-    const float* Ly_row = Ly.ptr<float>(y);
-    float* dst_row = dst.ptr<float>(y);
-
-    for (int x = 0; x < sz.width; x++) {
-      float dL = inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]);
-      dst_row[x] = -3.315f/(dL*dL*dL*dL);
-    }
-  }
-
-  exp(dst, dst);
-  dst = 1.0 - dst;
-}
-
-
-/* ************************************************************************* */
-/**
-* @brief This function computes Charbonnier conductivity coefficient gc
-* gc = 1 / sqrt(1 + dL^2 / k^2)
-* @param _Lx First order image derivative in X-direction (horizontal)
-* @param _Ly First order image derivative in Y-direction (vertical)
-* @param _dst Output image
-* @param k Contrast factor parameter
-* @note For more information check the following paper: J. Weickert
-* Applications of nonlinear diffusion in image processing and computer vision,
-* Proceedings of Algorithmy 2000
-*/
-void charbonnier_diffusivity(InputArray _Lx, InputArray _Ly, OutputArray _dst, float k) {
-  _dst.create(_Lx.size(), _Lx.type());
-  Mat Lx = _Lx.getMat();
-  Mat Ly = _Ly.getMat();
-  Mat dst = _dst.getMat();
-
-  Size sz = Lx.size();
-  float inv_k = 1.0f / (k*k);
-  for (int y = 0; y < sz.height; y++) {
-
-    const float* Lx_row = Lx.ptr<float>(y);
-    const float* Ly_row = Ly.ptr<float>(y);
-    float* dst_row = dst.ptr<float>(y);
-
-    for (int x = 0; x < sz.width; x++) {
-      float den = sqrt(1.0f+inv_k*(Lx_row[x]*Lx_row[x] + Ly_row[x]*Ly_row[x]));
-      dst_row[x] = 1.0f / den;
-    }
-  }
-}
-
-
-/* ************************************************************************* */
-/**
- * @brief This function computes a good empirical value for the k contrast factor
- * given an input image, the percentile (0-1), the gradient scale and the number of
- * bins in the histogram
- * @param img Input image
- * @param perc Percentile of the image gradient histogram (0-1)
- * @param gscale Scale for computing the image gradient histogram
- * @param nbins Number of histogram bins
- * @param ksize_x Kernel size in X-direction (horizontal) for the Gaussian smoothing kernel
- * @param ksize_y Kernel size in Y-direction (vertical) for the Gaussian smoothing kernel
- * @return k contrast factor
- */
-float compute_k_percentile(const cv::Mat& img, float perc, float gscale, int nbins, int ksize_x, int ksize_y) {
-    CV_INSTRUMENT_REGION();
-
-    int nbin = 0, nelements = 0, nthreshold = 0, k = 0;
-    float kperc = 0.0, modg = 0.0;
-    float npoints = 0.0;
-    float hmax = 0.0;
-
-    // Create the array for the histogram
-    std::vector<int> hist(nbins, 0);
-
-    // Create the matrices
-    Mat gaussian = Mat::zeros(img.rows, img.cols, CV_32F);
-    Mat Lx = Mat::zeros(img.rows, img.cols, CV_32F);
-    Mat Ly = Mat::zeros(img.rows, img.cols, CV_32F);
-
-    // Perform the Gaussian convolution
-    gaussian_2D_convolution(img, gaussian, ksize_x, ksize_y, gscale);
-
-    // Compute the Gaussian derivatives Lx and Ly
-    Scharr(gaussian, Lx, CV_32F, 1, 0, 1, 0, cv::BORDER_DEFAULT);
-    Scharr(gaussian, Ly, CV_32F, 0, 1, 1, 0, cv::BORDER_DEFAULT);
-
-    // Skip the borders for computing the histogram
-    for (int i = 1; i < gaussian.rows - 1; i++) {
-        const float *lx = Lx.ptr<float>(i);
-        const float *ly = Ly.ptr<float>(i);
-        for (int j = 1; j < gaussian.cols - 1; j++) {
-            modg = lx[j]*lx[j] + ly[j]*ly[j];
-
-            // Get the maximum
-            if (modg > hmax) {
-                hmax = modg;
-            }
-        }
-    }
-    hmax = sqrt(hmax);
-    // Skip the borders for computing the histogram
-    for (int i = 1; i < gaussian.rows - 1; i++) {
-        const float *lx = Lx.ptr<float>(i);
-        const float *ly = Ly.ptr<float>(i);
-        for (int j = 1; j < gaussian.cols - 1; j++) {
-            modg = lx[j]*lx[j] + ly[j]*ly[j];
-
-            // Find the correspondent bin
-            if (modg != 0.0) {
-                nbin = (int)floor(nbins*(sqrt(modg) / hmax));
-
-                if (nbin == nbins) {
-                    nbin--;
-                }
-
-                hist[nbin]++;
-                npoints++;
-            }
-        }
-    }
-
-    // Now find the perc of the histogram percentile
-    nthreshold = (int)(npoints*perc);
-
-    for (k = 0; nelements < nthreshold && k < nbins; k++) {
-        nelements = nelements + hist[k];
-    }
-
-    if (nelements < nthreshold)  {
-        kperc = 0.03f;
-    }
-    else {
-        kperc = hmax*((float)(k) / (float)nbins);
-    }
-
-    return kperc;
-}
-
-/* ************************************************************************* */
-/**
- * @brief This function computes Scharr image derivatives
- * @param src Input image
- * @param dst Output image
- * @param xorder Derivative order in X-direction (horizontal)
- * @param yorder Derivative order in Y-direction (vertical)
- * @param scale Scale factor for the derivative size
- */
-void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale) {
-    Mat kx, ky;
-    compute_derivative_kernels(kx, ky, xorder, yorder, scale);
-    sepFilter2D(src, dst, CV_32F, kx, ky);
-}
-
-/* ************************************************************************* */
-/**
- * @brief Compute derivative kernels for sizes different than 3
- * @param _kx Horizontal kernel ues
- * @param _ky Vertical kernel values
- * @param dx Derivative order in X-direction (horizontal)
- * @param dy Derivative order in Y-direction (vertical)
- * @param scale Scale factor or derivative size
- */
-void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale) {
-    CV_INSTRUMENT_REGION();
-
-    int ksize = 3 + 2 * (scale - 1);
-
-    // The standard Scharr kernel
-    if (scale == 1) {
-        getDerivKernels(_kx, _ky, dx, dy, 0, true, CV_32F);
-        return;
-    }
-
-    _kx.create(ksize, 1, CV_32F, -1, true);
-    _ky.create(ksize, 1, CV_32F, -1, true);
-    Mat kx = _kx.getMat();
-    Mat ky = _ky.getMat();
-    std::vector<float> kerI;
-
-    float w = 10.0f / 3.0f;
-    float norm = 1.0f / (2.0f*scale*(w + 2.0f));
-
-    for (int k = 0; k < 2; k++) {
-        Mat* kernel = k == 0 ? &kx : &ky;
-        int order = k == 0 ? dx : dy;
-        kerI.assign(ksize, 0.0f);
-
-        if (order == 0) {
-            kerI[0] = norm, kerI[ksize / 2] = w*norm, kerI[ksize - 1] = norm;
-        }
-        else if (order == 1) {
-            kerI[0] = -1, kerI[ksize / 2] = 0, kerI[ksize - 1] = 1;
-        }
-
-        Mat temp(kernel->rows, kernel->cols, CV_32F, &kerI[0]);
-        temp.copyTo(*kernel);
-    }
-}
-
-class Nld_Step_Scalar_Invoker : public cv::ParallelLoopBody
-{
-public:
-    Nld_Step_Scalar_Invoker(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float _stepsize)
-        : _Ld(&Ld)
-        , _c(&c)
-        , _Lstep(&Lstep)
-        , stepsize(_stepsize)
-    {
-    }
-
-    virtual ~Nld_Step_Scalar_Invoker()
-    {
-
-    }
-
-    void operator()(const cv::Range& range) const CV_OVERRIDE
-    {
-        cv::Mat& Ld = *_Ld;
-        const cv::Mat& c = *_c;
-        cv::Mat& Lstep = *_Lstep;
-
-        for (int i = range.start; i < range.end; i++)
-        {
-            const float *c_prev  = c.ptr<float>(i - 1);
-            const float *c_curr  = c.ptr<float>(i);
-            const float *c_next  = c.ptr<float>(i + 1);
-            const float *ld_prev = Ld.ptr<float>(i - 1);
-            const float *ld_curr = Ld.ptr<float>(i);
-            const float *ld_next = Ld.ptr<float>(i + 1);
-
-            float *dst  = Lstep.ptr<float>(i);
-
-            for (int j = 1; j < Lstep.cols - 1; j++)
-            {
-                float xpos = (c_curr[j]   + c_curr[j+1])*(ld_curr[j+1] - ld_curr[j]);
-                float xneg = (c_curr[j-1] + c_curr[j])  *(ld_curr[j]   - ld_curr[j-1]);
-                float ypos = (c_curr[j]   + c_next[j])  *(ld_next[j]   - ld_curr[j]);
-                float yneg = (c_prev[j]   + c_curr[j])  *(ld_curr[j]   - ld_prev[j]);
-                dst[j] = 0.5f*stepsize*(xpos - xneg + ypos - yneg);
-            }
-        }
-    }
-private:
-    cv::Mat * _Ld;
-    const cv::Mat * _c;
-    cv::Mat * _Lstep;
-    float stepsize;
-};
-
-/* ************************************************************************* */
-/**
-* @brief This function performs a scalar non-linear diffusion step
-* @param Ld Output image in the evolution
-* @param c Conductivity image
-* @param Lstep Previous image in the evolution
-* @param stepsize The step size in time units
-* @note Forward Euler Scheme 3x3 stencil
-* The function c is a scalar value that depends on the gradient norm
-* dL_by_ds = d(c dL_by_dx)_by_dx + d(c dL_by_dy)_by_dy
-*/
-void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize) {
-    CV_INSTRUMENT_REGION();
-
-    cv::parallel_for_(cv::Range(1, Lstep.rows - 1), Nld_Step_Scalar_Invoker(Ld, c, Lstep, stepsize), (double)Ld.total()/(1 << 16));
-
-    float xneg, xpos, yneg, ypos;
-    float* dst = Lstep.ptr<float>(0);
-    const float* cprv = NULL;
-    const float* ccur  = c.ptr<float>(0);
-    const float* cnxt  = c.ptr<float>(1);
-    const float* ldprv = NULL;
-    const float* ldcur = Ld.ptr<float>(0);
-    const float* ldnxt = Ld.ptr<float>(1);
-    for (int j = 1; j < Lstep.cols - 1; j++) {
-        xpos = (ccur[j]   + ccur[j+1]) * (ldcur[j+1] - ldcur[j]);
-        xneg = (ccur[j-1] + ccur[j])   * (ldcur[j]   - ldcur[j-1]);
-        ypos = (ccur[j]   + cnxt[j])   * (ldnxt[j]   - ldcur[j]);
-        dst[j] = 0.5f*stepsize*(xpos - xneg + ypos);
-    }
-
-    dst = Lstep.ptr<float>(Lstep.rows - 1);
-    ccur = c.ptr<float>(Lstep.rows - 1);
-    cprv = c.ptr<float>(Lstep.rows - 2);
-    ldcur = Ld.ptr<float>(Lstep.rows - 1);
-    ldprv = Ld.ptr<float>(Lstep.rows - 2);
-
-    for (int j = 1; j < Lstep.cols - 1; j++) {
-        xpos = (ccur[j] + ccur[j+1]) * (ldcur[j+1] - ldcur[j]);
-        xneg = (ccur[j-1] + ccur[j]) * (ldcur[j] - ldcur[j-1]);
-        yneg = (cprv[j] + ccur[j])   * (ldcur[j] - ldprv[j]);
-        dst[j] = 0.5f*stepsize*(xpos - xneg - yneg);
-    }
-
-    ccur = c.ptr<float>(1);
-    ldcur = Ld.ptr<float>(1);
-    cprv = c.ptr<float>(0);
-    ldprv = Ld.ptr<float>(0);
-
-    int r0 = Lstep.cols - 1;
-    int r1 = Lstep.cols - 2;
-
-    for (int i = 1; i < Lstep.rows - 1; i++) {
-        cnxt = c.ptr<float>(i + 1);
-        ldnxt = Ld.ptr<float>(i + 1);
-        dst = Lstep.ptr<float>(i);
-
-        xpos = (ccur[0] + ccur[1]) * (ldcur[1] - ldcur[0]);
-        ypos = (ccur[0] + cnxt[0]) * (ldnxt[0] - ldcur[0]);
-        yneg = (cprv[0] + ccur[0]) * (ldcur[0] - ldprv[0]);
-        dst[0] = 0.5f*stepsize*(xpos + ypos - yneg);
-
-        xneg = (ccur[r1] + ccur[r0]) * (ldcur[r0] - ldcur[r1]);
-        ypos = (ccur[r0] + cnxt[r0]) * (ldnxt[r0] - ldcur[r0]);
-        yneg = (cprv[r0] + ccur[r0]) * (ldcur[r0] - ldprv[r0]);
-        dst[r0] = 0.5f*stepsize*(-xneg + ypos - yneg);
-
-        cprv = ccur;
-        ccur = cnxt;
-        ldprv = ldcur;
-        ldcur = ldnxt;
-    }
-    Ld += Lstep;
-}
-
-/* ************************************************************************* */
-/**
-* @brief This function downsamples the input image using OpenCV resize
-* @param src Input image to be downsampled
-* @param dst Output image with half of the resolution of the input image
-*/
-void halfsample_image(const cv::Mat& src, cv::Mat& dst) {
-    // Make sure the destination image is of the right size
-    CV_Assert(src.cols / 2 == dst.cols);
-    CV_Assert(src.rows / 2 == dst.rows);
-    resize(src, dst, dst.size(), 0, 0, cv::INTER_AREA);
-}
-
-/* ************************************************************************* */
-/**
- * @brief This function checks if a given pixel is a maximum in a local neighbourhood
- * @param img Input image where we will perform the maximum search
- * @param dsize Half size of the neighbourhood
- * @param value Response value at (x,y) position
- * @param row Image row coordinate
- * @param col Image column coordinate
- * @param same_img Flag to indicate if the image value at (x,y) is in the input image
- * @return 1->is maximum, 0->otherwise
- */
-bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img) {
-
-    bool response = true;
-
-    for (int i = row - dsize; i <= row + dsize; i++) {
-        for (int j = col - dsize; j <= col + dsize; j++) {
-            if (i >= 0 && i < img.rows && j >= 0 && j < img.cols) {
-                if (same_img == true) {
-                    if (i != row || j != col) {
-                        if ((*(img.ptr<float>(i)+j)) > value) {
-                            response = false;
-                            return response;
-                        }
-                    }
-                }
-                else {
-                    if ((*(img.ptr<float>(i)+j)) > value) {
-                        response = false;
-                        return response;
-                    }
-                }
-            }
-        }
-    }
-
-    return response;
-}
-
-}

modules/features2d/src/kaze/nldiffusion_functions.h

@@ -1,47 +0,0 @@
-/**
- * @file nldiffusion_functions.h
- * @brief Functions for non-linear diffusion applications:
- * 2D Gaussian Derivatives
- * Perona and Malik conductivity equations
- * Perona and Malik evolution
- * @date Dec 27, 2011
- * @author Pablo F. Alcantarilla
- */
-
-#ifndef __OPENCV_FEATURES_2D_NLDIFFUSION_FUNCTIONS_H__
-#define __OPENCV_FEATURES_2D_NLDIFFUSION_FUNCTIONS_H__
-
-/* ************************************************************************* */
-// Declaration of functions
-
-namespace cv
-{
-
-// Gaussian 2D convolution
-void gaussian_2D_convolution(const cv::Mat& src, cv::Mat& dst, int ksize_x, int ksize_y, float sigma);
-
-// Diffusivity functions
-void pm_g1(InputArray Lx, InputArray Ly, OutputArray dst, float k);
-void pm_g2(InputArray Lx, InputArray Ly, OutputArray dst, float k);
-void weickert_diffusivity(InputArray Lx, InputArray Ly, OutputArray dst, float k);
-void charbonnier_diffusivity(InputArray Lx, InputArray Ly, OutputArray dst, float k);
-
-float compute_k_percentile(const cv::Mat& img, float perc, float gscale, int nbins, int ksize_x, int ksize_y);
-
-// Image derivatives
-void compute_scharr_derivatives(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder, int scale);
-void compute_derivative_kernels(cv::OutputArray _kx, cv::OutputArray _ky, int dx, int dy, int scale);
-void image_derivatives_scharr(const cv::Mat& src, cv::Mat& dst, int xorder, int yorder);
-
-// Nonlinear diffusion filtering scalar step
-void nld_step_scalar(cv::Mat& Ld, const cv::Mat& c, cv::Mat& Lstep, float stepsize);
-
-// For non-maxima suppression
-bool check_maximum_neighbourhood(const cv::Mat& img, int dsize, float value, int row, int col, bool same_img);
-
-// Image downsampling
-void halfsample_image(const cv::Mat& src, cv::Mat& dst);
-
-}
-
-#endif

modules/features2d/src/kaze/utils.h

@@ -1,42 +0,0 @@
-#ifndef __OPENCV_FEATURES_2D_KAZE_UTILS_H__
-#define __OPENCV_FEATURES_2D_KAZE_UTILS_H__
-
-/* ************************************************************************* */
-/**
- * @brief This function computes the value of a 2D Gaussian function
- * @param x X Position
- * @param y Y Position
- * @param sigma Standard Deviation
- */
-inline float gaussian(float x, float y, float sigma) {
-  return expf(-(x*x + y*y) / (2.0f*sigma*sigma));
-}
-
-/* ************************************************************************* */
-/**
- * @brief This function checks descriptor limits
- * @param x X Position
- * @param y Y Position
- * @param width Image width
- * @param height Image height
- */
-inline void checkDescriptorLimits(int &x, int &y, int width, int height) {
-
-  if (x < 0) {
-    x = 0;
-  }
-
-  if (y < 0) {
-    y = 0;
-  }
-
-  if (x > width - 1) {
-    x = width - 1;
-  }
-
-  if (y > height - 1) {
-    y = height - 1;
-  }
-}
-
-#endif

modules/features2d/src/opencl/akaze.cl

@@ -1,122 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html
-
-
-/**
- * @brief This function computes the Perona and Malik conductivity coefficient g2
- * g2 = 1 / (1 + dL^2 / k^2)
- * @param lx First order image derivative in X-direction (horizontal)
- * @param ly First order image derivative in Y-direction (vertical)
- * @param dst Output image
- * @param k Contrast factor parameter
- */
-__kernel void
-AKAZE_pm_g2(__global const float* lx, __global const float* ly, __global float* dst,
-    float k, int size)
-{
-    int i = get_global_id(0);
-    // OpenCV plays with dimensions so we need explicit check for this
-    if (!(i < size))
-    {
-        return;
-    }
-
-    const float k2inv = 1.0f / (k * k);
-    dst[i] = 1.0f / (1.0f + ((lx[i] * lx[i] + ly[i] * ly[i]) * k2inv));
-}
-
-__kernel void
-AKAZE_nld_step_scalar(__global const float* lt, int lt_step, int lt_offset, int rows, int cols,
-    __global const float* lf, __global float* dst, float step_size)
-{
-    /* The labeling scheme for this five star stencil:
-        [    a    ]
-        [ -1 c +1 ]
-        [    b    ]
-    */
-    // column-first indexing
-    int i = get_global_id(1);
-    int j = get_global_id(0);
-
-    // OpenCV plays with dimensions so we need explicit check for this
-    if (!(i < rows && j < cols))
-    {
-        return;
-    }
-
-    // get row indexes
-    int a = (i - 1) * cols;
-    int c = (i    ) * cols;
-    int b = (i + 1) * cols;
-    // compute stencil
-    float res = 0.0f;
-    if (i == 0) // first rows
-    {
-        if (j == 0 || j == (cols - 1))
-        {
-            res = 0.0f;
-        } else
-        {
-            res = (lf[c + j] + lf[c + j + 1])*(lt[c + j + 1] - lt[c + j]) +
-                  (lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) +
-                  (lf[c + j] + lf[b + j    ])*(lt[b + j    ] - lt[c + j]);
-        }
-    } else if (i == (rows - 1)) // last row
-    {
-        if (j == 0 || j == (cols - 1))
-        {
-            res = 0.0f;
-        } else
-        {
-            res = (lf[c + j] + lf[c + j + 1])*(lt[c + j + 1] - lt[c + j]) +
-                  (lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) +
-                  (lf[c + j] + lf[a + j    ])*(lt[a + j    ] - lt[c + j]);
-        }
-    } else // inner rows
-    {
-        if (j == 0) // first column
-        {
-            res = (lf[c + 0] + lf[c + 1])*(lt[c + 1] - lt[c + 0]) +
-                  (lf[c + 0] + lf[b + 0])*(lt[b + 0] - lt[c + 0]) +
-                  (lf[c + 0] + lf[a + 0])*(lt[a + 0] - lt[c + 0]);
-        } else if (j == (cols - 1)) // last column
-        {
-            res = (lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) +
-                  (lf[c + j] + lf[b + j    ])*(lt[b + j    ] - lt[c + j]) +
-                  (lf[c + j] + lf[a + j    ])*(lt[a + j    ] - lt[c + j]);
-        } else // inner stencil
-        {
-            res = (lf[c + j] + lf[c + j + 1])*(lt[c + j + 1] - lt[c + j]) +
-                  (lf[c + j] + lf[c + j - 1])*(lt[c + j - 1] - lt[c + j]) +
-                  (lf[c + j] + lf[b + j    ])*(lt[b + j    ] - lt[c + j]) +
-                  (lf[c + j] + lf[a + j    ])*(lt[a + j    ] - lt[c + j]);
-        }
-    }
-
-    dst[c + j] = res * step_size;
-}
-
-/**
- * @brief Compute determinant from hessians
- * @details Compute Ldet by (Lxx.mul(Lyy) - Lxy.mul(Lxy)) * sigma
- *
- * @param lxx spatial derivates
- * @param lxy spatial derivates
- * @param lyy spatial derivates
- * @param dst output determinant
- * @param sigma determinant will be scaled by this sigma
- */
-__kernel void
-AKAZE_compute_determinant(__global const float* lxx, __global const float* lxy, __global const float* lyy,
-    __global float* dst, float sigma, int size)
-{
-    int i = get_global_id(0);
-    // OpenCV plays with dimensions so we need explicit check for this
-    if (!(i < size))
-    {
-        return;
-    }
-
-    dst[i] = (lxx[i] * lyy[i] - lxy[i] * lxy[i]) * sigma;
-}

modules/features2d/test/ocl/test_feature2d.cpp

@@ -1,73 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html
-
-#include "../test_precomp.hpp"
-#include "cvconfig.h"
-#include "opencv2/ts/ocl_test.hpp"
-#include <functional>
-
-#ifdef HAVE_OPENCL
-
-namespace opencv_test {
-namespace ocl {
-
-#define TEST_IMAGES testing::Values(\
-    "detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
-    "../stitching/a3.png", \
-    "../stitching/s2.jpg")
-
-PARAM_TEST_CASE(Feature2DFixture, std::function<Ptr<Feature2D>()>, std::string)
-{
-    std::string filename;
-    Mat image, descriptors;
-    vector<KeyPoint> keypoints;
-    UMat uimage, udescriptors;
-    vector<KeyPoint> ukeypoints;
-    Ptr<Feature2D> feature;
-
-    virtual void SetUp()
-    {
-        feature = GET_PARAM(0)();
-        filename = GET_PARAM(1);
-
-        image = readImage(filename);
-
-        ASSERT_FALSE(image.empty());
-
-        image.copyTo(uimage);
-
-        OCL_OFF(feature->detect(image, keypoints));
-        OCL_ON(feature->detect(uimage, ukeypoints));
-        // note: we use keypoints from CPU for GPU too, to test descriptors separately
-        OCL_OFF(feature->compute(image, keypoints, descriptors));
-        OCL_ON(feature->compute(uimage, keypoints, udescriptors));
-    }
-};
-
-OCL_TEST_P(Feature2DFixture, KeypointsSame)
-{
-    EXPECT_EQ(keypoints.size(), ukeypoints.size());
-
-    for (size_t i = 0; i < keypoints.size(); ++i)
-    {
-        EXPECT_GE(KeyPoint::overlap(keypoints[i], ukeypoints[i]), 0.95);
-        EXPECT_NEAR(keypoints[i].angle, ukeypoints[i].angle, 0.05);
-    }
-}
-
-OCL_TEST_P(Feature2DFixture, DescriptorsSame)
-{
-    EXPECT_MAT_NEAR(descriptors, udescriptors, 0.001);
-}
-
-OCL_INSTANTIATE_TEST_CASE_P(AKAZE, Feature2DFixture,
-    testing::Combine(testing::Values([]() { return AKAZE::create(); }), TEST_IMAGES));
-
-OCL_INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, Feature2DFixture,
-    testing::Combine(testing::Values([]() { return AKAZE::create(AKAZE::DESCRIPTOR_KAZE); }), TEST_IMAGES));
-
-}//ocl
-}//cvtest
-
-#endif //HAVE_OPENCL

modules/features2d/test/test_agast.cpp

@@ -1,138 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "test_precomp.hpp"
-
-namespace opencv_test { namespace {
-
-class CV_AgastTest : public cvtest::BaseTest
-{
-public:
-    CV_AgastTest();
-    ~CV_AgastTest();
-protected:
-    void run(int);
-};
-
-CV_AgastTest::CV_AgastTest() {}
-CV_AgastTest::~CV_AgastTest() {}
-
-void CV_AgastTest::run( int )
-{
-  for(int type=0; type <= 2; ++type) {
-    Mat image1 = imread(string(ts->get_data_path()) + "inpaint/orig.png");
-    Mat image2 = imread(string(ts->get_data_path()) + "cameracalibration/chess9.png");
-    string xml = string(ts->get_data_path()) + format("agast/result%d.xml", type);
-
-    if (image1.empty() || image2.empty())
-    {
-        ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
-        return;
-    }
-
-    Mat gray1, gray2;
-    cvtColor(image1, gray1, COLOR_BGR2GRAY);
-    cvtColor(image2, gray2, COLOR_BGR2GRAY);
-
-    vector<KeyPoint> keypoints1;
-    vector<KeyPoint> keypoints2;
-    AGAST(gray1, keypoints1, 30, true, static_cast<AgastFeatureDetector::DetectorType>(type));
-    AGAST(gray2, keypoints2, (type > 0 ? 30 : 20), true, static_cast<AgastFeatureDetector::DetectorType>(type));
-
-    for(size_t i = 0; i < keypoints1.size(); ++i)
-    {
-        const KeyPoint& kp = keypoints1[i];
-        cv::circle(image1, kp.pt, cvRound(kp.size/2), Scalar(255, 0, 0));
-    }
-
-    for(size_t i = 0; i < keypoints2.size(); ++i)
-    {
-        const KeyPoint& kp = keypoints2[i];
-        cv::circle(image2, kp.pt, cvRound(kp.size/2), Scalar(255, 0, 0));
-    }
-
-    Mat kps1(1, (int)(keypoints1.size() * sizeof(KeyPoint)), CV_8U, &keypoints1[0]);
-    Mat kps2(1, (int)(keypoints2.size() * sizeof(KeyPoint)), CV_8U, &keypoints2[0]);
-
-    FileStorage fs(xml, FileStorage::READ);
-    if (!fs.isOpened())
-    {
-        fs.open(xml, FileStorage::WRITE);
-        if (!fs.isOpened())
-        {
-            ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
-            return;
-        }
-        fs << "exp_kps1" << kps1;
-        fs << "exp_kps2" << kps2;
-        fs.release();
-        fs.open(xml, FileStorage::READ);
-        if (!fs.isOpened())
-        {
-            ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
-            return;
-        }
-    }
-
-    Mat exp_kps1, exp_kps2;
-    read( fs["exp_kps1"], exp_kps1, Mat() );
-    read( fs["exp_kps2"], exp_kps2, Mat() );
-    fs.release();
-
-     if ( exp_kps1.size != kps1.size || 0 != cvtest::norm(exp_kps1, kps1, NORM_L2) ||
-          exp_kps2.size != kps2.size || 0 != cvtest::norm(exp_kps2, kps2, NORM_L2))
-    {
-        ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
-        return;
-    }
-
-    /*cv::namedWindow("Img1"); cv::imshow("Img1", image1);
-    cv::namedWindow("Img2"); cv::imshow("Img2", image2);
-    cv::waitKey(0);*/
-  }
-
-  ts->set_failed_test_info(cvtest::TS::OK);
-}
-
-TEST(Features2d_AGAST, regression) { CV_AgastTest test; test.safe_run(); }
-
-}} // namespace

modules/features2d/test/test_akaze.cpp

@@ -1,48 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html
-
-#include "test_precomp.hpp"
-
-namespace opencv_test { namespace {
-
-TEST(Features2d_AKAZE, detect_and_compute_split)
-{
-    Mat testImg(100, 100, CV_8U);
-    RNG rng(101);
-    rng.fill(testImg, RNG::UNIFORM, Scalar(0), Scalar(255), true);
-
-    Ptr<Feature2D> ext = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 0, 3, 0.001f, 1, 1, KAZE::DIFF_PM_G2);
-    vector<KeyPoint> detAndCompKps;
-    Mat desc;
-    ext->detectAndCompute(testImg, noArray(), detAndCompKps, desc);
-
-    vector<KeyPoint> detKps;
-    ext->detect(testImg, detKps);
-
-    ASSERT_EQ(detKps.size(), detAndCompKps.size());
-
-    for(size_t i = 0; i < detKps.size(); i++)
-        ASSERT_EQ(detKps[i].hash(), detAndCompKps[i].hash());
-}
-
-/**
- * This test is here to guard propagation of NaNs that happens on this image. NaNs are guarded
- * by debug asserts in AKAZE, which should fire for you if you are lucky.
- *
- * This test also reveals problems with uninitialized memory that happens only on this image.
- * This is very hard to hit and depends a lot on particular allocator. Run this test in valgrind and check
- * for uninitialized values if you think you are hitting this problem again.
- */
-TEST(Features2d_AKAZE, uninitialized_and_nans)
-{
-    Mat b1 = imread(cvtest::TS::ptr()->get_data_path() + "../stitching/b1.png");
-    ASSERT_FALSE(b1.empty());
-
-    vector<KeyPoint> keypoints;
-    Mat desc;
-    Ptr<Feature2D> akaze = AKAZE::create();
-    akaze->detectAndCompute(b1, noArray(), keypoints, desc);
-}
-
-}} // namespace

modules/features2d/test/test_brisk.cpp

@@ -1,108 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "test_precomp.hpp"
-
-namespace opencv_test { namespace {
-
-class CV_BRISKTest : public cvtest::BaseTest
-{
-public:
-    CV_BRISKTest();
-    ~CV_BRISKTest();
-protected:
-    void run(int);
-};
-
-CV_BRISKTest::CV_BRISKTest() {}
-CV_BRISKTest::~CV_BRISKTest() {}
-
-void CV_BRISKTest::run( int )
-{
-  Mat image1 = imread(string(ts->get_data_path()) + "inpaint/orig.png");
-  Mat image2 = imread(string(ts->get_data_path()) + "cameracalibration/chess9.png");
-
-  if (image1.empty() || image2.empty())
-    {
-      ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
-      return;
-    }
-
-  Mat gray1, gray2;
-  cvtColor(image1, gray1, COLOR_BGR2GRAY);
-  cvtColor(image2, gray2, COLOR_BGR2GRAY);
-
-  Ptr<FeatureDetector> detector = BRISK::create();
-
-  // Check parameter get/set functions.
-  BRISK* detectorTyped = dynamic_cast<BRISK*>(detector.get());
-  ASSERT_NE(nullptr, detectorTyped);
-  detectorTyped->setOctaves(3);
-  detectorTyped->setThreshold(30);
-  ASSERT_EQ(detectorTyped->getOctaves(), 3);
-  ASSERT_EQ(detectorTyped->getThreshold(), 30);
-  detectorTyped->setOctaves(4);
-  detectorTyped->setThreshold(29);
-  ASSERT_EQ(detectorTyped->getOctaves(), 4);
-  ASSERT_EQ(detectorTyped->getThreshold(), 29);
-
-  vector<KeyPoint> keypoints1;
-  vector<KeyPoint> keypoints2;
-  detector->detect(image1, keypoints1);
-  detector->detect(image2, keypoints2);
-
-  for(size_t i = 0; i < keypoints1.size(); ++i)
-    {
-      const KeyPoint& kp = keypoints1[i];
-      ASSERT_NE(kp.angle, -1);
-    }
-
-  for(size_t i = 0; i < keypoints2.size(); ++i)
-    {
-      const KeyPoint& kp = keypoints2[i];
-      ASSERT_NE(kp.angle, -1);
-    }
-}
-
-TEST(Features2d_BRISK, regression) { CV_BRISKTest test; test.safe_run(); }
-
-}} // namespace

modules/features2d/test/test_descriptors_invariance.cpp

@@ -20,17 +20,9 @@ const static std::string IMAGE_BIKES = "detectors_descriptors_evaluation/images_
 INSTANTIATE_TEST_CASE_P(SIFT, DescriptorRotationInvariance,
                         Value(IMAGE_TSUKUBA, []() { return SIFT::create(); }, []() { return SIFT::create(); }, 0.98f));
 
-INSTANTIATE_TEST_CASE_P(BRISK, DescriptorRotationInvariance,
-                        Value(IMAGE_TSUKUBA, []() { return BRISK::create(); }, []() { return BRISK::create(); }, 0.99f));
-
 INSTANTIATE_TEST_CASE_P(ORB, DescriptorRotationInvariance,
                         Value(IMAGE_TSUKUBA, []() { return ORB::create(); }, []() { return ORB::create(); }, 0.99f));
 
-INSTANTIATE_TEST_CASE_P(AKAZE, DescriptorRotationInvariance,
-                        Value(IMAGE_TSUKUBA, []() { return AKAZE::create(); }, []() { return AKAZE::create(); }, 0.99f));
-
-INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DescriptorRotationInvariance,
-                        Value(IMAGE_TSUKUBA, []() { return AKAZE::create(AKAZE::DESCRIPTOR_KAZE); }, []() { return AKAZE::create(AKAZE::DESCRIPTOR_KAZE); }, 0.99f));
 
 /*
  * Descriptor's scale invariance check
@@ -39,10 +31,4 @@ INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DescriptorRotationInvariance,
 INSTANTIATE_TEST_CASE_P(SIFT, DescriptorScaleInvariance,
                         Value(IMAGE_BIKES, []() { return SIFT::create(0, 3, 0.09); }, []() { return SIFT::create(0, 3, 0.09); }, 0.78f));
 
-INSTANTIATE_TEST_CASE_P(AKAZE, DescriptorScaleInvariance,
-                        Value(IMAGE_BIKES, []() { return AKAZE::create(); }, []() { return AKAZE::create(); }, 0.6f));
-
-INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DescriptorScaleInvariance,
-                        Value(IMAGE_BIKES, []() { return AKAZE::create(AKAZE::DESCRIPTOR_KAZE); }, []() { return AKAZE::create(AKAZE::DESCRIPTOR_KAZE); }, 0.55f));
-
 }} // namespace

modules/features2d/test/test_descriptors_regression.cpp

@@ -25,14 +25,6 @@ TEST( Features2d_DescriptorExtractor_SIFT, regression )
     test.safe_run();
 }
 
-TEST( Features2d_DescriptorExtractor_BRISK, regression )
-{
-    CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk",
-                                             (CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f,
-                                            BRISK::create() );
-    test.safe_run();
-}
-
 TEST( Features2d_DescriptorExtractor_ORB, regression )
 {
     // TODO adjust the parameters below
@@ -46,31 +38,6 @@ TEST( Features2d_DescriptorExtractor_ORB, regression )
     test.safe_run();
 }
 
-TEST( Features2d_DescriptorExtractor_KAZE, regression )
-{
-    CV_DescriptorExtractorTest< L2<float> > test( "descriptor-kaze",  0.03f,
-                                                 KAZE::create(),
-                                                 L2<float>(), KAZE::create() );
-    test.safe_run();
-}
-
-TEST( Features2d_DescriptorExtractor_AKAZE, regression )
-{
-    CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",
-                                              (CV_DescriptorExtractorTest<Hamming>::DistanceType)(486*0.05f),
-                                              AKAZE::create(),
-                                              Hamming(), AKAZE::create());
-    test.safe_run();
-}
-
-TEST( Features2d_DescriptorExtractor_AKAZE_DESCRIPTOR_KAZE, regression )
-{
-    CV_DescriptorExtractorTest< L2<float> > test( "descriptor-akaze-with-kaze-desc", 0.03f,
-                                              AKAZE::create(AKAZE::DESCRIPTOR_KAZE),
-                                              L2<float>(), AKAZE::create(AKAZE::DESCRIPTOR_KAZE));
-    test.safe_run();
-}
-
 TEST( Features2d_DescriptorExtractor, batch_ORB )
 {
     string path = string(cvtest::TS::ptr()->get_data_path() + "detectors_descriptors_evaluation/images_datasets/graf");
@@ -144,15 +111,7 @@ TEST_P(DescriptorImage, no_crash)
     glob(cvtest::TS::ptr()->get_data_path() + pattern, fnames, false);
     std::sort(fnames.begin(), fnames.end());
 
-    Ptr<AKAZE> akaze_mldb = AKAZE::create(AKAZE::DESCRIPTOR_MLDB);
-    Ptr<AKAZE> akaze_mldb_upright = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT);
-    Ptr<AKAZE> akaze_mldb_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 256);
-    Ptr<AKAZE> akaze_mldb_upright_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT, 256);
-    Ptr<AKAZE> akaze_kaze = AKAZE::create(AKAZE::DESCRIPTOR_KAZE);
-    Ptr<AKAZE> akaze_kaze_upright = AKAZE::create(AKAZE::DESCRIPTOR_KAZE_UPRIGHT);
     Ptr<ORB> orb = ORB::create();
-    Ptr<KAZE> kaze = KAZE::create();
-    Ptr<BRISK> brisk = BRISK::create();
     size_t n = fnames.size();
     vector<KeyPoint> keypoints;
     Mat descriptors;
@@ -183,15 +142,7 @@ TEST_P(DescriptorImage, no_crash)
         } \
         ASSERT_EQ(descriptors.rows, (int)keypoints.size());
 
-        TEST_DETECTOR("AKAZE:MLDB", akaze_mldb);
-        TEST_DETECTOR("AKAZE:MLDB_UPRIGHT", akaze_mldb_upright);
-        TEST_DETECTOR("AKAZE:MLDB_256", akaze_mldb_256);
-        TEST_DETECTOR("AKAZE:MLDB_UPRIGHT_256", akaze_mldb_upright_256);
-        TEST_DETECTOR("AKAZE:KAZE", akaze_kaze);
-        TEST_DETECTOR("AKAZE:KAZE_UPRIGHT", akaze_kaze_upright);
-        TEST_DETECTOR("KAZE", kaze);
         TEST_DETECTOR("ORB", orb);
-        TEST_DETECTOR("BRISK", brisk);
     }
 }
 

modules/features2d/test/test_detectors_invariance.cpp

@@ -20,17 +20,9 @@ const static std::string IMAGE_BIKES = "detectors_descriptors_evaluation/images_
 INSTANTIATE_TEST_CASE_P(SIFT, DetectorRotationInvariance,
                         Value(IMAGE_TSUKUBA, []() { return SIFT::create(); }, 0.45f, 0.70f));
 
-INSTANTIATE_TEST_CASE_P(BRISK, DetectorRotationInvariance,
-                        Value(IMAGE_TSUKUBA, []() { return BRISK::create(); }, 0.45f, 0.76f));
-
 INSTANTIATE_TEST_CASE_P(ORB, DetectorRotationInvariance,
                         Value(IMAGE_TSUKUBA, []() { return ORB::create(); }, 0.5f, 0.76f));
 
-INSTANTIATE_TEST_CASE_P(AKAZE, DetectorRotationInvariance,
-                        Value(IMAGE_TSUKUBA, []() { return AKAZE::create(); }, 0.5f, 0.71f));
-
-INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DetectorRotationInvariance,
-                        Value(IMAGE_TSUKUBA, []() { return AKAZE::create(AKAZE::DESCRIPTOR_KAZE); }, 0.5f, 0.71f));
 
 /*
  * Detector's scale invariance check
@@ -39,19 +31,7 @@ INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DetectorRotationInvariance,
 INSTANTIATE_TEST_CASE_P(SIFT, DetectorScaleInvariance,
                         Value(IMAGE_BIKES, []() { return SIFT::create(0, 3, 0.09); }, 0.60f, 0.98f));
 
-INSTANTIATE_TEST_CASE_P(BRISK, DetectorScaleInvariance,
-                        Value(IMAGE_BIKES, []() { return BRISK::create(); }, 0.08f, 0.49f));
-
 INSTANTIATE_TEST_CASE_P(ORB, DetectorScaleInvariance,
                         Value(IMAGE_BIKES, []() { return ORB::create(); }, 0.08f, 0.49f));
 
-INSTANTIATE_TEST_CASE_P(KAZE, DetectorScaleInvariance,
-                        Value(IMAGE_BIKES, []() { return KAZE::create(); }, 0.08f, 0.49f));
-
-INSTANTIATE_TEST_CASE_P(AKAZE, DetectorScaleInvariance,
-                        Value(IMAGE_BIKES, []() { return AKAZE::create(); }, 0.08f, 0.49f));
-
-INSTANTIATE_TEST_CASE_P(AKAZE_DESCRIPTOR_KAZE, DetectorScaleInvariance,
-                        Value(IMAGE_BIKES, []() { return AKAZE::create(AKAZE::DESCRIPTOR_KAZE); }, 0.08f, 0.49f));
-
 }} // namespace

modules/features2d/test/test_detectors_regression.cpp

@@ -24,24 +24,12 @@ TEST( Features2d_Detector_SIFT, regression )
     test.safe_run();
 }
 
-TEST( Features2d_Detector_BRISK, regression )
-{
-    CV_FeatureDetectorTest test( "detector-brisk", BRISK::create() );
-    test.safe_run();
-}
-
 TEST( Features2d_Detector_FAST, regression )
 {
     CV_FeatureDetectorTest test( "detector-fast", FastFeatureDetector::create() );
     test.safe_run();
 }
 
-TEST( Features2d_Detector_AGAST, regression )
-{
-    CV_FeatureDetectorTest test( "detector-agast", AgastFeatureDetector::create() );
-    test.safe_run();
-}
-
 TEST( Features2d_Detector_GFTT, regression )
 {
     CV_FeatureDetectorTest test( "detector-gftt", GFTTDetector::create() );
@@ -68,22 +56,4 @@ TEST( Features2d_Detector_ORB, regression )
     test.safe_run();
 }
 
-TEST( Features2d_Detector_KAZE, regression )
-{
-    CV_FeatureDetectorTest test( "detector-kaze", KAZE::create() );
-    test.safe_run();
-}
-
-TEST( Features2d_Detector_AKAZE, regression )
-{
-    CV_FeatureDetectorTest test( "detector-akaze", AKAZE::create() );
-    test.safe_run();
-}
-
-TEST( Features2d_Detector_AKAZE_DESCRIPTOR_KAZE, regression )
-{
-    CV_FeatureDetectorTest test( "detector-akaze-with-kaze-desc", AKAZE::create(AKAZE::DESCRIPTOR_KAZE) );
-    test.safe_run();
-}
-
 }} // namespace

modules/features2d/test/test_keypoints.cpp

@@ -115,25 +115,12 @@ protected:
 
 
 // Registration of tests
-
-TEST(Features2d_Detector_Keypoints_BRISK, validation)
-{
-    CV_FeatureDetectorKeypointsTest test(BRISK::create());
-    test.safe_run();
-}
-
 TEST(Features2d_Detector_Keypoints_FAST, validation)
 {
     CV_FeatureDetectorKeypointsTest test(FastFeatureDetector::create());
     test.safe_run();
 }
 
-TEST(Features2d_Detector_Keypoints_AGAST, validation)
-{
-    CV_FeatureDetectorKeypointsTest test(AgastFeatureDetector::create());
-    test.safe_run();
-}
-
 TEST(Features2d_Detector_Keypoints_HARRIS, validation)
 {
 
@@ -161,21 +148,6 @@ TEST(Features2d_Detector_Keypoints_ORB, validation)
     test.safe_run();
 }
 
-TEST(Features2d_Detector_Keypoints_KAZE, validation)
-{
-    CV_FeatureDetectorKeypointsTest test(KAZE::create());
-    test.safe_run();
-}
-
-TEST(Features2d_Detector_Keypoints_AKAZE, validation)
-{
-    CV_FeatureDetectorKeypointsTest test_kaze(AKAZE::create(AKAZE::DESCRIPTOR_KAZE));
-    test_kaze.safe_run();
-
-    CV_FeatureDetectorKeypointsTest test_mldb(AKAZE::create(AKAZE::DESCRIPTOR_MLDB));
-    test_mldb.safe_run();
-}
-
 TEST(Features2d_Detector_Keypoints_SIFT, validation)
 {
     CV_FeatureDetectorKeypointsTest test(SIFT::create());

modules/js/test/test_features2d.js

@@ -35,29 +35,13 @@ QUnit.test('Detectors', function(assert) {
   assert.equal(kp.size(), 7, 'MSER');
   */
 
-  let brisk = new cv.BRISK();
-  brisk.detect(image, kp);
-  assert.equal(kp.size(), 191, 'BRISK');
-
   let ffd = new cv.FastFeatureDetector();
   ffd.detect(image, kp);
   assert.equal(kp.size(), 12, 'FastFeatureDetector');
 
-  let afd = new cv.AgastFeatureDetector();
-  afd.detect(image, kp);
-  assert.equal(kp.size(), 67, 'AgastFeatureDetector');
-
   let gftt = new cv.GFTTDetector();
   gftt.detect(image, kp);
   assert.equal(kp.size(), 168, 'GFTTDetector');
-
-  let kaze = new cv.KAZE();
-  kaze.detect(image, kp);
-  assert.equal(kp.size(), 159, 'KAZE');
-
-  let akaze = new cv.AKAZE();
-  akaze.detect(image, kp);
-  assert.equal(kp.size(), 53, 'AKAZE');
 });
 
 QUnit.test('SimpleBlobDetector', function(assert) {

modules/python/test/test_algorithm_rw.py

@@ -12,16 +12,16 @@ class algorithm_rw_test(NewOpenCVTests):
         os.close(fd)
 
         # some arbitrary non-default parameters
-        gold = cv.AKAZE_create(descriptor_size=1, descriptor_channels=2, nOctaves=3, threshold=4.0)
-        gold.write(cv.FileStorage(fname, cv.FILE_STORAGE_WRITE), "AKAZE")
+        gold = cv.ORB_create(nfeatures=200, scaleFactor=1.3, nlevels=5, edgeThreshold=28)
+        gold.write(cv.FileStorage(fname, cv.FILE_STORAGE_WRITE), "ORB")
 
         fs = cv.FileStorage(fname, cv.FILE_STORAGE_READ)
-        algorithm = cv.AKAZE_create()
-        algorithm.read(fs.getNode("AKAZE"))
+        algorithm = cv.ORB_create()
+        algorithm.read(fs.getNode("ORB"))
 
-        self.assertEqual(algorithm.getDescriptorSize(), 1)
-        self.assertEqual(algorithm.getDescriptorChannels(), 2)
-        self.assertEqual(algorithm.getNOctaves(), 3)
-        self.assertEqual(algorithm.getThreshold(), 4.0)
+        self.assertEqual(algorithm.getMaxFeatures(), 200)
+        self.assertAlmostEqual(algorithm.getScaleFactor(), 1.3, places=6)
+        self.assertEqual(algorithm.getNLevels(), 5)
+        self.assertEqual(algorithm.getEdgeThreshold(), 28)
 
         os.remove(fname)

modules/stitching/perf/opencl/perf_stitch.cpp

@@ -20,9 +20,9 @@ namespace ocl {
 typedef TestBaseWithParam<string> stitch;
 
 #if defined(HAVE_OPENCV_XFEATURES2D) && defined(OPENCV_ENABLE_NONFREE)
-#define TEST_DETECTORS testing::Values("surf", "orb", "akaze")
+#define TEST_DETECTORS testing::Values("surf", "sift", "orb", "akaze")
 #else
-#define TEST_DETECTORS testing::Values("orb", "akaze")
+#define TEST_DETECTORS testing::Values("orb", "sift")
 #endif
 
 OCL_PERF_TEST_P(stitch, a123, TEST_DETECTORS)

modules/stitching/perf/perf_precomp.hpp

@@ -6,6 +6,7 @@
 
 #ifdef HAVE_OPENCV_XFEATURES2D
 #include "opencv2/xfeatures2d/nonfree.hpp"
+#include "opencv2/xfeatures2d.hpp"
 #endif
 
 namespace cv
@@ -15,12 +16,16 @@ static inline Ptr<Feature2D> getFeatureFinder(const std::string& name)
 {
     if (name == "orb")
         return ORB::create();
+    else if (name == "sift")
+        return SIFT::create();
 #if defined(HAVE_OPENCV_XFEATURES2D) && defined(OPENCV_ENABLE_NONFREE)
     else if (name == "surf")
         return xfeatures2d::SURF::create();
 #endif
+#if defined(HAVE_OPENCV_XFEATURES2D)
     else if (name == "akaze")
-        return AKAZE::create();
+        return xfeatures2d::AKAZE::create();
+#endif
     else
         return Ptr<Feature2D>();
 }

modules/stitching/perf/perf_stich.cpp

@@ -20,7 +20,7 @@ typedef TestBaseWithParam<tuple<string, int>> stitchExposureCompMultiFeed;
 #if defined(HAVE_OPENCV_XFEATURES2D) && defined(OPENCV_ENABLE_NONFREE)
 #define TEST_DETECTORS testing::Values("surf", "orb", "akaze")
 #else
-#define TEST_DETECTORS testing::Values("orb", "akaze")
+#define TEST_DETECTORS testing::Values("orb")
 #endif
 #define TEST_EXP_COMP_BS testing::Values(32, 16, 12, 10, 8)
 #define TEST_EXP_COMP_NR_FEED testing::Values(1, 2, 3, 4, 5)

platforms/js/opencv_js.config.py

@@ -152,16 +152,12 @@ dnn = {'dnn_Net': ['setInput', 'forward', 'setPreferableBackend','getUnconnected
             'readNetFromONNX', 'readNetFromTFLite', 'readNet', 'blobFromImage']}
 
 features2d = {'Feature2D': ['detect', 'compute', 'detectAndCompute', 'descriptorSize', 'descriptorType', 'defaultNorm', 'empty', 'getDefaultName'],
-              'BRISK': ['create', 'getDefaultName'],
               'ORB': ['create', 'setMaxFeatures', 'setScaleFactor', 'setNLevels', 'setEdgeThreshold', 'setFastThreshold', 'setFirstLevel', 'setWTA_K', 'setScoreType', 'setPatchSize', 'getFastThreshold', 'getDefaultName'],
               'MSER': ['create', 'detectRegions', 'setDelta', 'getDelta', 'setMinArea', 'getMinArea', 'setMaxArea', 'getMaxArea', 'setPass2Only', 'getPass2Only', 'getDefaultName'],
               'FastFeatureDetector': ['create', 'setThreshold', 'getThreshold', 'setNonmaxSuppression', 'getNonmaxSuppression', 'setType', 'getType', 'getDefaultName'],
-              'AgastFeatureDetector': ['create', 'setThreshold', 'getThreshold', 'setNonmaxSuppression', 'getNonmaxSuppression', 'setType', 'getType', 'getDefaultName'],
               'GFTTDetector': ['create', 'setMaxFeatures', 'getMaxFeatures', 'setQualityLevel', 'getQualityLevel', 'setMinDistance', 'getMinDistance', 'setBlockSize', 'getBlockSize', 'setHarrisDetector', 'getHarrisDetector', 'setK', 'getK', 'getDefaultName'],
               'SimpleBlobDetector': ['create', 'setParams', 'getParams', 'getDefaultName'],
               'SimpleBlobDetector_Params': [],
-              'KAZE': ['create', 'setExtended', 'getExtended', 'setUpright', 'getUpright', 'setThreshold', 'getThreshold', 'setNOctaves', 'getNOctaves', 'setNOctaveLayers', 'getNOctaveLayers', 'setDiffusivity', 'getDiffusivity', 'getDefaultName'],
-              'AKAZE': ['create', 'setDescriptorType', 'getDescriptorType', 'setDescriptorSize', 'getDescriptorSize', 'setDescriptorChannels', 'getDescriptorChannels', 'setThreshold', 'getThreshold', 'setNOctaves', 'getNOctaves', 'setNOctaveLayers', 'getNOctaveLayers', 'setDiffusivity', 'getDiffusivity', 'getDefaultName'],
               'DescriptorMatcher': ['add', 'clear', 'empty', 'isMaskSupported', 'train', 'match', 'knnMatch', 'radiusMatch', 'clone', 'create'],
               'BFMatcher': ['isMaskSupported', 'create'],
               '': ['drawKeypoints', 'drawMatches', 'drawMatchesKnn']}

samples/cpp/asift.cpp

@@ -5,6 +5,9 @@
 #include <opencv2/3d.hpp>
 #include <iostream>
 #include <iomanip>
+#ifdef HAVE_OPENCV_XFEATURES2D
+#include "opencv2/xfeatures2d.hpp"
+#endif
 
 using namespace std;
 using namespace cv;
@@ -33,7 +36,7 @@ int main(int argc, char** argv)
     vector<String> fileName;
     cv::CommandLineParser parser(argc, argv,
         "{help h ||}"
-        "{feature|brisk|}"
+        "{feature|orb|}"
         "{flann||}"
         "{maxlines|50|}"
         "{image1|aero1.jpg|}{image2|aero3.jpg|}");
@@ -88,11 +91,16 @@ int main(int argc, char** argv)
     }
     else if (feature == "brisk")
     {
-        backend = BRISK::create();
+#ifdef HAVE_OPENCV_XFEATURES2D
+        backend = xfeatures2d::BRISK::create();
         if (useFlann)
             matcher = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(6, 12, 1));
         else
             matcher = DescriptorMatcher::create("BruteForce-Hamming");
+#else
+        cout << "OpenCV is built without opencv_contrib modules. BRISK algorithm is not available!" << std::endl;
+        return -1;
+#endif
     }
     else
     {

samples/cpp/stitching_detailed.cpp

@@ -424,14 +424,22 @@ int main(int argc, char* argv[])
     }
     else if (features_type == "akaze")
     {
-        finder = AKAZE::create();
-    }
 #ifdef HAVE_OPENCV_XFEATURES2D
+        finder = xfeatures2d::AKAZE::create();
+#else
+        cout << "OpenCV is built without opencv_contrib modules. AKAZE algorithm is not available!" << std::endl;
+        return -1;
+#endif
+    }
     else if (features_type == "surf")
     {
+#if defined(HAVE_OPENCV_XFEATURES2D) && defined(HAVE_OPENCV_NONFREE)
         finder = xfeatures2d::SURF::create();
-    }
+#else
+        cout << "OpenCV is built without NONFREE modules. SURF algorithm is not available!" << std::endl;
+        return -1;
 #endif
+    }
     else if (features_type == "sift")
     {
         finder = SIFT::create();

samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/Utils.cpp

@@ -308,18 +308,39 @@ void createFeatures(const std::string &featureName, int numKeypoints, cv::Ptr<cv
     }
     else if (featureName == "KAZE")
     {
-        detector = cv::KAZE::create();
-        descriptor = cv::KAZE::create();
+#if defined (HAVE_OPENCV_XFEATURES2D)
+        detector = cv::xfeatures2d::KAZE::create();
+        descriptor = cv::xfeatures2d::KAZE::create();
+#else
+        std::cout << "xfeatures2d module is not available." << std::endl;
+        std::cout << "Default to ORB." << std::endl;
+        detector = cv::ORB::create(numKeypoints);
+        descriptor = cv::ORB::create(numKeypoints);
+#endif
     }
     else if (featureName == "AKAZE")
     {
-        detector = cv::AKAZE::create();
-        descriptor = cv::AKAZE::create();
+#if defined (HAVE_OPENCV_XFEATURES2D)
+        detector = cv::xfeatures2d::AKAZE::create();
+        descriptor = cv::xfeatures2d::AKAZE::create();
+#else
+        std::cout << "xfeatures2d module is not available." << std::endl;
+        std::cout << "Default to ORB." << std::endl;
+        detector = cv::ORB::create(numKeypoints);
+        descriptor = cv::ORB::create(numKeypoints);
+#endif
     }
     else if (featureName == "BRISK")
     {
-        detector = cv::BRISK::create();
-        descriptor = cv::BRISK::create();
+#if defined (HAVE_OPENCV_XFEATURES2D)
+        detector = cv::xfeatures2d::BRISK::create();
+        descriptor = cv::xfeatures2d::BRISK::create();
+#else
+        std::cout << "xfeatures2d module is not available." << std::endl;
+        std::cout << "Default to ORB." << std::endl;
+        detector = cv::ORB::create(numKeypoints);
+        descriptor = cv::ORB::create(numKeypoints);
+#endif
     }
     else if (featureName == "SIFT")
     {
@@ -341,7 +362,7 @@ void createFeatures(const std::string &featureName, int numKeypoints, cv::Ptr<cv
     else if (featureName == "BINBOOST")
     {
 #if defined (HAVE_OPENCV_XFEATURES2D)
-        detector = cv::KAZE::create();
+        detector = cv::xfeatures2d::KAZE::create();
         descriptor = cv::xfeatures2d::BoostDesc::create();
 #else
         std::cout << "xfeatures2d module is not available." << std::endl;
@@ -353,7 +374,7 @@ void createFeatures(const std::string &featureName, int numKeypoints, cv::Ptr<cv
     else if (featureName == "VGG")
     {
 #if defined (HAVE_OPENCV_XFEATURES2D)
-        detector = cv::KAZE::create();
+        detector = cv::xfeatures2d::KAZE::create();
         descriptor = cv::xfeatures2d::VGG::create();
 #else
         std::cout << "xfeatures2d module is not available." << std::endl;

samples/cpp/tutorial_code/features2D/AKAZE_match.cpp

@@ -1,7 +1,9 @@
+#include <iostream>
+#ifdef HAVE_OPENCV_XFEATURES2D
 #include <opencv2/features2d.hpp>
+#include "opencv2/xfeatures2d.hpp"
 #include <opencv2/imgproc.hpp>
 #include <opencv2/highgui.hpp>
-#include <iostream>
 
 using namespace std;
 using namespace cv;
@@ -28,7 +30,7 @@ int main(int argc, char* argv[])
     vector<KeyPoint> kpts1, kpts2;
     Mat desc1, desc2;
 
-    Ptr<AKAZE> akaze = AKAZE::create();
+    Ptr<xfeatures2d::AKAZE> akaze = xfeatures2d::AKAZE::create();
     akaze->detectAndCompute(img1, noArray(), kpts1, desc1);
     akaze->detectAndCompute(img2, noArray(), kpts2, desc2);
     //! [AKAZE]
@@ -96,3 +98,10 @@ int main(int argc, char* argv[])
 
     return 0;
 }
+#else
+int main()
+{
+    std::cout << "This tutorial code needs the xfeatures2d contrib module to be run." << std::endl;
+    return 0;
+}
+#endif

samples/cpp/tutorial_code/features2D/AKAZE_tracking/planar_tracking.cpp

@@ -1,11 +1,14 @@
+#include <vector>
+#include <iostream>
+#include <iomanip>
+
+#ifdef HAVE_OPENCV_XFEATURES2D
 #include <opencv2/features2d.hpp>
+#include "opencv2/xfeatures2d.hpp"
 #include <opencv2/videoio.hpp>
 #include <opencv2/imgproc.hpp>
 #include <opencv2/3d.hpp>
 #include <opencv2/highgui.hpp>      //for imshow
-#include <vector>
-#include <iostream>
-#include <iomanip>
 
 #include "stats.h" // Stats structure definition
 #include "utils.h" // Drawing and printing functions
@@ -148,7 +151,7 @@ int main(int argc, char **argv)
     }
 
     Stats stats, akaze_stats, orb_stats;
-    Ptr<AKAZE> akaze = AKAZE::create();
+    Ptr<xfeatures2d::AKAZE> akaze = xfeatures2d::AKAZE::create();
     akaze->setThreshold(akaze_thresh);
     Ptr<ORB> orb = ORB::create();
     Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
@@ -211,3 +214,10 @@ int main(int argc, char **argv)
     printStatistics("ORB", orb_stats);
     return 0;
 }
+#else
+int main()
+{
+    std::cout << "This tutorial code needs the xfeatures2d contrib module to be run." << std::endl;
+    return 0;
+}
+#endif

samples/cpp/tutorial_code/xfeatures2D/LATCH_match.cpp

@@ -1,115 +0,0 @@
-#include <iostream>
-
-#include "opencv2/opencv_modules.hpp"
-
-#ifdef HAVE_OPENCV_XFEATURES2D
-
-#include <opencv2/core.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/highgui.hpp>
-#include <opencv2/features2d.hpp>
-#include <opencv2/xfeatures2d.hpp>
-#include <opencv2/imgcodecs.hpp>
-#include <vector>
-
-// If you find this code useful, please add a reference to the following paper in your work:
-// Gil Levi and Tal Hassner, "LATCH: Learned Arrangements of Three Patch Codes", arXiv preprint arXiv:1501.03719, 15 Jan. 2015
-
-using namespace std;
-using namespace cv;
-
-const float inlier_threshold = 2.5f; // Distance threshold to identify inliers
-const float nn_match_ratio = 0.8f;   // Nearest neighbor matching ratio
-
-int main(int argc, char* argv[])
-{
-    CommandLineParser parser(argc, argv,
-                             "{@img1 | graf1.png | input image 1}"
-                             "{@img2 | graf3.png | input image 2}"
-                             "{@homography | H1to3p.xml | homography matrix}");
-    Mat img1 = imread( samples::findFile( parser.get<String>("@img1") ), IMREAD_GRAYSCALE);
-    Mat img2 = imread( samples::findFile( parser.get<String>("@img2") ), IMREAD_GRAYSCALE);
-
-    Mat homography;
-    FileStorage fs( samples::findFile( parser.get<String>("@homography") ), FileStorage::READ);
-    fs.getFirstTopLevelNode() >> homography;
-
-    vector<KeyPoint> kpts1, kpts2;
-    Mat desc1, desc2;
-
-    Ptr<cv::ORB> orb_detector = cv::ORB::create(10000);
-
-    Ptr<xfeatures2d::LATCH> latch = xfeatures2d::LATCH::create();
-
-
-    orb_detector->detect(img1, kpts1);
-    latch->compute(img1, kpts1, desc1);
-
-    orb_detector->detect(img2, kpts2);
-    latch->compute(img2, kpts2, desc2);
-
-    BFMatcher matcher(NORM_HAMMING);
-    vector< vector<DMatch> > nn_matches;
-    matcher.knnMatch(desc1, desc2, nn_matches, 2);
-
-    vector<KeyPoint> matched1, matched2, inliers1, inliers2;
-    vector<DMatch> good_matches;
-    for (size_t i = 0; i < nn_matches.size(); i++) {
-        DMatch first = nn_matches[i][0];
-        float dist1 = nn_matches[i][0].distance;
-        float dist2 = nn_matches[i][1].distance;
-
-        if (dist1 < nn_match_ratio * dist2) {
-            matched1.push_back(kpts1[first.queryIdx]);
-            matched2.push_back(kpts2[first.trainIdx]);
-        }
-    }
-
-    for (unsigned i = 0; i < matched1.size(); i++) {
-        Mat col = Mat::ones(3, 1, CV_64F);
-        col.at<double>(0) = matched1[i].pt.x;
-        col.at<double>(1) = matched1[i].pt.y;
-
-        col = homography * col;
-        col /= col.at<double>(2);
-        double dist = sqrt(pow(col.at<double>(0) - matched2[i].pt.x, 2) +
-            pow(col.at<double>(1) - matched2[i].pt.y, 2));
-
-        if (dist < inlier_threshold) {
-            int new_i = static_cast<int>(inliers1.size());
-            inliers1.push_back(matched1[i]);
-            inliers2.push_back(matched2[i]);
-            good_matches.push_back(DMatch(new_i, new_i, 0));
-        }
-    }
-
-    Mat res;
-    drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
-    imwrite("latch_result.png", res);
-
-
-    double inlier_ratio = inliers1.size() * 1.0 / matched1.size();
-    cout << "LATCH Matching Results" << endl;
-    cout << "*******************************" << endl;
-    cout << "# Keypoints 1:                        \t" << kpts1.size() << endl;
-    cout << "# Keypoints 2:                        \t" << kpts2.size() << endl;
-    cout << "# Matches:                            \t" << matched1.size() << endl;
-    cout << "# Inliers:                            \t" << inliers1.size() << endl;
-    cout << "# Inliers Ratio:                      \t" << inlier_ratio << endl;
-    cout << endl;
-
-    imshow("result", res);
-    waitKey();
-
-    return 0;
-}
-
-#else
-
-int main()
-{
-    std::cerr << "OpenCV was built without xfeatures2d module" << std::endl;
-    return 0;
-}
-
-#endif

samples/gpu/surf_keypoint_matcher.cpp

@@ -1,96 +0,0 @@
-#include <iostream>
-
-#include "opencv2/opencv_modules.hpp"
-
-#ifdef HAVE_OPENCV_XFEATURES2D
-
-#include "opencv2/core.hpp"
-#include "opencv2/features2d.hpp"
-#include "opencv2/highgui.hpp"
-#include "opencv2/cudafeatures2d.hpp"
-#include "opencv2/xfeatures2d/cuda.hpp"
-
-using namespace std;
-using namespace cv;
-using namespace cv::cuda;
-
-static void help()
-{
-    cout << "\nThis program demonstrates using SURF_CUDA features detector, descriptor extractor and BruteForceMatcher_CUDA" << endl;
-    cout << "\nUsage:\n\tsurf_keypoint_matcher --left <image1> --right <image2>" << endl;
-}
-
-int main(int argc, char* argv[])
-{
-    if (argc != 5)
-    {
-        help();
-        return -1;
-    }
-
-    GpuMat img1, img2;
-    for (int i = 1; i < argc; ++i)
-    {
-        if (string(argv[i]) == "--left")
-        {
-            img1.upload(imread(argv[++i], IMREAD_GRAYSCALE));
-            CV_Assert(!img1.empty());
-        }
-        else if (string(argv[i]) == "--right")
-        {
-            img2.upload(imread(argv[++i], IMREAD_GRAYSCALE));
-            CV_Assert(!img2.empty());
-        }
-        else if (string(argv[i]) == "--help")
-        {
-            help();
-            return -1;
-        }
-    }
-
-    cv::cuda::printShortCudaDeviceInfo(cv::cuda::getDevice());
-
-    SURF_CUDA surf;
-
-    // detecting keypoints & computing descriptors
-    GpuMat keypoints1GPU, keypoints2GPU;
-    GpuMat descriptors1GPU, descriptors2GPU;
-    surf(img1, GpuMat(), keypoints1GPU, descriptors1GPU);
-    surf(img2, GpuMat(), keypoints2GPU, descriptors2GPU);
-
-    cout << "FOUND " << keypoints1GPU.cols << " keypoints on first image" << endl;
-    cout << "FOUND " << keypoints2GPU.cols << " keypoints on second image" << endl;
-
-    // matching descriptors
-    Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher(surf.defaultNorm());
-    vector<DMatch> matches;
-    matcher->match(descriptors1GPU, descriptors2GPU, matches);
-
-    // downloading results
-    vector<KeyPoint> keypoints1, keypoints2;
-    vector<float> descriptors1, descriptors2;
-    surf.downloadKeypoints(keypoints1GPU, keypoints1);
-    surf.downloadKeypoints(keypoints2GPU, keypoints2);
-    surf.downloadDescriptors(descriptors1GPU, descriptors1);
-    surf.downloadDescriptors(descriptors2GPU, descriptors2);
-
-    // drawing the results
-    Mat img_matches;
-    drawMatches(Mat(img1), keypoints1, Mat(img2), keypoints2, matches, img_matches);
-
-    namedWindow("matches", 0);
-    imshow("matches", img_matches);
-    waitKey(0);
-
-    return 0;
-}
-
-#else
-
-int main()
-{
-    std::cerr << "OpenCV was built without xfeatures2d module" << std::endl;
-    return 0;
-}
-
-#endif

samples/java/tutorial_code/features2D/akaze_matching/AKAZEMatchDemo.java

@@ -15,7 +15,7 @@ import org.opencv.core.Mat;
 import org.opencv.core.MatOfDMatch;
 import org.opencv.core.MatOfKeyPoint;
 import org.opencv.core.Scalar;
-import org.opencv.features2d.AKAZE;
+import org.opencv.xfeatures2d.AKAZE;
 import org.opencv.features2d.DescriptorMatcher;
 import org.opencv.features2d.Features2d;
 import org.opencv.highgui.HighGui;

samples/python/stitching_detailed.py

@@ -40,11 +40,13 @@ try:
 except AttributeError:
     print("SIFT not available")
 try:
-    FEATURES_FIND_CHOICES['brisk'] = cv.BRISK_create
+    cv.xfeatures2d_BRISK.create() # check if the function can be called
+    FEATURES_FIND_CHOICES['brisk'] = cv.xfeatures2d_BRISK.create
 except AttributeError:
     print("BRISK not available")
 try:
-    FEATURES_FIND_CHOICES['akaze'] = cv.AKAZE_create
+    cv.xfeatures2d_AKAZE.create() # check if the function can be called
+    FEATURES_FIND_CHOICES['akaze'] = cv.xfeatures2d_AKAZE.create
 except AttributeError:
     print("AKAZE not available")
 
@@ -276,7 +278,6 @@ def get_compensator(args):
 def main():
     args = parser.parse_args()
     img_names = args.img_names
-    print(img_names)
     work_megapix = args.work_megapix
     seam_megapix = args.seam_megapix
     compose_megapix = args.compose_megapix

samples/python/tutorial_code/features2D/akaze_matching/AKAZE_match.py

@@ -22,7 +22,7 @@ homography = fs.getFirstTopLevelNode().mat()
 ## [load]
 
 ## [AKAZE]
-akaze = cv.AKAZE_create()
+akaze = cv.xfeatures2d.AKAZE_create()
 kpts1, desc1 = akaze.detectAndCompute(img1, None)
 kpts2, desc2 = akaze.detectAndCompute(img2, None)
 ## [AKAZE]