add part of opencv

Lib/opencv/sources/modules/features2d/src/agast_score.hpp

@@ -0,0 +1,69 @@
+/* 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

Lib/opencv/sources/modules/features2d/src/akaze.cpp

@@ -0,0 +1,253 @@
+/*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)
+        : descriptor(_descriptor_type)
+        , descriptor_channels(_descriptor_channels)
+        , descriptor_size(_descriptor_size)
+        , threshold(_threshold)
+        , octaves(_octaves)
+        , sublevels(_sublevels)
+        , diffusivity(_diffusivity)
+        {
+        }
+
+        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; }
+
+        // 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(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 << "descriptor" << descriptor;
+            fs << "descriptor_channels" << descriptor_channels;
+            fs << "descriptor_size" << descriptor_size;
+            fs << "threshold" << threshold;
+            fs << "octaves" << octaves;
+            fs << "sublevels" << sublevels;
+            fs << "diffusivity" << diffusivity;
+        }
+
+        void read(const FileNode& fn) CV_OVERRIDE
+        {
+            descriptor = static_cast<DescriptorType>((int)fn["descriptor"]);
+            descriptor_channels = (int)fn["descriptor_channels"];
+            descriptor_size = (int)fn["descriptor_size"];
+            threshold = (float)fn["threshold"];
+            octaves = (int)fn["octaves"];
+            sublevels = (int)fn["sublevels"];
+            diffusivity = static_cast<KAZE::DiffusivityType>((int)fn["diffusivity"]);
+        }
+
+        DescriptorType descriptor;
+        int descriptor_channels;
+        int descriptor_size;
+        float threshold;
+        int octaves;
+        int sublevels;
+        KAZE::DiffusivityType diffusivity;
+    };
+
+    Ptr<AKAZE> AKAZE::create(DescriptorType descriptor_type,
+                             int descriptor_size, int descriptor_channels,
+                             float threshold, int octaves,
+                             int sublevels, KAZE::DiffusivityType diffusivity)
+    {
+        return makePtr<AKAZE_Impl>(descriptor_type, descriptor_size, descriptor_channels,
+                                   threshold, octaves, sublevels, diffusivity);
+    }
+
+    String AKAZE::getDefaultName() const
+    {
+        return (Feature2D::getDefaultName() + ".AKAZE");
+    }
+
+}

Lib/opencv/sources/modules/features2d/src/bagofwords.cpp

@@ -0,0 +1,216 @@
+/*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;
+}
+
+}

Lib/opencv/sources/modules/features2d/src/blobdetector.cpp

@@ -0,0 +1,389 @@
+/*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 "precomp.hpp"
+#include <iterator>
+#include <limits>
+
+// Requires CMake flag: DEBUG_opencv_features2d=ON
+//#define DEBUG_BLOB_DETECTOR
+
+#ifdef DEBUG_BLOB_DETECTOR
+#include "opencv2/highgui.hpp"
+#endif
+
+namespace cv
+{
+
+class CV_EXPORTS_W SimpleBlobDetectorImpl : public SimpleBlobDetector
+{
+public:
+
+  explicit SimpleBlobDetectorImpl(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
+
+  virtual void read( const FileNode& fn ) CV_OVERRIDE;
+  virtual void write( FileStorage& fs ) const CV_OVERRIDE;
+
+protected:
+  struct CV_EXPORTS Center
+  {
+      Point2d location;
+      double radius;
+      double confidence;
+  };
+
+  virtual void detect( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() ) CV_OVERRIDE;
+  virtual void findBlobs(InputArray image, InputArray binaryImage, std::vector<Center> &centers) const;
+
+  Params params;
+};
+
+/*
+*  SimpleBlobDetector
+*/
+SimpleBlobDetector::Params::Params()
+{
+    thresholdStep = 10;
+    minThreshold = 50;
+    maxThreshold = 220;
+    minRepeatability = 2;
+    minDistBetweenBlobs = 10;
+
+    filterByColor = true;
+    blobColor = 0;
+
+    filterByArea = true;
+    minArea = 25;
+    maxArea = 5000;
+
+    filterByCircularity = false;
+    minCircularity = 0.8f;
+    maxCircularity = std::numeric_limits<float>::max();
+
+    filterByInertia = true;
+    //minInertiaRatio = 0.6;
+    minInertiaRatio = 0.1f;
+    maxInertiaRatio = std::numeric_limits<float>::max();
+
+    filterByConvexity = true;
+    //minConvexity = 0.8;
+    minConvexity = 0.95f;
+    maxConvexity = std::numeric_limits<float>::max();
+}
+
+void SimpleBlobDetector::Params::read(const cv::FileNode& fn )
+{
+    thresholdStep = fn["thresholdStep"];
+    minThreshold = fn["minThreshold"];
+    maxThreshold = fn["maxThreshold"];
+
+    minRepeatability = (size_t)(int)fn["minRepeatability"];
+    minDistBetweenBlobs = fn["minDistBetweenBlobs"];
+
+    filterByColor = (int)fn["filterByColor"] != 0 ? true : false;
+    blobColor = (uchar)(int)fn["blobColor"];
+
+    filterByArea = (int)fn["filterByArea"] != 0 ? true : false;
+    minArea = fn["minArea"];
+    maxArea = fn["maxArea"];
+
+    filterByCircularity = (int)fn["filterByCircularity"] != 0 ? true : false;
+    minCircularity = fn["minCircularity"];
+    maxCircularity = fn["maxCircularity"];
+
+    filterByInertia = (int)fn["filterByInertia"] != 0 ? true : false;
+    minInertiaRatio = fn["minInertiaRatio"];
+    maxInertiaRatio = fn["maxInertiaRatio"];
+
+    filterByConvexity = (int)fn["filterByConvexity"] != 0 ? true : false;
+    minConvexity = fn["minConvexity"];
+    maxConvexity = fn["maxConvexity"];
+}
+
+void SimpleBlobDetector::Params::write(cv::FileStorage& fs) const
+{
+    fs << "thresholdStep" << thresholdStep;
+    fs << "minThreshold" << minThreshold;
+    fs << "maxThreshold" << maxThreshold;
+
+    fs << "minRepeatability" << (int)minRepeatability;
+    fs << "minDistBetweenBlobs" << minDistBetweenBlobs;
+
+    fs << "filterByColor" << (int)filterByColor;
+    fs << "blobColor" << (int)blobColor;
+
+    fs << "filterByArea" << (int)filterByArea;
+    fs << "minArea" << minArea;
+    fs << "maxArea" << maxArea;
+
+    fs << "filterByCircularity" << (int)filterByCircularity;
+    fs << "minCircularity" << minCircularity;
+    fs << "maxCircularity" << maxCircularity;
+
+    fs << "filterByInertia" << (int)filterByInertia;
+    fs << "minInertiaRatio" << minInertiaRatio;
+    fs << "maxInertiaRatio" << maxInertiaRatio;
+
+    fs << "filterByConvexity" << (int)filterByConvexity;
+    fs << "minConvexity" << minConvexity;
+    fs << "maxConvexity" << maxConvexity;
+}
+
+SimpleBlobDetectorImpl::SimpleBlobDetectorImpl(const SimpleBlobDetector::Params &parameters) :
+params(parameters)
+{
+}
+
+void SimpleBlobDetectorImpl::read( const cv::FileNode& fn )
+{
+    params.read(fn);
+}
+
+void SimpleBlobDetectorImpl::write( cv::FileStorage& fs ) const
+{
+    writeFormat(fs);
+    params.write(fs);
+}
+
+void SimpleBlobDetectorImpl::findBlobs(InputArray _image, InputArray _binaryImage, std::vector<Center> &centers) const
+{
+    CV_INSTRUMENT_REGION();
+
+    Mat image = _image.getMat(), binaryImage = _binaryImage.getMat();
+    CV_UNUSED(image);
+    centers.clear();
+
+    std::vector < std::vector<Point> > contours;
+    findContours(binaryImage, contours, RETR_LIST, CHAIN_APPROX_NONE);
+
+#ifdef DEBUG_BLOB_DETECTOR
+    Mat keypointsImage;
+    cvtColor(binaryImage, keypointsImage, COLOR_GRAY2RGB);
+
+    Mat contoursImage;
+    cvtColor(binaryImage, contoursImage, COLOR_GRAY2RGB);
+    drawContours( contoursImage, contours, -1, Scalar(0,255,0) );
+    imshow("contours", contoursImage );
+#endif
+
+    for (size_t contourIdx = 0; contourIdx < contours.size(); contourIdx++)
+    {
+        Center center;
+        center.confidence = 1;
+        Moments moms = moments(contours[contourIdx]);
+        if (params.filterByArea)
+        {
+            double area = moms.m00;
+            if (area < params.minArea || area >= params.maxArea)
+                continue;
+        }
+
+        if (params.filterByCircularity)
+        {
+            double area = moms.m00;
+            double perimeter = arcLength(contours[contourIdx], true);
+            double ratio = 4 * CV_PI * area / (perimeter * perimeter);
+            if (ratio < params.minCircularity || ratio >= params.maxCircularity)
+                continue;
+        }
+
+        if (params.filterByInertia)
+        {
+            double denominator = std::sqrt(std::pow(2 * moms.mu11, 2) + std::pow(moms.mu20 - moms.mu02, 2));
+            const double eps = 1e-2;
+            double ratio;
+            if (denominator > eps)
+            {
+                double cosmin = (moms.mu20 - moms.mu02) / denominator;
+                double sinmin = 2 * moms.mu11 / denominator;
+                double cosmax = -cosmin;
+                double sinmax = -sinmin;
+
+                double imin = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmin - moms.mu11 * sinmin;
+                double imax = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmax - moms.mu11 * sinmax;
+                ratio = imin / imax;
+            }
+            else
+            {
+                ratio = 1;
+            }
+
+            if (ratio < params.minInertiaRatio || ratio >= params.maxInertiaRatio)
+                continue;
+
+            center.confidence = ratio * ratio;
+        }
+
+        if (params.filterByConvexity)
+        {
+            std::vector < Point > hull;
+            convexHull(contours[contourIdx], hull);
+            double area = contourArea(contours[contourIdx]);
+            double hullArea = contourArea(hull);
+            if (fabs(hullArea) < DBL_EPSILON)
+                continue;
+            double ratio = area / hullArea;
+            if (ratio < params.minConvexity || ratio >= params.maxConvexity)
+                continue;
+        }
+
+        if(moms.m00 == 0.0)
+            continue;
+        center.location = Point2d(moms.m10 / moms.m00, moms.m01 / moms.m00);
+
+        if (params.filterByColor)
+        {
+            if (binaryImage.at<uchar> (cvRound(center.location.y), cvRound(center.location.x)) != params.blobColor)
+                continue;
+        }
+
+        //compute blob radius
+        {
+            std::vector<double> dists;
+            for (size_t pointIdx = 0; pointIdx < contours[contourIdx].size(); pointIdx++)
+            {
+                Point2d pt = contours[contourIdx][pointIdx];
+                dists.push_back(norm(center.location - pt));
+            }
+            std::sort(dists.begin(), dists.end());
+            center.radius = (dists[(dists.size() - 1) / 2] + dists[dists.size() / 2]) / 2.;
+        }
+
+        centers.push_back(center);
+
+
+#ifdef DEBUG_BLOB_DETECTOR
+        circle( keypointsImage, center.location, 1, Scalar(0,0,255), 1 );
+#endif
+    }
+#ifdef DEBUG_BLOB_DETECTOR
+    imshow("bk", keypointsImage );
+    waitKey();
+#endif
+}
+
+void SimpleBlobDetectorImpl::detect(InputArray image, std::vector<cv::KeyPoint>& keypoints, InputArray mask)
+{
+    CV_INSTRUMENT_REGION();
+
+    keypoints.clear();
+    CV_Assert(params.minRepeatability != 0);
+    Mat grayscaleImage;
+    if (image.channels() == 3 || image.channels() == 4)
+        cvtColor(image, grayscaleImage, COLOR_BGR2GRAY);
+    else
+        grayscaleImage = image.getMat();
+
+    if (grayscaleImage.type() != CV_8UC1) {
+        CV_Error(Error::StsUnsupportedFormat, "Blob detector only supports 8-bit images!");
+    }
+
+    std::vector < std::vector<Center> > centers;
+    for (double thresh = params.minThreshold; thresh < params.maxThreshold; thresh += params.thresholdStep)
+    {
+        Mat binarizedImage;
+        threshold(grayscaleImage, binarizedImage, thresh, 255, THRESH_BINARY);
+
+        std::vector < Center > curCenters;
+        findBlobs(grayscaleImage, binarizedImage, curCenters);
+        std::vector < std::vector<Center> > newCenters;
+        for (size_t i = 0; i < curCenters.size(); i++)
+        {
+            bool isNew = true;
+            for (size_t j = 0; j < centers.size(); j++)
+            {
+                double dist = norm(centers[j][ centers[j].size() / 2 ].location - curCenters[i].location);
+                isNew = dist >= params.minDistBetweenBlobs && dist >= centers[j][ centers[j].size() / 2 ].radius && dist >= curCenters[i].radius;
+                if (!isNew)
+                {
+                    centers[j].push_back(curCenters[i]);
+
+                    size_t k = centers[j].size() - 1;
+                    while( k > 0 && curCenters[i].radius < centers[j][k-1].radius )
+                    {
+                        centers[j][k] = centers[j][k-1];
+                        k--;
+                    }
+                    centers[j][k] = curCenters[i];
+
+                    break;
+                }
+            }
+            if (isNew)
+                newCenters.push_back(std::vector<Center> (1, curCenters[i]));
+        }
+        std::copy(newCenters.begin(), newCenters.end(), std::back_inserter(centers));
+    }
+
+    for (size_t i = 0; i < centers.size(); i++)
+    {
+        if (centers[i].size() < params.minRepeatability)
+            continue;
+        Point2d sumPoint(0, 0);
+        double normalizer = 0;
+        for (size_t j = 0; j < centers[i].size(); j++)
+        {
+            sumPoint += centers[i][j].confidence * centers[i][j].location;
+            normalizer += centers[i][j].confidence;
+        }
+        sumPoint *= (1. / normalizer);
+        KeyPoint kpt(sumPoint, (float)(centers[i][centers[i].size() / 2].radius) * 2.0f);
+        keypoints.push_back(kpt);
+    }
+
+    if (!mask.empty())
+    {
+        KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
+    }
+}
+
+Ptr<SimpleBlobDetector> SimpleBlobDetector::create(const SimpleBlobDetector::Params& params)
+{
+    return makePtr<SimpleBlobDetectorImpl>(params);
+}
+
+String SimpleBlobDetector::getDefaultName() const
+{
+    return (Feature2D::getDefaultName() + ".SimpleBlobDetector");
+}
+
+}

Lib/opencv/sources/modules/features2d/src/draw.cpp

@@ -0,0 +1,262 @@
+/*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"
+
+const int draw_shift_bits = 4;
+const int draw_multiplier = 1 << draw_shift_bits;
+
+namespace cv
+{
+
+/*
+ * Functions to draw keypoints and matches.
+ */
+static inline void _drawKeypoint( InputOutputArray img, const KeyPoint& p, const Scalar& color, DrawMatchesFlags flags )
+{
+    CV_Assert( !img.empty() );
+    Point center( cvRound(p.pt.x * draw_multiplier), cvRound(p.pt.y * draw_multiplier) );
+
+    if( !!(flags & DrawMatchesFlags::DRAW_RICH_KEYPOINTS) )
+    {
+        int radius = cvRound(p.size/2 * draw_multiplier); // KeyPoint::size is a diameter
+
+        // draw the circles around keypoints with the keypoints size
+        circle( img, center, radius, color, 1, LINE_AA, draw_shift_bits );
+
+        // draw orientation of the keypoint, if it is applicable
+        if( p.angle != -1 )
+        {
+            float srcAngleRad = p.angle*(float)CV_PI/180.f;
+            Point orient( cvRound(cos(srcAngleRad)*radius ),
+                          cvRound(sin(srcAngleRad)*radius )
+                        );
+            line( img, center, center+orient, color, 1, LINE_AA, draw_shift_bits );
+        }
+#if 0
+        else
+        {
+            // draw center with R=1
+            int radius = 1 * draw_multiplier;
+            circle( img, center, radius, color, 1, LINE_AA, draw_shift_bits );
+        }
+#endif
+    }
+    else
+    {
+        // draw center with R=3
+        int radius = 3 * draw_multiplier;
+        circle( img, center, radius, color, 1, LINE_AA, draw_shift_bits );
+    }
+}
+
+void drawKeypoints( InputArray image, const std::vector<KeyPoint>& keypoints, InputOutputArray outImage,
+                    const Scalar& _color, DrawMatchesFlags flags )
+{
+    CV_INSTRUMENT_REGION();
+
+    if( !(flags & DrawMatchesFlags::DRAW_OVER_OUTIMG) )
+    {
+        if (image.type() == CV_8UC3 || image.type() == CV_8UC4)
+        {
+            image.copyTo(outImage);
+        }
+        else if( image.type() == CV_8UC1 )
+        {
+            cvtColor( image, outImage, COLOR_GRAY2BGR );
+        }
+        else
+        {
+            CV_Error( Error::StsBadArg, "Incorrect type of input image: " + typeToString(image.type()) );
+        }
+    }
+
+    RNG& rng=theRNG();
+    bool isRandColor = _color == Scalar::all(-1);
+
+    CV_Assert( !outImage.empty() );
+    std::vector<KeyPoint>::const_iterator it = keypoints.begin(),
+                                     end = keypoints.end();
+    for( ; it != end; ++it )
+    {
+        Scalar color = isRandColor ? Scalar( rng(256), rng(256), rng(256), 255 ) : _color;
+        _drawKeypoint( outImage, *it, color, flags );
+    }
+}
+
+static void _prepareImage(InputArray src, const Mat& dst)
+{
+    CV_CheckType(src.type(), src.type() == CV_8UC1 || src.type() == CV_8UC3 || src.type() == CV_8UC4, "Unsupported source image");
+    CV_CheckType(dst.type(), dst.type() == CV_8UC3 || dst.type() == CV_8UC4, "Unsupported destination image");
+    const int src_cn = src.channels();
+    const int dst_cn = dst.channels();
+
+    if (src_cn == dst_cn)
+        src.copyTo(dst);
+    else if (src_cn == 1)
+        cvtColor(src, dst, dst_cn == 3 ? COLOR_GRAY2BGR : COLOR_GRAY2BGRA);
+    else if (src_cn == 3 && dst_cn == 4)
+        cvtColor(src, dst, COLOR_BGR2BGRA);
+    else if (src_cn == 4 && dst_cn == 3)
+        cvtColor(src, dst, COLOR_BGRA2BGR);
+    else
+        CV_Error(Error::StsInternal, "");
+}
+
+static void _prepareImgAndDrawKeypoints( InputArray img1, const std::vector<KeyPoint>& keypoints1,
+                                         InputArray img2, const std::vector<KeyPoint>& keypoints2,
+                                         InputOutputArray _outImg, Mat& outImg1, Mat& outImg2,
+                                         const Scalar& singlePointColor, DrawMatchesFlags flags )
+{
+    Mat outImg;
+    Size img1size = img1.size(), img2size = img2.size();
+    Size size( img1size.width + img2size.width, MAX(img1size.height, img2size.height) );
+    if( !!(flags & DrawMatchesFlags::DRAW_OVER_OUTIMG) )
+    {
+        outImg = _outImg.getMat();
+        if( size.width > outImg.cols || size.height > outImg.rows )
+            CV_Error( Error::StsBadSize, "outImg has size less than need to draw img1 and img2 together" );
+        outImg1 = outImg( Rect(0, 0, img1size.width, img1size.height) );
+        outImg2 = outImg( Rect(img1size.width, 0, img2size.width, img2size.height) );
+    }
+    else
+    {
+        const int cn1 = img1.channels(), cn2 = img2.channels();
+        const int out_cn = std::max(3, std::max(cn1, cn2));
+        _outImg.create(size, CV_MAKETYPE(img1.depth(), out_cn));
+        outImg = _outImg.getMat();
+        outImg = Scalar::all(0);
+        outImg1 = outImg( Rect(0, 0, img1size.width, img1size.height) );
+        outImg2 = outImg( Rect(img1size.width, 0, img2size.width, img2size.height) );
+
+        _prepareImage(img1, outImg1);
+        _prepareImage(img2, outImg2);
+    }
+
+    // draw keypoints
+    if( !(flags & DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS) )
+    {
+        Mat _outImg1 = outImg( Rect(0, 0, img1size.width, img1size.height) );
+        drawKeypoints( _outImg1, keypoints1, _outImg1, singlePointColor, flags | DrawMatchesFlags::DRAW_OVER_OUTIMG );
+
+        Mat _outImg2 = outImg( Rect(img1size.width, 0, img2size.width, img2size.height) );
+        drawKeypoints( _outImg2, keypoints2, _outImg2, singlePointColor, flags | DrawMatchesFlags::DRAW_OVER_OUTIMG );
+    }
+}
+
+static inline void _drawMatch( InputOutputArray outImg, InputOutputArray outImg1, InputOutputArray outImg2 ,
+                          const KeyPoint& kp1, const KeyPoint& kp2, const Scalar& matchColor, DrawMatchesFlags flags )
+{
+    RNG& rng = theRNG();
+    bool isRandMatchColor = matchColor == Scalar::all(-1);
+    Scalar color = isRandMatchColor ? Scalar( rng(256), rng(256), rng(256), 255 ) : matchColor;
+
+    _drawKeypoint( outImg1, kp1, color, flags );
+    _drawKeypoint( outImg2, kp2, color, flags );
+
+    Point2f pt1 = kp1.pt,
+            pt2 = kp2.pt,
+            dpt2 = Point2f( std::min(pt2.x+outImg1.size().width, float(outImg.size().width-1)), pt2.y );
+
+    line( outImg,
+          Point(cvRound(pt1.x*draw_multiplier), cvRound(pt1.y*draw_multiplier)),
+          Point(cvRound(dpt2.x*draw_multiplier), cvRound(dpt2.y*draw_multiplier)),
+          color, 1, LINE_AA, draw_shift_bits );
+}
+
+void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
+                  InputArray img2, const std::vector<KeyPoint>& keypoints2,
+                  const std::vector<DMatch>& matches1to2, InputOutputArray outImg,
+                  const Scalar& matchColor, const Scalar& singlePointColor,
+                  const std::vector<char>& matchesMask, DrawMatchesFlags flags )
+{
+    if( !matchesMask.empty() && matchesMask.size() != matches1to2.size() )
+        CV_Error( Error::StsBadSize, "matchesMask must have the same size as matches1to2" );
+
+    Mat outImg1, outImg2;
+    _prepareImgAndDrawKeypoints( img1, keypoints1, img2, keypoints2,
+                                 outImg, outImg1, outImg2, singlePointColor, flags );
+
+    // draw matches
+    for( size_t m = 0; m < matches1to2.size(); m++ )
+    {
+        if( matchesMask.empty() || matchesMask[m] )
+        {
+            int i1 = matches1to2[m].queryIdx;
+            int i2 = matches1to2[m].trainIdx;
+            CV_Assert(i1 >= 0 && i1 < static_cast<int>(keypoints1.size()));
+            CV_Assert(i2 >= 0 && i2 < static_cast<int>(keypoints2.size()));
+
+            const KeyPoint &kp1 = keypoints1[i1], &kp2 = keypoints2[i2];
+            _drawMatch( outImg, outImg1, outImg2, kp1, kp2, matchColor, flags );
+        }
+    }
+}
+
+void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
+                  InputArray img2, const std::vector<KeyPoint>& keypoints2,
+                  const std::vector<std::vector<DMatch> >& matches1to2, InputOutputArray outImg,
+                  const Scalar& matchColor, const Scalar& singlePointColor,
+                  const std::vector<std::vector<char> >& matchesMask, DrawMatchesFlags flags )
+{
+    if( !matchesMask.empty() && matchesMask.size() != matches1to2.size() )
+        CV_Error( Error::StsBadSize, "matchesMask must have the same size as matches1to2" );
+
+    Mat outImg1, outImg2;
+    _prepareImgAndDrawKeypoints( img1, keypoints1, img2, keypoints2,
+                                 outImg, outImg1, outImg2, singlePointColor, flags );
+
+    // draw matches
+    for( size_t i = 0; i < matches1to2.size(); i++ )
+    {
+        for( size_t j = 0; j < matches1to2[i].size(); j++ )
+        {
+            int i1 = matches1to2[i][j].queryIdx;
+            int i2 = matches1to2[i][j].trainIdx;
+            if( matchesMask.empty() || matchesMask[i][j] )
+            {
+                const KeyPoint &kp1 = keypoints1[i1], &kp2 = keypoints2[i2];
+                _drawMatch( outImg, outImg1, outImg2, kp1, kp2, matchColor, flags );
+            }
+        }
+    }
+}
+}

Lib/opencv/sources/modules/features2d/src/dynamic.cpp

@@ -0,0 +1,47 @@
+/*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) 2009-2010, 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 "precomp.hpp"
+namespace cv
+{
+
+}

Lib/opencv/sources/modules/features2d/src/evaluation.cpp

@@ -0,0 +1,570 @@
+//*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 "precomp.hpp"
+#include <limits>
+
+using namespace cv;
+
+template<typename _Tp> static int solveQuadratic(_Tp a, _Tp b, _Tp c, _Tp& x1, _Tp& x2)
+{
+    if( a == 0 )
+    {
+        if( b == 0 )
+        {
+            x1 = x2 = 0;
+            return c == 0;
+        }
+        x1 = x2 = -c/b;
+        return 1;
+    }
+
+    _Tp d = b*b - 4*a*c;
+    if( d < 0 )
+    {
+        x1 = x2 = 0;
+        return 0;
+    }
+    if( d > 0 )
+    {
+        d = std::sqrt(d);
+        double s = 1/(2*a);
+        x1 = (-b - d)*s;
+        x2 = (-b + d)*s;
+        if( x1 > x2 )
+            std::swap(x1, x2);
+        return 2;
+    }
+    x1 = x2 = -b/(2*a);
+    return 1;
+}
+
+//for android ndk
+#undef _S
+static inline Point2f applyHomography( const Mat_<double>& H, const Point2f& pt )
+{
+    double z = H(2,0)*pt.x + H(2,1)*pt.y + H(2,2);
+    if( z )
+    {
+        double w = 1./z;
+        return Point2f( (float)((H(0,0)*pt.x + H(0,1)*pt.y + H(0,2))*w), (float)((H(1,0)*pt.x + H(1,1)*pt.y + H(1,2))*w) );
+    }
+    return Point2f( std::numeric_limits<float>::max(), std::numeric_limits<float>::max() );
+}
+
+static inline void linearizeHomographyAt( const Mat_<double>& H, const Point2f& pt, Mat_<double>& A )
+{
+    A.create(2,2);
+    double p1 = H(0,0)*pt.x + H(0,1)*pt.y + H(0,2),
+           p2 = H(1,0)*pt.x + H(1,1)*pt.y + H(1,2),
+           p3 = H(2,0)*pt.x + H(2,1)*pt.y + H(2,2),
+           p3_2 = p3*p3;
+    if( p3 )
+    {
+        A(0,0) = H(0,0)/p3 - p1*H(2,0)/p3_2; // fxdx
+        A(0,1) = H(0,1)/p3 - p1*H(2,1)/p3_2; // fxdy
+
+        A(1,0) = H(1,0)/p3 - p2*H(2,0)/p3_2; // fydx
+        A(1,1) = H(1,1)/p3 - p2*H(2,1)/p3_2; // fydx
+    }
+    else
+        A.setTo(Scalar::all(std::numeric_limits<double>::max()));
+}
+
+class EllipticKeyPoint
+{
+public:
+    EllipticKeyPoint();
+    EllipticKeyPoint( const Point2f& _center, const Scalar& _ellipse );
+
+    static void convert( const std::vector<KeyPoint>& src, std::vector<EllipticKeyPoint>& dst );
+    static void convert( const std::vector<EllipticKeyPoint>& src, std::vector<KeyPoint>& dst );
+
+    static Mat_<double> getSecondMomentsMatrix( const Scalar& _ellipse );
+    Mat_<double> getSecondMomentsMatrix() const;
+
+    void calcProjection( const Mat_<double>& H, EllipticKeyPoint& projection ) const;
+    static void calcProjection( const std::vector<EllipticKeyPoint>& src, const Mat_<double>& H, std::vector<EllipticKeyPoint>& dst );
+
+    Point2f center;
+    Scalar ellipse; // 3 elements a, b, c: ax^2+2bxy+cy^2=1
+    Size_<float> axes; // half length of ellipse axes
+    Size_<float> boundingBox; // half sizes of bounding box which sides are parallel to the coordinate axes
+};
+
+EllipticKeyPoint::EllipticKeyPoint()
+{
+    *this = EllipticKeyPoint(Point2f(0,0), Scalar(1, 0, 1) );
+}
+
+EllipticKeyPoint::EllipticKeyPoint( const Point2f& _center, const Scalar& _ellipse )
+{
+    center = _center;
+    ellipse = _ellipse;
+
+    double a = ellipse[0], b = ellipse[1], c = ellipse[2];
+    double ac_b2 = a*c - b*b;
+    double x1, x2;
+    solveQuadratic(1., -(a+c), ac_b2, x1, x2);
+    axes.width = (float)(1/sqrt(x1));
+    axes.height = (float)(1/sqrt(x2));
+
+    boundingBox.width = (float)sqrt(ellipse[2]/ac_b2);
+    boundingBox.height = (float)sqrt(ellipse[0]/ac_b2);
+}
+
+Mat_<double> EllipticKeyPoint::getSecondMomentsMatrix( const Scalar& _ellipse )
+{
+    Mat_<double> M(2, 2);
+    M(0,0) = _ellipse[0];
+    M(1,0) = M(0,1) = _ellipse[1];
+    M(1,1) = _ellipse[2];
+    return M;
+}
+
+Mat_<double> EllipticKeyPoint::getSecondMomentsMatrix() const
+{
+    return getSecondMomentsMatrix(ellipse);
+}
+
+void EllipticKeyPoint::calcProjection( const Mat_<double>& H, EllipticKeyPoint& projection ) const
+{
+    Point2f dstCenter = applyHomography(H, center);
+
+    Mat_<double> invM; invert(getSecondMomentsMatrix(), invM);
+    Mat_<double> Aff; linearizeHomographyAt(H, center, Aff);
+    Mat_<double> dstM; invert(Aff*invM*Aff.t(), dstM);
+
+    projection = EllipticKeyPoint( dstCenter, Scalar(dstM(0,0), dstM(0,1), dstM(1,1)) );
+}
+
+void EllipticKeyPoint::convert( const std::vector<KeyPoint>& src, std::vector<EllipticKeyPoint>& dst )
+{
+    CV_INSTRUMENT_REGION();
+
+    if( !src.empty() )
+    {
+        dst.resize(src.size());
+        for( size_t i = 0; i < src.size(); i++ )
+        {
+            float rad = src[i].size/2;
+            CV_Assert( rad );
+            float fac = 1.f/(rad*rad);
+            dst[i] = EllipticKeyPoint( src[i].pt, Scalar(fac, 0, fac) );
+        }
+    }
+}
+
+void EllipticKeyPoint::convert( const std::vector<EllipticKeyPoint>& src, std::vector<KeyPoint>& dst )
+{
+    CV_INSTRUMENT_REGION();
+
+    if( !src.empty() )
+    {
+        dst.resize(src.size());
+        for( size_t i = 0; i < src.size(); i++ )
+        {
+            Size_<float> axes = src[i].axes;
+            float rad = sqrt(axes.height*axes.width);
+            dst[i] = KeyPoint(src[i].center, 2*rad );
+        }
+    }
+}
+
+void EllipticKeyPoint::calcProjection( const std::vector<EllipticKeyPoint>& src, const Mat_<double>& H, std::vector<EllipticKeyPoint>& dst )
+{
+    if( !src.empty() )
+    {
+        CV_Assert( !H.empty() && H.cols == 3 && H.rows == 3);
+        dst.resize(src.size());
+        std::vector<EllipticKeyPoint>::const_iterator srcIt = src.begin();
+        std::vector<EllipticKeyPoint>::iterator       dstIt = dst.begin();
+        for( ; srcIt != src.end() && dstIt != dst.end(); ++srcIt, ++dstIt )
+            srcIt->calcProjection(H, *dstIt);
+    }
+}
+
+static void filterEllipticKeyPointsByImageSize( std::vector<EllipticKeyPoint>& keypoints, const Size& imgSize )
+{
+    if( !keypoints.empty() )
+    {
+        std::vector<EllipticKeyPoint> filtered;
+        filtered.reserve(keypoints.size());
+        std::vector<EllipticKeyPoint>::const_iterator it = keypoints.begin();
+        for( int i = 0; it != keypoints.end(); ++it, i++ )
+        {
+            if( it->center.x + it->boundingBox.width < imgSize.width &&
+                it->center.x - it->boundingBox.width > 0 &&
+                it->center.y + it->boundingBox.height < imgSize.height &&
+                it->center.y - it->boundingBox.height > 0 )
+                filtered.push_back(*it);
+        }
+        keypoints.assign(filtered.begin(), filtered.end());
+    }
+}
+
+struct IntersectAreaCounter
+{
+    IntersectAreaCounter( float _dr, int _minx,
+                          int _miny, int _maxy,
+                          const Point2f& _diff,
+                          const Scalar& _ellipse1, const Scalar& _ellipse2 ) :
+               dr(_dr), bua(0), bna(0), minx(_minx), miny(_miny), maxy(_maxy),
+               diff(_diff), ellipse1(_ellipse1), ellipse2(_ellipse2) {}
+    IntersectAreaCounter( const IntersectAreaCounter& counter, Split )
+    {
+        *this = counter;
+        bua = 0;
+        bna = 0;
+    }
+
+    void operator()( const BlockedRange& range )
+    {
+        CV_Assert( miny < maxy );
+        CV_Assert( dr > FLT_EPSILON );
+
+        int temp_bua = bua, temp_bna = bna;
+        for( int i = range.begin(); i != range.end(); i++ )
+        {
+            float rx1 = minx + i*dr;
+            float rx2 = rx1 - diff.x;
+            for( float ry1 = (float)miny; ry1 <= (float)maxy; ry1 += dr )
+            {
+                float ry2 = ry1 - diff.y;
+                //compute the distance from the ellipse center
+                float e1 = (float)(ellipse1[0]*rx1*rx1 + 2*ellipse1[1]*rx1*ry1 + ellipse1[2]*ry1*ry1);
+                float e2 = (float)(ellipse2[0]*rx2*rx2 + 2*ellipse2[1]*rx2*ry2 + ellipse2[2]*ry2*ry2);
+                //compute the area
+                if( e1<1 && e2<1 ) temp_bna++;
+                if( e1<1 || e2<1 ) temp_bua++;
+            }
+        }
+        bua = temp_bua;
+        bna = temp_bna;
+    }
+
+    void join( IntersectAreaCounter& ac )
+    {
+        bua += ac.bua;
+        bna += ac.bna;
+    }
+
+    float dr;
+    int bua, bna;
+
+    int minx;
+    int miny, maxy;
+
+    Point2f diff;
+    Scalar ellipse1, ellipse2;
+
+};
+
+struct SIdx
+{
+    SIdx() : S(-1), i1(-1), i2(-1) {}
+    SIdx(float _S, int _i1, int _i2) : S(_S), i1(_i1), i2(_i2) {}
+    float S;
+    int i1;
+    int i2;
+
+    bool operator<(const SIdx& v) const { return S > v.S; }
+
+    struct UsedFinder
+    {
+        UsedFinder(const SIdx& _used) : used(_used) {}
+        const SIdx& used;
+        bool operator()(const SIdx& v) const { return  (v.i1 == used.i1 || v.i2 == used.i2); }
+        UsedFinder& operator=(const UsedFinder&);
+    };
+};
+
+static void computeOneToOneMatchedOverlaps( const std::vector<EllipticKeyPoint>& keypoints1, const std::vector<EllipticKeyPoint>& keypoints2t,
+                                            bool commonPart, std::vector<SIdx>& overlaps, float minOverlap )
+{
+    CV_Assert( minOverlap >= 0.f );
+    overlaps.clear();
+    if( keypoints1.empty() || keypoints2t.empty() )
+        return;
+
+    overlaps.clear();
+    overlaps.reserve(cvRound(keypoints1.size() * keypoints2t.size() * 0.01));
+
+    for( size_t i1 = 0; i1 < keypoints1.size(); i1++ )
+    {
+        EllipticKeyPoint kp1 = keypoints1[i1];
+        float maxDist = sqrt(kp1.axes.width*kp1.axes.height),
+              fac = 30.f/maxDist;
+        if( !commonPart )
+            fac=3;
+
+        maxDist = maxDist*4;
+        fac = 1.f/(fac*fac);
+
+        EllipticKeyPoint keypoint1a = EllipticKeyPoint( kp1.center, Scalar(fac*kp1.ellipse[0], fac*kp1.ellipse[1], fac*kp1.ellipse[2]) );
+
+        for( size_t i2 = 0; i2 < keypoints2t.size(); i2++ )
+        {
+            EllipticKeyPoint kp2 = keypoints2t[i2];
+            Point2f diff = kp2.center - kp1.center;
+
+            if( norm(diff) < maxDist )
+            {
+                EllipticKeyPoint keypoint2a = EllipticKeyPoint( kp2.center, Scalar(fac*kp2.ellipse[0], fac*kp2.ellipse[1], fac*kp2.ellipse[2]) );
+                //find the largest eigenvalue
+                int maxx =  (int)ceil(( keypoint1a.boundingBox.width > (diff.x+keypoint2a.boundingBox.width)) ?
+                                     keypoint1a.boundingBox.width : (diff.x+keypoint2a.boundingBox.width));
+                int minx = (int)floor((-keypoint1a.boundingBox.width < (diff.x-keypoint2a.boundingBox.width)) ?
+                                    -keypoint1a.boundingBox.width : (diff.x-keypoint2a.boundingBox.width));
+
+                int maxy =  (int)ceil(( keypoint1a.boundingBox.height > (diff.y+keypoint2a.boundingBox.height)) ?
+                                     keypoint1a.boundingBox.height : (diff.y+keypoint2a.boundingBox.height));
+                int miny = (int)floor((-keypoint1a.boundingBox.height < (diff.y-keypoint2a.boundingBox.height)) ?
+                                    -keypoint1a.boundingBox.height : (diff.y-keypoint2a.boundingBox.height));
+                int mina = (maxx-minx) < (maxy-miny) ? (maxx-minx) : (maxy-miny) ;
+
+                //compute the area
+                float dr = (float)mina/50.f;
+                int N = (int)floor((float)(maxx - minx) / dr);
+                IntersectAreaCounter ac( dr, minx, miny, maxy, diff, keypoint1a.ellipse, keypoint2a.ellipse );
+                parallel_reduce( BlockedRange(0, N+1), ac );
+                if( ac.bna > 0 )
+                {
+                    float ov =  (float)ac.bna / (float)ac.bua;
+                    if( ov >= minOverlap )
+                        overlaps.push_back(SIdx(ov, (int)i1, (int)i2));
+                }
+            }
+        }
+    }
+
+    std::sort( overlaps.begin(), overlaps.end() );
+
+    typedef std::vector<SIdx>::iterator It;
+
+    It pos = overlaps.begin();
+    It end = overlaps.end();
+
+    while(pos != end)
+    {
+        It prev = pos++;
+        end = std::remove_if(pos, end, SIdx::UsedFinder(*prev));
+    }
+    overlaps.erase(pos, overlaps.end());
+}
+
+static void calculateRepeatability( const Mat& img1, const Mat& img2, const Mat& H1to2,
+                                    const std::vector<KeyPoint>& _keypoints1, const std::vector<KeyPoint>& _keypoints2,
+                                    float& repeatability, int& correspondencesCount,
+                                    Mat* thresholdedOverlapMask=0  )
+{
+    std::vector<EllipticKeyPoint> keypoints1, keypoints2, keypoints1t, keypoints2t;
+    EllipticKeyPoint::convert( _keypoints1, keypoints1 );
+    EllipticKeyPoint::convert( _keypoints2, keypoints2 );
+
+    // calculate projections of key points
+    EllipticKeyPoint::calcProjection( keypoints1, H1to2, keypoints1t );
+    Mat H2to1; invert(H1to2, H2to1);
+    EllipticKeyPoint::calcProjection( keypoints2, H2to1, keypoints2t );
+
+    float overlapThreshold;
+    bool ifEvaluateDetectors = thresholdedOverlapMask == 0;
+    if( ifEvaluateDetectors )
+    {
+        overlapThreshold = 1.f - 0.4f;
+
+        // remove key points from outside of the common image part
+        Size sz1 = img1.size(), sz2 = img2.size();
+        filterEllipticKeyPointsByImageSize( keypoints1, sz1 );
+        filterEllipticKeyPointsByImageSize( keypoints1t, sz2 );
+        filterEllipticKeyPointsByImageSize( keypoints2, sz2 );
+        filterEllipticKeyPointsByImageSize( keypoints2t, sz1 );
+    }
+    else
+    {
+        overlapThreshold = 1.f - 0.5f;
+
+        thresholdedOverlapMask->create( (int)keypoints1.size(), (int)keypoints2t.size(), CV_8UC1 );
+        thresholdedOverlapMask->setTo( Scalar::all(0) );
+    }
+    size_t size1 = keypoints1.size(), size2 = keypoints2t.size();
+    size_t minCount = MIN( size1, size2 );
+
+    // calculate overlap errors
+    std::vector<SIdx> overlaps;
+    computeOneToOneMatchedOverlaps( keypoints1, keypoints2t, ifEvaluateDetectors, overlaps, overlapThreshold/*min overlap*/ );
+
+    correspondencesCount = -1;
+    repeatability = -1.f;
+    if( overlaps.empty() )
+        return;
+
+    if( ifEvaluateDetectors )
+    {
+        // regions one-to-one matching
+        correspondencesCount = (int)overlaps.size();
+        repeatability = minCount ? (float)correspondencesCount / minCount : -1;
+    }
+    else
+    {
+        for( size_t i = 0; i < overlaps.size(); i++ )
+        {
+            int y = overlaps[i].i1;
+            int x = overlaps[i].i2;
+            thresholdedOverlapMask->at<uchar>(y,x) = 1;
+        }
+    }
+}
+
+void cv::evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2,
+                              std::vector<KeyPoint>* _keypoints1, std::vector<KeyPoint>* _keypoints2,
+                              float& repeatability, int& correspCount,
+                              const Ptr<FeatureDetector>& _fdetector )
+{
+    CV_INSTRUMENT_REGION();
+
+    Ptr<FeatureDetector> fdetector(_fdetector);
+    std::vector<KeyPoint> *keypoints1, *keypoints2, buf1, buf2;
+    keypoints1 = _keypoints1 != 0 ? _keypoints1 : &buf1;
+    keypoints2 = _keypoints2 != 0 ? _keypoints2 : &buf2;
+
+    if( (keypoints1->empty() || keypoints2->empty()) && !fdetector )
+        CV_Error( Error::StsBadArg, "fdetector must not be empty when keypoints1 or keypoints2 is empty" );
+
+    if( keypoints1->empty() )
+        fdetector->detect( img1, *keypoints1 );
+    if( keypoints2->empty() )
+        fdetector->detect( img2, *keypoints2 );
+
+    calculateRepeatability( img1, img2, H1to2, *keypoints1, *keypoints2, repeatability, correspCount );
+}
+
+struct DMatchForEvaluation : public DMatch
+{
+    uchar isCorrect;
+    DMatchForEvaluation( const DMatch &dm ) : DMatch( dm ), isCorrect(0) {}
+};
+
+static inline float recall( int correctMatchCount, int correspondenceCount )
+{
+    return correspondenceCount ? (float)correctMatchCount / (float)correspondenceCount : -1;
+}
+
+static inline float precision( int correctMatchCount, int falseMatchCount )
+{
+    return correctMatchCount + falseMatchCount ? (float)correctMatchCount / (float)(correctMatchCount + falseMatchCount) : -1;
+}
+
+void cv::computeRecallPrecisionCurve( const std::vector<std::vector<DMatch> >& matches1to2,
+                                      const std::vector<std::vector<uchar> >& correctMatches1to2Mask,
+                                      std::vector<Point2f>& recallPrecisionCurve )
+{
+    CV_INSTRUMENT_REGION();
+
+    CV_Assert( matches1to2.size() == correctMatches1to2Mask.size() );
+
+    std::vector<DMatchForEvaluation> allMatches;
+    int correspondenceCount = 0;
+    for( size_t i = 0; i < matches1to2.size(); i++ )
+    {
+        for( size_t j = 0; j < matches1to2[i].size(); j++ )
+        {
+            DMatchForEvaluation match = matches1to2[i][j];
+            match.isCorrect = correctMatches1to2Mask[i][j] ;
+            allMatches.push_back( match );
+            correspondenceCount += match.isCorrect != 0 ? 1 : 0;
+        }
+    }
+
+    std::sort( allMatches.begin(), allMatches.end() );
+
+    int correctMatchCount = 0, falseMatchCount = 0;
+    recallPrecisionCurve.resize( allMatches.size() );
+    for( size_t i = 0; i < allMatches.size(); i++ )
+    {
+        if( allMatches[i].isCorrect )
+            correctMatchCount++;
+        else
+            falseMatchCount++;
+
+        float r = recall( correctMatchCount, correspondenceCount );
+        float p =  precision( correctMatchCount, falseMatchCount );
+        recallPrecisionCurve[i] = Point2f(1-p, r);
+    }
+}
+
+float cv::getRecall( const std::vector<Point2f>& recallPrecisionCurve, float l_precision )
+{
+    CV_INSTRUMENT_REGION();
+
+    int nearestPointIndex = getNearestPoint( recallPrecisionCurve, l_precision );
+
+    float recall = -1.f;
+
+    if( nearestPointIndex >= 0 )
+        recall = recallPrecisionCurve[nearestPointIndex].y;
+
+    return recall;
+}
+
+int cv::getNearestPoint( const std::vector<Point2f>& recallPrecisionCurve, float l_precision )
+{
+    CV_INSTRUMENT_REGION();
+
+    int nearestPointIndex = -1;
+
+    if( l_precision >= 0 && l_precision <= 1 )
+    {
+        float minDiff = FLT_MAX;
+        for( size_t i = 0; i < recallPrecisionCurve.size(); i++ )
+        {
+            float curDiff = std::fabs(l_precision - recallPrecisionCurve[i].x);
+            if( curDiff <= minDiff )
+            {
+                nearestPointIndex = (int)i;
+                minDiff = curDiff;
+            }
+        }
+    }
+
+    return nearestPointIndex;
+}

Lib/opencv/sources/modules/features2d/src/fast.avx2.cpp

@@ -0,0 +1,184 @@
+/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
+   Below is the original copyright and the references */
+
+/*
+Copyright (c) 2006, 2008 Edward Rosten
+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:
+ * Machine learning for high-speed corner detection,
+   E. Rosten and T. Drummond, ECCV 2006
+ * Faster and better: A machine learning approach to corner detection
+   E. Rosten, R. Porter and T. Drummond, PAMI, 2009
+*/
+
+#include "precomp.hpp"
+#include "fast.hpp"
+#include "opencv2/core/hal/intrin.hpp"
+
+namespace cv
+{
+namespace opt_AVX2
+{
+
+class FAST_t_patternSize16_AVX2_Impl CV_FINAL: public FAST_t_patternSize16_AVX2
+{
+public:
+    FAST_t_patternSize16_AVX2_Impl(int _cols, int _threshold, bool _nonmax_suppression, const int* _pixel):
+                                   cols(_cols), nonmax_suppression(_nonmax_suppression), pixel(_pixel)
+    {
+        //patternSize = 16
+        t256c = (char)_threshold;
+        threshold = std::min(std::max(_threshold, 0), 255);
+    }
+
+    virtual void process(int &j, const uchar* &ptr, uchar* curr, int* cornerpos, int &ncorners) CV_OVERRIDE
+    {
+        static const __m256i delta256 = _mm256_broadcastsi128_si256(_mm_set1_epi8((char)(-128))), K16_256 = _mm256_broadcastsi128_si256(_mm_set1_epi8((char)8));
+        const __m256i t256 = _mm256_broadcastsi128_si256(_mm_set1_epi8(t256c));
+        for (; j < cols - 32 - 3; j += 32, ptr += 32)
+        {
+            __m256i m0, m1;
+            __m256i v0 = _mm256_loadu_si256((const __m256i*)ptr);
+
+            __m256i v1 = _mm256_xor_si256(_mm256_subs_epu8(v0, t256), delta256);
+            v0 = _mm256_xor_si256(_mm256_adds_epu8(v0, t256), delta256);
+
+            __m256i x0 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[0])), delta256);
+            __m256i x1 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[4])), delta256);
+            __m256i x2 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[8])), delta256);
+            __m256i x3 = _mm256_sub_epi8(_mm256_loadu_si256((const __m256i*)(ptr + pixel[12])), delta256);
+
+            m0 = _mm256_and_si256(_mm256_cmpgt_epi8(x0, v0), _mm256_cmpgt_epi8(x1, v0));
+            m1 = _mm256_and_si256(_mm256_cmpgt_epi8(v1, x0), _mm256_cmpgt_epi8(v1, x1));
+            m0 = _mm256_or_si256(m0, _mm256_and_si256(_mm256_cmpgt_epi8(x1, v0), _mm256_cmpgt_epi8(x2, v0)));
+            m1 = _mm256_or_si256(m1, _mm256_and_si256(_mm256_cmpgt_epi8(v1, x1), _mm256_cmpgt_epi8(v1, x2)));
+            m0 = _mm256_or_si256(m0, _mm256_and_si256(_mm256_cmpgt_epi8(x2, v0), _mm256_cmpgt_epi8(x3, v0)));
+            m1 = _mm256_or_si256(m1, _mm256_and_si256(_mm256_cmpgt_epi8(v1, x2), _mm256_cmpgt_epi8(v1, x3)));
+            m0 = _mm256_or_si256(m0, _mm256_and_si256(_mm256_cmpgt_epi8(x3, v0), _mm256_cmpgt_epi8(x0, v0)));
+            m1 = _mm256_or_si256(m1, _mm256_and_si256(_mm256_cmpgt_epi8(v1, x3), _mm256_cmpgt_epi8(v1, x0)));
+            m0 = _mm256_or_si256(m0, m1);
+
+            unsigned int mask = _mm256_movemask_epi8(m0); //unsigned is important!
+            if (mask == 0){
+                continue;
+            }
+            if ((mask & 0xffff) == 0)
+            {
+                j -= 16;
+                ptr -= 16;
+                continue;
+            }
+
+            __m256i c0 = _mm256_setzero_si256(), c1 = c0, max0 = c0, max1 = c0;
+            for (int k = 0; k < 25; k++)
+            {
+                __m256i x = _mm256_xor_si256(_mm256_loadu_si256((const __m256i*)(ptr + pixel[k])), delta256);
+                m0 = _mm256_cmpgt_epi8(x, v0);
+                m1 = _mm256_cmpgt_epi8(v1, x);
+
+                c0 = _mm256_and_si256(_mm256_sub_epi8(c0, m0), m0);
+                c1 = _mm256_and_si256(_mm256_sub_epi8(c1, m1), m1);
+
+                max0 = _mm256_max_epu8(max0, c0);
+                max1 = _mm256_max_epu8(max1, c1);
+            }
+
+            max0 = _mm256_max_epu8(max0, max1);
+            unsigned int m = _mm256_movemask_epi8(_mm256_cmpgt_epi8(max0, K16_256));
+
+            for (int k = 0; m > 0 && k < 32; k++, m >>= 1)
+                if (m & 1)
+                {
+                    cornerpos[ncorners++] = j + k;
+                    if (nonmax_suppression)
+                    {
+                        short d[25];
+                        for (int q = 0; q < 25; q++)
+                            d[q] = (short)(ptr[k] - ptr[k + pixel[q]]);
+                        v_int16x8 q0 = v_setall_s16(-1000), q1 = v_setall_s16(1000);
+                        for (int q = 0; q < 16; q += 8)
+                        {
+                            v_int16x8 v0_ = v_load(d + q + 1);
+                            v_int16x8 v1_ = v_load(d + q + 2);
+                            v_int16x8 a = v_min(v0_, v1_);
+                            v_int16x8 b = v_max(v0_, v1_);
+                            v0_ = v_load(d + q + 3);
+                            a = v_min(a, v0_);
+                            b = v_max(b, v0_);
+                            v0_ = v_load(d + q + 4);
+                            a = v_min(a, v0_);
+                            b = v_max(b, v0_);
+                            v0_ = v_load(d + q + 5);
+                            a = v_min(a, v0_);
+                            b = v_max(b, v0_);
+                            v0_ = v_load(d + q + 6);
+                            a = v_min(a, v0_);
+                            b = v_max(b, v0_);
+                            v0_ = v_load(d + q + 7);
+                            a = v_min(a, v0_);
+                            b = v_max(b, v0_);
+                            v0_ = v_load(d + q + 8);
+                            a = v_min(a, v0_);
+                            b = v_max(b, v0_);
+                            v0_ = v_load(d + q);
+                            q0 = v_max(q0, v_min(a, v0_));
+                            q1 = v_min(q1, v_max(b, v0_));
+                            v0_ = v_load(d + q + 9);
+                            q0 = v_max(q0, v_min(a, v0_));
+                            q1 = v_min(q1, v_max(b, v0_));
+                        }
+                        q0 = v_max(q0, v_setzero_s16() - q1);
+                        curr[j + k] = (uchar)(v_reduce_max(q0) - 1);
+                    }
+                }
+        }
+        _mm256_zeroupper();
+    }
+
+    virtual ~FAST_t_patternSize16_AVX2_Impl() CV_OVERRIDE {};
+
+private:
+    int cols;
+    char t256c;
+    int threshold;
+    bool nonmax_suppression;
+    const int* pixel;
+};
+
+Ptr<FAST_t_patternSize16_AVX2> FAST_t_patternSize16_AVX2::getImpl(int _cols, int _threshold, bool _nonmax_suppression, const int* _pixel)
+{
+    return Ptr<FAST_t_patternSize16_AVX2>(new FAST_t_patternSize16_AVX2_Impl(_cols, _threshold, _nonmax_suppression, _pixel));
+}
+
+}
+}

Lib/opencv/sources/modules/features2d/src/fast.cpp

@@ -0,0 +1,606 @@
+/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
+   Below is the original copyright and the references */
+
+/*
+Copyright (c) 2006, 2008 Edward Rosten
+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:
+ * Machine learning for high-speed corner detection,
+   E. Rosten and T. Drummond, ECCV 2006
+ * Faster and better: A machine learning approach to corner detection
+   E. Rosten, R. Porter and T. Drummond, PAMI, 2009
+*/
+
+#include "precomp.hpp"
+#include "fast.hpp"
+#include "fast_score.hpp"
+#include "opencl_kernels_features2d.hpp"
+#include "hal_replacement.hpp"
+#include "opencv2/core/hal/intrin.hpp"
+
+#include "opencv2/core/openvx/ovx_defs.hpp"
+
+namespace cv
+{
+
+template<int patternSize>
+void FAST_t(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression)
+{
+    Mat img = _img.getMat();
+    const int K = patternSize/2, N = patternSize + K + 1;
+    int i, j, k, pixel[25];
+    makeOffsets(pixel, (int)img.step, patternSize);
+
+#if CV_SIMD128
+    const int quarterPatternSize = patternSize/4;
+    v_uint8x16 delta = v_setall_u8(0x80), t = v_setall_u8((char)threshold), K16 = v_setall_u8((char)K);
+#if CV_TRY_AVX2
+    Ptr<opt_AVX2::FAST_t_patternSize16_AVX2> fast_t_impl_avx2;
+    if(CV_CPU_HAS_SUPPORT_AVX2)
+        fast_t_impl_avx2 = opt_AVX2::FAST_t_patternSize16_AVX2::getImpl(img.cols, threshold, nonmax_suppression, pixel);
+#endif
+
+#endif
+
+    keypoints.clear();
+
+    threshold = std::min(std::max(threshold, 0), 255);
+
+    uchar threshold_tab[512];
+    for( i = -255; i <= 255; i++ )
+        threshold_tab[i+255] = (uchar)(i < -threshold ? 1 : i > threshold ? 2 : 0);
+
+    AutoBuffer<uchar> _buf((img.cols+16)*3*(sizeof(int) + sizeof(uchar)) + 128);
+    uchar* buf[3];
+    buf[0] = _buf.data(); buf[1] = buf[0] + img.cols; buf[2] = buf[1] + img.cols;
+    int* cpbuf[3];
+    cpbuf[0] = (int*)alignPtr(buf[2] + img.cols, sizeof(int)) + 1;
+    cpbuf[1] = cpbuf[0] + img.cols + 1;
+    cpbuf[2] = cpbuf[1] + img.cols + 1;
+    memset(buf[0], 0, img.cols*3);
+
+    for(i = 3; i < img.rows-2; i++)
+    {
+        const uchar* ptr = img.ptr<uchar>(i) + 3;
+        uchar* curr = buf[(i - 3)%3];
+        int* cornerpos = cpbuf[(i - 3)%3];
+        memset(curr, 0, img.cols);
+        int ncorners = 0;
+
+        if( i < img.rows - 3 )
+        {
+            j = 3;
+#if CV_SIMD128
+            {
+                if( patternSize == 16 )
+                {
+#if CV_TRY_AVX2
+                    if (fast_t_impl_avx2)
+                        fast_t_impl_avx2->process(j, ptr, curr, cornerpos, ncorners);
+#endif
+                    //vz if (j <= (img.cols - 27)) //it doesn't make sense using vectors for less than 8 elements
+                    {
+                        for (; j < img.cols - 16 - 3; j += 16, ptr += 16)
+                        {
+                            v_uint8x16 v = v_load(ptr);
+                            v_int8x16 v0 = v_reinterpret_as_s8((v + t) ^ delta);
+                            v_int8x16 v1 = v_reinterpret_as_s8((v - t) ^ delta);
+
+                            v_int8x16 x0 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[0]), delta));
+                            v_int8x16 x1 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[quarterPatternSize]), delta));
+                            v_int8x16 x2 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[2*quarterPatternSize]), delta));
+                            v_int8x16 x3 = v_reinterpret_as_s8(v_sub_wrap(v_load(ptr + pixel[3*quarterPatternSize]), delta));
+
+                            v_int8x16 m0, m1;
+                            m0 = (v0 < x0) & (v0 < x1);
+                            m1 = (x0 < v1) & (x1 < v1);
+                            m0 = m0 | ((v0 < x1) & (v0 < x2));
+                            m1 = m1 | ((x1 < v1) & (x2 < v1));
+                            m0 = m0 | ((v0 < x2) & (v0 < x3));
+                            m1 = m1 | ((x2 < v1) & (x3 < v1));
+                            m0 = m0 | ((v0 < x3) & (v0 < x0));
+                            m1 = m1 | ((x3 < v1) & (x0 < v1));
+                            m0 = m0 | m1;
+
+                            if( !v_check_any(m0) )
+                                continue;
+                            if( !v_check_any(v_combine_low(m0, m0)) )
+                            {
+                                j -= 8;
+                                ptr -= 8;
+                                continue;
+                            }
+
+                            v_int8x16 c0 = v_setzero_s8();
+                            v_int8x16 c1 = v_setzero_s8();
+                            v_uint8x16 max0 = v_setzero_u8();
+                            v_uint8x16 max1 = v_setzero_u8();
+                            for( k = 0; k < N; k++ )
+                            {
+                                v_int8x16 x = v_reinterpret_as_s8(v_load((ptr + pixel[k])) ^ delta);
+                                m0 = v0 < x;
+                                m1 = x < v1;
+
+                                c0 = v_sub_wrap(c0, m0) & m0;
+                                c1 = v_sub_wrap(c1, m1) & m1;
+
+                                max0 = v_max(max0, v_reinterpret_as_u8(c0));
+                                max1 = v_max(max1, v_reinterpret_as_u8(c1));
+                            }
+
+                            max0 = K16 < v_max(max0, max1);
+                            unsigned int m = v_signmask(v_reinterpret_as_s8(max0));
+
+                            for( k = 0; m > 0 && k < 16; k++, m >>= 1 )
+                            {
+                                if( m & 1 )
+                                {
+                                    cornerpos[ncorners++] = j+k;
+                                    if(nonmax_suppression)
+                                    {
+                                        short d[25];
+                                        for (int _k = 0; _k < 25; _k++)
+                                            d[_k] = (short)(ptr[k] - ptr[k + pixel[_k]]);
+
+                                        v_int16x8 a0, b0, a1, b1;
+                                        a0 = b0 = a1 = b1 = v_load(d + 8);
+                                        for(int shift = 0; shift < 8; ++shift)
+                                        {
+                                            v_int16x8 v_nms = v_load(d + shift);
+                                            a0 = v_min(a0, v_nms);
+                                            b0 = v_max(b0, v_nms);
+                                            v_nms = v_load(d + 9 + shift);
+                                            a1 = v_min(a1, v_nms);
+                                            b1 = v_max(b1, v_nms);
+                                        }
+                                        curr[j + k] = (uchar)(v_reduce_max(v_max(v_max(a0, a1), v_setzero_s16() - v_min(b0, b1))) - 1);
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+#endif
+            for( ; j < img.cols - 3; j++, ptr++ )
+            {
+                int v = ptr[0];
+                const uchar* tab = &threshold_tab[0] - v + 255;
+                int d = tab[ptr[pixel[0]]] | tab[ptr[pixel[8]]];
+
+                if( d == 0 )
+                    continue;
+
+                d &= tab[ptr[pixel[2]]] | tab[ptr[pixel[10]]];
+                d &= tab[ptr[pixel[4]]] | tab[ptr[pixel[12]]];
+                d &= tab[ptr[pixel[6]]] | tab[ptr[pixel[14]]];
+
+                if( d == 0 )
+                    continue;
+
+                d &= tab[ptr[pixel[1]]] | tab[ptr[pixel[9]]];
+                d &= tab[ptr[pixel[3]]] | tab[ptr[pixel[11]]];
+                d &= tab[ptr[pixel[5]]] | tab[ptr[pixel[13]]];
+                d &= tab[ptr[pixel[7]]] | tab[ptr[pixel[15]]];
+
+                if( d & 1 )
+                {
+                    int vt = v - threshold, count = 0;
+
+                    for( k = 0; k < N; k++ )
+                    {
+                        int x = ptr[pixel[k]];
+                        if(x < vt)
+                        {
+                            if( ++count > K )
+                            {
+                                cornerpos[ncorners++] = j;
+                                if(nonmax_suppression)
+                                    curr[j] = (uchar)cornerScore<patternSize>(ptr, pixel, threshold);
+                                break;
+                            }
+                        }
+                        else
+                            count = 0;
+                    }
+                }
+
+                if( d & 2 )
+                {
+                    int vt = v + threshold, count = 0;
+
+                    for( k = 0; k < N; k++ )
+                    {
+                        int x = ptr[pixel[k]];
+                        if(x > vt)
+                        {
+                            if( ++count > K )
+                            {
+                                cornerpos[ncorners++] = j;
+                                if(nonmax_suppression)
+                                    curr[j] = (uchar)cornerScore<patternSize>(ptr, pixel, threshold);
+                                break;
+                            }
+                        }
+                        else
+                            count = 0;
+                    }
+                }
+            }
+        }
+
+        cornerpos[-1] = ncorners;
+
+        if( i == 3 )
+            continue;
+
+        const uchar* prev = buf[(i - 4 + 3)%3];
+        const uchar* pprev = buf[(i - 5 + 3)%3];
+        cornerpos = cpbuf[(i - 4 + 3)%3];
+        ncorners = cornerpos[-1];
+
+        for( k = 0; k < ncorners; k++ )
+        {
+            j = cornerpos[k];
+            int score = prev[j];
+            if( !nonmax_suppression ||
+               (score > prev[j+1] && score > prev[j-1] &&
+                score > pprev[j-1] && score > pprev[j] && score > pprev[j+1] &&
+                score > curr[j-1] && score > curr[j] && score > curr[j+1]) )
+            {
+                keypoints.push_back(KeyPoint((float)j, (float)(i-1), 7.f, -1, (float)score));
+            }
+        }
+    }
+}
+
+#ifdef HAVE_OPENCL
+template<typename pt>
+struct cmp_pt
+{
+    bool operator ()(const pt& a, const pt& b) const { return a.y < b.y || (a.y == b.y && a.x < b.x); }
+};
+
+static bool ocl_FAST( InputArray _img, std::vector<KeyPoint>& keypoints,
+                     int threshold, bool nonmax_suppression, int maxKeypoints )
+{
+    UMat img = _img.getUMat();
+    if( img.cols < 7 || img.rows < 7 )
+        return false;
+    size_t globalsize[] = { (size_t)img.cols-6, (size_t)img.rows-6 };
+
+    ocl::Kernel fastKptKernel("FAST_findKeypoints", ocl::features2d::fast_oclsrc);
+    if (fastKptKernel.empty())
+        return false;
+
+    UMat kp1(1, maxKeypoints*2+1, CV_32S);
+
+    UMat ucounter1(kp1, Rect(0,0,1,1));
+    ucounter1.setTo(Scalar::all(0));
+
+    if( !fastKptKernel.args(ocl::KernelArg::ReadOnly(img),
+                            ocl::KernelArg::PtrReadWrite(kp1),
+                            maxKeypoints, threshold).run(2, globalsize, 0, true))
+        return false;
+
+    Mat mcounter;
+    ucounter1.copyTo(mcounter);
+    int i, counter = mcounter.at<int>(0);
+    counter = std::min(counter, maxKeypoints);
+
+    keypoints.clear();
+
+    if( counter == 0 )
+        return true;
+
+    if( !nonmax_suppression )
+    {
+        Mat m;
+        kp1(Rect(0, 0, counter*2+1, 1)).copyTo(m);
+        const Point* pt = (const Point*)(m.ptr<int>() + 1);
+        for( i = 0; i < counter; i++ )
+            keypoints.push_back(KeyPoint((float)pt[i].x, (float)pt[i].y, 7.f, -1, 1.f));
+    }
+    else
+    {
+        UMat kp2(1, maxKeypoints*3+1, CV_32S);
+        UMat ucounter2 = kp2(Rect(0,0,1,1));
+        ucounter2.setTo(Scalar::all(0));
+
+        ocl::Kernel fastNMSKernel("FAST_nonmaxSupression", ocl::features2d::fast_oclsrc);
+        if (fastNMSKernel.empty())
+            return false;
+
+        size_t globalsize_nms[] = { (size_t)counter };
+        if( !fastNMSKernel.args(ocl::KernelArg::PtrReadOnly(kp1),
+                                ocl::KernelArg::PtrReadWrite(kp2),
+                                ocl::KernelArg::ReadOnly(img),
+                                counter, counter).run(1, globalsize_nms, 0, true))
+            return false;
+
+        Mat m2;
+        kp2(Rect(0, 0, counter*3+1, 1)).copyTo(m2);
+        Point3i* pt2 = (Point3i*)(m2.ptr<int>() + 1);
+        int newcounter = std::min(m2.at<int>(0), counter);
+
+        std::sort(pt2, pt2 + newcounter, cmp_pt<Point3i>());
+
+        for( i = 0; i < newcounter; i++ )
+            keypoints.push_back(KeyPoint((float)pt2[i].x, (float)pt2[i].y, 7.f, -1, (float)pt2[i].z));
+    }
+
+    return true;
+}
+#endif
+
+
+#ifdef HAVE_OPENVX
+namespace ovx {
+    template <> inline bool skipSmallImages<VX_KERNEL_FAST_CORNERS>(int w, int h) { return w*h < 800 * 600; }
+}
+static bool openvx_FAST(InputArray _img, std::vector<KeyPoint>& keypoints,
+                        int _threshold, bool nonmaxSuppression, int type)
+{
+    using namespace ivx;
+
+    // Nonmax suppression is done differently in OpenCV than in OpenVX
+    // 9/16 is the only supported mode in OpenVX
+    if(nonmaxSuppression || type != FastFeatureDetector::TYPE_9_16)
+        return false;
+
+    Mat imgMat = _img.getMat();
+    if(imgMat.empty() || imgMat.type() != CV_8UC1)
+        return false;
+
+    if (ovx::skipSmallImages<VX_KERNEL_FAST_CORNERS>(imgMat.cols, imgMat.rows))
+        return false;
+
+    try
+    {
+        Context context = ovx::getOpenVXContext();
+        Image img = Image::createFromHandle(context, Image::matTypeToFormat(imgMat.type()),
+                                            Image::createAddressing(imgMat), (void*)imgMat.data);
+        ivx::Scalar threshold = ivx::Scalar::create<VX_TYPE_FLOAT32>(context, _threshold);
+        vx_size capacity = imgMat.cols * imgMat.rows;
+        Array corners = Array::create(context, VX_TYPE_KEYPOINT, capacity);
+
+        ivx::Scalar numCorners = ivx::Scalar::create<VX_TYPE_SIZE>(context, 0);
+
+        IVX_CHECK_STATUS(vxuFastCorners(context, img, threshold, (vx_bool)nonmaxSuppression, corners, numCorners));
+
+        size_t nPoints = numCorners.getValue<vx_size>();
+        keypoints.clear(); keypoints.reserve(nPoints);
+        std::vector<vx_keypoint_t> vxCorners;
+        corners.copyTo(vxCorners);
+        for(size_t i = 0; i < nPoints; i++)
+        {
+            vx_keypoint_t kp = vxCorners[i];
+            //if nonmaxSuppression is false, kp.strength is undefined
+            keypoints.push_back(KeyPoint((float)kp.x, (float)kp.y, 7.f, -1, kp.strength));
+        }
+
+#ifdef VX_VERSION_1_1
+        //we should take user memory back before release
+        //(it's not done automatically according to standard)
+        img.swapHandle();
+#endif
+    }
+    catch (const RuntimeError & e)
+    {
+        VX_DbgThrow(e.what());
+    }
+    catch (const WrapperError & e)
+    {
+        VX_DbgThrow(e.what());
+    }
+
+    return true;
+}
+
+#endif
+
+static inline int hal_FAST(cv::Mat& src, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression, FastFeatureDetector::DetectorType type)
+{
+    if (threshold > 20)
+        return CV_HAL_ERROR_NOT_IMPLEMENTED;
+
+    cv::Mat scores(src.size(), src.type());
+
+    int error = cv_hal_FAST_dense(src.data, src.step, scores.data, scores.step, src.cols, src.rows, type);
+
+    if (error != CV_HAL_ERROR_OK)
+        return error;
+
+    cv::Mat suppressedScores(src.size(), src.type());
+
+    if (nonmax_suppression)
+    {
+        error = cv_hal_FAST_NMS(scores.data, scores.step, suppressedScores.data, suppressedScores.step, scores.cols, scores.rows);
+
+        if (error != CV_HAL_ERROR_OK)
+            return error;
+    }
+    else
+    {
+        suppressedScores = scores;
+    }
+
+    if (!threshold && nonmax_suppression) threshold = 1;
+
+    cv::KeyPoint kpt(0, 0, 7.f, -1, 0);
+
+    unsigned uthreshold = (unsigned) threshold;
+
+    int ofs = 3;
+
+    int stride = (int)suppressedScores.step;
+    const unsigned char* pscore = suppressedScores.data;
+
+    keypoints.clear();
+
+    for (int y = ofs; y + ofs < suppressedScores.rows; ++y)
+    {
+        kpt.pt.y = (float)(y);
+        for (int x = ofs; x + ofs < suppressedScores.cols; ++x)
+        {
+            unsigned score = pscore[y * stride + x];
+            if (score > uthreshold)
+            {
+                kpt.pt.x = (float)(x);
+                kpt.response = (nonmax_suppression != 0) ? (float)((int)score - 1) : 0.f;
+                keypoints.push_back(kpt);
+            }
+        }
+    }
+
+    return CV_HAL_ERROR_OK;
+}
+
+void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression, FastFeatureDetector::DetectorType type)
+{
+    CV_INSTRUMENT_REGION();
+
+    CV_OCL_RUN(_img.isUMat() && type == FastFeatureDetector::TYPE_9_16,
+               ocl_FAST(_img, keypoints, threshold, nonmax_suppression, 10000));
+
+    cv::Mat img = _img.getMat();
+    CALL_HAL(fast_dense, hal_FAST, img, keypoints, threshold, nonmax_suppression, type);
+
+    size_t keypoints_count;
+    CALL_HAL(fast, cv_hal_FAST, img.data, img.step, img.cols, img.rows,
+             (uchar*)(keypoints.data()), &keypoints_count, threshold, nonmax_suppression, type);
+
+    CV_OVX_RUN(true,
+               openvx_FAST(_img, keypoints, threshold, nonmax_suppression, type))
+
+    switch(type) {
+    case FastFeatureDetector::TYPE_5_8:
+        FAST_t<8>(_img, keypoints, threshold, nonmax_suppression);
+        break;
+    case FastFeatureDetector::TYPE_7_12:
+        FAST_t<12>(_img, keypoints, threshold, nonmax_suppression);
+        break;
+    case FastFeatureDetector::TYPE_9_16:
+        FAST_t<16>(_img, keypoints, threshold, nonmax_suppression);
+        break;
+    }
+}
+
+
+void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression)
+{
+    CV_INSTRUMENT_REGION();
+
+    FAST(_img, keypoints, threshold, nonmax_suppression, FastFeatureDetector::TYPE_9_16);
+}
+
+
+class FastFeatureDetector_Impl CV_FINAL : public FastFeatureDetector
+{
+public:
+    FastFeatureDetector_Impl( int _threshold, bool _nonmaxSuppression, FastFeatureDetector::DetectorType _type )
+    : threshold(_threshold), nonmaxSuppression(_nonmaxSuppression), type(_type)
+    {}
+
+    void detect( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask ) CV_OVERRIDE
+    {
+        CV_INSTRUMENT_REGION();
+
+        if(_image.empty())
+        {
+            keypoints.clear();
+            return;
+        }
+
+        Mat mask = _mask.getMat(), grayImage;
+        UMat ugrayImage;
+        _InputArray gray = _image;
+        if( _image.type() != CV_8U )
+        {
+            _OutputArray ogray = _image.isUMat() ? _OutputArray(ugrayImage) : _OutputArray(grayImage);
+            cvtColor( _image, ogray, COLOR_BGR2GRAY );
+            gray = ogray;
+        }
+        FAST( gray, keypoints, threshold, nonmaxSuppression, type );
+        KeyPointsFilter::runByPixelsMask( keypoints, mask );
+    }
+
+    void set(int prop, double value)
+    {
+        if(prop == THRESHOLD)
+            threshold = cvRound(value);
+        else if(prop == NONMAX_SUPPRESSION)
+            nonmaxSuppression = value != 0;
+        else if(prop == FAST_N)
+            type = static_cast<FastFeatureDetector::DetectorType>(cvRound(value));
+        else
+            CV_Error(Error::StsBadArg, "");
+    }
+
+    double get(int prop) const
+    {
+        if(prop == THRESHOLD)
+            return threshold;
+        if(prop == NONMAX_SUPPRESSION)
+            return nonmaxSuppression;
+        if(prop == FAST_N)
+            return static_cast<int>(type);
+        CV_Error(Error::StsBadArg, "");
+        return 0;
+    }
+
+    void setThreshold(int threshold_) CV_OVERRIDE { threshold = threshold_; }
+    int getThreshold() const CV_OVERRIDE { return threshold; }
+
+    void setNonmaxSuppression(bool f) CV_OVERRIDE { nonmaxSuppression = f; }
+    bool getNonmaxSuppression() const CV_OVERRIDE { return nonmaxSuppression; }
+
+    void setType(FastFeatureDetector::DetectorType type_) CV_OVERRIDE{ type = type_; }
+    FastFeatureDetector::DetectorType getType() const CV_OVERRIDE{ return type; }
+
+    int threshold;
+    bool nonmaxSuppression;
+    FastFeatureDetector::DetectorType type;
+};
+
+Ptr<FastFeatureDetector> FastFeatureDetector::create( int threshold, bool nonmaxSuppression, FastFeatureDetector::DetectorType type )
+{
+    return makePtr<FastFeatureDetector_Impl>(threshold, nonmaxSuppression, type);
+}
+
+String FastFeatureDetector::getDefaultName() const
+{
+    return (Feature2D::getDefaultName() + ".FastFeatureDetector");
+}
+
+}

Lib/opencv/sources/modules/features2d/src/fast.hpp

@@ -0,0 +1,62 @@
+/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
+   Below is the original copyright and the references */
+
+/*
+Copyright (c) 2006, 2008 Edward Rosten
+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:
+ * Machine learning for high-speed corner detection,
+   E. Rosten and T. Drummond, ECCV 2006
+ * Faster and better: A machine learning approach to corner detection
+   E. Rosten, R. Porter and T. Drummond, PAMI, 2009
+*/
+
+#ifndef OPENCV_FEATURES2D_FAST_HPP
+#define OPENCV_FEATURES2D_FAST_HPP
+
+namespace cv
+{
+namespace opt_AVX2
+{
+#if CV_TRY_AVX2
+class FAST_t_patternSize16_AVX2
+{
+public:
+    static Ptr<FAST_t_patternSize16_AVX2> getImpl(int _cols, int _threshold, bool _nonmax_suppression, const int* _pixel);
+    virtual void process(int &j, const uchar* &ptr, uchar* curr, int* cornerpos, int &ncorners) = 0;
+    virtual ~FAST_t_patternSize16_AVX2() {};
+};
+#endif
+}
+}
+#endif

Lib/opencv/sources/modules/features2d/src/fast_score.cpp

@@ -0,0 +1,365 @@
+/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
+   Below is the original copyright and the references */
+
+/*
+Copyright (c) 2006, 2008 Edward Rosten
+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:
+ * Machine learning for high-speed corner detection,
+   E. Rosten and T. Drummond, ECCV 2006
+ * Faster and better: A machine learning approach to corner detection
+   E. Rosten, R. Porter and T. Drummond, PAMI, 2009
+*/
+
+#include "fast_score.hpp"
+#include "opencv2/core/hal/intrin.hpp"
+#define VERIFY_CORNERS 0
+
+namespace cv {
+
+void makeOffsets(int pixel[25], int rowStride, int patternSize)
+{
+    static const int offsets16[][2] =
+    {
+        {0,  3}, { 1,  3}, { 2,  2}, { 3,  1}, { 3, 0}, { 3, -1}, { 2, -2}, { 1, -3},
+        {0, -3}, {-1, -3}, {-2, -2}, {-3, -1}, {-3, 0}, {-3,  1}, {-2,  2}, {-1,  3}
+    };
+
+    static const int offsets12[][2] =
+    {
+        {0,  2}, { 1,  2}, { 2,  1}, { 2, 0}, { 2, -1}, { 1, -2},
+        {0, -2}, {-1, -2}, {-2, -1}, {-2, 0}, {-2,  1}, {-1,  2}
+    };
+
+    static const int offsets8[][2] =
+    {
+        {0,  1}, { 1,  1}, { 1, 0}, { 1, -1},
+        {0, -1}, {-1, -1}, {-1, 0}, {-1,  1}
+    };
+
+    const int (*offsets)[2] = patternSize == 16 ? offsets16 :
+                              patternSize == 12 ? offsets12 :
+                              patternSize == 8  ? offsets8  : 0;
+
+    CV_Assert(pixel && offsets);
+
+    int k = 0;
+    for( ; k < patternSize; k++ )
+        pixel[k] = offsets[k][0] + offsets[k][1] * rowStride;
+    for( ; k < 25; k++ )
+        pixel[k] = pixel[k - patternSize];
+}
+
+#if VERIFY_CORNERS
+static void testCorner(const uchar* ptr, const int pixel[], int K, int N, int threshold) {
+    // check that with the computed "threshold" the pixel is still a corner
+    // and that with the increased-by-1 "threshold" the pixel is not a corner anymore
+    for( int delta = 0; delta <= 1; delta++ )
+    {
+        int v0 = std::min(ptr[0] + threshold + delta, 255);
+        int v1 = std::max(ptr[0] - threshold - delta, 0);
+        int c0 = 0, c1 = 0;
+
+        for( int k = 0; k < N; k++ )
+        {
+            int x = ptr[pixel[k]];
+            if(x > v0)
+            {
+                if( ++c0 > K )
+                    break;
+                c1 = 0;
+            }
+            else if( x < v1 )
+            {
+                if( ++c1 > K )
+                    break;
+                c0 = 0;
+            }
+            else
+            {
+                c0 = c1 = 0;
+            }
+        }
+        CV_Assert( (delta == 0 && std::max(c0, c1) > K) ||
+                   (delta == 1 && std::max(c0, c1) <= K) );
+    }
+}
+#endif
+
+template<>
+int cornerScore<16>(const uchar* ptr, const int pixel[], int threshold)
+{
+    const int K = 8, N = K*3 + 1;
+    int k, v = ptr[0];
+    short d[N];
+    for( k = 0; k < N; k++ )
+        d[k] = (short)(v - ptr[pixel[k]]);
+
+#if CV_SIMD128
+    if (true)
+    {
+        v_int16x8 q0 = v_setall_s16(-1000), q1 = v_setall_s16(1000);
+        for (k = 0; k < 16; k += 8)
+        {
+            v_int16x8 v0 = v_load(d + k + 1);
+            v_int16x8 v1 = v_load(d + k + 2);
+            v_int16x8 a = v_min(v0, v1);
+            v_int16x8 b = v_max(v0, v1);
+            v0 = v_load(d + k + 3);
+            a = v_min(a, v0);
+            b = v_max(b, v0);
+            v0 = v_load(d + k + 4);
+            a = v_min(a, v0);
+            b = v_max(b, v0);
+            v0 = v_load(d + k + 5);
+            a = v_min(a, v0);
+            b = v_max(b, v0);
+            v0 = v_load(d + k + 6);
+            a = v_min(a, v0);
+            b = v_max(b, v0);
+            v0 = v_load(d + k + 7);
+            a = v_min(a, v0);
+            b = v_max(b, v0);
+            v0 = v_load(d + k + 8);
+            a = v_min(a, v0);
+            b = v_max(b, v0);
+            v0 = v_load(d + k);
+            q0 = v_max(q0, v_min(a, v0));
+            q1 = v_min(q1, v_max(b, v0));
+            v0 = v_load(d + k + 9);
+            q0 = v_max(q0, v_min(a, v0));
+            q1 = v_min(q1, v_max(b, v0));
+        }
+        q0 = v_max(q0, v_setzero_s16() - q1);
+        threshold = v_reduce_max(q0) - 1;
+    }
+    else
+#endif
+    {
+
+        int a0 = threshold;
+        for( k = 0; k < 16; k += 2 )
+        {
+            int a = std::min((int)d[k+1], (int)d[k+2]);
+            a = std::min(a, (int)d[k+3]);
+            if( a <= a0 )
+                continue;
+            a = std::min(a, (int)d[k+4]);
+            a = std::min(a, (int)d[k+5]);
+            a = std::min(a, (int)d[k+6]);
+            a = std::min(a, (int)d[k+7]);
+            a = std::min(a, (int)d[k+8]);
+            a0 = std::max(a0, std::min(a, (int)d[k]));
+            a0 = std::max(a0, std::min(a, (int)d[k+9]));
+        }
+
+        int b0 = -a0;
+        for( k = 0; k < 16; k += 2 )
+        {
+            int b = std::max((int)d[k+1], (int)d[k+2]);
+            b = std::max(b, (int)d[k+3]);
+            b = std::max(b, (int)d[k+4]);
+            b = std::max(b, (int)d[k+5]);
+            if( b >= b0 )
+                continue;
+            b = std::max(b, (int)d[k+6]);
+            b = std::max(b, (int)d[k+7]);
+            b = std::max(b, (int)d[k+8]);
+
+            b0 = std::min(b0, std::max(b, (int)d[k]));
+            b0 = std::min(b0, std::max(b, (int)d[k+9]));
+        }
+
+        threshold = -b0 - 1;
+    }
+
+#if VERIFY_CORNERS
+    testCorner(ptr, pixel, K, N, threshold);
+#endif
+    return threshold;
+}
+
+template<>
+int cornerScore<12>(const uchar* ptr, const int pixel[], int threshold)
+{
+    const int K = 6, N = K*3 + 1;
+    int k, v = ptr[0];
+    short d[N + 4];
+    for( k = 0; k < N; k++ )
+        d[k] = (short)(v - ptr[pixel[k]]);
+#if CV_SIMD128
+    for( k = 0; k < 4; k++ )
+        d[N+k] = d[k];
+#endif
+
+#if CV_SIMD128
+    if (true)
+    {
+        v_int16x8 q0 = v_setall_s16(-1000), q1 = v_setall_s16(1000);
+        for (k = 0; k < 16; k += 8)
+        {
+            v_int16x8 v0 = v_load(d + k + 1);
+            v_int16x8 v1 = v_load(d + k + 2);
+            v_int16x8 a = v_min(v0, v1);
+            v_int16x8 b = v_max(v0, v1);
+            v0 = v_load(d + k + 3);
+            a = v_min(a, v0);
+            b = v_max(b, v0);
+            v0 = v_load(d + k + 4);
+            a = v_min(a, v0);
+            b = v_max(b, v0);
+            v0 = v_load(d + k + 5);
+            a = v_min(a, v0);
+            b = v_max(b, v0);
+            v0 = v_load(d + k + 6);
+            a = v_min(a, v0);
+            b = v_max(b, v0);
+            v0 = v_load(d + k);
+            q0 = v_max(q0, v_min(a, v0));
+            q1 = v_min(q1, v_max(b, v0));
+            v0 = v_load(d + k + 7);
+            q0 = v_max(q0, v_min(a, v0));
+            q1 = v_min(q1, v_max(b, v0));
+        }
+        q0 = v_max(q0, v_setzero_s16() - q1);
+        threshold = v_reduce_max(q0) - 1;
+    }
+    else
+#endif
+    {
+        int a0 = threshold;
+        for( k = 0; k < 12; k += 2 )
+        {
+            int a = std::min((int)d[k+1], (int)d[k+2]);
+            if( a <= a0 )
+                continue;
+            a = std::min(a, (int)d[k+3]);
+            a = std::min(a, (int)d[k+4]);
+            a = std::min(a, (int)d[k+5]);
+            a = std::min(a, (int)d[k+6]);
+            a0 = std::max(a0, std::min(a, (int)d[k]));
+            a0 = std::max(a0, std::min(a, (int)d[k+7]));
+        }
+
+        int b0 = -a0;
+        for( k = 0; k < 12; k += 2 )
+        {
+            int b = std::max((int)d[k+1], (int)d[k+2]);
+            b = std::max(b, (int)d[k+3]);
+            b = std::max(b, (int)d[k+4]);
+            if( b >= b0 )
+                continue;
+            b = std::max(b, (int)d[k+5]);
+            b = std::max(b, (int)d[k+6]);
+
+            b0 = std::min(b0, std::max(b, (int)d[k]));
+            b0 = std::min(b0, std::max(b, (int)d[k+7]));
+        }
+
+        threshold = -b0-1;
+    }
+#if VERIFY_CORNERS
+    testCorner(ptr, pixel, K, N, threshold);
+#endif
+    return threshold;
+}
+
+template<>
+int cornerScore<8>(const uchar* ptr, const int pixel[], int threshold)
+{
+    const int K = 4, N = K * 3 + 1;
+    int k, v = ptr[0];
+    short d[N];
+    for (k = 0; k < N; k++)
+        d[k] = (short)(v - ptr[pixel[k]]);
+
+#if CV_SIMD128
+    if (true)
+    {
+        v_int16x8 v0 = v_load(d + 1);
+        v_int16x8 v1 = v_load(d + 2);
+        v_int16x8 a = v_min(v0, v1);
+        v_int16x8 b = v_max(v0, v1);
+        v0 = v_load(d + 3);
+        a = v_min(a, v0);
+        b = v_max(b, v0);
+        v0 = v_load(d + 4);
+        a = v_min(a, v0);
+        b = v_max(b, v0);
+        v0 = v_load(d);
+        v_int16x8 q0 = v_min(a, v0);
+        v_int16x8 q1 = v_max(b, v0);
+        v0 = v_load(d + 5);
+        q0 = v_max(q0, v_min(a, v0));
+        q1 = v_min(q1, v_max(b, v0));
+        q0 = v_max(q0, v_setzero_s16() - q1);
+        threshold = v_reduce_max(q0) - 1;
+    }
+    else
+#endif
+    {
+        int a0 = threshold;
+        for( k = 0; k < 8; k += 2 )
+        {
+            int a = std::min((int)d[k+1], (int)d[k+2]);
+            if( a <= a0 )
+                continue;
+            a = std::min(a, (int)d[k+3]);
+            a = std::min(a, (int)d[k+4]);
+            a0 = std::max(a0, std::min(a, (int)d[k]));
+            a0 = std::max(a0, std::min(a, (int)d[k+5]));
+        }
+
+        int b0 = -a0;
+        for( k = 0; k < 8; k += 2 )
+        {
+            int b = std::max((int)d[k+1], (int)d[k+2]);
+            b = std::max(b, (int)d[k+3]);
+            if( b >= b0 )
+                continue;
+            b = std::max(b, (int)d[k+4]);
+
+            b0 = std::min(b0, std::max(b, (int)d[k]));
+            b0 = std::min(b0, std::max(b, (int)d[k+5]));
+        }
+
+        threshold = -b0-1;
+    }
+
+#if VERIFY_CORNERS
+    testCorner(ptr, pixel, K, N, threshold);
+#endif
+    return threshold;
+}
+
+} // namespace cv

Lib/opencv/sources/modules/features2d/src/fast_score.hpp

@@ -0,0 +1,62 @@
+/* This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten.
+   Below is the original copyright and the references */
+
+/*
+Copyright (c) 2006, 2008 Edward Rosten
+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:
+ * Machine learning for high-speed corner detection,
+   E. Rosten and T. Drummond, ECCV 2006
+ * Faster and better: A machine learning approach to corner detection
+   E. Rosten, R. Porter and T. Drummond, PAMI, 2009
+*/
+
+#ifndef __OPENCV_FEATURES_2D_FAST_HPP__
+#define __OPENCV_FEATURES_2D_FAST_HPP__
+
+#ifdef __cplusplus
+
+#include "precomp.hpp"
+
+namespace cv
+{
+
+void makeOffsets(int pixel[25], int row_stride, int patternSize);
+
+template<int patternSize>
+int cornerScore(const uchar* ptr, const int pixel[], int threshold);
+
+}
+
+#endif
+#endif

Lib/opencv/sources/modules/features2d/src/feature2d.cpp

@@ -0,0 +1,224 @@
+/*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 "precomp.hpp"
+
+namespace cv
+{
+
+using std::vector;
+
+Feature2D::~Feature2D() {}
+
+/*
+ * Detect keypoints in an image.
+ * image        The image.
+ * keypoints    The detected keypoints.
+ * mask         Mask specifying where to look for keypoints (optional). Must be a char
+ *              matrix with non-zero values in the region of interest.
+ */
+void Feature2D::detect( InputArray image,
+                        std::vector<KeyPoint>& keypoints,
+                        InputArray mask )
+{
+    CV_INSTRUMENT_REGION();
+
+    if( image.empty() )
+    {
+        keypoints.clear();
+        return;
+    }
+    detectAndCompute(image, mask, keypoints, noArray(), false);
+}
+
+
+void Feature2D::detect( InputArrayOfArrays images,
+                        std::vector<std::vector<KeyPoint> >& keypoints,
+                        InputArrayOfArrays masks )
+{
+    CV_INSTRUMENT_REGION();
+
+    int nimages = (int)images.total();
+
+    if (!masks.empty())
+    {
+        CV_Assert(masks.total() == (size_t)nimages);
+    }
+
+    keypoints.resize(nimages);
+
+    if (images.isMatVector())
+    {
+       for (int i = 0; i < nimages; i++)
+       {
+           detect(images.getMat(i), keypoints[i], masks.empty() ? noArray() : masks.getMat(i));
+       }
+    }
+    else
+    {
+        // assume UMats
+        for (int i = 0; i < nimages; i++)
+        {
+            detect(images.getUMat(i), keypoints[i], masks.empty() ? noArray() : masks.getUMat(i));
+        }
+    }
+
+
+}
+
+/*
+ * Compute the descriptors for a set of keypoints in an image.
+ * image        The image.
+ * keypoints    The input keypoints. Keypoints for which a descriptor cannot be computed are removed.
+ * descriptors  Copmputed descriptors. Row i is the descriptor for keypoint i.
+ */
+void Feature2D::compute( InputArray image,
+                         std::vector<KeyPoint>& keypoints,
+                         OutputArray descriptors )
+{
+    CV_INSTRUMENT_REGION();
+
+    if( image.empty() )
+    {
+        descriptors.release();
+        return;
+    }
+    detectAndCompute(image, noArray(), keypoints, descriptors, true);
+}
+
+void Feature2D::compute( InputArrayOfArrays images,
+                         std::vector<std::vector<KeyPoint> >& keypoints,
+                         OutputArrayOfArrays descriptors )
+{
+    CV_INSTRUMENT_REGION();
+
+    if( !descriptors.needed() )
+        return;
+
+    int nimages = (int)images.total();
+
+    CV_Assert( keypoints.size() == (size_t)nimages );
+    // resize descriptors to appropriate size and compute
+    if (descriptors.isMatVector())
+    {
+        vector<Mat>& vec = *(vector<Mat>*)descriptors.getObj();
+        vec.resize(nimages);
+        for (int i = 0; i < nimages; i++)
+        {
+            compute(images.getMat(i), keypoints[i], vec[i]);
+        }
+    }
+    else if (descriptors.isUMatVector())
+    {
+        vector<UMat>& vec = *(vector<UMat>*)descriptors.getObj();
+        vec.resize(nimages);
+        for (int i = 0; i < nimages; i++)
+        {
+            compute(images.getUMat(i), keypoints[i], vec[i]);
+        }
+    }
+    else
+    {
+        CV_Error(Error::StsBadArg, "descriptors must be vector<Mat> or vector<UMat>");
+    }
+}
+
+
+/* Detects keypoints and computes the descriptors */
+void Feature2D::detectAndCompute( InputArray, InputArray,
+                                  std::vector<KeyPoint>&,
+                                  OutputArray,
+                                  bool )
+{
+    CV_INSTRUMENT_REGION();
+
+    CV_Error(Error::StsNotImplemented, "");
+}
+
+void Feature2D::write( const String& fileName ) const
+{
+    FileStorage fs(fileName, FileStorage::WRITE);
+    write(fs);
+}
+
+void Feature2D::read( const String& fileName )
+{
+    FileStorage fs(fileName, FileStorage::READ);
+    read(fs.root());
+}
+
+void Feature2D::write( FileStorage&) const
+{
+}
+
+void Feature2D::read( const FileNode&)
+{
+}
+
+int Feature2D::descriptorSize() const
+{
+    return 0;
+}
+
+int Feature2D::descriptorType() const
+{
+    return CV_32F;
+}
+
+int Feature2D::defaultNorm() const
+{
+    int tp = descriptorType();
+    return tp == CV_8U ? NORM_HAMMING : NORM_L2;
+}
+
+// Return true if detector object is empty
+bool Feature2D::empty() const
+{
+    return true;
+}
+
+String Feature2D::getDefaultName() const
+{
+    return "Feature2D";
+}
+
+}

Lib/opencv/sources/modules/features2d/src/gftt.cpp

@@ -0,0 +1,151 @@
+/*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
+{
+
+class GFTTDetector_Impl CV_FINAL : public GFTTDetector
+{
+public:
+    GFTTDetector_Impl( int _nfeatures, double _qualityLevel,
+                      double _minDistance, int _blockSize, int _gradientSize,
+                      bool _useHarrisDetector, double _k )
+        : nfeatures(_nfeatures), qualityLevel(_qualityLevel), minDistance(_minDistance),
+        blockSize(_blockSize), gradSize(_gradientSize), useHarrisDetector(_useHarrisDetector), k(_k)
+    {
+    }
+
+    void setMaxFeatures(int maxFeatures) CV_OVERRIDE { nfeatures = maxFeatures; }
+    int getMaxFeatures() const CV_OVERRIDE { return nfeatures; }
+
+    void setQualityLevel(double qlevel) CV_OVERRIDE { qualityLevel = qlevel; }
+    double getQualityLevel() const CV_OVERRIDE { return qualityLevel; }
+
+    void setMinDistance(double minDistance_) CV_OVERRIDE { minDistance = minDistance_; }
+    double getMinDistance() const CV_OVERRIDE { return minDistance; }
+
+    void setBlockSize(int blockSize_) CV_OVERRIDE { blockSize = blockSize_; }
+    int getBlockSize() const CV_OVERRIDE { return blockSize; }
+
+    //void setGradientSize(int gradientSize_) { gradSize = gradientSize_; }
+    //int getGradientSize() { return gradSize; }
+
+    void setHarrisDetector(bool val) CV_OVERRIDE { useHarrisDetector = val; }
+    bool getHarrisDetector() const CV_OVERRIDE { return useHarrisDetector; }
+
+    void setK(double k_) CV_OVERRIDE { k = k_; }
+    double getK() const CV_OVERRIDE { return k; }
+
+    void detect( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask ) CV_OVERRIDE
+    {
+        CV_INSTRUMENT_REGION();
+
+        if(_image.empty())
+        {
+            keypoints.clear();
+            return;
+        }
+
+        std::vector<Point2f> corners;
+
+        if (_image.isUMat())
+        {
+            UMat ugrayImage;
+            if( _image.type() != CV_8U )
+                cvtColor( _image, ugrayImage, COLOR_BGR2GRAY );
+            else
+                ugrayImage = _image.getUMat();
+
+            goodFeaturesToTrack( ugrayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
+                                 blockSize, gradSize, useHarrisDetector, k );
+        }
+        else
+        {
+            Mat image = _image.getMat(), grayImage = image;
+            if( image.type() != CV_8U )
+                cvtColor( image, grayImage, COLOR_BGR2GRAY );
+
+            goodFeaturesToTrack( grayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
+                                blockSize, gradSize, useHarrisDetector, k );
+        }
+
+        keypoints.resize(corners.size());
+        std::vector<Point2f>::const_iterator corner_it = corners.begin();
+        std::vector<KeyPoint>::iterator keypoint_it = keypoints.begin();
+        for( ; corner_it != corners.end() && keypoint_it != keypoints.end(); ++corner_it, ++keypoint_it )
+            *keypoint_it = KeyPoint( *corner_it, (float)blockSize );
+
+    }
+
+    int nfeatures;
+    double qualityLevel;
+    double minDistance;
+    int blockSize;
+    int gradSize;
+    bool useHarrisDetector;
+    double k;
+};
+
+
+Ptr<GFTTDetector> GFTTDetector::create( int _nfeatures, double _qualityLevel,
+                         double _minDistance, int _blockSize, int _gradientSize,
+                         bool _useHarrisDetector, double _k )
+{
+    return makePtr<GFTTDetector_Impl>(_nfeatures, _qualityLevel,
+                                      _minDistance, _blockSize, _gradientSize, _useHarrisDetector, _k);
+}
+
+Ptr<GFTTDetector> GFTTDetector::create( int _nfeatures, double _qualityLevel,
+                         double _minDistance, int _blockSize,
+                         bool _useHarrisDetector, double _k )
+{
+    return makePtr<GFTTDetector_Impl>(_nfeatures, _qualityLevel,
+                                      _minDistance, _blockSize, 3, _useHarrisDetector, _k);
+}
+
+String GFTTDetector::getDefaultName() const
+{
+    return (Feature2D::getDefaultName() + ".GFTTDetector");
+}
+
+}

Lib/opencv/sources/modules/features2d/src/hal_replacement.hpp

@@ -0,0 +1,135 @@
+/*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) 2017, 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 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*/
+
+#ifndef OPENCV_FEATURES2D_HAL_REPLACEMENT_HPP
+#define OPENCV_FEATURES2D_HAL_REPLACEMENT_HPP
+
+#include "opencv2/core/hal/interface.h"
+
+#if defined __GNUC__
+#  pragma GCC diagnostic push
+#  pragma GCC diagnostic ignored "-Wunused-parameter"
+#elif defined _MSC_VER
+#  pragma warning( push )
+#  pragma warning( disable: 4100 )
+#endif
+
+//! @addtogroup features2d_hal_interface
+//! @note Define your functions to override default implementations:
+//! @code
+//! #undef hal_add8u
+//! #define hal_add8u my_add8u
+//! @endcode
+//! @{
+/**
+   @brief Detects corners using the FAST algorithm, returns mask.
+   @param src_data,src_step Source image
+   @param dst_data,dst_step Destination mask
+   @param width,height Source image dimensions
+   @param type FAST type
+*/
+inline int hal_ni_FAST_dense(const uchar* src_data, size_t src_step, uchar* dst_data, size_t dst_step, int width, int height, cv::FastFeatureDetector::DetectorType type) { return CV_HAL_ERROR_NOT_IMPLEMENTED; }
+
+//! @cond IGNORED
+#define cv_hal_FAST_dense hal_ni_FAST_dense
+//! @endcond
+
+/**
+   @brief Non-maximum suppression for FAST_9_16.
+   @param src_data,src_step Source mask
+   @param dst_data,dst_step Destination mask after NMS
+   @param width,height Source mask dimensions
+*/
+inline int hal_ni_FAST_NMS(const uchar* src_data, size_t src_step, uchar* dst_data, size_t dst_step, int width, int height) { return CV_HAL_ERROR_NOT_IMPLEMENTED; }
+
+//! @cond IGNORED
+#define cv_hal_FAST_NMS hal_ni_FAST_NMS
+//! @endcond
+
+/**
+   @brief Detects corners using the FAST algorithm.
+   @param src_data,src_step Source image
+   @param width,height Source image dimensions
+   @param keypoints_data Pointer to keypoints
+   @param keypoints_count Count of keypoints
+   @param threshold Threshold for keypoint
+   @param nonmax_suppression Indicates if make nonmaxima suppression or not.
+   @param type FAST type
+*/
+inline int hal_ni_FAST(const uchar* src_data, size_t src_step, int width, int height, uchar* keypoints_data, size_t* keypoints_count, int threshold, bool nonmax_suppression, int /*cv::FastFeatureDetector::DetectorType*/ type) { return CV_HAL_ERROR_NOT_IMPLEMENTED; }
+
+//! @cond IGNORED
+#define cv_hal_FAST hal_ni_FAST
+//! @endcond
+
+//! @}
+
+
+#if defined __GNUC__
+#  pragma GCC diagnostic pop
+#elif defined _MSC_VER
+#  pragma warning( pop )
+#endif
+
+#include "custom_hal.hpp"
+
+//! @cond IGNORED
+#define CALL_HAL_RET(name, fun, retval, ...) \
+    int res = __CV_EXPAND(fun(__VA_ARGS__, &retval)); \
+    if (res == CV_HAL_ERROR_OK) \
+        return retval; \
+    else if (res != CV_HAL_ERROR_NOT_IMPLEMENTED) \
+        CV_Error_(cv::Error::StsInternal, \
+            ("HAL implementation " CVAUX_STR(name) " ==> " CVAUX_STR(fun) " returned %d (0x%08x)", res, res));
+
+
+#define CALL_HAL(name, fun, ...) \
+{                                           \
+    int res = __CV_EXPAND(fun(__VA_ARGS__)); \
+    if (res == CV_HAL_ERROR_OK) \
+        return; \
+    else if (res != CV_HAL_ERROR_NOT_IMPLEMENTED) \
+        CV_Error_(cv::Error::StsInternal, \
+            ("HAL implementation " CVAUX_STR(name) " ==> " CVAUX_STR(fun) " returned %d (0x%08x)", res, res)); \
+}
+//! @endcond
+
+#endif

Lib/opencv/sources/modules/features2d/src/kaze.cpp

@@ -0,0 +1,205 @@
+/*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 << "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
+        {
+            extended = (int)fn["extended"] != 0;
+            upright = (int)fn["upright"] != 0;
+            threshold = (float)fn["threshold"];
+            octaves = (int)fn["octaves"];
+            sublevels = (int)fn["sublevels"];
+            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");
+    }
+
+}

Lib/opencv/sources/modules/features2d/src/kaze/AKAZEConfig.h

@@ -0,0 +1,65 @@
+/**
+ * @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

Lib/opencv/sources/modules/features2d/src/kaze/AKAZEFeatures.h

@@ -0,0 +1,115 @@
+/**
+ * @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

Lib/opencv/sources/modules/features2d/src/kaze/KAZEConfig.h

@@ -0,0 +1,56 @@
+/**
+ * @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

Lib/opencv/sources/modules/features2d/src/kaze/KAZEFeatures.h

@@ -0,0 +1,64 @@
+
+/**
+ * @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

Lib/opencv/sources/modules/features2d/src/kaze/TEvolution.h

@@ -0,0 +1,41 @@
+/**
+ * @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

Lib/opencv/sources/modules/features2d/src/kaze/fed.cpp

@@ -0,0 +1,192 @@
+//=============================================================================
+//
+// 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;
+  }
+}

Lib/opencv/sources/modules/features2d/src/kaze/fed.h

@@ -0,0 +1,25 @@
+#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__

Lib/opencv/sources/modules/features2d/src/kaze/nldiffusion_functions.cpp

@@ -0,0 +1,542 @@
+//=============================================================================
+//
+// 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 Ld2 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 img 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;
+}
+
+}

Lib/opencv/sources/modules/features2d/src/kaze/nldiffusion_functions.h

@@ -0,0 +1,47 @@
+/**
+ * @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 suppresion
+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

Lib/opencv/sources/modules/features2d/src/kaze/utils.h

@@ -0,0 +1,42 @@
+#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 sig 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

Lib/opencv/sources/modules/features2d/src/keypoint.cpp

@@ -0,0 +1,266 @@
+/*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*/
+
+#include "precomp.hpp"
+
+namespace cv
+{
+
+struct KeypointResponseGreaterThanThreshold
+{
+    KeypointResponseGreaterThanThreshold(float _value) :
+    value(_value)
+    {
+    }
+    inline bool operator()(const KeyPoint& kpt) const
+    {
+        return kpt.response >= value;
+    }
+    float value;
+};
+
+struct KeypointResponseGreater
+{
+    inline bool operator()(const KeyPoint& kp1, const KeyPoint& kp2) const
+    {
+        return kp1.response > kp2.response;
+    }
+};
+
+// takes keypoints and culls them by the response
+void KeyPointsFilter::retainBest(std::vector<KeyPoint>& keypoints, int n_points)
+{
+    //this is only necessary if the keypoints size is greater than the number of desired points.
+    if( n_points >= 0 && keypoints.size() > (size_t)n_points )
+    {
+        if (n_points==0)
+        {
+            keypoints.clear();
+            return;
+        }
+        //first use nth element to partition the keypoints into the best and worst.
+        std::nth_element(keypoints.begin(), keypoints.begin() + n_points - 1, keypoints.end(), KeypointResponseGreater());
+        //this is the boundary response, and in the case of FAST may be ambiguous
+        float ambiguous_response = keypoints[n_points - 1].response;
+        //use std::partition to grab all of the keypoints with the boundary response.
+        std::vector<KeyPoint>::const_iterator new_end =
+        std::partition(keypoints.begin() + n_points, keypoints.end(),
+                       KeypointResponseGreaterThanThreshold(ambiguous_response));
+        //resize the keypoints, given this new end point. nth_element and partition reordered the points inplace
+        keypoints.resize(new_end - keypoints.begin());
+    }
+}
+
+struct RoiPredicate
+{
+    RoiPredicate( const Rect& _r ) : r(_r)
+    {}
+
+    bool operator()( const KeyPoint& keyPt ) const
+    {
+        return !r.contains( keyPt.pt );
+    }
+
+    Rect r;
+};
+
+void KeyPointsFilter::runByImageBorder( std::vector<KeyPoint>& keypoints, Size imageSize, int borderSize )
+{
+    if( borderSize > 0)
+    {
+        if (imageSize.height <= borderSize * 2 || imageSize.width <= borderSize * 2)
+            keypoints.clear();
+        else
+            keypoints.erase( std::remove_if(keypoints.begin(), keypoints.end(),
+                                       RoiPredicate(Rect(Point(borderSize, borderSize),
+                                                         Point(imageSize.width - borderSize, imageSize.height - borderSize)))),
+                             keypoints.end() );
+    }
+}
+
+struct SizePredicate
+{
+    SizePredicate( float _minSize, float _maxSize ) : minSize(_minSize), maxSize(_maxSize)
+    {}
+
+    bool operator()( const KeyPoint& keyPt ) const
+    {
+        float size = keyPt.size;
+        return (size < minSize) || (size > maxSize);
+    }
+
+    float minSize, maxSize;
+};
+
+void KeyPointsFilter::runByKeypointSize( std::vector<KeyPoint>& keypoints, float minSize, float maxSize )
+{
+    CV_Assert( minSize >= 0 );
+    CV_Assert( maxSize >= 0);
+    CV_Assert( minSize <= maxSize );
+
+    keypoints.erase( std::remove_if(keypoints.begin(), keypoints.end(), SizePredicate(minSize, maxSize)),
+                     keypoints.end() );
+}
+
+class MaskPredicate
+{
+public:
+    MaskPredicate( const Mat& _mask ) : mask(_mask) {}
+    bool operator() (const KeyPoint& key_pt) const
+    {
+        return mask.at<uchar>( (int)(key_pt.pt.y + 0.5f), (int)(key_pt.pt.x + 0.5f) ) == 0;
+    }
+
+private:
+    const Mat mask;
+    MaskPredicate& operator=(const MaskPredicate&);
+};
+
+void KeyPointsFilter::runByPixelsMask( std::vector<KeyPoint>& keypoints, const Mat& mask )
+{
+    CV_INSTRUMENT_REGION();
+
+    if( mask.empty() )
+        return;
+
+    keypoints.erase(std::remove_if(keypoints.begin(), keypoints.end(), MaskPredicate(mask)), keypoints.end());
+}
+
+struct KeyPoint_LessThan
+{
+    KeyPoint_LessThan(const std::vector<KeyPoint>& _kp) : kp(&_kp) {}
+    bool operator()(int i, int j) const
+    {
+        const KeyPoint& kp1 = (*kp)[i];
+        const KeyPoint& kp2 = (*kp)[j];
+        if( kp1.pt.x != kp2.pt.x )
+            return kp1.pt.x < kp2.pt.x;
+        if( kp1.pt.y != kp2.pt.y )
+            return kp1.pt.y < kp2.pt.y;
+        if( kp1.size != kp2.size )
+            return kp1.size > kp2.size;
+        if( kp1.angle != kp2.angle )
+            return kp1.angle < kp2.angle;
+        if( kp1.response != kp2.response )
+            return kp1.response > kp2.response;
+        if( kp1.octave != kp2.octave )
+            return kp1.octave > kp2.octave;
+        if( kp1.class_id != kp2.class_id )
+            return kp1.class_id > kp2.class_id;
+
+        return i < j;
+    }
+    const std::vector<KeyPoint>* kp;
+};
+
+void KeyPointsFilter::removeDuplicated( std::vector<KeyPoint>& keypoints )
+{
+    int i, j, n = (int)keypoints.size();
+    std::vector<int> kpidx(n);
+    std::vector<uchar> mask(n, (uchar)1);
+
+    for( i = 0; i < n; i++ )
+        kpidx[i] = i;
+    std::sort(kpidx.begin(), kpidx.end(), KeyPoint_LessThan(keypoints));
+    for( i = 1, j = 0; i < n; i++ )
+    {
+        KeyPoint& kp1 = keypoints[kpidx[i]];
+        KeyPoint& kp2 = keypoints[kpidx[j]];
+        if( kp1.pt.x != kp2.pt.x || kp1.pt.y != kp2.pt.y ||
+            kp1.size != kp2.size || kp1.angle != kp2.angle )
+            j = i;
+        else
+            mask[kpidx[i]] = 0;
+    }
+
+    for( i = j = 0; i < n; i++ )
+    {
+        if( mask[i] )
+        {
+            if( i != j )
+                keypoints[j] = keypoints[i];
+            j++;
+        }
+    }
+    keypoints.resize(j);
+}
+
+struct KeyPoint12_LessThan
+{
+    bool operator()(const KeyPoint &kp1, const KeyPoint &kp2) const
+    {
+        if( kp1.pt.x != kp2.pt.x )
+            return kp1.pt.x < kp2.pt.x;
+        if( kp1.pt.y != kp2.pt.y )
+            return kp1.pt.y < kp2.pt.y;
+        if( kp1.size != kp2.size )
+            return kp1.size > kp2.size;
+        if( kp1.angle != kp2.angle )
+            return kp1.angle < kp2.angle;
+        if( kp1.response != kp2.response )
+            return kp1.response > kp2.response;
+        if( kp1.octave != kp2.octave )
+            return kp1.octave > kp2.octave;
+        return kp1.class_id > kp2.class_id;
+    }
+};
+
+void KeyPointsFilter::removeDuplicatedSorted( std::vector<KeyPoint>& keypoints )
+{
+    int i, j, n = (int)keypoints.size();
+
+    if (n < 2) return;
+
+    std::sort(keypoints.begin(), keypoints.end(), KeyPoint12_LessThan());
+
+    for( i = 0, j = 1; j < n; ++j )
+    {
+        const KeyPoint& kp1 = keypoints[i];
+        const KeyPoint& kp2 = keypoints[j];
+        if( kp1.pt.x != kp2.pt.x || kp1.pt.y != kp2.pt.y ||
+            kp1.size != kp2.size || kp1.angle != kp2.angle ) {
+            keypoints[++i] = keypoints[j];
+        }
+    }
+    keypoints.resize(i + 1);
+}
+
+}

Lib/opencv/sources/modules/features2d/src/main.cpp

@@ -0,0 +1,52 @@
+/*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.
+// Copyright (C) 2015, Itseez 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*/
+
+//
+// Library initialization file
+//
+
+#include "precomp.hpp"
+
+IPP_INITIALIZER_AUTO
+
+/* End of file. */

Lib/opencv/sources/modules/features2d/src/opencl/akaze.cl

@@ -0,0 +1,122 @@
+// 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;
+}

Lib/opencv/sources/modules/features2d/src/opencl/brute_force_match.cl

@@ -0,0 +1,560 @@
+/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
+// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// @Authors
+//    Nathan, liujun@multicorewareinc.com
+//    Peng Xiao, pengxiao@outlook.com
+//    Baichuan Su, baichuan@multicorewareinc.com
+//
+// 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*/
+
+#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics:enable
+#define MAX_FLOAT 3.40282e+038f
+
+#ifndef T
+#define T float
+#endif
+
+#ifndef BLOCK_SIZE
+#define BLOCK_SIZE 16
+#endif
+#ifndef MAX_DESC_LEN
+#define MAX_DESC_LEN 64
+#endif
+
+#define BLOCK_SIZE_ODD          (BLOCK_SIZE + 1)
+#ifndef SHARED_MEM_SZ
+#  if (BLOCK_SIZE < MAX_DESC_LEN)
+#    define SHARED_MEM_SZ      (kercn * (BLOCK_SIZE * MAX_DESC_LEN + BLOCK_SIZE * BLOCK_SIZE))
+#  else
+#    define SHARED_MEM_SZ      (kercn * 2 * BLOCK_SIZE_ODD * BLOCK_SIZE)
+#  endif
+#endif
+
+#ifndef DIST_TYPE
+#define DIST_TYPE 2
+#endif
+
+// dirty fix for non-template support
+#if (DIST_TYPE == 2) // L1Dist
+#   ifdef T_FLOAT
+        typedef float result_type;
+#       if (8 == kercn)
+            typedef float8 value_type;
+#           define DIST(x, y) {value_type d = fabs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3 + d.s4 + d.s5 + d.s6 + d.s7;}
+#       elif (4 == kercn)
+            typedef float4 value_type;
+#           define DIST(x, y) {value_type d = fabs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3;}
+#       else
+            typedef float value_type;
+#           define DIST(x, y) result += fabs((x) - (y))
+#       endif
+#   else
+        typedef int result_type;
+#       if (8 == kercn)
+            typedef int8 value_type;
+#           define DIST(x, y) {value_type d = abs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3 + d.s4 + d.s5 + d.s6 + d.s7;}
+#       elif (4 == kercn)
+            typedef int4 value_type;
+#           define DIST(x, y) {value_type d = abs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3;}
+#       else
+            typedef int  value_type;
+#           define DIST(x, y) result += abs((x) - (y))
+#       endif
+#   endif
+#   define DIST_RES(x) (x)
+#elif (DIST_TYPE == 4) // L2Dist
+    typedef float result_type;
+#   if (8 == kercn)
+        typedef float8 value_type;
+#       define DIST(x, y)   {value_type d = ((x) - (y)); result += dot(d.s0123, d.s0123) + dot(d.s4567, d.s4567);}
+#   elif (4 == kercn)
+        typedef float4      value_type;
+#       define DIST(x, y)   {value_type d = ((x) - (y)); result += dot(d, d);}
+#   else
+        typedef float       value_type;
+#       define DIST(x, y)   {value_type d = ((x) - (y)); result = mad(d, d, result);}
+#   endif
+#   define DIST_RES(x) sqrt(x)
+#elif (DIST_TYPE == 6) // Hamming
+#   if (8 == kercn)
+        typedef int8 value_type;
+#   elif (4 == kercn)
+        typedef int4 value_type;
+#   else
+        typedef int value_type;
+#   endif
+    typedef int result_type;
+#   define DIST(x, y) result += popcount( (x) ^ (y) )
+#   define DIST_RES(x) (x)
+#endif
+
+inline result_type reduce_block(
+    __local value_type *s_query,
+    __local value_type *s_train,
+    int lidx,
+    int lidy
+    )
+{
+    result_type result = 0;
+    #pragma unroll
+    for (int j = 0 ; j < BLOCK_SIZE ; j++)
+    {
+        DIST(s_query[lidy * BLOCK_SIZE_ODD + j], s_train[j * BLOCK_SIZE_ODD + lidx]);
+    }
+    return DIST_RES(result);
+}
+
+inline result_type reduce_block_match(
+    __local value_type *s_query,
+    __local value_type *s_train,
+    int lidx,
+    int lidy
+    )
+{
+    result_type result = 0;
+    #pragma unroll
+    for (int j = 0 ; j < BLOCK_SIZE ; j++)
+    {
+        DIST(s_query[lidy * BLOCK_SIZE_ODD + j], s_train[j * BLOCK_SIZE_ODD + lidx]);
+    }
+    return result;
+}
+
+inline result_type reduce_multi_block(
+    __local value_type *s_query,
+    __local value_type *s_train,
+    int block_index,
+    int lidx,
+    int lidy
+    )
+{
+    result_type result = 0;
+    #pragma unroll
+    for (int j = 0 ; j < BLOCK_SIZE ; j++)
+    {
+        DIST(s_query[lidy * MAX_DESC_LEN + block_index * BLOCK_SIZE + j], s_train[j * BLOCK_SIZE + lidx]);
+    }
+    return result;
+}
+
+__kernel void BruteForceMatch_Match(
+    __global T *query,
+    __global T *train,
+    __global int *bestTrainIdx,
+    __global float *bestDistance,
+    int query_rows,
+    int query_cols,
+    int train_rows,
+    int train_cols,
+    int step
+)
+{
+    const int lidx = get_local_id(0);
+    const int lidy = get_local_id(1);
+    const int groupidx = get_group_id(0);
+
+    const int queryIdx = mad24(BLOCK_SIZE, groupidx, lidy);
+    const int queryOffset = min(queryIdx, query_rows - 1) * step;
+    __global TN *query_vec = (__global TN *)(query + queryOffset);
+    query_cols /= kercn;
+
+    __local float sharebuffer[SHARED_MEM_SZ];
+    __local value_type *s_query = (__local value_type *)sharebuffer;
+
+#if 0 < MAX_DESC_LEN
+    __local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
+    // load the query into local memory.
+    #pragma unroll
+    for (int i = 0; i < MAX_DESC_LEN / BLOCK_SIZE; i++)
+    {
+        const int loadx = mad24(BLOCK_SIZE, i, lidx);
+        s_query[mad24(MAX_DESC_LEN, lidy, loadx)] = loadx < query_cols ? query_vec[loadx] : 0;
+    }
+#else
+    __local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
+    const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx);
+    const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy);
+#endif
+
+    float myBestDistance = MAX_FLOAT;
+    int myBestTrainIdx = -1;
+
+    // loopUnrolledCached to find the best trainIdx and best distance.
+    for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++)
+    {
+        result_type result = 0;
+
+        const int trainOffset = min(mad24(BLOCK_SIZE, t, lidy), train_rows - 1) * step;
+        __global TN *train_vec = (__global TN *)(train + trainOffset);
+#if 0 < MAX_DESC_LEN
+        #pragma unroll
+        for (int i = 0; i < MAX_DESC_LEN / BLOCK_SIZE; i++)
+        {
+            //load a BLOCK_SIZE * BLOCK_SIZE block into local train.
+            const int loadx = mad24(BLOCK_SIZE, i, lidx);
+            s_train[mad24(BLOCK_SIZE, lidx, lidy)] = loadx < train_cols ? train_vec[loadx] : 0;
+
+            //synchronize to make sure each elem for reduceIteration in share memory is written already.
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            result += reduce_multi_block(s_query, s_train, i, lidx, lidy);
+
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+#else
+        for (int i = 0, endq = (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE; i < endq; i++)
+        {
+            const int loadx = mad24(i, BLOCK_SIZE, lidx);
+            //load query and train into local memory
+            if (loadx < query_cols)
+            {
+                s_query[s_query_i] = query_vec[loadx];
+                s_train[s_train_i] = train_vec[loadx];
+            }
+            else
+            {
+                s_query[s_query_i] = 0;
+                s_train[s_train_i] = 0;
+            }
+
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            result += reduce_block_match(s_query, s_train, lidx, lidy);
+
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+#endif
+        result = DIST_RES(result);
+
+        const int trainIdx = mad24(BLOCK_SIZE, t, lidx);
+
+        if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance /*&& mask(queryIdx, trainIdx)*/)
+        {
+            myBestDistance = result;
+            myBestTrainIdx = trainIdx;
+        }
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    __local float *s_distance = (__local float *)sharebuffer;
+    __local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE);
+
+    //findBestMatch
+    s_distance += lidy * BLOCK_SIZE_ODD;
+    s_trainIdx += lidy * BLOCK_SIZE_ODD;
+    s_distance[lidx] = myBestDistance;
+    s_trainIdx[lidx] = myBestTrainIdx;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    //reduce -- now all reduce implement in each threads.
+    #pragma unroll
+    for (int k = 0 ; k < BLOCK_SIZE; k++)
+    {
+        if (myBestDistance > s_distance[k])
+        {
+            myBestDistance = s_distance[k];
+            myBestTrainIdx = s_trainIdx[k];
+        }
+    }
+
+    if (queryIdx < query_rows && lidx == 0)
+    {
+        bestTrainIdx[queryIdx] = myBestTrainIdx;
+        bestDistance[queryIdx] = myBestDistance;
+    }
+}
+
+//radius_match
+__kernel void BruteForceMatch_RadiusMatch(
+    __global T *query,
+    __global T *train,
+    float maxDistance,
+    __global int *bestTrainIdx,
+    __global float *bestDistance,
+    __global int *nMatches,
+    int query_rows,
+    int query_cols,
+    int train_rows,
+    int train_cols,
+    int bestTrainIdx_cols,
+    int step,
+    int ostep
+)
+{
+    const int lidx = get_local_id(0);
+    const int lidy = get_local_id(1);
+    const int groupidx = get_group_id(0);
+    const int groupidy = get_group_id(1);
+
+    const int queryIdx = mad24(BLOCK_SIZE, groupidy, lidy);
+    const int queryOffset = min(queryIdx, query_rows - 1) * step;
+    __global TN *query_vec = (__global TN *)(query + queryOffset);
+
+    const int trainIdx = mad24(BLOCK_SIZE, groupidx, lidx);
+    const int trainOffset = min(mad24(BLOCK_SIZE, groupidx, lidy), train_rows - 1) * step;
+    __global TN *train_vec = (__global TN *)(train + trainOffset);
+
+    query_cols /= kercn;
+
+    __local float sharebuffer[SHARED_MEM_SZ];
+    __local value_type *s_query = (__local value_type *)sharebuffer;
+    __local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
+
+    result_type result = 0;
+    const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx);
+    const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy);
+    for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; ++i)
+    {
+        //load a BLOCK_SIZE * BLOCK_SIZE block into local train.
+        const int loadx = mad24(BLOCK_SIZE, i, lidx);
+
+        if (loadx < query_cols)
+        {
+            s_query[s_query_i] = query_vec[loadx];
+            s_train[s_train_i] = train_vec[loadx];
+        }
+        else
+        {
+            s_query[s_query_i] = 0;
+            s_train[s_train_i] = 0;
+        }
+
+        //synchronize to make sure each elem for reduceIteration in share memory is written already.
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        result += reduce_block(s_query, s_train, lidx, lidy);
+
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if (queryIdx < query_rows && trainIdx < train_rows && convert_float(result) < maxDistance)
+    {
+        int ind = atom_inc(nMatches + queryIdx);
+
+        if(ind < bestTrainIdx_cols)
+        {
+            bestTrainIdx[mad24(queryIdx, ostep, ind)] = trainIdx;
+            bestDistance[mad24(queryIdx, ostep, ind)] = result;
+        }
+    }
+}
+
+__kernel void BruteForceMatch_knnMatch(
+    __global T *query,
+    __global T *train,
+    __global int2 *bestTrainIdx,
+    __global float2 *bestDistance,
+    int query_rows,
+    int query_cols,
+    int train_rows,
+    int train_cols,
+    int step
+)
+{
+    const int lidx = get_local_id(0);
+    const int lidy = get_local_id(1);
+    const int groupidx = get_group_id(0);
+
+    const int queryIdx = mad24(BLOCK_SIZE, groupidx, lidy);
+    const int queryOffset = min(queryIdx, query_rows - 1) * step;
+    __global TN *query_vec = (__global TN *)(query + queryOffset);
+    query_cols /= kercn;
+
+    __local float sharebuffer[SHARED_MEM_SZ];
+    __local value_type *s_query = (__local value_type *)sharebuffer;
+
+#if 0 < MAX_DESC_LEN
+    __local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
+    // load the query into local memory.
+    #pragma unroll
+    for (int i = 0 ;  i <  MAX_DESC_LEN / BLOCK_SIZE; i ++)
+    {
+        int loadx = mad24(BLOCK_SIZE, i, lidx);
+        s_query[mad24(MAX_DESC_LEN, lidy, loadx)] = loadx < query_cols ? query_vec[loadx] : 0;
+    }
+#else
+    __local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
+    const int s_query_i = mad24(BLOCK_SIZE_ODD, lidy, lidx);
+    const int s_train_i = mad24(BLOCK_SIZE_ODD, lidx, lidy);
+#endif
+
+    float myBestDistance1 = MAX_FLOAT;
+    float myBestDistance2 = MAX_FLOAT;
+    int myBestTrainIdx1 = -1;
+    int myBestTrainIdx2 = -1;
+
+    for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt ; t++)
+    {
+        result_type result = 0;
+
+        int trainOffset = min(mad24(BLOCK_SIZE, t, lidy), train_rows - 1) * step;
+        __global TN *train_vec = (__global TN *)(train + trainOffset);
+#if 0 < MAX_DESC_LEN
+        #pragma unroll
+        for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++)
+        {
+            //load a BLOCK_SIZE * BLOCK_SIZE block into local train.
+            const int loadx = mad24(BLOCK_SIZE, i, lidx);
+            s_train[mad24(BLOCK_SIZE, lidx, lidy)] = loadx < train_cols ? train_vec[loadx] : 0;
+
+            //synchronize to make sure each elem for reduceIteration in share memory is written already.
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            result += reduce_multi_block(s_query, s_train, i, lidx, lidy);
+
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+#else
+        for (int i = 0, endq = (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE; i < endq ; i++)
+        {
+            const int loadx = mad24(BLOCK_SIZE, i, lidx);
+            //load query and train into local memory
+            if (loadx < query_cols)
+            {
+                s_query[s_query_i] = query_vec[loadx];
+                s_train[s_train_i] = train_vec[loadx];
+            }
+            else
+            {
+                s_query[s_query_i] = 0;
+                s_train[s_train_i] = 0;
+            }
+
+            barrier(CLK_LOCAL_MEM_FENCE);
+
+            result += reduce_block_match(s_query, s_train, lidx, lidy);
+
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+#endif
+        result = DIST_RES(result);
+
+        const int trainIdx = mad24(BLOCK_SIZE, t, lidx);
+
+        if (queryIdx < query_rows && trainIdx < train_rows)
+        {
+            if (result < myBestDistance1)
+            {
+                myBestDistance2 = myBestDistance1;
+                myBestTrainIdx2 = myBestTrainIdx1;
+                myBestDistance1 = result;
+                myBestTrainIdx1 = trainIdx;
+            }
+            else if (result < myBestDistance2)
+            {
+                myBestDistance2 = result;
+                myBestTrainIdx2 = trainIdx;
+            }
+        }
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    __local float *s_distance = (__local float *)sharebuffer;
+    __local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE);
+
+    // find BestMatch
+    s_distance += lidy * BLOCK_SIZE_ODD;
+    s_trainIdx += lidy * BLOCK_SIZE_ODD;
+    s_distance[lidx] = myBestDistance1;
+    s_trainIdx[lidx] = myBestTrainIdx1;
+
+    float bestDistance1 = MAX_FLOAT;
+    float bestDistance2 = MAX_FLOAT;
+    int bestTrainIdx1 = -1;
+    int bestTrainIdx2 = -1;
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (lidx == 0)
+    {
+        for (int i = 0 ; i < BLOCK_SIZE ; i++)
+        {
+            float val = s_distance[i];
+            if (val < bestDistance1)
+            {
+                bestDistance2 = bestDistance1;
+                bestTrainIdx2 = bestTrainIdx1;
+
+                bestDistance1 = val;
+                bestTrainIdx1 = s_trainIdx[i];
+            }
+            else if (val < bestDistance2)
+            {
+                bestDistance2 = val;
+                bestTrainIdx2 = s_trainIdx[i];
+            }
+        }
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    s_distance[lidx] = myBestDistance2;
+    s_trainIdx[lidx] = myBestTrainIdx2;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (lidx == 0)
+    {
+        for (int i = 0 ; i < BLOCK_SIZE ; i++)
+        {
+            float val = s_distance[i];
+
+            if (val < bestDistance2)
+            {
+                bestDistance2 = val;
+                bestTrainIdx2 = s_trainIdx[i];
+            }
+        }
+    }
+
+    myBestDistance1 = bestDistance1;
+    myBestDistance2 = bestDistance2;
+
+    myBestTrainIdx1 = bestTrainIdx1;
+    myBestTrainIdx2 = bestTrainIdx2;
+
+    if (queryIdx < query_rows && lidx == 0)
+    {
+        bestTrainIdx[queryIdx] = (int2)(myBestTrainIdx1, myBestTrainIdx2);
+        bestDistance[queryIdx] = (float2)(myBestDistance1, myBestDistance2);
+    }
+}

Lib/opencv/sources/modules/features2d/src/opencl/fast.cl

@@ -0,0 +1,162 @@
+// OpenCL port of the FAST corner detector.
+// Copyright (C) 2014, Itseez Inc. See the license at http://opencv.org
+
+inline int cornerScore(__global const uchar* img, int step)
+{
+    int k, tofs, v = img[0], a0 = 0, b0;
+    int d[16];
+    #define LOAD2(idx, ofs) \
+        tofs = ofs; d[idx] = (short)(v - img[tofs]); d[idx+8] = (short)(v - img[-tofs])
+    LOAD2(0, 3);
+    LOAD2(1, -step+3);
+    LOAD2(2, -step*2+2);
+    LOAD2(3, -step*3+1);
+    LOAD2(4, -step*3);
+    LOAD2(5, -step*3-1);
+    LOAD2(6, -step*2-2);
+    LOAD2(7, -step-3);
+
+    #pragma unroll
+    for( k = 0; k < 16; k += 2 )
+    {
+        int a = min((int)d[(k+1)&15], (int)d[(k+2)&15]);
+        a = min(a, (int)d[(k+3)&15]);
+        a = min(a, (int)d[(k+4)&15]);
+        a = min(a, (int)d[(k+5)&15]);
+        a = min(a, (int)d[(k+6)&15]);
+        a = min(a, (int)d[(k+7)&15]);
+        a = min(a, (int)d[(k+8)&15]);
+        a0 = max(a0, min(a, (int)d[k&15]));
+        a0 = max(a0, min(a, (int)d[(k+9)&15]));
+    }
+
+    b0 = -a0;
+    #pragma unroll
+    for( k = 0; k < 16; k += 2 )
+    {
+        int b = max((int)d[(k+1)&15], (int)d[(k+2)&15]);
+        b = max(b, (int)d[(k+3)&15]);
+        b = max(b, (int)d[(k+4)&15]);
+        b = max(b, (int)d[(k+5)&15]);
+        b = max(b, (int)d[(k+6)&15]);
+        b = max(b, (int)d[(k+7)&15]);
+        b = max(b, (int)d[(k+8)&15]);
+
+        b0 = min(b0, max(b, (int)d[k]));
+        b0 = min(b0, max(b, (int)d[(k+9)&15]));
+    }
+
+    return -b0-1;
+}
+
+__kernel
+void FAST_findKeypoints(
+    __global const uchar * _img, int step, int img_offset,
+    int img_rows, int img_cols,
+    volatile __global int* kp_loc,
+    int max_keypoints, int threshold )
+{
+    int j = get_global_id(0) + 3;
+    int i = get_global_id(1) + 3;
+
+    if (i < img_rows - 3 && j < img_cols - 3)
+    {
+        __global const uchar* img = _img + mad24(i, step, j + img_offset);
+        int v = img[0], t0 = v - threshold, t1 = v + threshold;
+        int k, tofs, v0, v1;
+        int m0 = 0, m1 = 0;
+
+        #define UPDATE_MASK(idx, ofs) \
+            tofs = ofs; v0 = img[tofs]; v1 = img[-tofs]; \
+            m0 |= ((v0 < t0) << idx) | ((v1 < t0) << (8 + idx)); \
+            m1 |= ((v0 > t1) << idx) | ((v1 > t1) << (8 + idx))
+
+        UPDATE_MASK(0, 3);
+        if( (m0 | m1) == 0 )
+            return;
+
+        UPDATE_MASK(2, -step*2+2);
+        UPDATE_MASK(4, -step*3);
+        UPDATE_MASK(6, -step*2-2);
+
+        #define EVEN_MASK (1+4+16+64)
+
+        if( ((m0 | (m0 >> 8)) & EVEN_MASK) != EVEN_MASK &&
+            ((m1 | (m1 >> 8)) & EVEN_MASK) != EVEN_MASK )
+            return;
+
+        UPDATE_MASK(1, -step+3);
+        UPDATE_MASK(3, -step*3+1);
+        UPDATE_MASK(5, -step*3-1);
+        UPDATE_MASK(7, -step-3);
+        if( ((m0 | (m0 >> 8)) & 255) != 255 &&
+            ((m1 | (m1 >> 8)) & 255) != 255 )
+            return;
+
+        m0 |= m0 << 16;
+        m1 |= m1 << 16;
+
+        #define CHECK0(i) ((m0 & (511 << i)) == (511 << i))
+        #define CHECK1(i) ((m1 & (511 << i)) == (511 << i))
+
+        if( CHECK0(0) + CHECK0(1) + CHECK0(2) + CHECK0(3) +
+            CHECK0(4) + CHECK0(5) + CHECK0(6) + CHECK0(7) +
+            CHECK0(8) + CHECK0(9) + CHECK0(10) + CHECK0(11) +
+            CHECK0(12) + CHECK0(13) + CHECK0(14) + CHECK0(15) +
+
+            CHECK1(0) + CHECK1(1) + CHECK1(2) + CHECK1(3) +
+            CHECK1(4) + CHECK1(5) + CHECK1(6) + CHECK1(7) +
+            CHECK1(8) + CHECK1(9) + CHECK1(10) + CHECK1(11) +
+            CHECK1(12) + CHECK1(13) + CHECK1(14) + CHECK1(15) == 0 )
+            return;
+
+        {
+            int idx = atomic_inc(kp_loc);
+            if( idx < max_keypoints )
+            {
+                kp_loc[1 + 2*idx] = j;
+                kp_loc[2 + 2*idx] = i;
+            }
+        }
+    }
+}
+
+///////////////////////////////////////////////////////////////////////////
+// nonmaxSupression
+
+__kernel
+void FAST_nonmaxSupression(
+    __global const int* kp_in, volatile __global int* kp_out,
+    __global const uchar * _img, int step, int img_offset,
+    int rows, int cols, int counter, int max_keypoints)
+{
+    const int idx = get_global_id(0);
+
+    if (idx < counter)
+    {
+        int x = kp_in[1 + 2*idx];
+        int y = kp_in[2 + 2*idx];
+        __global const uchar* img = _img + mad24(y, step, x + img_offset);
+
+        int s = cornerScore(img, step);
+
+        if( (x < 4 || s > cornerScore(img-1, step)) +
+            (y < 4 || s > cornerScore(img-step, step)) != 2 )
+            return;
+        if( (x >= cols - 4 || s > cornerScore(img+1, step)) +
+            (y >= rows - 4 || s > cornerScore(img+step, step)) +
+            (x < 4 || y < 4 || s > cornerScore(img-step-1, step)) +
+            (x >= cols - 4 || y < 4 || s > cornerScore(img-step+1, step)) +
+            (x < 4 || y >= rows - 4 || s > cornerScore(img+step-1, step)) +
+            (x >= cols - 4 || y >= rows - 4 || s > cornerScore(img+step+1, step)) == 6)
+        {
+            int new_idx = atomic_inc(kp_out);
+            if( new_idx < max_keypoints )
+            {
+                kp_out[1 + 3*new_idx] = x;
+                kp_out[2 + 3*new_idx] = y;
+                kp_out[3 + 3*new_idx] = s;
+            }
+        }
+    }
+}

Lib/opencv/sources/modules/features2d/src/opencl/orb.cl

@@ -0,0 +1,254 @@
+// OpenCL port of the ORB feature detector and descriptor extractor
+// Copyright (C) 2014, Itseez Inc. See the license at http://opencv.org
+//
+// The original code has been contributed by Peter Andreas Entschev, peter@entschev.com
+
+#define LAYERINFO_SIZE 1
+#define LAYERINFO_OFS 0
+#define KEYPOINT_SIZE 3
+#define ORIENTED_KEYPOINT_SIZE 4
+#define KEYPOINT_X 0
+#define KEYPOINT_Y 1
+#define KEYPOINT_Z 2
+#define KEYPOINT_ANGLE 3
+
+/////////////////////////////////////////////////////////////
+
+#ifdef ORB_RESPONSES
+
+__kernel void
+ORB_HarrisResponses(__global const uchar* imgbuf, int imgstep, int imgoffset0,
+                    __global const int* layerinfo, __global const int* keypoints,
+                    __global float* responses, int nkeypoints )
+{
+    int idx = get_global_id(0);
+    if( idx < nkeypoints )
+    {
+        __global const int* kpt = keypoints + idx*KEYPOINT_SIZE;
+        __global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE;
+        __global const uchar* img = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] +
+            (kpt[KEYPOINT_Y] - blockSize/2)*imgstep + (kpt[KEYPOINT_X] - blockSize/2);
+
+        int i, j;
+        int a = 0, b = 0, c = 0;
+        for( i = 0; i < blockSize; i++, img += imgstep-blockSize )
+        {
+            for( j = 0; j < blockSize; j++, img++ )
+            {
+                int Ix = (img[1] - img[-1])*2 + img[-imgstep+1] - img[-imgstep-1] + img[imgstep+1] - img[imgstep-1];
+                int Iy = (img[imgstep] - img[-imgstep])*2 + img[imgstep-1] - img[-imgstep-1] + img[imgstep+1] - img[-imgstep+1];
+                a += Ix*Ix;
+                b += Iy*Iy;
+                c += Ix*Iy;
+            }
+        }
+        responses[idx] = ((float)a * b - (float)c * c - HARRIS_K * (float)(a + b) * (a + b))*scale_sq_sq;
+    }
+}
+
+#endif
+
+/////////////////////////////////////////////////////////////
+
+#ifdef ORB_ANGLES
+
+#define _DBL_EPSILON 2.2204460492503131e-16f
+#define atan2_p1 (0.9997878412794807f*57.29577951308232f)
+#define atan2_p3 (-0.3258083974640975f*57.29577951308232f)
+#define atan2_p5 (0.1555786518463281f*57.29577951308232f)
+#define atan2_p7 (-0.04432655554792128f*57.29577951308232f)
+
+inline float fastAtan2( float y, float x )
+{
+    float ax = fabs(x), ay = fabs(y);
+    float a, c, c2;
+    if( ax >= ay )
+    {
+        c = ay/(ax + _DBL_EPSILON);
+        c2 = c*c;
+        a = (((atan2_p7*c2 + atan2_p5)*c2 + atan2_p3)*c2 + atan2_p1)*c;
+    }
+    else
+    {
+        c = ax/(ay + _DBL_EPSILON);
+        c2 = c*c;
+        a = 90.f - (((atan2_p7*c2 + atan2_p5)*c2 + atan2_p3)*c2 + atan2_p1)*c;
+    }
+    if( x < 0 )
+        a = 180.f - a;
+    if( y < 0 )
+        a = 360.f - a;
+    return a;
+}
+
+
+__kernel void
+ORB_ICAngle(__global const uchar* imgbuf, int imgstep, int imgoffset0,
+            __global const int* layerinfo, __global const int* keypoints,
+            __global float* responses, const __global int* u_max,
+            int nkeypoints, int half_k )
+{
+    int idx = get_global_id(0);
+    if( idx < nkeypoints )
+    {
+        __global const int* kpt = keypoints + idx*KEYPOINT_SIZE;
+
+        __global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE;
+        __global const uchar* center = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] +
+            kpt[KEYPOINT_Y]*imgstep + kpt[KEYPOINT_X];
+
+        int u, v, m_01 = 0, m_10 = 0;
+
+        // Treat the center line differently, v=0
+        for( u = -half_k; u <= half_k; u++ )
+            m_10 += u * center[u];
+
+        // Go line by line in the circular patch
+        for( v = 1; v <= half_k; v++ )
+        {
+            // Proceed over the two lines
+            int v_sum = 0;
+            int d = u_max[v];
+            for( u = -d; u <= d; u++ )
+            {
+                int val_plus = center[u + v*imgstep], val_minus = center[u - v*imgstep];
+                v_sum += (val_plus - val_minus);
+                m_10 += u * (val_plus + val_minus);
+            }
+            m_01 += v * v_sum;
+        }
+
+        // we do not use OpenCL's atan2 intrinsic,
+        // because we want to get _exactly_ the same results as the CPU version
+        responses[idx] = fastAtan2((float)m_01, (float)m_10);
+    }
+}
+
+#endif
+
+/////////////////////////////////////////////////////////////
+
+#ifdef ORB_DESCRIPTORS
+
+__kernel void
+ORB_computeDescriptor(__global const uchar* imgbuf, int imgstep, int imgoffset0,
+                      __global const int* layerinfo, __global const int* keypoints,
+                      __global uchar* _desc, const __global int* pattern,
+                      int nkeypoints, int dsize )
+{
+    int idx = get_global_id(0);
+    if( idx < nkeypoints )
+    {
+        int i;
+        __global const int* kpt = keypoints + idx*ORIENTED_KEYPOINT_SIZE;
+
+        __global const int* layer = layerinfo + kpt[KEYPOINT_Z]*LAYERINFO_SIZE;
+        __global const uchar* center = imgbuf + imgoffset0 + layer[LAYERINFO_OFS] +
+                                kpt[KEYPOINT_Y]*imgstep + kpt[KEYPOINT_X];
+        float angle = as_float(kpt[KEYPOINT_ANGLE]);
+        angle *= 0.01745329251994329547f;
+
+        float cosa;
+        float sina = sincos(angle, &cosa);
+
+        __global uchar* desc = _desc + idx*dsize;
+
+        #define GET_VALUE(idx) \
+            center[mad24(convert_int_rte(pattern[(idx)*2] * sina + pattern[(idx)*2+1] * cosa), imgstep, \
+                        convert_int_rte(pattern[(idx)*2] * cosa - pattern[(idx)*2+1] * sina))]
+
+        for( i = 0; i < dsize; i++ )
+        {
+            int val;
+        #if WTA_K == 2
+            int t0, t1;
+
+            t0 = GET_VALUE(0); t1 = GET_VALUE(1);
+            val = t0 < t1;
+
+            t0 = GET_VALUE(2); t1 = GET_VALUE(3);
+            val |= (t0 < t1) << 1;
+
+            t0 = GET_VALUE(4); t1 = GET_VALUE(5);
+            val |= (t0 < t1) << 2;
+
+            t0 = GET_VALUE(6); t1 = GET_VALUE(7);
+            val |= (t0 < t1) << 3;
+
+            t0 = GET_VALUE(8); t1 = GET_VALUE(9);
+            val |= (t0 < t1) << 4;
+
+            t0 = GET_VALUE(10); t1 = GET_VALUE(11);
+            val |= (t0 < t1) << 5;
+
+            t0 = GET_VALUE(12); t1 = GET_VALUE(13);
+            val |= (t0 < t1) << 6;
+
+            t0 = GET_VALUE(14); t1 = GET_VALUE(15);
+            val |= (t0 < t1) << 7;
+
+            pattern += 16*2;
+
+        #elif WTA_K == 3
+            int t0, t1, t2;
+
+            t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
+            val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);
+
+            t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);
+            val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;
+
+            t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);
+            val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;
+
+            t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);
+            val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;
+
+            pattern += 12*2;
+
+        #elif WTA_K == 4
+            int t0, t1, t2, t3, k;
+            int a, b;
+
+            t0 = GET_VALUE(0); t1 = GET_VALUE(1);
+            t2 = GET_VALUE(2); t3 = GET_VALUE(3);
+            a = 0, b = 2;
+            if( t1 > t0 ) t0 = t1, a = 1;
+            if( t3 > t2 ) t2 = t3, b = 3;
+            k = t0 > t2 ? a : b;
+            val = k;
+
+            t0 = GET_VALUE(4); t1 = GET_VALUE(5);
+            t2 = GET_VALUE(6); t3 = GET_VALUE(7);
+            a = 0, b = 2;
+            if( t1 > t0 ) t0 = t1, a = 1;
+            if( t3 > t2 ) t2 = t3, b = 3;
+            k = t0 > t2 ? a : b;
+            val |= k << 2;
+
+            t0 = GET_VALUE(8); t1 = GET_VALUE(9);
+            t2 = GET_VALUE(10); t3 = GET_VALUE(11);
+            a = 0, b = 2;
+            if( t1 > t0 ) t0 = t1, a = 1;
+            if( t3 > t2 ) t2 = t3, b = 3;
+            k = t0 > t2 ? a : b;
+            val |= k << 4;
+
+            t0 = GET_VALUE(12); t1 = GET_VALUE(13);
+            t2 = GET_VALUE(14); t3 = GET_VALUE(15);
+            a = 0, b = 2;
+            if( t1 > t0 ) t0 = t1, a = 1;
+            if( t3 > t2 ) t2 = t3, b = 3;
+            k = t0 > t2 ? a : b;
+            val |= k << 6;
+
+            pattern += 16*2;
+        #else
+            #error "unknown/undefined WTA_K value; should be 2, 3 or 4"
+        #endif
+            desc[i] = (uchar)val;
+        }
+    }
+}
+
+#endif

Lib/opencv/sources/modules/features2d/src/precomp.hpp

@@ -0,0 +1,56 @@
+/*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*/
+
+#ifndef __OPENCV_PRECOMP_H__
+#define __OPENCV_PRECOMP_H__
+
+#include "opencv2/features2d.hpp"
+#include "opencv2/imgproc.hpp"
+
+#include "opencv2/core/utility.hpp"
+#include "opencv2/core/private.hpp"
+#include "opencv2/core/ocl.hpp"
+#include "opencv2/core/hal/hal.hpp"
+
+#include <algorithm>
+
+#endif