MSD Detector Added

- Saliency stl array instead of pointer array added
- Perf test in Extra Repository
- Epsilon change to 10E-3

modules/xfeatures2d/doc/xfeatures2d.bib

@@ -64,3 +64,10 @@
   volume = "32",
   number = "5"
 }
+
+@inproceedings{Tombari14,
+  title={Interest Points via Maximal Self-Dissimilarities},
+  author={Tombari, Federico and Di Stefano, Luigi},
+  booktitle={Asian Conference on Computer Vision -- ACCV 2014},
+  year={2014}
+}

modules/xfeatures2d/include/opencv2/xfeatures2d.hpp

@@ -159,7 +159,7 @@ LATCH is a binary descriptor based on learned comparisons of triplets of image p
 * half_ssd_size - the size of half of the mini-patches size. For example, if we would like to compare triplets of patches of size 7x7x
     then the half_ssd_size should be (7-1)/2 = 3.
 
-Note: the descriptor can be coupled with any keypoint extractor. The only demand is that if you use set rotationInvariance = True then 
+Note: the descriptor can be coupled with any keypoint extractor. The only demand is that if you use set rotationInvariance = True then
     you will have to use an extractor which estimates the patch orientation (in degrees). Examples for such extractors are ORB and SIFT.
 
 Note: a complete example can be found under /samples/cpp/tutorial_code/xfeatures2D/latch_match.cpp
@@ -258,6 +258,27 @@ public:
 
 };
 
+/** @brief Class implementing the MSD (*Maximal Self-Dissimilarity*) keypoint detector, described in @cite Tombari14.
+
+The algorithm implements a novel interest point detector stemming from the intuition that image patches
+which are highly dissimilar over a relatively large extent of their surroundings hold the property of
+being repeatable and distinctive. This concept of "contextual self-dissimilarity" reverses the key
+paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local
+Means filter, which build upon the presence of similar - rather than dissimilar - patches. Moreover,
+it extends to contextual information the local self-dissimilarity notion embedded in established
+detectors of corner-like interest points, thereby achieving enhanced repeatability, distinctiveness and
+localization accuracy.
+
+*/
+
+class CV_EXPORTS_W MSDDetector : public Feature2D {
+
+public:
+
+    static Ptr<MSDDetector> create(int m_patch_radius = 3, int m_search_area_radius = 5,
+            int m_nms_radius = 5, int m_nms_scale_radius = 0, float m_th_saliency = 250.0f, int m_kNN = 4,
+            float m_scale_factor = 1.25f, int m_n_scales = -1, bool m_compute_orientation = false);
+};
 
 //! @}
 

modules/xfeatures2d/perf/perf_msd.cpp

+#include "perf_precomp.hpp"
+
+using namespace std;
+using namespace cv;
+using namespace xfeatures2d;
+using namespace perf;
+using std::tr1::make_tuple;
+using std::tr1::get;
+
+typedef perf::TestBaseWithParam<std::string> msd;
+
+#define MSD_IMAGES \
+    "cv/detectors_descriptors_evaluation/images_datasets/leuven/img1.png",\
+    "stitching/a3.png"
+
+PERF_TEST_P(msd, detect, testing::Values(MSD_IMAGES))
+{
+    string filename = getDataPath(GetParam());
+    Mat frame = imread(filename, IMREAD_GRAYSCALE);
+
+    if (frame.empty())
+        FAIL() << "Unable to load source image " << filename;
+
+    Mat mask;
+    declare.in(frame);
+    Ptr<MSDDetector> detector = MSDDetector::create();
+    vector<KeyPoint> points;
+
+    TEST_CYCLE() detector->detect(frame, points, mask);
+
+    sort(points.begin(), points.end(), comparators::KeypointGreater());
+    SANITY_CHECK_KEYPOINTS(points, 1e-3);
+}

modules/xfeatures2d/src/msd.cpp

+/*
+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
+(3-clause BSD License)
+
+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 names of the copyright holders nor the names of the 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 copyright holders 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.
+
+***********************************************************************************
+Maximal Self-Dissimilarity (MSD) Interest Point Detector
+
+This is an implementation of the MSD interest point detector
+presented in the scientific publication:
+
+[1] F. Tombari, L. Di Stefano
+"Interest Points via Maximal Self-Dissimilarities"
+12th Asian Conference on Computer Vision (ACCV), 2014
+
+The code is ported from the stand-alone implementation available
+at this repository:
+www.github.com/fedassa/msdDetector
+
+AUTHORS:  Federico Tombari (fedassa@gmail.com) (original code), 
+          Daniele De Gregorio (degregorio.daniele@gmail.com) (OpenCV porting)
+
+University of Bologna, Open Perception
+
+ */
+
+#include "precomp.hpp"
+#include <limits>
+
+namespace cv
+{
+    namespace xfeatures2d
+    {
+        /*!
+            MSD Image Pyramid.
+         */
+        class MSDImagePyramid
+        {
+            // Multi-threaded construction of the scale-space pyramid
+            struct MSDImagePyramidBuilder : ParallelLoopBody
+            {
+
+                MSDImagePyramidBuilder(const cv::Mat& _im, std::vector<cv::Mat>* _m_imPyr, float _scaleFactor)
+                {
+                    im = &_im;
+                    m_imPyr = _m_imPyr;
+                    scaleFactor = _scaleFactor;
+
+                }
+
+                void operator()(const Range& range) const
+                {
+                    for (int lvl = range.start; lvl < range.end; lvl++)
+                    {
+                        float scale = 1 / std::pow(scaleFactor, (float) lvl);
+                        (*m_imPyr)[lvl] = cv::Mat(cv::Size(cvRound(im->cols * scale), cvRound(im->rows * scale)), im->type());
+                        cv::resize(*im, (*m_imPyr)[lvl], cv::Size((*m_imPyr)[lvl].cols, (*m_imPyr)[lvl].rows), 0.0, 0.0, cv::INTER_AREA);
+                    }
+                }
+                const cv::Mat* im;
+                std::vector<cv::Mat>* m_imPyr;
+                float scaleFactor;
+            };
+
+        public:
+
+            MSDImagePyramid(const cv::Mat &im, const int nLevels, const float scaleFactor = 1.6f);
+            ~MSDImagePyramid();
+
+            const std::vector<cv::Mat> getImPyr() const
+            {
+                return m_imPyr;
+            };
+
+        private:
+
+            std::vector<cv::Mat> m_imPyr;
+            int m_nLevels;
+            float m_scaleFactor;
+        };
+
+        MSDImagePyramid::MSDImagePyramid(const cv::Mat & im, const int nLevels, const float scaleFactor)
+        {
+            m_nLevels = nLevels;
+            m_scaleFactor = scaleFactor;
+            m_imPyr.clear();
+            m_imPyr.resize(nLevels);
+
+            m_imPyr[0] = im.clone();
+
+            if (m_nLevels > 1)
+            {
+                parallel_for_(Range(1, nLevels), MSDImagePyramidBuilder(im, &m_imPyr, scaleFactor));
+            }
+        }
+
+        MSDImagePyramid::~MSDImagePyramid()
+        {
+        }
+
+        /*!
+            MSD Implementation.
+         */
+        class MSDDetector_Impl : public MSDDetector
+        {
+        public:
+
+            // Multi-threaded contextualSelfDissimilarity method
+            struct MSDSelfDissimilarityScan : ParallelLoopBody
+            {
+
+                MSDSelfDissimilarityScan(MSDDetector_Impl& _detector, std::vector< std::vector<float> >* _saliency, cv::Mat& _img, int _level, int _border, int _split)
+                {
+                    detector = &_detector;
+                    saliency = _saliency;
+                    img = &_img;
+                    split = _split;
+                    level = _level;
+                    border = _border;
+                    int w = img->cols - border * 2;
+                    chunkSize = w / split;
+                    remains = w - chunkSize*split;
+                }
+
+                void operator()(const Range& range) const
+                {
+                    for (int i = range.start; i < range.end; i++)
+                    {
+                        int start = border + i*chunkSize;
+                        int end = border + (i + 1) * chunkSize;
+                        if (remains > 0)
+                            if (i == split - 1)
+                            {
+                                end = img->cols - border;
+                            }
+                        detector->contextualSelfDissimilarity(*img, start, end, &saliency->at(level)[0]);
+                    }
+                }
+
+                MSDDetector_Impl* detector;
+                std::vector< std::vector<float> >* saliency;
+                cv::Mat* img;
+                int level;
+                int split;
+                int border;
+                int chunkSize;
+                int remains;
+            };
+
+            /**
+             * Constructor
+             * @param patch_radius Patch radius
+             * @param search_area_radius Search Area radius
+             * @param nms_radius  Non Maxima Suppression spatial radius
+             * @param nms_scale_radius Non Maxima Suppression scale radius
+             * @param th_saliency Saliency threshold
+             * @param kNN number of nearest neighbors (k)
+             * @param scale_factor Scale factor for building up the image pyramid
+             * @param n_scales Number of scales number of scales for building up the image pyramid (if set to -1, this number is automatically determined)
+             * @param compute_orientation Flag for associating a canoncial orientation to each keypoint
+             */
+            MSDDetector_Impl(int patch_radius, int search_area_radius,
+                    int nms_radius, int nms_scale_radius, float th_saliency, int kNN, float scale_factor,
+                    int n_scales, bool compute_orientation)
+            : m_patch_radius(patch_radius), m_search_area_radius(search_area_radius), m_nms_radius(nms_radius),
+              m_nms_scale_radius(nms_scale_radius), m_th_saliency(th_saliency), m_kNN(kNN), m_scale_factor(scale_factor),
+              m_n_scales(n_scales), m_compute_orientation(compute_orientation)
+
+            {
+            }
+
+            void detect(InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask)
+            {
+                m_mask = _mask.getMat();
+
+                int border = m_search_area_radius + m_patch_radius;
+
+                cv::Mat img = _image.getMat();
+                if (m_n_scales == -1)
+                    m_cur_n_scales = cvFloor(std::log(cv::min(img.cols, img.rows) / ((m_patch_radius + m_search_area_radius)*2.0 + 1)) / std::log(m_scale_factor));
+                else
+                    m_cur_n_scales = m_n_scales;
+
+                cv::Mat imgG;
+                if (img.channels() == 1)
+                    imgG = img;
+                else
+                    cv::cvtColor(img, imgG, cv::COLOR_BGR2GRAY);
+
+                MSDImagePyramid scaleSpacer(imgG, m_cur_n_scales, m_scale_factor);
+                m_scaleSpace = scaleSpacer.getImPyr();
+
+                keypoints.clear();
+                std::vector< std::vector<float> > saliency;
+                saliency.resize(m_cur_n_scales);
+
+                for (int r = 0; r < m_cur_n_scales; r++)
+                {
+                    saliency[r].resize(m_scaleSpace[r].rows * m_scaleSpace[r].cols);
+                    fill(saliency[r].begin(), saliency[r].end(), 0.0f);
+                }
+
+                for (int r = 0; r < m_cur_n_scales; r++)
+                {
+                    int steps = cv::getNumThreads();
+                    parallel_for_(Range(0, steps), MSDSelfDissimilarityScan((*this), &saliency, m_scaleSpace[r], r, border, steps));
+                }
+
+                nonMaximaSuppression(saliency, keypoints);
+
+                for (int r = 0; r < m_cur_n_scales; r++)
+                {
+                    saliency[r].clear();
+                }
+
+                m_scaleSpace.clear();
+
+            }
+
+        protected:
+
+            // Patch radius
+            int m_patch_radius;
+            // Search area radius
+            int m_search_area_radius;
+            // Non Maxima Suppression Spatial Radius
+            int m_nms_radius;
+            // Non Maxima Suppression Scale Radius
+            int m_nms_scale_radius;
+            //Saliency threshold
+            float m_th_saliency;
+            //k nearest neighbors
+            int m_kNN;
+            //Scale factor
+            float m_scale_factor;
+            //Number of scales
+            int m_n_scales;
+            //Current number of scales
+            int m_cur_n_scales;
+            //Compute orientation flag
+            bool m_compute_orientation;
+
+        private:
+
+
+            // Scale-space image pyramid
+            std::vector<cv::Mat> m_scaleSpace;
+            // Input binary mask
+            cv::Mat m_mask;
+
+            /**
+             * Computes the normalized average value of input vector
+             * @param minVals input vector
+             * @param den normalization factor (pre-multiplied by the number of elements of the input vector, assumed constant)
+             * @return normalized average value
+             */
+            inline float computeAvgDistance(std::vector<int> &minVals, int den)
+            {
+                float avg_dist = 0.0f;
+                for (unsigned int i = 0; i < minVals.size(); i++)
+                    avg_dist += minVals[i];
+
+                avg_dist /= den;
+                return avg_dist;
+            }
+
+            /**
+             * Computer the Contextual Self-Dissimilarity (CSD, [1]) for a specific range of image pixels (row-wise)
+             * @param img input image
+             * @param xmin left-most range limit for the image pixels being processed
+             * @param xmax right-most range limit for the image pixels being processed
+             * @param saliency output array being filled with the CSD value computed at each input pixel
+             */
+            void contextualSelfDissimilarity(cv::Mat &img, int xmin, int xmax, float* saliency);
+
+            /**
+             * Associates a canonical orientation (computed as in [1]) to each extracted key-point
+             * @param img input image
+             * @param x column index of the key-point on the input image
+             * @param y row index of the key-point on the input image
+             * @param circle pre-computed LUT used in the function
+             * @return angle of the canonical orientation (in radians)
+             */
+            float computeOrientation(cv::Mat &img, int x, int y, std::vector<cv::Point2f> circle);
+
+            /**
+             * Computes the Non-Maxima Suppression (NMS) over the scale-space as in [1] for all elements of the image pyramid
+             * @param saliency input saliency associated to each element of the image pyramid
+             * @param keypoints key-points obtained as local maxima of the saliency
+             */
+            void nonMaximaSuppression(std::vector< std::vector<float> > & saliency, std::vector<cv::KeyPoint> & keypoints);
+
+            /**
+             * Computes the floating point interpolation of a key-point coordinates
+             * @param x column index of the key-point at its scale of the image pyramid
+             * @param yrow index of the key-point at its scale of the image pyramid
+             * @param scale scale of the key-point over the image pyramid
+             * @param saliency pointer to the saliency array
+             * @param p_res interpolated coordinates of the key-point referred to the lowest level of the pyramid (i.e. in the ref. frame of the input image)
+             * @return false if the current key-point has to be rejected, true otherwise
+             */
+            bool rescalePoint(int x, int y, int scale, std::vector< std::vector<float> > & saliency, cv::Point2f & p_res);
+
+        };
+
+        bool MSDDetector_Impl::rescalePoint(int i, int j, int scale, std::vector< std::vector<float> > & saliency, cv::Point2f &p_res)
+        {
+
+            const float deriv_scale = 0.5f;
+            int width_s = m_scaleSpace[scale].cols;
+            //const float second_deriv_scale = 1.0f;
+            const float cross_deriv_scale = 0.25f;
+
+            cv::Vec2f dD((saliency[scale][j * width_s + i + 1] - saliency[scale][j * width_s + i - 1]) * deriv_scale,
+                    (saliency[scale][(j + 1) * width_s + i] - saliency[scale][(j - 1) * width_s + i]) * deriv_scale);
+
+            float cc = saliency[scale][j * width_s + i] * 2;
+            float dxx = (saliency[scale][j * width_s + i + 1] + saliency[scale][j * width_s + i - 1] - cc); // * second_deriv_scale;
+            float dyy = (saliency[scale][(j + 1) * width_s + i] + saliency[scale][(j - 1) * width_s + i] - cc); // * second_deriv_scale;
+            float dxy = (saliency[scale][(j + 1) * width_s + i + 1] - saliency[scale][(j + 1) * width_s + i - 1] -
+                    saliency[scale][(j - 1) * width_s + i + 1] + saliency[scale][(j - 1) * width_s + i - 1]) * cross_deriv_scale;
+
+            cv::Matx22f H(dxx, dxy, dxy, dyy);
+
+            cv::Vec2f X;
+            cv::solve(H, dD, X, cv::DECOMP_LU);
+
+            float xr = -X[1];
+            float xc = -X[0];
+
+            if (std::abs(xr) > 5 || std::abs(xc) > 5)
+                return false;
+
+            if (scale == 0)
+            {
+                p_res.x = i + xc + 0.5f;
+                p_res.y = j + xr + 0.5f;
+            } else
+            {
+                float effectiveScaleFactor = std::pow(m_scale_factor, scale);
+                p_res.x = (i + xc + 0.5f) * effectiveScaleFactor;
+                p_res.y = (j + xr + 0.5f) * effectiveScaleFactor;
+
+                p_res.x -= 0.5f;
+                p_res.y -= 0.5f;
+
+                if (p_res.x < 0 || p_res.x >= m_scaleSpace[0].cols || p_res.y < 0 || p_res.y >= m_scaleSpace[0].rows)
+                {
+                    return false;
+                }
+            }
+
+            return true;
+        }
+
+        void MSDDetector_Impl::contextualSelfDissimilarity(cv::Mat &img, int xmin, int xmax, float* saliency)
+        {
+            int r_s = m_patch_radius;
+            int r_b = m_search_area_radius;
+            int k = m_kNN;
+
+            int w = img.cols;
+            int h = img.rows;
+
+            int side_s = 2 * r_s + 1;
+            int side_b = 2 * r_b + 1;
+            int border = r_s + r_b;
+            int temp;
+            int den = side_s * side_s * k;
+
+            std::vector<int> minVals(k);
+            int *acc = new int[side_b * side_b];
+            int **vCol = new int *[w];
+            for (int i = 0; i < w; i++)
+                vCol[i] = new int[side_b * side_b];
+
+            //first position
+            int x = xmin;
+            int y = border;
+
+            int ctrInd = 0;
+            for (int kk = 0; kk < k; kk++)
+                minVals[kk] = std::numeric_limits<int>::max();
+
+            for (int j = y - r_b; j <= y + r_b; j++)
+            {
+
+                for (int i = x - r_b; i <= x + r_b; i++)
+                {
+                    if (j == y && i == x)
+                        continue;
+
+                    acc[ctrInd] = 0;
+                    for (int u = -r_s; u <= r_s; u++)
+                    {
+                        vCol[x + u][ctrInd] = 0;
+                        for (int v = -r_s; v <= r_s; v++)
+                        {
+
+                            temp = img.at<unsigned char>(j + v, i + u) - img.at<unsigned char>(y + v, x + u);
+                            vCol[x + u][ctrInd] += (temp * temp);
+                        }
+                        acc[ctrInd] += vCol[x + u][ctrInd];
+                    }
+
+                    if (acc[ctrInd] < minVals[k - 1])
+                    {
+                        minVals[k - 1] = acc[ctrInd];
+
+                        for (int kk = k - 2; kk >= 0; kk--)
+                        {
+                            if (minVals[kk] > minVals[kk + 1])
+                            {
+                                std::swap(minVals[kk], minVals[kk + 1]);
+                            } else
+                                break;
+                        }
+                    }
+
+                    ctrInd++;
+                }
+            }
+            saliency[y * w + x] = computeAvgDistance(minVals, den);
+
+            for (x = xmin + 1; x < xmax; x++)
+            {
+                ctrInd = 0;
+                for (int kk = 0; kk < k; kk++)
+                    minVals[kk] = std::numeric_limits<int>::max();
+
+                for (int j = y - r_b; j <= y + r_b; j++)
+                {
+                    for (int i = x - r_b; i <= x + r_b; i++)
+                    {
+                        if (j == y && i == x)
+                            continue;
+
+                        vCol[x + r_s][ctrInd] = 0;
+                        for (int v = -r_s; v <= r_s; v++)
+                        {
+                            temp = img.at<unsigned char>(j + v, i + r_s) - img.at<unsigned char>(y + v, x + r_s);
+                            vCol[x + r_s][ctrInd] += (temp * temp);
+                        }
+
+                        acc[ctrInd] = acc[ctrInd] + vCol[x + r_s][ctrInd] - vCol[x - r_s - 1][ctrInd];
+
+                        if (acc[ctrInd] < minVals[k - 1])
+                        {
+                            minVals[k - 1] = acc[ctrInd];
+                            for (int kk = k - 2; kk >= 0; kk--)
+                            {
+                                if (minVals[kk] > minVals[kk + 1])
+                                {
+                                    std::swap(minVals[kk], minVals[kk + 1]);
+                                } else
+                                    break;
+                            }
+                        }
+
+                        ctrInd++;
+                    }
+                }
+                saliency[y * w + x] = computeAvgDistance(minVals, den);
+            }
+
+            for (y = border + 1; y < h - border; y++)
+            {
+                ctrInd = 0;
+                for (int kk = 0; kk < k; kk++)
+                    minVals[kk] = std::numeric_limits<int>::max();
+                x = xmin;
+
+                for (int j = y - r_b; j <= y + r_b; j++)
+                {
+                    for (int i = x - r_b; i <= x + r_b; i++)
+                    {
+                        if (j == y && i == x)
+                            continue;
+
+                        acc[ctrInd] = 0;
+                        for (int u = -r_s; u <= r_s; u++)
+                        {
+                            temp = img.at<unsigned char>(j + r_s, i + u) - img.at<unsigned char>(y + r_s, x + u);
+                            vCol[x + u][ctrInd] += (temp * temp);
+
+                            temp = img.at<unsigned char>(j - r_s - 1, i + u) - img.at<unsigned char>(y - r_s - 1, x + u);
+                            vCol[x + u][ctrInd] -= (temp * temp);
+
+                            acc[ctrInd] += vCol[x + u][ctrInd];
+                        }
+
+                        if (acc[ctrInd] < minVals[k - 1])
+                        {
+                            minVals[k - 1] = acc[ctrInd];
+
+                            for (int kk = k - 2; kk >= 0; kk--)
+                            {
+                                if (minVals[kk] > minVals[kk + 1])
+                                {
+                                    std::swap(minVals[kk], minVals[kk + 1]);
+                                } else
+                                    break;
+                            }
+                        }
+
+                        ctrInd++;
+                    }
+                }
+                saliency[y * w + x] = computeAvgDistance(minVals, den);
+
+                for (x = xmin + 1; x < xmax; x++)
+                {
+                    ctrInd = 0;
+                    for (int kk = 0; kk < k; kk++)
+                        minVals[kk] = std::numeric_limits<int>::max();
+
+                    for (int j = y - r_b; j <= y + r_b; j++)
+                    {
+                        for (int i = x - r_b; i <= x + r_b; i++)
+                        {
+                            if (j == y && i == x)
+                                continue;
+
+                            temp = img.at<unsigned char>(j + r_s, i + r_s) - img.at<unsigned char>(y + r_s, x + r_s);
+                            vCol[x + r_s][ctrInd] += (temp * temp);
+
+                            temp = img.at<unsigned char>(j - r_s - 1, i + r_s) - img.at<unsigned char>(y - r_s - 1, x + r_s);
+                            vCol[x + r_s][ctrInd] -= (temp * temp);
+
+                            acc[ctrInd] = acc[ctrInd] + vCol[x + r_s][ctrInd] - vCol[x - r_s - 1][ctrInd];
+
+                            if (acc[ctrInd] < minVals[k - 1])
+                            {
+                                minVals[k - 1] = acc[ctrInd];
+
+                                for (int kk = k - 2; kk >= 0; kk--)
+                                {
+                                    if (minVals[kk] > minVals[kk + 1])
+                                    {
+                                        std::swap(minVals[kk], minVals[kk + 1]);
+                                    } else
+                                        break;
+                                }
+                            }
+                            ctrInd++;
+                        }
+                    }
+                    saliency[y * w + x] = computeAvgDistance(minVals, den);
+                }
+            }
+
+            for (int i = 0; i < w; i++)
+                delete[] vCol[i];
+            delete[] vCol;
+            delete[] acc;
+        }
+
+        float MSDDetector_Impl::computeOrientation(cv::Mat &img, int x, int y, std::vector<cv::Point2f> circle)
+        {
+            int temp;
+
+            int nBins = 36;
+            float step = float((2 * CV_PI) / nBins);
+            std::vector<float> hist(nBins, 0);
+            std::vector<int> dists(circle.size(), 0);
+
+            int minDist = std::numeric_limits<int>::max();
+            int maxDist = -1;
+
+            for (int k = 0; k < (int) circle.size(); k++)
+            {
+
+                int j = y + static_cast<int> (circle[k].y);
+                int i = x + static_cast<int> (circle[k].x);
+
+                for (int v = -m_patch_radius; v <= m_patch_radius; v++)
+                {
+                    for (int u = -m_patch_radius; u <= m_patch_radius; u++)
+                    {
+                        temp = img.at<unsigned char>(j + v, i + u) - img.at<unsigned char>(y + v, x + u);
+                        dists[k] += temp*temp;
+                    }
+                }
+
+                if (dists[k] > maxDist)
+                    maxDist = dists[k];
+                if (dists[k] < minDist)
+                    minDist = dists[k];
+            }
+
+            float deltaAngle = 0.0f;
+            for (int k = 0; k < (int) circle.size(); k++)
+            {
+                float angle = deltaAngle;
+                float weight = (1.0f * maxDist - dists[k]) / (maxDist - minDist);
+
+                float binF;
+                if (angle >= 2 * CV_PI)
+                    binF = 0.0f;
+                else
+                    binF = angle / step;
+                int bin = static_cast<int> (std::floor(binF));
+
+                CV_Assert(bin >= 0 && bin < nBins);
+                float binDist = abs(binF - bin - 0.5f);
+
+                float weightA = weight * (1.0f - binDist);
+                float weightB = weight * binDist;
+                hist[bin] += weightA;
+
+                if (2 * (binF - bin) < step)
+                    hist[(bin + nBins - 1) % nBins] += weightB;
+                else
+                    hist[(bin + 1) % nBins] += weightB;
+
+                deltaAngle += step;
+            }
+
+            int bestBin = -1;
+            float maxBin = -1;
+            for (int i = 0; i < nBins; i++)
+            {
+                if (hist[i] > maxBin)
+                {
+                    maxBin = hist[i];
+                    bestBin = i;
+                }
+            }
+
+            int l = (bestBin == 0) ? nBins - 1 : bestBin - 1;
+            int r = (bestBin + 1) % nBins;
+            float bestAngle2 = bestBin + 0.5f * ((hist[l]) - (hist[r])) / ((hist[l]) - 2.0f * (hist[bestBin]) + (hist[r]));
+            bestAngle2 = (bestAngle2 < 0) ? nBins + bestAngle2 : (bestAngle2 >= nBins) ? bestAngle2 - nBins : bestAngle2;
+            bestAngle2 *= step;
+
+            return bestAngle2;
+        }
+
+        void MSDDetector_Impl::nonMaximaSuppression(std::vector< std::vector<float> > & saliency, std::vector<cv::KeyPoint> & keypoints)
+        {
+            cv::KeyPoint kp_temp;
+            int border = m_search_area_radius + m_patch_radius;
+
+            std::vector<cv::Point2f> orientPoints;
+            if (m_compute_orientation)
+            {
+                int nBins = 36;
+                float step = float((2 * CV_PI) / nBins);
+                float deltaAngle = 0.0f;
+
+                for (int i = 0; i < nBins; i++)
+                {
+                    cv::Point2f pt;
+                    pt.x = m_search_area_radius * cos(deltaAngle);
+                    pt.y = m_search_area_radius * sin(deltaAngle);
+
+                    orientPoints.push_back(pt);
+
+                    deltaAngle += step;
+                }
+            }
+
+            for (int r = 0; r < m_cur_n_scales; r++)
+            {
+                int cW = m_scaleSpace[r].cols;
+                int cH = m_scaleSpace[r].rows;
+
+                for (int j = border; j < cH - border; j++)
+                {
+                    for (int i = border; i < cW - border; i++)
+                    {
+                        if (saliency[r][j * cW + i] <= m_th_saliency)
+                            continue;
+
+                        if (m_mask.rows > 0)
+                        {
+                            int j_full = cvRound(j * std::pow(m_scale_factor, r));
+                            int i_full = cvRound(i * std::pow(m_scale_factor, r));
+                            if ((int) m_mask.at<unsigned char>(j_full, i_full) == 0)
+                                continue;
+                        }
+
+                        bool is_max = true;
+
+                        for (int k = cv::max(0, r - m_nms_scale_radius); k <= cv::min(m_cur_n_scales - 1, r + m_nms_scale_radius); k++)
+                        {
+                            if (k != r)
+                            {
+                                int j_sc = cvRound(j * std::pow(m_scale_factor, r - k));
+                                int i_sc = cvRound(i * std::pow(m_scale_factor, r - k));
+
+                                if (saliency[r][j * cW + i] < saliency[k][j_sc * cW + i_sc])
+                                {
+                                    is_max = false;
+                                    break;
+                                }
+                            }
+                        }
+
+                        for (int v = cv::max(border, j - m_nms_radius); v <= cv::min(cH - border - 1, j + m_nms_radius); v++)
+                        {
+                            for (int u = cv::max(border, i - m_nms_radius); u <= cv::min(cW - border - 1, i + m_nms_radius); u++)
+                            {
+                                if (saliency[r][j * cW + i] < saliency[r][v * cW + u])
+                                {
+                                    is_max = false;
+                                    break;
+                                }
+                            }
+
+                            if (!is_max)
+                                break;
+                        }
+
+                        if (is_max)
+                        {
+                            bool resInt = rescalePoint(i, j, r, saliency, kp_temp.pt);
+                            if (!resInt)
+                                continue;
+
+
+                            if (m_mask.rows > 0)
+                            {
+                                if (m_mask.at<unsigned char>((int) kp_temp.pt.y, (int) kp_temp.pt.x) == 0)
+                                    continue;
+                            }
+                            kp_temp.response = saliency[r][j * cW + i];
+                            kp_temp.size = (m_patch_radius * 2.0f + 1) * std::pow(m_scale_factor, r);
+                            kp_temp.octave = r;
+                            if (m_compute_orientation)
+                                kp_temp.angle = computeOrientation(m_scaleSpace[r], i, j, orientPoints);
+
+                            keypoints.push_back(kp_temp);
+                        }
+                    }
+                }
+            }
+
+        }
+
+        Ptr<MSDDetector> MSDDetector::create(int m_patch_radius, int m_search_area_radius,
+                int m_nms_radius, int m_nms_scale_radius, float m_th_saliency, int m_kNN, float m_scale_factor,
+                int m_n_scales, bool m_compute_orientation)
+        {
+            return makePtr<MSDDetector_Impl>(m_patch_radius, m_search_area_radius,
+                    m_nms_radius, m_nms_scale_radius, m_th_saliency, m_kNN, m_scale_factor,
+                    m_n_scales, m_compute_orientation);
+        }
+
+    }
+}

modules/xfeatures2d/test/test_keypoints.cpp

@@ -135,3 +135,10 @@ TEST(Features2d_Detector_Keypoints_Star, validation)
     CV_FeatureDetectorKeypointsTest test(xfeatures2d::StarDetector::create());
     test.safe_run();
 }
+
+
+TEST(Features2d_Detector_Keypoints_MSDDetector, validation)
+{
+    CV_FeatureDetectorKeypointsTest test(xfeatures2d::MSDDetector::create());
+    test.safe_run();
+}