--- /dev/null
+/*********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * Copyright (C) 2011 The Autonomous Systems Lab (ASL), ETH Zurich,
+ * Stefan Leutenegger, Simon Lynen and Margarita Chli.
+ * Copyright (c) 2009, Willow Garage, Inc.
+ * 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 Willow Garage 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.
+ *********************************************************************/
+
+/*
+ BRISK - Binary Robust Invariant Scalable Keypoints
+ Reference implementation of
+ [1] Stefan Leutenegger,Margarita Chli and Roland Siegwart, BRISK:
+ Binary Robust Invariant Scalable Keypoints, in Proceedings of
+ the IEEE International Conference on Computer Vision (ICCV2011).
+ */
+
+#include <opencv2/features2d/features2d.hpp>
+#include <opencv2/core/core.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <fstream>
+#include <stdlib.h>
+
+#include "fast_score.hpp"
+
+namespace cv
+{
+
+// a layer in the Brisk detector pyramid
+class CV_EXPORTS BriskLayer
+{
+public:
+ // constructor arguments
+ struct CV_EXPORTS CommonParams
+ {
+ static const int HALFSAMPLE = 0;
+ static const int TWOTHIRDSAMPLE = 1;
+ };
+ // construct a base layer
+ BriskLayer(const cv::Mat& img, float scale = 1.0f, float offset = 0.0f);
+ // derive a layer
+ BriskLayer(const BriskLayer& layer, int mode);
+
+ // Fast/Agast without non-max suppression
+ void
+ getAgastPoints(uint8_t threshold, std::vector<cv::KeyPoint>& keypoints);
+
+ // get scores - attention, this is in layer coordinates, not scale=1 coordinates!
+ inline uint8_t
+ getAgastScore(int x, int y, uint8_t threshold);
+ inline uint8_t
+ getAgastScore_5_8(int x, int y, uint8_t threshold);
+ inline uint8_t
+ getAgastScore(float xf, float yf, uint8_t threshold, float scale = 1.0f);
+
+ // accessors
+ inline const cv::Mat&
+ img() const
+ {
+ return img_;
+ }
+ inline const cv::Mat&
+ scores() const
+ {
+ return scores_;
+ }
+ inline float
+ scale() const
+ {
+ return scale_;
+ }
+ inline float
+ offset() const
+ {
+ return offset_;
+ }
+
+ // half sampling
+ static inline void
+ halfsample(const cv::Mat& srcimg, cv::Mat& dstimg);
+ // two third sampling
+ static inline void
+ twothirdsample(const cv::Mat& srcimg, cv::Mat& dstimg);
+
+private:
+ // access gray values (smoothed/interpolated)
+ __inline__ uint8_t
+ value(const cv::Mat& mat, float xf, float yf, float scale);
+ // the image
+ cv::Mat img_;
+ // its Fast scores
+ cv::Mat_<uchar> scores_;
+ // coordinate transformation
+ float scale_;
+ float offset_;
+ // agast
+ cv::Ptr<cv::FastFeatureDetector> fast_9_16_;
+ int pixel_5_8_[25];
+ int pixel_9_16_[25];
+};
+
+class CV_EXPORTS BriskScaleSpace
+{
+public:
+ // construct telling the octaves number:
+ BriskScaleSpace(uint8_t _octaves = 3);
+ ~BriskScaleSpace();
+
+ // construct the image pyramids
+ void
+ constructPyramid(const cv::Mat& image);
+
+ // get Keypoints
+ void
+ getKeypoints(const uint8_t _threshold, std::vector<cv::KeyPoint>& keypoints);
+
+protected:
+ // nonmax suppression:
+ __inline__ bool
+ isMax2D(const uint8_t layer, const int x_layer, const int y_layer);
+ // 1D (scale axis) refinement:
+ __inline__ float
+ refine1D(const float s_05, const float s0, const float s05, float& max); // around octave
+ __inline__ float
+ refine1D_1(const float s_05, const float s0, const float s05, float& max); // around intra
+ __inline__ float
+ refine1D_2(const float s_05, const float s0, const float s05, float& max); // around octave 0 only
+ // 2D maximum refinement:
+ __inline__ float
+ subpixel2D(const int s_0_0, const int s_0_1, const int s_0_2, const int s_1_0, const int s_1_1, const int s_1_2,
+ const int s_2_0, const int s_2_1, const int s_2_2, float& delta_x, float& delta_y);
+ // 3D maximum refinement centered around (x_layer,y_layer)
+ __inline__ float
+ refine3D(const uint8_t layer, const int x_layer, const int y_layer, float& x, float& y, float& scale, bool& ismax);
+
+ // interpolated score access with recalculation when needed:
+ __inline__ int
+ getScoreAbove(const uint8_t layer, const int x_layer, const int y_layer);
+ __inline__ int
+ getScoreBelow(const uint8_t layer, const int x_layer, const int y_layer);
+
+ // return the maximum of score patches above or below
+ __inline__ float
+ getScoreMaxAbove(const uint8_t layer, const int x_layer, const int y_layer, const int threshold, bool& ismax,
+ float& dx, float& dy);
+ __inline__ float
+ getScoreMaxBelow(const uint8_t layer, const int x_layer, const int y_layer, const int threshold, bool& ismax,
+ float& dx, float& dy);
+
+ // the image pyramids:
+ uint8_t layers_;
+ std::vector<BriskLayer> pyramid_;
+
+ // some constant parameters:
+ static const float safetyFactor_;
+ static const float basicSize_;
+};
+
+using namespace cv;
+
+const float BRISK::basicSize_ = 12.0;
+const unsigned int BRISK::scales_ = 64;
+const float BRISK::scalerange_ = 30; // 40->4 Octaves - else, this needs to be adjusted...
+const unsigned int BRISK::n_rot_ = 1024; // discretization of the rotation look-up
+
+const float BriskScaleSpace::safetyFactor_ = 1.0;
+const float BriskScaleSpace::basicSize_ = 12.0;
+
+// constructors
+BRISK::BRISK(int thresh, int octaves_in, float patternScale)
+{
+ threshold = thresh;
+ octaves = octaves_in;
+
+ std::vector<float> rList;
+ std::vector<int> nList;
+
+ // this is the standard pattern found to be suitable also
+ rList.resize(5);
+ nList.resize(5);
+ const double f = 0.85 * patternScale;
+
+ rList[0] = f * 0;
+ rList[1] = f * 2.9;
+ rList[2] = f * 4.9;
+ rList[3] = f * 7.4;
+ rList[4] = f * 10.8;
+
+ nList[0] = 1;
+ nList[1] = 10;
+ nList[2] = 14;
+ nList[3] = 15;
+ nList[4] = 20;
+
+ generateKernel(rList, nList, 5.85 * patternScale, 8.2 * patternScale);
+
+}
+BRISK::BRISK(std::vector<float> &radiusList, std::vector<int> &numberList, float dMax, float dMin,
+ std::vector<int> indexChange)
+{
+ generateKernel(radiusList, numberList, dMax, dMin, indexChange);
+}
+
+void
+BRISK::generateKernel(std::vector<float> &radiusList, std::vector<int> &numberList, float dMax,
+ float dMin, std::vector<int> indexChange)
+{
+
+ dMax_ = dMax;
+ dMin_ = dMin;
+
+ // get the total number of points
+ const int rings = radiusList.size();
+ assert(radiusList.size()!=0&&radiusList.size()==numberList.size());
+ points_ = 0; // remember the total number of points
+ for (int ring = 0; ring < rings; ring++)
+ {
+ points_ += numberList[ring];
+ }
+ // set up the patterns
+ patternPoints_ = new BriskPatternPoint[points_ * scales_ * n_rot_];
+ BriskPatternPoint* patternIterator = patternPoints_;
+
+ // define the scale discretization:
+ static const float lb_scale = log(scalerange_) / log(2.0);
+ static const float lb_scale_step = lb_scale / (scales_);
+
+ scaleList_ = new float[scales_];
+ sizeList_ = new unsigned int[scales_];
+
+ const float sigma_scale = 1.3;
+
+ for (unsigned int scale = 0; scale < scales_; ++scale)
+ {
+ scaleList_[scale] = pow((double) 2.0, (double) (scale * lb_scale_step));
+ sizeList_[scale] = 0;
+
+ // generate the pattern points look-up
+ double alpha, theta;
+ for (size_t rot = 0; rot < n_rot_; ++rot)
+ {
+ theta = double(rot) * 2 * M_PI / double(n_rot_); // this is the rotation of the feature
+ for (int ring = 0; ring < rings; ++ring)
+ {
+ for (int num = 0; num < numberList[ring]; ++num)
+ {
+ // the actual coordinates on the circle
+ alpha = (double(num)) * 2 * M_PI / double(numberList[ring]);
+ patternIterator->x = scaleList_[scale] * radiusList[ring] * cos(alpha + theta); // feature rotation plus angle of the point
+ patternIterator->y = scaleList_[scale] * radiusList[ring] * sin(alpha + theta);
+ // and the gaussian kernel sigma
+ if (ring == 0)
+ {
+ patternIterator->sigma = sigma_scale * scaleList_[scale] * 0.5;
+ }
+ else
+ {
+ patternIterator->sigma = sigma_scale * scaleList_[scale] * (double(radiusList[ring]))
+ * sin(M_PI / numberList[ring]);
+ }
+ // adapt the sizeList if necessary
+ const unsigned int size = ceil(((scaleList_[scale] * radiusList[ring]) + patternIterator->sigma)) + 1;
+ if (sizeList_[scale] < size)
+ {
+ sizeList_[scale] = size;
+ }
+
+ // increment the iterator
+ ++patternIterator;
+ }
+ }
+ }
+ }
+
+ // now also generate pairings
+ shortPairs_ = new BriskShortPair[points_ * (points_ - 1) / 2];
+ longPairs_ = new BriskLongPair[points_ * (points_ - 1) / 2];
+ noShortPairs_ = 0;
+ noLongPairs_ = 0;
+
+ // fill indexChange with 0..n if empty
+ unsigned int indSize = indexChange.size();
+ if (indSize == 0)
+ {
+ indexChange.resize(points_ * (points_ - 1) / 2);
+ indSize = indexChange.size();
+ }
+ for (unsigned int i = 0; i < indSize; i++)
+ {
+ indexChange[i] = i;
+ }
+ const float dMin_sq = dMin_ * dMin_;
+ const float dMax_sq = dMax_ * dMax_;
+ for (unsigned int i = 1; i < points_; i++)
+ {
+ for (unsigned int j = 0; j < i; j++)
+ { //(find all the pairs)
+ // point pair distance:
+ const float dx = patternPoints_[j].x - patternPoints_[i].x;
+ const float dy = patternPoints_[j].y - patternPoints_[i].y;
+ const float norm_sq = (dx * dx + dy * dy);
+ if (norm_sq > dMin_sq)
+ {
+ // save to long pairs
+ BriskLongPair& longPair = longPairs_[noLongPairs_];
+ longPair.weighted_dx = int((dx / (norm_sq)) * 2048.0 + 0.5);
+ longPair.weighted_dy = int((dy / (norm_sq)) * 2048.0 + 0.5);
+ longPair.i = i;
+ longPair.j = j;
+ ++noLongPairs_;
+ }
+ else if (norm_sq < dMax_sq)
+ {
+ // save to short pairs
+ assert(noShortPairs_<indSize);
+ // make sure the user passes something sensible
+ BriskShortPair& shortPair = shortPairs_[indexChange[noShortPairs_]];
+ shortPair.j = j;
+ shortPair.i = i;
+ ++noShortPairs_;
+ }
+ }
+ }
+
+ // no bits:
+ strings_ = (int) ceil((float(noShortPairs_)) / 128.0) * 4 * 4;
+}
+
+// simple alternative:
+__inline__ int
+BRISK::smoothedIntensity(const cv::Mat& image, const cv::Mat& integral, const float key_x,
+ const float key_y, const unsigned int scale, const unsigned int rot,
+ const unsigned int point) const
+{
+
+ // get the float position
+ const BriskPatternPoint& briskPoint = patternPoints_[scale * n_rot_ * points_ + rot * points_ + point];
+ const float xf = briskPoint.x + key_x;
+ const float yf = briskPoint.y + key_y;
+ const int x = int(xf);
+ const int y = int(yf);
+ const int& imagecols = image.cols;
+
+ // get the sigma:
+ const float sigma_half = briskPoint.sigma;
+ const float area = 4.0 * sigma_half * sigma_half;
+
+ // calculate output:
+ int ret_val;
+ if (sigma_half < 0.5)
+ {
+ //interpolation multipliers:
+ const int r_x = (xf - x) * 1024;
+ const int r_y = (yf - y) * 1024;
+ const int r_x_1 = (1024 - r_x);
+ const int r_y_1 = (1024 - r_y);
+ uchar* ptr = image.data + x + y * imagecols;
+ // just interpolate:
+ ret_val = (r_x_1 * r_y_1 * int(*ptr));
+ ptr++;
+ ret_val += (r_x * r_y_1 * int(*ptr));
+ ptr += imagecols;
+ ret_val += (r_x * r_y * int(*ptr));
+ ptr--;
+ ret_val += (r_x_1 * r_y * int(*ptr));
+ return (ret_val + 512) / 1024;
+ }
+
+ // this is the standard case (simple, not speed optimized yet):
+
+ // scaling:
+ const int scaling = 4194304.0 / area;
+ const int scaling2 = float(scaling) * area / 1024.0;
+
+ // the integral image is larger:
+ const int integralcols = imagecols + 1;
+
+ // calculate borders
+ const float x_1 = xf - sigma_half;
+ const float x1 = xf + sigma_half;
+ const float y_1 = yf - sigma_half;
+ const float y1 = yf + sigma_half;
+
+ const int x_left = int(x_1 + 0.5);
+ const int y_top = int(y_1 + 0.5);
+ const int x_right = int(x1 + 0.5);
+ const int y_bottom = int(y1 + 0.5);
+
+ // overlap area - multiplication factors:
+ const float r_x_1 = float(x_left) - x_1 + 0.5;
+ const float r_y_1 = float(y_top) - y_1 + 0.5;
+ const float r_x1 = x1 - float(x_right) + 0.5;
+ const float r_y1 = y1 - float(y_bottom) + 0.5;
+ const int dx = x_right - x_left - 1;
+ const int dy = y_bottom - y_top - 1;
+ const int A = (r_x_1 * r_y_1) * scaling;
+ const int B = (r_x1 * r_y_1) * scaling;
+ const int C = (r_x1 * r_y1) * scaling;
+ const int D = (r_x_1 * r_y1) * scaling;
+ const int r_x_1_i = r_x_1 * scaling;
+ const int r_y_1_i = r_y_1 * scaling;
+ const int r_x1_i = r_x1 * scaling;
+ const int r_y1_i = r_y1 * scaling;
+
+ if (dx + dy > 2)
+ {
+ // now the calculation:
+ uchar* ptr = image.data + x_left + imagecols * y_top;
+ // first the corners:
+ ret_val = A * int(*ptr);
+ ptr += dx + 1;
+ ret_val += B * int(*ptr);
+ ptr += dy * imagecols + 1;
+ ret_val += C * int(*ptr);
+ ptr -= dx + 1;
+ ret_val += D * int(*ptr);
+
+ // next the edges:
+ int* ptr_integral = (int*) integral.data + x_left + integralcols * y_top + 1;
+ // find a simple path through the different surface corners
+ const int tmp1 = (*ptr_integral);
+ ptr_integral += dx;
+ const int tmp2 = (*ptr_integral);
+ ptr_integral += integralcols;
+ const int tmp3 = (*ptr_integral);
+ ptr_integral++;
+ const int tmp4 = (*ptr_integral);
+ ptr_integral += dy * integralcols;
+ const int tmp5 = (*ptr_integral);
+ ptr_integral--;
+ const int tmp6 = (*ptr_integral);
+ ptr_integral += integralcols;
+ const int tmp7 = (*ptr_integral);
+ ptr_integral -= dx;
+ const int tmp8 = (*ptr_integral);
+ ptr_integral -= integralcols;
+ const int tmp9 = (*ptr_integral);
+ ptr_integral--;
+ const int tmp10 = (*ptr_integral);
+ ptr_integral -= dy * integralcols;
+ const int tmp11 = (*ptr_integral);
+ ptr_integral++;
+ const int tmp12 = (*ptr_integral);
+
+ // assign the weighted surface integrals:
+ const int upper = (tmp3 - tmp2 + tmp1 - tmp12) * r_y_1_i;
+ const int middle = (tmp6 - tmp3 + tmp12 - tmp9) * scaling;
+ const int left = (tmp9 - tmp12 + tmp11 - tmp10) * r_x_1_i;
+ const int right = (tmp5 - tmp4 + tmp3 - tmp6) * r_x1_i;
+ const int bottom = (tmp7 - tmp6 + tmp9 - tmp8) * r_y1_i;
+
+ return (ret_val + upper + middle + left + right + bottom + scaling2 / 2) / scaling2;
+ }
+
+ // now the calculation:
+ uchar* ptr = image.data + x_left + imagecols * y_top;
+ // first row:
+ ret_val = A * int(*ptr);
+ ptr++;
+ const uchar* end1 = ptr + dx;
+ for (; ptr < end1; ptr++)
+ {
+ ret_val += r_y_1_i * int(*ptr);
+ }
+ ret_val += B * int(*ptr);
+ // middle ones:
+ ptr += imagecols - dx - 1;
+ uchar* end_j = ptr + dy * imagecols;
+ for (; ptr < end_j; ptr += imagecols - dx - 1)
+ {
+ ret_val += r_x_1_i * int(*ptr);
+ ptr++;
+ const uchar* end2 = ptr + dx;
+ for (; ptr < end2; ptr++)
+ {
+ ret_val += int(*ptr) * scaling;
+ }
+ ret_val += r_x1_i * int(*ptr);
+ }
+ // last row:
+ ret_val += D * int(*ptr);
+ ptr++;
+ const uchar* end3 = ptr + dx;
+ for (; ptr < end3; ptr++)
+ {
+ ret_val += r_y1_i * int(*ptr);
+ }
+ ret_val += C * int(*ptr);
+
+ return (ret_val + scaling2 / 2) / scaling2;
+}
+
+inline bool
+RoiPredicate(const float minX, const float minY, const float maxX, const float maxY, const KeyPoint& keyPt)
+{
+ const Point2f& pt = keyPt.pt;
+ return (pt.x < minX) || (pt.x >= maxX) || (pt.y < minY) || (pt.y >= maxY);
+}
+
+// computes the descriptor
+void
+BRISK::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& keypoints,
+ OutputArray _descriptors, bool useProvidedKeypoints) const
+{
+ Mat image = _image.getMat(), mask = _mask.getMat();
+ if (!useProvidedKeypoints)
+ detectImpl(image, keypoints, mask);
+
+ //Remove keypoints very close to the border
+ size_t ksize = keypoints.size();
+ std::vector<int> kscales; // remember the scale per keypoint
+ kscales.resize(ksize);
+ static const float log2 = 0.693147180559945;
+ static const float lb_scalerange = log(scalerange_) / (log2);
+ std::vector<cv::KeyPoint>::iterator beginning = keypoints.begin();
+ std::vector<int>::iterator beginningkscales = kscales.begin();
+ static const float basicSize06 = basicSize_ * 0.6;
+ for (size_t k = 0; k < ksize; k++)
+ {
+ unsigned int scale;
+ scale = std::max((int) (scales_ / lb_scalerange * (log(keypoints[k].size / (basicSize06)) / log2) + 0.5), 0);
+ // saturate
+ if (scale >= scales_)
+ scale = scales_ - 1;
+ kscales[k] = scale;
+ const int border = sizeList_[scale];
+ const int border_x = image.cols - border;
+ const int border_y = image.rows - border;
+ if (RoiPredicate(border, border, border_x, border_y, keypoints[k]))
+ {
+ keypoints.erase(beginning + k);
+ kscales.erase(beginningkscales + k);
+ if (k == 0)
+ {
+ beginning = keypoints.begin();
+ beginningkscales = kscales.begin();
+ }
+ ksize--;
+ k--;
+ }
+ }
+
+ // first, calculate the integral image over the whole image:
+ // current integral image
+ cv::Mat _integral; // the integral image
+ cv::integral(image, _integral);
+
+ int* _values = new int[points_]; // for temporary use
+
+ // resize the descriptors:
+ _descriptors.create(ksize, strings_, CV_8U);
+ cv::Mat descriptors = _descriptors.getMat();
+ descriptors.setTo(0);
+
+ // now do the extraction for all keypoints:
+
+ // temporary variables containing gray values at sample points:
+ int t1;
+ int t2;
+
+ // the feature orientation
+ uchar* ptr = descriptors.data;
+ for (size_t k = 0; k < ksize; k++)
+ {
+ int theta;
+ cv::KeyPoint& kp = keypoints[k];
+ const int& scale = kscales[k];
+ int shifter = 0;
+ int* pvalues = _values;
+ const float& x = kp.pt.x;
+ const float& y = kp.pt.y;
+ if (kp.angle==-1)
+ {
+ // don't compute the gradient direction, just assign a rotation of 0°
+ theta = 0;
+ }
+ else
+ {
+ theta = (int) (n_rot_ * (kp.angle / (360.0)) + 0.5);
+ if (theta < 0)
+ theta += n_rot_;
+ if (theta >= int(n_rot_))
+ theta -= n_rot_;
+ }
+
+ // now also extract the stuff for the actual direction:
+ // let us compute the smoothed values
+ shifter = 0;
+
+ //unsigned int mean=0;
+ pvalues = _values;
+ // get the gray values in the rotated pattern
+ for (unsigned int i = 0; i < points_; i++)
+ {
+ *(pvalues++) = smoothedIntensity(image, _integral, x, y, scale, theta, i);
+ }
+
+ // now iterate through all the pairings
+ unsigned int* ptr2 = (unsigned int*) ptr;
+ const BriskShortPair* max = shortPairs_ + noShortPairs_;
+ for (BriskShortPair* iter = shortPairs_; iter < max; ++iter)
+ {
+ t1 = *(_values + iter->i);
+ t2 = *(_values + iter->j);
+ if (t1 > t2)
+ {
+ *ptr2 |= ((1) << shifter);
+
+ } // else already initialized with zero
+ // take care of the iterators:
+ ++shifter;
+ if (shifter == 32)
+ {
+ shifter = 0;
+ ++ptr2;
+ }
+ }
+
+ ptr += strings_;
+ }
+
+ // clean-up
+ _integral.release();
+ delete[] _values;
+}
+
+int
+BRISK::descriptorSize() const
+{
+ return strings_;
+}
+
+int
+BRISK::descriptorType() const
+{
+ return CV_8U;
+}
+
+BRISK::~BRISK()
+{
+ delete[] patternPoints_;
+ delete[] shortPairs_;
+ delete[] longPairs_;
+ delete[] scaleList_;
+ delete[] sizeList_;
+}
+
+void
+BRISK::operator()(InputArray _image, InputArray _mask, vector<KeyPoint>& keypoints) const
+{
+ Mat image = _image.getMat(), mask = _mask.getMat();
+ if( image.type() != CV_8UC1 )
+ cvtColor(_image, image, CV_BGR2GRAY);
+
+ BriskScaleSpace briskScaleSpace(octaves);
+ briskScaleSpace.constructPyramid(image);
+ briskScaleSpace.getKeypoints(threshold, keypoints);
+
+ // remove invalid points
+ removeInvalidPoints(mask, keypoints);
+
+ // Compute the orientations of the keypoints
+ //Remove keypoints very close to the border
+ size_t ksize = keypoints.size();
+ std::vector<int> kscales; // remember the scale per keypoint
+ kscales.resize(ksize);
+ static const float log2 = 0.693147180559945;
+ static const float lb_scalerange = log(scalerange_) / (log2);
+ std::vector<cv::KeyPoint>::iterator beginning = keypoints.begin();
+ std::vector<int>::iterator beginningkscales = kscales.begin();
+ static const float basicSize06 = basicSize_ * 0.6;
+ for (size_t k = 0; k < ksize; k++)
+ {
+ unsigned int scale;
+ scale = std::max((int) (scales_ / lb_scalerange * (log(keypoints[k].size / (basicSize06)) / log2) + 0.5), 0);
+ // saturate
+ if (scale >= scales_)
+ scale = scales_ - 1;
+ kscales[k] = scale;
+ const int border = sizeList_[scale];
+ const int border_x = image.cols - border;
+ const int border_y = image.rows - border;
+ if (RoiPredicate(border, border, border_x, border_y, keypoints[k]))
+ {
+ keypoints.erase(beginning + k);
+ kscales.erase(beginningkscales + k);
+ if (k == 0)
+ {
+ beginning = keypoints.begin();
+ beginningkscales = kscales.begin();
+ }
+ ksize--;
+ k--;
+ }
+ }
+
+ // first, calculate the integral image over the whole image:
+ // current integral image
+ cv::Mat _integral; // the integral image
+ cv::integral(image, _integral);
+
+ int* _values = new int[points_]; // for temporary use
+
+ // now do the extraction for all keypoints:
+
+ // temporary variables containing gray values at sample points:
+ int t1;
+ int t2;
+
+ // the feature orientation
+ int direction0;
+ int direction1;
+
+ for (size_t k = 0; k < ksize; k++)
+ {
+ cv::KeyPoint& kp = keypoints[k];
+ const int& scale = kscales[k];
+ int* pvalues = _values;
+ const float& x = kp.pt.x;
+ const float& y = kp.pt.y;
+ // get the gray values in the unrotated pattern
+ for (unsigned int i = 0; i < points_; i++)
+ {
+ *(pvalues++) = smoothedIntensity(image, _integral, x, y, scale, 0, i);
+ }
+
+ direction0 = 0;
+ direction1 = 0;
+ // now iterate through the long pairings
+ const BriskLongPair* max = longPairs_ + noLongPairs_;
+ for (BriskLongPair* iter = longPairs_; iter < max; ++iter)
+ {
+ t1 = *(_values + iter->i);
+ t2 = *(_values + iter->j);
+ const int delta_t = (t1 - t2);
+ // update the direction:
+ const int tmp0 = delta_t * (iter->weighted_dx) / 1024;
+ const int tmp1 = delta_t * (iter->weighted_dy) / 1024;
+ direction0 += tmp0;
+ direction1 += tmp1;
+ }
+ kp.angle = atan2((float) direction1, (float) direction0) / M_PI * 180.0;
+ if (kp.angle < 0)
+ kp.angle += 360;
+ }
+}
+
+
+void
+BRISK::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
+{
+ (*this)(image, mask, keypoints);
+}
+
+void
+BRISK::computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors) const
+{
+ (*this)(image, Mat(), keypoints, descriptors, true);
+}
+
+// construct telling the octaves number:
+BriskScaleSpace::BriskScaleSpace(uint8_t _octaves)
+{
+ if (_octaves == 0)
+ layers_ = 1;
+ else
+ layers_ = 2 * _octaves;
+}
+BriskScaleSpace::~BriskScaleSpace()
+{
+
+}
+// construct the image pyramids
+void
+BriskScaleSpace::constructPyramid(const cv::Mat& image)
+{
+
+ // set correct size:
+ pyramid_.clear();
+
+ // fill the pyramid:
+ pyramid_.push_back(BriskLayer(image.clone()));
+ if (layers_ > 1)
+ {
+ pyramid_.push_back(BriskLayer(pyramid_.back(), BriskLayer::CommonParams::TWOTHIRDSAMPLE));
+ }
+ const int octaves2 = layers_;
+
+ for (uint8_t i = 2; i < octaves2; i += 2)
+ {
+ pyramid_.push_back(BriskLayer(pyramid_[i - 2], BriskLayer::CommonParams::HALFSAMPLE));
+ pyramid_.push_back(BriskLayer(pyramid_[i - 1], BriskLayer::CommonParams::HALFSAMPLE));
+ }
+}
+
+void
+BriskScaleSpace::getKeypoints(const uint8_t _threshold, std::vector<cv::KeyPoint>& keypoints)
+{
+ // make sure keypoints is empty
+ keypoints.resize(0);
+ keypoints.reserve(2000);
+
+ // assign thresholds
+ uint8_t threshold_ = _threshold;
+ uint8_t safeThreshold_ = threshold_ * safetyFactor_;
+ std::vector<std::vector<cv::KeyPoint> > agastPoints;
+ agastPoints.resize(layers_);
+
+ // go through the octaves and intra layers and calculate fast corner scores:
+ for (uint8_t i = 0; i < layers_; i++)
+ {
+ // call OAST16_9 without nms
+ BriskLayer& l = pyramid_[i];
+ l.getAgastPoints(safeThreshold_, agastPoints[i]);
+ }
+
+ if (layers_ == 1)
+ {
+ // just do a simple 2d subpixel refinement...
+ const int num = agastPoints[0].size();
+ for (int n = 0; n < num; n++)
+ {
+ const cv::Point2f& point = agastPoints.at(0)[n].pt;
+ // first check if it is a maximum:
+ if (!isMax2D(0, point.x, point.y))
+ continue;
+
+ // let's do the subpixel and float scale refinement:
+ BriskLayer& l = pyramid_[0];
+ register int s_0_0 = l.getAgastScore(point.x - 1, point.y - 1, 1);
+ register int s_1_0 = l.getAgastScore(point.x, point.y - 1, 1);
+ register int s_2_0 = l.getAgastScore(point.x + 1, point.y - 1, 1);
+ register int s_2_1 = l.getAgastScore(point.x + 1, point.y, 1);
+ register int s_1_1 = l.getAgastScore(point.x, point.y, 1);
+ register int s_0_1 = l.getAgastScore(point.x - 1, point.y, 1);
+ register int s_0_2 = l.getAgastScore(point.x - 1, point.y + 1, 1);
+ register int s_1_2 = l.getAgastScore(point.x, point.y + 1, 1);
+ register int s_2_2 = l.getAgastScore(point.x + 1, point.y + 1, 1);
+ float delta_x, delta_y;
+ float max = subpixel2D(s_0_0, s_0_1, s_0_2, s_1_0, s_1_1, s_1_2, s_2_0, s_2_1, s_2_2, delta_x, delta_y);
+
+ // store:
+ keypoints.push_back(cv::KeyPoint(float(point.x) + delta_x, float(point.y) + delta_y, basicSize_, -1, max, 0));
+
+ }
+
+ return;
+ }
+
+ float x, y, scale, score;
+ for (uint8_t i = 0; i < layers_; i++)
+ {
+ BriskLayer& l = pyramid_[i];
+ const int num = agastPoints[i].size();
+ if (i == layers_ - 1)
+ {
+ for (int n = 0; n < num; n++)
+ {
+ const cv::Point2f& point = agastPoints.at(i)[n].pt;
+ // consider only 2D maxima...
+ if (!isMax2D(i, point.x, point.y))
+ continue;
+
+ bool ismax;
+ float dx, dy;
+ getScoreMaxBelow(i, point.x, point.y, l.getAgastScore(point.x, point.y, safeThreshold_), ismax, dx, dy);
+ if (!ismax)
+ continue;
+
+ // get the patch on this layer:
+ register int s_0_0 = l.getAgastScore(point.x - 1, point.y - 1, 1);
+ register int s_1_0 = l.getAgastScore(point.x, point.y - 1, 1);
+ register int s_2_0 = l.getAgastScore(point.x + 1, point.y - 1, 1);
+ register int s_2_1 = l.getAgastScore(point.x + 1, point.y, 1);
+ register int s_1_1 = l.getAgastScore(point.x, point.y, 1);
+ register int s_0_1 = l.getAgastScore(point.x - 1, point.y, 1);
+ register int s_0_2 = l.getAgastScore(point.x - 1, point.y + 1, 1);
+ register int s_1_2 = l.getAgastScore(point.x, point.y + 1, 1);
+ register int s_2_2 = l.getAgastScore(point.x + 1, point.y + 1, 1);
+ float delta_x, delta_y;
+ float max = subpixel2D(s_0_0, s_0_1, s_0_2, s_1_0, s_1_1, s_1_2, s_2_0, s_2_1, s_2_2, delta_x, delta_y);
+
+ // store:
+ keypoints.push_back(
+ cv::KeyPoint((float(point.x) + delta_x) * l.scale() + l.offset(),
+ (float(point.y) + delta_y) * l.scale() + l.offset(), basicSize_ * l.scale(), -1, max, i));
+ }
+ }
+ else
+ {
+ // not the last layer:
+ for (int n = 0; n < num; n++)
+ {
+ const cv::Point2f& point = agastPoints.at(i)[n].pt;
+
+ // first check if it is a maximum:
+ if (!isMax2D(i, point.x, point.y))
+ continue;
+
+ // let's do the subpixel and float scale refinement:
+ bool ismax;
+ score = refine3D(i, point.x, point.y, x, y, scale, ismax);
+ if (!ismax)
+ {
+ continue;
+ }
+
+ // finally store the detected keypoint:
+ if (score > float(threshold_))
+ {
+ keypoints.push_back(cv::KeyPoint(x, y, basicSize_ * scale, -1, score, i));
+ }
+ }
+ }
+ }
+}
+
+// interpolated score access with recalculation when needed:
+__inline__ int
+BriskScaleSpace::getScoreAbove(const uint8_t layer, const int x_layer, const int y_layer)
+{
+ assert(layer<layers_-1);
+ BriskLayer& l = pyramid_[layer + 1];
+ if (layer % 2 == 0)
+ { // octave
+ const int sixths_x = 4 * x_layer - 1;
+ const int x_above = sixths_x / 6;
+ const int sixths_y = 4 * y_layer - 1;
+ const int y_above = sixths_y / 6;
+ const int r_x = (sixths_x % 6);
+ const int r_x_1 = 6 - r_x;
+ const int r_y = (sixths_y % 6);
+ const int r_y_1 = 6 - r_y;
+ uint8_t score = 0xFF
+ & ((r_x_1 * r_y_1 * l.getAgastScore(x_above, y_above, 1) + r_x * r_y_1
+ * l.getAgastScore(x_above + 1, y_above, 1)
+ + r_x_1 * r_y * l.getAgastScore(x_above, y_above + 1, 1)
+ + r_x * r_y * l.getAgastScore(x_above + 1, y_above + 1, 1) + 18)
+ / 36);
+
+ return score;
+ }
+ else
+ { // intra
+ const int eighths_x = 6 * x_layer - 1;
+ const int x_above = eighths_x / 8;
+ const int eighths_y = 6 * y_layer - 1;
+ const int y_above = eighths_y / 8;
+ const int r_x = (eighths_x % 8);
+ const int r_x_1 = 8 - r_x;
+ const int r_y = (eighths_y % 8);
+ const int r_y_1 = 8 - r_y;
+ uint8_t score = 0xFF
+ & ((r_x_1 * r_y_1 * l.getAgastScore(x_above, y_above, 1) + r_x * r_y_1
+ * l.getAgastScore(x_above + 1, y_above, 1)
+ + r_x_1 * r_y * l.getAgastScore(x_above, y_above + 1, 1)
+ + r_x * r_y * l.getAgastScore(x_above + 1, y_above + 1, 1) + 32)
+ / 64);
+ return score;
+ }
+}
+__inline__ int
+BriskScaleSpace::getScoreBelow(const uint8_t layer, const int x_layer, const int y_layer)
+{
+ assert(layer);
+ BriskLayer& l = pyramid_[layer - 1];
+ int sixth_x;
+ int quarter_x;
+ float xf;
+ int sixth_y;
+ int quarter_y;
+ float yf;
+
+ // scaling:
+ float offs;
+ float area;
+ int scaling;
+ int scaling2;
+
+ if (layer % 2 == 0)
+ { // octave
+ sixth_x = 8 * x_layer + 1;
+ xf = float(sixth_x) / 6.0;
+ sixth_y = 8 * y_layer + 1;
+ yf = float(sixth_y) / 6.0;
+
+ // scaling:
+ offs = 2.0 / 3.0;
+ area = 4.0 * offs * offs;
+ scaling = 4194304.0 / area;
+ scaling2 = float(scaling) * area;
+ }
+ else
+ {
+ quarter_x = 6 * x_layer + 1;
+ xf = float(quarter_x) / 4.0;
+ quarter_y = 6 * y_layer + 1;
+ yf = float(quarter_y) / 4.0;
+
+ // scaling:
+ offs = 3.0 / 4.0;
+ area = 4.0 * offs * offs;
+ scaling = 4194304.0 / area;
+ scaling2 = float(scaling) * area;
+ }
+
+ // calculate borders
+ const float x_1 = xf - offs;
+ const float x1 = xf + offs;
+ const float y_1 = yf - offs;
+ const float y1 = yf + offs;
+
+ const int x_left = int(x_1 + 0.5);
+ const int y_top = int(y_1 + 0.5);
+ const int x_right = int(x1 + 0.5);
+ const int y_bottom = int(y1 + 0.5);
+
+ // overlap area - multiplication factors:
+ const float r_x_1 = float(x_left) - x_1 + 0.5;
+ const float r_y_1 = float(y_top) - y_1 + 0.5;
+ const float r_x1 = x1 - float(x_right) + 0.5;
+ const float r_y1 = y1 - float(y_bottom) + 0.5;
+ const int dx = x_right - x_left - 1;
+ const int dy = y_bottom - y_top - 1;
+ const int A = (r_x_1 * r_y_1) * scaling;
+ const int B = (r_x1 * r_y_1) * scaling;
+ const int C = (r_x1 * r_y1) * scaling;
+ const int D = (r_x_1 * r_y1) * scaling;
+ const int r_x_1_i = r_x_1 * scaling;
+ const int r_y_1_i = r_y_1 * scaling;
+ const int r_x1_i = r_x1 * scaling;
+ const int r_y1_i = r_y1 * scaling;
+
+ // first row:
+ int ret_val = A * int(l.getAgastScore(x_left, y_top, 1));
+ for (int X = 1; X <= dx; X++)
+ {
+ ret_val += r_y_1_i * int(l.getAgastScore(x_left + X, y_top, 1));
+ }
+ ret_val += B * int(l.getAgastScore(x_left + dx + 1, y_top, 1));
+ // middle ones:
+ for (int Y = 1; Y <= dy; Y++)
+ {
+ ret_val += r_x_1_i * int(l.getAgastScore(x_left, y_top + Y, 1));
+
+ for (int X = 1; X <= dx; X++)
+ {
+ ret_val += int(l.getAgastScore(x_left + X, y_top + Y, 1)) * scaling;
+ }
+ ret_val += r_x1_i * int(l.getAgastScore(x_left + dx + 1, y_top + Y, 1));
+ }
+ // last row:
+ ret_val += D * int(l.getAgastScore(x_left, y_top + dy + 1, 1));
+ for (int X = 1; X <= dx; X++)
+ {
+ ret_val += r_y1_i * int(l.getAgastScore(x_left + X, y_top + dy + 1, 1));
+ }
+ ret_val += C * int(l.getAgastScore(x_left + dx + 1, y_top + dy + 1, 1));
+
+ return ((ret_val + scaling2 / 2) / scaling2);
+}
+
+__inline__ bool
+BriskScaleSpace::isMax2D(const uint8_t layer, const int x_layer, const int y_layer)
+{
+ const cv::Mat& scores = pyramid_[layer].scores();
+ const int scorescols = scores.cols;
+ uchar* data = scores.data + y_layer * scorescols + x_layer;
+ // decision tree:
+ const uchar center = (*data);
+ data--;
+ const uchar s_10 = *data;
+ if (center < s_10)
+ return false;
+ data += 2;
+ const uchar s10 = *data;
+ if (center < s10)
+ return false;
+ data -= (scorescols + 1);
+ const uchar s0_1 = *data;
+ if (center < s0_1)
+ return false;
+ data += 2 * scorescols;
+ const uchar s01 = *data;
+ if (center < s01)
+ return false;
+ data--;
+ const uchar s_11 = *data;
+ if (center < s_11)
+ return false;
+ data += 2;
+ const uchar s11 = *data;
+ if (center < s11)
+ return false;
+ data -= 2 * scorescols;
+ const uchar s1_1 = *data;
+ if (center < s1_1)
+ return false;
+ data -= 2;
+ const uchar s_1_1 = *data;
+ if (center < s_1_1)
+ return false;
+
+ // reject neighbor maxima
+ std::vector<int> delta;
+ // put together a list of 2d-offsets to where the maximum is also reached
+ if (center == s_1_1)
+ {
+ delta.push_back(-1);
+ delta.push_back(-1);
+ }
+ if (center == s0_1)
+ {
+ delta.push_back(0);
+ delta.push_back(-1);
+ }
+ if (center == s1_1)
+ {
+ delta.push_back(1);
+ delta.push_back(-1);
+ }
+ if (center == s_10)
+ {
+ delta.push_back(-1);
+ delta.push_back(0);
+ }
+ if (center == s10)
+ {
+ delta.push_back(1);
+ delta.push_back(0);
+ }
+ if (center == s_11)
+ {
+ delta.push_back(-1);
+ delta.push_back(1);
+ }
+ if (center == s01)
+ {
+ delta.push_back(0);
+ delta.push_back(1);
+ }
+ if (center == s11)
+ {
+ delta.push_back(1);
+ delta.push_back(1);
+ }
+ const unsigned int deltasize = delta.size();
+ if (deltasize != 0)
+ {
+ // in this case, we have to analyze the situation more carefully:
+ // the values are gaussian blurred and then we really decide
+ data = scores.data + y_layer * scorescols + x_layer;
+ int smoothedcenter = 4 * center + 2 * (s_10 + s10 + s0_1 + s01) + s_1_1 + s1_1 + s_11 + s11;
+ for (unsigned int i = 0; i < deltasize; i += 2)
+ {
+ data = scores.data + (y_layer - 1 + delta[i + 1]) * scorescols + x_layer + delta[i] - 1;
+ int othercenter = *data;
+ data++;
+ othercenter += 2 * (*data);
+ data++;
+ othercenter += *data;
+ data += scorescols;
+ othercenter += 2 * (*data);
+ data--;
+ othercenter += 4 * (*data);
+ data--;
+ othercenter += 2 * (*data);
+ data += scorescols;
+ othercenter += *data;
+ data++;
+ othercenter += 2 * (*data);
+ data++;
+ othercenter += *data;
+ if (othercenter > smoothedcenter)
+ return false;
+ }
+ }
+ return true;
+}
+
+// 3D maximum refinement centered around (x_layer,y_layer)
+__inline__ float
+BriskScaleSpace::refine3D(const uint8_t layer, const int x_layer, const int y_layer, float& x, float& y, float& scale,
+ bool& ismax)
+{
+ ismax = true;
+ BriskLayer& thisLayer = pyramid_[layer];
+ const int center = thisLayer.getAgastScore(x_layer, y_layer, 1);
+
+ // check and get above maximum:
+ float delta_x_above, delta_y_above;
+ float max_above = getScoreMaxAbove(layer, x_layer, y_layer, center, ismax, delta_x_above, delta_y_above);
+
+ if (!ismax)
+ return 0.0;
+
+ float max; // to be returned
+
+ if (layer % 2 == 0)
+ { // on octave
+ // treat the patch below:
+ float delta_x_below, delta_y_below;
+ float max_below_float;
+ uchar max_below_uchar = 0;
+ if (layer == 0)
+ {
+ // guess the lower intra octave...
+ BriskLayer& l = pyramid_[0];
+ register int s_0_0 = l.getAgastScore_5_8(x_layer - 1, y_layer - 1, 1);
+ max_below_uchar = s_0_0;
+ register int s_1_0 = l.getAgastScore_5_8(x_layer, y_layer - 1, 1);
+ if (s_1_0 > max_below_uchar)
+ max_below_uchar = s_1_0;
+ register int s_2_0 = l.getAgastScore_5_8(x_layer + 1, y_layer - 1, 1);
+ if (s_2_0 > max_below_uchar)
+ max_below_uchar = s_2_0;
+ register int s_2_1 = l.getAgastScore_5_8(x_layer + 1, y_layer, 1);
+ if (s_2_1 > max_below_uchar)
+ max_below_uchar = s_2_1;
+ register int s_1_1 = l.getAgastScore_5_8(x_layer, y_layer, 1);
+ if (s_1_1 > max_below_uchar)
+ max_below_uchar = s_1_1;
+ register int s_0_1 = l.getAgastScore_5_8(x_layer - 1, y_layer, 1);
+ if (s_0_1 > max_below_uchar)
+ max_below_uchar = s_0_1;
+ register int s_0_2 = l.getAgastScore_5_8(x_layer - 1, y_layer + 1, 1);
+ if (s_0_2 > max_below_uchar)
+ max_below_uchar = s_0_2;
+ register int s_1_2 = l.getAgastScore_5_8(x_layer, y_layer + 1, 1);
+ if (s_1_2 > max_below_uchar)
+ max_below_uchar = s_1_2;
+ register int s_2_2 = l.getAgastScore_5_8(x_layer + 1, y_layer + 1, 1);
+ if (s_2_2 > max_below_uchar)
+ max_below_uchar = s_2_2;
+
+ max_below_float = subpixel2D(s_0_0, s_0_1, s_0_2, s_1_0, s_1_1, s_1_2, s_2_0, s_2_1, s_2_2, delta_x_below,
+ delta_y_below);
+ max_below_float = max_below_uchar;
+ }
+ else
+ {
+ max_below_float = getScoreMaxBelow(layer, x_layer, y_layer, center, ismax, delta_x_below, delta_y_below);
+ if (!ismax)
+ return 0;
+ }
+
+ // get the patch on this layer:
+ register int s_0_0 = thisLayer.getAgastScore(x_layer - 1, y_layer - 1, 1);
+ register int s_1_0 = thisLayer.getAgastScore(x_layer, y_layer - 1, 1);
+ register int s_2_0 = thisLayer.getAgastScore(x_layer + 1, y_layer - 1, 1);
+ register int s_2_1 = thisLayer.getAgastScore(x_layer + 1, y_layer, 1);
+ register int s_1_1 = thisLayer.getAgastScore(x_layer, y_layer, 1);
+ register int s_0_1 = thisLayer.getAgastScore(x_layer - 1, y_layer, 1);
+ register int s_0_2 = thisLayer.getAgastScore(x_layer - 1, y_layer + 1, 1);
+ register int s_1_2 = thisLayer.getAgastScore(x_layer, y_layer + 1, 1);
+ register int s_2_2 = thisLayer.getAgastScore(x_layer + 1, y_layer + 1, 1);
+ float delta_x_layer, delta_y_layer;
+ float max_layer = subpixel2D(s_0_0, s_0_1, s_0_2, s_1_0, s_1_1, s_1_2, s_2_0, s_2_1, s_2_2, delta_x_layer,
+ delta_y_layer);
+
+ // calculate the relative scale (1D maximum):
+ if (layer == 0)
+ {
+ scale = refine1D_2(max_below_float, std::max(float(center), max_layer), max_above, max);
+ }
+ else
+ scale = refine1D(max_below_float, std::max(float(center), max_layer), max_above, max);
+
+ if (scale > 1.0)
+ {
+ // interpolate the position:
+ const float r0 = (1.5 - scale) / .5;
+ const float r1 = 1.0 - r0;
+ x = (r0 * delta_x_layer + r1 * delta_x_above + float(x_layer)) * thisLayer.scale() + thisLayer.offset();
+ y = (r0 * delta_y_layer + r1 * delta_y_above + float(y_layer)) * thisLayer.scale() + thisLayer.offset();
+ }
+ else
+ {
+ if (layer == 0)
+ {
+ // interpolate the position:
+ const float r0 = (scale - 0.5) / 0.5;
+ const float r_1 = 1.0 - r0;
+ x = r0 * delta_x_layer + r_1 * delta_x_below + float(x_layer);
+ y = r0 * delta_y_layer + r_1 * delta_y_below + float(y_layer);
+ }
+ else
+ {
+ // interpolate the position:
+ const float r0 = (scale - 0.75) / 0.25;
+ const float r_1 = 1.0 - r0;
+ x = (r0 * delta_x_layer + r_1 * delta_x_below + float(x_layer)) * thisLayer.scale() + thisLayer.offset();
+ y = (r0 * delta_y_layer + r_1 * delta_y_below + float(y_layer)) * thisLayer.scale() + thisLayer.offset();
+ }
+ }
+ }
+ else
+ {
+ // on intra
+ // check the patch below:
+ float delta_x_below, delta_y_below;
+ float max_below = getScoreMaxBelow(layer, x_layer, y_layer, center, ismax, delta_x_below, delta_y_below);
+ if (!ismax)
+ return 0.0;
+
+ // get the patch on this layer:
+ register int s_0_0 = thisLayer.getAgastScore(x_layer - 1, y_layer - 1, 1);
+ register int s_1_0 = thisLayer.getAgastScore(x_layer, y_layer - 1, 1);
+ register int s_2_0 = thisLayer.getAgastScore(x_layer + 1, y_layer - 1, 1);
+ register int s_2_1 = thisLayer.getAgastScore(x_layer + 1, y_layer, 1);
+ register int s_1_1 = thisLayer.getAgastScore(x_layer, y_layer, 1);
+ register int s_0_1 = thisLayer.getAgastScore(x_layer - 1, y_layer, 1);
+ register int s_0_2 = thisLayer.getAgastScore(x_layer - 1, y_layer + 1, 1);
+ register int s_1_2 = thisLayer.getAgastScore(x_layer, y_layer + 1, 1);
+ register int s_2_2 = thisLayer.getAgastScore(x_layer + 1, y_layer + 1, 1);
+ float delta_x_layer, delta_y_layer;
+ float max_layer = subpixel2D(s_0_0, s_0_1, s_0_2, s_1_0, s_1_1, s_1_2, s_2_0, s_2_1, s_2_2, delta_x_layer,
+ delta_y_layer);
+
+ // calculate the relative scale (1D maximum):
+ scale = refine1D_1(max_below, std::max(float(center), max_layer), max_above, max);
+ if (scale > 1.0)
+ {
+ // interpolate the position:
+ const float r0 = 4.0 - scale * 3.0;
+ const float r1 = 1.0 - r0;
+ x = (r0 * delta_x_layer + r1 * delta_x_above + float(x_layer)) * thisLayer.scale() + thisLayer.offset();
+ y = (r0 * delta_y_layer + r1 * delta_y_above + float(y_layer)) * thisLayer.scale() + thisLayer.offset();
+ }
+ else
+ {
+ // interpolate the position:
+ const float r0 = scale * 3.0 - 2.0;
+ const float r_1 = 1.0 - r0;
+ x = (r0 * delta_x_layer + r_1 * delta_x_below + float(x_layer)) * thisLayer.scale() + thisLayer.offset();
+ y = (r0 * delta_y_layer + r_1 * delta_y_below + float(y_layer)) * thisLayer.scale() + thisLayer.offset();
+ }
+ }
+
+ // calculate the absolute scale:
+ scale *= thisLayer.scale();
+
+ // that's it, return the refined maximum:
+ return max;
+}
+
+// return the maximum of score patches above or below
+__inline__ float
+BriskScaleSpace::getScoreMaxAbove(const uint8_t layer, const int x_layer, const int y_layer, const int threshold,
+ bool& ismax, float& dx, float& dy)
+{
+
+ ismax = false;
+ // relevant floating point coordinates
+ float x_1;
+ float x1;
+ float y_1;
+ float y1;
+
+ // the layer above
+ assert(layer+1<layers_);
+ BriskLayer& layerAbove = pyramid_[layer + 1];
+
+ if (layer % 2 == 0)
+ {
+ // octave
+ x_1 = float(4 * (x_layer) - 1 - 2) / 6.0;
+ x1 = float(4 * (x_layer) - 1 + 2) / 6.0;
+ y_1 = float(4 * (y_layer) - 1 - 2) / 6.0;
+ y1 = float(4 * (y_layer) - 1 + 2) / 6.0;
+ }
+ else
+ {
+ // intra
+ x_1 = float(6 * (x_layer) - 1 - 3) / 8.0f;
+ x1 = float(6 * (x_layer) - 1 + 3) / 8.0f;
+ y_1 = float(6 * (y_layer) - 1 - 3) / 8.0f;
+ y1 = float(6 * (y_layer) - 1 + 3) / 8.0f;
+ }
+
+ // check the first row
+ int max_x = x_1 + 1;
+ int max_y = y_1 + 1;
+ float tmp_max;
+ float max = layerAbove.getAgastScore(x_1, y_1, 1);
+ if (max > threshold)
+ return 0;
+ for (int x = x_1 + 1; x <= int(x1); x++)
+ {
+ tmp_max = layerAbove.getAgastScore(float(x), y_1, 1);
+ if (tmp_max > threshold)
+ return 0;
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = x;
+ }
+ }
+ tmp_max = layerAbove.getAgastScore(x1, y_1, 1);
+ if (tmp_max > threshold)
+ return 0;
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = int(x1);
+ }
+
+ // middle rows
+ for (int y = y_1 + 1; y <= int(y1); y++)
+ {
+ tmp_max = layerAbove.getAgastScore(x_1, float(y), 1);
+ if (tmp_max > threshold)
+ return 0;
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = int(x_1 + 1);
+ max_y = y;
+ }
+ for (int x = x_1 + 1; x <= int(x1); x++)
+ {
+ tmp_max = layerAbove.getAgastScore(x, y, 1);
+ if (tmp_max > threshold)
+ return 0;
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = x;
+ max_y = y;
+ }
+ }
+ tmp_max = layerAbove.getAgastScore(x1, float(y), 1);
+ if (tmp_max > threshold)
+ return 0;
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = int(x1);
+ max_y = y;
+ }
+ }
+
+ // bottom row
+ tmp_max = layerAbove.getAgastScore(x_1, y1, 1);
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = int(x_1 + 1);
+ max_y = int(y1);
+ }
+ for (int x = x_1 + 1; x <= int(x1); x++)
+ {
+ tmp_max = layerAbove.getAgastScore(float(x), y1, 1);
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = x;
+ max_y = int(y1);
+ }
+ }
+ tmp_max = layerAbove.getAgastScore(x1, y1, 1);
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = int(x1);
+ max_y = int(y1);
+ }
+
+ //find dx/dy:
+ register int s_0_0 = layerAbove.getAgastScore(max_x - 1, max_y - 1, 1);
+ register int s_1_0 = layerAbove.getAgastScore(max_x, max_y - 1, 1);
+ register int s_2_0 = layerAbove.getAgastScore(max_x + 1, max_y - 1, 1);
+ register int s_2_1 = layerAbove.getAgastScore(max_x + 1, max_y, 1);
+ register int s_1_1 = layerAbove.getAgastScore(max_x, max_y, 1);
+ register int s_0_1 = layerAbove.getAgastScore(max_x - 1, max_y, 1);
+ register int s_0_2 = layerAbove.getAgastScore(max_x - 1, max_y + 1, 1);
+ register int s_1_2 = layerAbove.getAgastScore(max_x, max_y + 1, 1);
+ register int s_2_2 = layerAbove.getAgastScore(max_x + 1, max_y + 1, 1);
+ float dx_1, dy_1;
+ float refined_max = subpixel2D(s_0_0, s_0_1, s_0_2, s_1_0, s_1_1, s_1_2, s_2_0, s_2_1, s_2_2, dx_1, dy_1);
+
+ // calculate dx/dy in above coordinates
+ float real_x = float(max_x) + dx_1;
+ float real_y = float(max_y) + dy_1;
+ bool returnrefined = true;
+ if (layer % 2 == 0)
+ {
+ dx = (real_x * 6.0f + 1.0f) / 4.0f - float(x_layer);
+ dy = (real_y * 6.0f + 1.0f) / 4.0f - float(y_layer);
+ }
+ else
+ {
+ dx = (real_x * 8.0 + 1.0) / 6.0 - float(x_layer);
+ dy = (real_y * 8.0 + 1.0) / 6.0 - float(y_layer);
+ }
+
+ // saturate
+ if (dx > 1.0f)
+ {
+ dx = 1.0f;
+ returnrefined = false;
+ }
+ if (dx < -1.0f)
+ {
+ dx = -1.0f;
+ returnrefined = false;
+ }
+ if (dy > 1.0f)
+ {
+ dy = 1.0f;
+ returnrefined = false;
+ }
+ if (dy < -1.0f)
+ {
+ dy = -1.0f;
+ returnrefined = false;
+ }
+
+ // done and ok.
+ ismax = true;
+ if (returnrefined)
+ {
+ return std::max(refined_max, max);
+ }
+ return max;
+}
+
+__inline__ float
+BriskScaleSpace::getScoreMaxBelow(const uint8_t layer, const int x_layer, const int y_layer, const int threshold,
+ bool& ismax, float& dx, float& dy)
+{
+ ismax = false;
+
+ // relevant floating point coordinates
+ float x_1;
+ float x1;
+ float y_1;
+ float y1;
+
+ if (layer % 2 == 0)
+ {
+ // octave
+ x_1 = float(8 * (x_layer) + 1 - 4) / 6.0;
+ x1 = float(8 * (x_layer) + 1 + 4) / 6.0;
+ y_1 = float(8 * (y_layer) + 1 - 4) / 6.0;
+ y1 = float(8 * (y_layer) + 1 + 4) / 6.0;
+ }
+ else
+ {
+ x_1 = float(6 * (x_layer) + 1 - 3) / 4.0;
+ x1 = float(6 * (x_layer) + 1 + 3) / 4.0;
+ y_1 = float(6 * (y_layer) + 1 - 3) / 4.0;
+ y1 = float(6 * (y_layer) + 1 + 3) / 4.0;
+ }
+
+ // the layer below
+ assert(layer>0);
+ BriskLayer& layerBelow = pyramid_[layer - 1];
+
+ // check the first row
+ int max_x = x_1 + 1;
+ int max_y = y_1 + 1;
+ float tmp_max;
+ float max = layerBelow.getAgastScore(x_1, y_1, 1);
+ if (max > threshold)
+ return 0;
+ for (int x = x_1 + 1; x <= int(x1); x++)
+ {
+ tmp_max = layerBelow.getAgastScore(float(x), y_1, 1);
+ if (tmp_max > threshold)
+ return 0;
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = x;
+ }
+ }
+ tmp_max = layerBelow.getAgastScore(x1, y_1, 1);
+ if (tmp_max > threshold)
+ return 0;
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = int(x1);
+ }
+
+ // middle rows
+ for (int y = y_1 + 1; y <= int(y1); y++)
+ {
+ tmp_max = layerBelow.getAgastScore(x_1, float(y), 1);
+ if (tmp_max > threshold)
+ return 0;
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = int(x_1 + 1);
+ max_y = y;
+ }
+ for (int x = x_1 + 1; x <= int(x1); x++)
+ {
+ tmp_max = layerBelow.getAgastScore(x, y, 1);
+ if (tmp_max > threshold)
+ return 0;
+ if (tmp_max == max)
+ {
+ const int t1 = 2
+ * (layerBelow.getAgastScore(x - 1, y, 1) + layerBelow.getAgastScore(x + 1, y, 1)
+ + layerBelow.getAgastScore(x, y + 1, 1) + layerBelow.getAgastScore(x, y - 1, 1))
+ + (layerBelow.getAgastScore(x + 1, y + 1, 1) + layerBelow.getAgastScore(x - 1, y + 1, 1)
+ + layerBelow.getAgastScore(x + 1, y - 1, 1) + layerBelow.getAgastScore(x - 1, y - 1, 1));
+ const int t2 = 2
+ * (layerBelow.getAgastScore(max_x - 1, max_y, 1) + layerBelow.getAgastScore(max_x + 1, max_y, 1)
+ + layerBelow.getAgastScore(max_x, max_y + 1, 1) + layerBelow.getAgastScore(max_x, max_y - 1, 1))
+ + (layerBelow.getAgastScore(max_x + 1, max_y + 1, 1) + layerBelow.getAgastScore(max_x - 1,
+ max_y + 1, 1)
+ + layerBelow.getAgastScore(max_x + 1, max_y - 1, 1)
+ + layerBelow.getAgastScore(max_x - 1, max_y - 1, 1));
+ if (t1 > t2)
+ {
+ max_x = x;
+ max_y = y;
+ }
+ }
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = x;
+ max_y = y;
+ }
+ }
+ tmp_max = layerBelow.getAgastScore(x1, float(y), 1);
+ if (tmp_max > threshold)
+ return 0;
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = int(x1);
+ max_y = y;
+ }
+ }
+
+ // bottom row
+ tmp_max = layerBelow.getAgastScore(x_1, y1, 1);
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = int(x_1 + 1);
+ max_y = int(y1);
+ }
+ for (int x = x_1 + 1; x <= int(x1); x++)
+ {
+ tmp_max = layerBelow.getAgastScore(float(x), y1, 1);
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = x;
+ max_y = int(y1);
+ }
+ }
+ tmp_max = layerBelow.getAgastScore(x1, y1, 1);
+ if (tmp_max > max)
+ {
+ max = tmp_max;
+ max_x = int(x1);
+ max_y = int(y1);
+ }
+
+ //find dx/dy:
+ register int s_0_0 = layerBelow.getAgastScore(max_x - 1, max_y - 1, 1);
+ register int s_1_0 = layerBelow.getAgastScore(max_x, max_y - 1, 1);
+ register int s_2_0 = layerBelow.getAgastScore(max_x + 1, max_y - 1, 1);
+ register int s_2_1 = layerBelow.getAgastScore(max_x + 1, max_y, 1);
+ register int s_1_1 = layerBelow.getAgastScore(max_x, max_y, 1);
+ register int s_0_1 = layerBelow.getAgastScore(max_x - 1, max_y, 1);
+ register int s_0_2 = layerBelow.getAgastScore(max_x - 1, max_y + 1, 1);
+ register int s_1_2 = layerBelow.getAgastScore(max_x, max_y + 1, 1);
+ register int s_2_2 = layerBelow.getAgastScore(max_x + 1, max_y + 1, 1);
+ float dx_1, dy_1;
+ float refined_max = subpixel2D(s_0_0, s_0_1, s_0_2, s_1_0, s_1_1, s_1_2, s_2_0, s_2_1, s_2_2, dx_1, dy_1);
+
+ // calculate dx/dy in above coordinates
+ float real_x = float(max_x) + dx_1;
+ float real_y = float(max_y) + dy_1;
+ bool returnrefined = true;
+ if (layer % 2 == 0)
+ {
+ dx = (real_x * 6.0 + 1.0) / 8.0 - float(x_layer);
+ dy = (real_y * 6.0 + 1.0) / 8.0 - float(y_layer);
+ }
+ else
+ {
+ dx = (real_x * 4.0 - 1.0) / 6.0 - float(x_layer);
+ dy = (real_y * 4.0 - 1.0) / 6.0 - float(y_layer);
+ }
+
+ // saturate
+ if (dx > 1.0)
+ {
+ dx = 1.0;
+ returnrefined = false;
+ }
+ if (dx < -1.0)
+ {
+ dx = -1.0;
+ returnrefined = false;
+ }
+ if (dy > 1.0)
+ {
+ dy = 1.0;
+ returnrefined = false;
+ }
+ if (dy < -1.0)
+ {
+ dy = -1.0;
+ returnrefined = false;
+ }
+
+ // done and ok.
+ ismax = true;
+ if (returnrefined)
+ {
+ return std::max(refined_max, max);
+ }
+ return max;
+}
+
+__inline__ float
+BriskScaleSpace::refine1D(const float s_05, const float s0, const float s05, float& max)
+{
+ int i_05 = int(1024.0 * s_05 + 0.5);
+ int i0 = int(1024.0 * s0 + 0.5);
+ int i05 = int(1024.0 * s05 + 0.5);
+
+ // 16.0000 -24.0000 8.0000
+ // -40.0000 54.0000 -14.0000
+ // 24.0000 -27.0000 6.0000
+
+ int three_a = 16 * i_05 - 24 * i0 + 8 * i05;
+ // second derivative must be negative:
+ if (three_a >= 0)
+ {
+ if (s0 >= s_05 && s0 >= s05)
+ {
+ max = s0;
+ return 1.0;
+ }
+ if (s_05 >= s0 && s_05 >= s05)
+ {
+ max = s_05;
+ return 0.75;
+ }
+ if (s05 >= s0 && s05 >= s_05)
+ {
+ max = s05;
+ return 1.5;
+ }
+ }
+
+ int three_b = -40 * i_05 + 54 * i0 - 14 * i05;
+ // calculate max location:
+ float ret_val = -float(three_b) / float(2 * three_a);
+ // saturate and return
+ if (ret_val < 0.75)
+ ret_val = 0.75;
+ else if (ret_val > 1.5)
+ ret_val = 1.5; // allow to be slightly off bounds ...?
+ int three_c = +24 * i_05 - 27 * i0 + 6 * i05;
+ max = float(three_c) + float(three_a) * ret_val * ret_val + float(three_b) * ret_val;
+ max /= 3072.0;
+ return ret_val;
+}
+
+__inline__ float
+BriskScaleSpace::refine1D_1(const float s_05, const float s0, const float s05, float& max)
+{
+ int i_05 = int(1024.0 * s_05 + 0.5);
+ int i0 = int(1024.0 * s0 + 0.5);
+ int i05 = int(1024.0 * s05 + 0.5);
+
+ // 4.5000 -9.0000 4.5000
+ //-10.5000 18.0000 -7.5000
+ // 6.0000 -8.0000 3.0000
+
+ int two_a = 9 * i_05 - 18 * i0 + 9 * i05;
+ // second derivative must be negative:
+ if (two_a >= 0)
+ {
+ if (s0 >= s_05 && s0 >= s05)
+ {
+ max = s0;
+ return 1.0;
+ }
+ if (s_05 >= s0 && s_05 >= s05)
+ {
+ max = s_05;
+ return 0.6666666666666666666666666667;
+ }
+ if (s05 >= s0 && s05 >= s_05)
+ {
+ max = s05;
+ return 1.3333333333333333333333333333;
+ }
+ }
+
+ int two_b = -21 * i_05 + 36 * i0 - 15 * i05;
+ // calculate max location:
+ float ret_val = -float(two_b) / float(2 * two_a);
+ // saturate and return
+ if (ret_val < 0.6666666666666666666666666667)
+ ret_val = 0.666666666666666666666666667;
+ else if (ret_val > 1.33333333333333333333333333)
+ ret_val = 1.333333333333333333333333333;
+ int two_c = +12 * i_05 - 16 * i0 + 6 * i05;
+ max = float(two_c) + float(two_a) * ret_val * ret_val + float(two_b) * ret_val;
+ max /= 2048.0;
+ return ret_val;
+}
+
+__inline__ float
+BriskScaleSpace::refine1D_2(const float s_05, const float s0, const float s05, float& max)
+{
+ int i_05 = int(1024.0 * s_05 + 0.5);
+ int i0 = int(1024.0 * s0 + 0.5);
+ int i05 = int(1024.0 * s05 + 0.5);
+
+ // 18.0000 -30.0000 12.0000
+ // -45.0000 65.0000 -20.0000
+ // 27.0000 -30.0000 8.0000
+
+ int a = 2 * i_05 - 4 * i0 + 2 * i05;
+ // second derivative must be negative:
+ if (a >= 0)
+ {
+ if (s0 >= s_05 && s0 >= s05)
+ {
+ max = s0;
+ return 1.0;
+ }
+ if (s_05 >= s0 && s_05 >= s05)
+ {
+ max = s_05;
+ return 0.7;
+ }
+ if (s05 >= s0 && s05 >= s_05)
+ {
+ max = s05;
+ return 1.5;
+ }
+ }
+
+ int b = -5 * i_05 + 8 * i0 - 3 * i05;
+ // calculate max location:
+ float ret_val = -float(b) / float(2 * a);
+ // saturate and return
+ if (ret_val < 0.7)
+ ret_val = 0.7;
+ else if (ret_val > 1.5)
+ ret_val = 1.5; // allow to be slightly off bounds ...?
+ int c = +3 * i_05 - 3 * i0 + 1 * i05;
+ max = float(c) + float(a) * ret_val * ret_val + float(b) * ret_val;
+ max /= 1024;
+ return ret_val;
+}
+
+__inline__ float
+BriskScaleSpace::subpixel2D(const int s_0_0, const int s_0_1, const int s_0_2, const int s_1_0, const int s_1_1,
+ const int s_1_2, const int s_2_0, const int s_2_1, const int s_2_2, float& delta_x,
+ float& delta_y)
+{
+
+ // the coefficients of the 2d quadratic function least-squares fit:
+ register int tmp1 = s_0_0 + s_0_2 - 2 * s_1_1 + s_2_0 + s_2_2;
+ register int coeff1 = 3 * (tmp1 + s_0_1 - ((s_1_0 + s_1_2) << 1) + s_2_1);
+ register int coeff2 = 3 * (tmp1 - ((s_0_1 + s_2_1) << 1) + s_1_0 + s_1_2);
+ register int tmp2 = s_0_2 - s_2_0;
+ register int tmp3 = (s_0_0 + tmp2 - s_2_2);
+ register int tmp4 = tmp3 - 2 * tmp2;
+ register int coeff3 = -3 * (tmp3 + s_0_1 - s_2_1);
+ register int coeff4 = -3 * (tmp4 + s_1_0 - s_1_2);
+ register int coeff5 = (s_0_0 - s_0_2 - s_2_0 + s_2_2) << 2;
+ register int coeff6 = -(s_0_0 + s_0_2 - ((s_1_0 + s_0_1 + s_1_2 + s_2_1) << 1) - 5 * s_1_1 + s_2_0 + s_2_2) << 1;
+
+ // 2nd derivative test:
+ register int H_det = 4 * coeff1 * coeff2 - coeff5 * coeff5;
+
+ if (H_det == 0)
+ {
+ delta_x = 0.0;
+ delta_y = 0.0;
+ return float(coeff6) / 18.0;
+ }
+
+ if (!(H_det > 0 && coeff1 < 0))
+ {
+ // The maximum must be at the one of the 4 patch corners.
+ int tmp_max = coeff3 + coeff4 + coeff5;
+ delta_x = 1.0;
+ delta_y = 1.0;
+
+ int tmp = -coeff3 + coeff4 - coeff5;
+ if (tmp > tmp_max)
+ {
+ tmp_max = tmp;
+ delta_x = -1.0;
+ delta_y = 1.0;
+ }
+ tmp = coeff3 - coeff4 - coeff5;
+ if (tmp > tmp_max)
+ {
+ tmp_max = tmp;
+ delta_x = 1.0;
+ delta_y = -1.0;
+ }
+ tmp = -coeff3 - coeff4 + coeff5;
+ if (tmp > tmp_max)
+ {
+ tmp_max = tmp;
+ delta_x = -1.0;
+ delta_y = -1.0;
+ }
+ return float(tmp_max + coeff1 + coeff2 + coeff6) / 18.0;
+ }
+
+ // this is hopefully the normal outcome of the Hessian test
+ delta_x = float(2 * coeff2 * coeff3 - coeff4 * coeff5) / float(-H_det);
+ delta_y = float(2 * coeff1 * coeff4 - coeff3 * coeff5) / float(-H_det);
+ // TODO: this is not correct, but easy, so perform a real boundary maximum search:
+ bool tx = false;
+ bool tx_ = false;
+ bool ty = false;
+ bool ty_ = false;
+ if (delta_x > 1.0)
+ tx = true;
+ else if (delta_x < -1.0)
+ tx_ = true;
+ if (delta_y > 1.0)
+ ty = true;
+ if (delta_y < -1.0)
+ ty_ = true;
+
+ if (tx || tx_ || ty || ty_)
+ {
+ // get two candidates:
+ float delta_x1 = 0.0, delta_x2 = 0.0, delta_y1 = 0.0, delta_y2 = 0.0;
+ if (tx)
+ {
+ delta_x1 = 1.0;
+ delta_y1 = -float(coeff4 + coeff5) / float(2 * coeff2);
+ if (delta_y1 > 1.0)
+ delta_y1 = 1.0;
+ else if (delta_y1 < -1.0)
+ delta_y1 = -1.0;
+ }
+ else if (tx_)
+ {
+ delta_x1 = -1.0;
+ delta_y1 = -float(coeff4 - coeff5) / float(2 * coeff2);
+ if (delta_y1 > 1.0)
+ delta_y1 = 1.0;
+ else if (delta_y1 < -1.0)
+ delta_y1 = -1.0;
+ }
+ if (ty)
+ {
+ delta_y2 = 1.0;
+ delta_x2 = -float(coeff3 + coeff5) / float(2 * coeff1);
+ if (delta_x2 > 1.0)
+ delta_x2 = 1.0;
+ else if (delta_x2 < -1.0)
+ delta_x2 = -1.0;
+ }
+ else if (ty_)
+ {
+ delta_y2 = -1.0;
+ delta_x2 = -float(coeff3 - coeff5) / float(2 * coeff1);
+ if (delta_x2 > 1.0)
+ delta_x2 = 1.0;
+ else if (delta_x2 < -1.0)
+ delta_x2 = -1.0;
+ }
+ // insert both options for evaluation which to pick
+ float max1 = (coeff1 * delta_x1 * delta_x1 + coeff2 * delta_y1 * delta_y1 + coeff3 * delta_x1 + coeff4 * delta_y1
+ + coeff5 * delta_x1 * delta_y1 + coeff6)
+ / 18.0;
+ float max2 = (coeff1 * delta_x2 * delta_x2 + coeff2 * delta_y2 * delta_y2 + coeff3 * delta_x2 + coeff4 * delta_y2
+ + coeff5 * delta_x2 * delta_y2 + coeff6)
+ / 18.0;
+ if (max1 > max2)
+ {
+ delta_x = delta_x1;
+ delta_y = delta_x1;
+ return max1;
+ }
+ else
+ {
+ delta_x = delta_x2;
+ delta_y = delta_x2;
+ return max2;
+ }
+ }
+
+ // this is the case of the maximum inside the boundaries:
+ return (coeff1 * delta_x * delta_x + coeff2 * delta_y * delta_y + coeff3 * delta_x + coeff4 * delta_y
+ + coeff5 * delta_x * delta_y + coeff6)
+ / 18.0;
+}
+
+// construct a layer
+BriskLayer::BriskLayer(const cv::Mat& img_in, float scale_in, float offset_in)
+{
+ img_ = img_in;
+ scores_ = cv::Mat_<uchar>::zeros(img_in.rows, img_in.cols);
+ // attention: this means that the passed image reference must point to persistent memory
+ scale_ = scale_in;
+ offset_ = offset_in;
+ // create an agast detector
+ fast_9_16_ = new FastFeatureDetector(1, true, FastFeatureDetector::TYPE_9_16);
+ makeOffsets(pixel_5_8_, img_.step, 8);
+ makeOffsets(pixel_9_16_, img_.step, 16);
+}
+// derive a layer
+BriskLayer::BriskLayer(const BriskLayer& layer, int mode)
+{
+ if (mode == CommonParams::HALFSAMPLE)
+ {
+ img_.create(layer.img().rows / 2, layer.img().cols / 2, CV_8U);
+ halfsample(layer.img(), img_);
+ scale_ = layer.scale() * 2;
+ offset_ = 0.5 * scale_ - 0.5;
+ }
+ else
+ {
+ img_.create(2 * (layer.img().rows / 3), 2 * (layer.img().cols / 3), CV_8U);
+ twothirdsample(layer.img(), img_);
+ scale_ = layer.scale() * 1.5;
+ offset_ = 0.5 * scale_ - 0.5;
+ }
+ scores_ = cv::Mat::zeros(img_.rows, img_.cols, CV_8U);
+ fast_9_16_ = new FastFeatureDetector(1, false, FastFeatureDetector::TYPE_9_16);
+ makeOffsets(pixel_5_8_, img_.step, 8);
+ makeOffsets(pixel_9_16_, img_.step, 16);
+}
+
+// Fast/Agast
+// wraps the agast class
+void
+BriskLayer::getAgastPoints(uint8_t threshold, std::vector<KeyPoint>& keypoints)
+{
+ fast_9_16_->set("threshold", threshold);
+ fast_9_16_->detect(img_, keypoints);
+
+ // also write scores
+ const int num = keypoints.size();
+
+ for (int i = 0; i < num; i++)
+ scores_(keypoints[i].pt.y, keypoints[i].pt.x) = keypoints[i].response;
+}
+inline uint8_t
+BriskLayer::getAgastScore(int x, int y, uint8_t threshold)
+{
+ if (x < 3 || y < 3)
+ return 0;
+ if (x >= img_.cols - 3 || y >= img_.rows - 3)
+ return 0;
+ uint8_t& score = *(scores_.data + x + y * scores_.cols);
+ if (score > 2)
+ {
+ return score;
+ }
+ score = cornerScore<16>(img_.data + x + y * img_.cols, pixel_9_16_, threshold - 1);
+ if (score < threshold)
+ score = 0;
+ return score;
+}
+
+inline uint8_t
+BriskLayer::getAgastScore_5_8(int x, int y, uint8_t threshold)
+{
+ if (x < 2 || y < 2)
+ return 0;
+ if (x >= img_.cols - 2 || y >= img_.rows - 2)
+ return 0;
+ uint8_t score = cornerScore<8>(img_.data + x + y * img_.cols, pixel_5_8_, threshold - 1);
+ if (score < threshold)
+ score = 0;
+ return score;
+}
+
+inline uint8_t
+BriskLayer::getAgastScore(float xf, float yf, uint8_t threshold_in, float scale_in)
+{
+ if (scale_in <= 1.0f)
+ {
+ // just do an interpolation inside the layer
+ const int x = int(xf);
+ const float rx1 = xf - float(x);
+ const float rx = 1.0f - rx1;
+ const int y = int(yf);
+ const float ry1 = yf - float(y);
+ const float ry = 1.0f - ry1;
+
+ return rx * ry * getAgastScore(x, y, threshold_in) + rx1 * ry * getAgastScore(x + 1, y, threshold_in)
+ + rx * ry1 * getAgastScore(x, y + 1, threshold_in) + rx1 * ry1 * getAgastScore(x + 1, y + 1, threshold_in);
+ }
+ else
+ {
+ // this means we overlap area smoothing
+ const float halfscale = scale_in / 2.0f;
+ // get the scores first:
+ for (int x = int(xf - halfscale); x <= int(xf + halfscale + 1.0f); x++)
+ {
+ for (int y = int(yf - halfscale); y <= int(yf + halfscale + 1.0f); y++)
+ {
+ getAgastScore(x, y, threshold_in);
+ }
+ }
+ // get the smoothed value
+ return value(scores_, xf, yf, scale_in);
+ }
+}
+
+// access gray values (smoothed/interpolated)
+__inline__ uint8_t
+BriskLayer::value(const cv::Mat& mat, float xf, float yf, float scale_in)
+{
+ assert(!mat.empty());
+ // get the position
+ const int x = floor(xf);
+ const int y = floor(yf);
+ const cv::Mat& image = mat;
+ const int& imagecols = image.cols;
+
+ // get the sigma_half:
+ const float sigma_half = scale_in / 2;
+ const float area = 4.0 * sigma_half * sigma_half;
+ // calculate output:
+ int ret_val;
+ if (sigma_half < 0.5)
+ {
+ //interpolation multipliers:
+ const int r_x = (xf - x) * 1024;
+ const int r_y = (yf - y) * 1024;
+ const int r_x_1 = (1024 - r_x);
+ const int r_y_1 = (1024 - r_y);
+ uchar* ptr = image.data + x + y * imagecols;
+ // just interpolate:
+ ret_val = (r_x_1 * r_y_1 * int(*ptr));
+ ptr++;
+ ret_val += (r_x * r_y_1 * int(*ptr));
+ ptr += imagecols;
+ ret_val += (r_x * r_y * int(*ptr));
+ ptr--;
+ ret_val += (r_x_1 * r_y * int(*ptr));
+ return 0xFF & ((ret_val + 512) / 1024 / 1024);
+ }
+
+ // this is the standard case (simple, not speed optimized yet):
+
+ // scaling:
+ const int scaling = 4194304.0 / area;
+ const int scaling2 = float(scaling) * area / 1024.0;
+
+ // calculate borders
+ const float x_1 = xf - sigma_half;
+ const float x1 = xf + sigma_half;
+ const float y_1 = yf - sigma_half;
+ const float y1 = yf + sigma_half;
+
+ const int x_left = int(x_1 + 0.5);
+ const int y_top = int(y_1 + 0.5);
+ const int x_right = int(x1 + 0.5);
+ const int y_bottom = int(y1 + 0.5);
+
+ // overlap area - multiplication factors:
+ const float r_x_1 = float(x_left) - x_1 + 0.5;
+ const float r_y_1 = float(y_top) - y_1 + 0.5;
+ const float r_x1 = x1 - float(x_right) + 0.5;
+ const float r_y1 = y1 - float(y_bottom) + 0.5;
+ const int dx = x_right - x_left - 1;
+ const int dy = y_bottom - y_top - 1;
+ const int A = (r_x_1 * r_y_1) * scaling;
+ const int B = (r_x1 * r_y_1) * scaling;
+ const int C = (r_x1 * r_y1) * scaling;
+ const int D = (r_x_1 * r_y1) * scaling;
+ const int r_x_1_i = r_x_1 * scaling;
+ const int r_y_1_i = r_y_1 * scaling;
+ const int r_x1_i = r_x1 * scaling;
+ const int r_y1_i = r_y1 * scaling;
+
+ // now the calculation:
+ uchar* ptr = image.data + x_left + imagecols * y_top;
+ // first row:
+ ret_val = A * int(*ptr);
+ ptr++;
+ const uchar* end1 = ptr + dx;
+ for (; ptr < end1; ptr++)
+ {
+ ret_val += r_y_1_i * int(*ptr);
+ }
+ ret_val += B * int(*ptr);
+ // middle ones:
+ ptr += imagecols - dx - 1;
+ uchar* end_j = ptr + dy * imagecols;
+ for (; ptr < end_j; ptr += imagecols - dx - 1)
+ {
+ ret_val += r_x_1_i * int(*ptr);
+ ptr++;
+ const uchar* end2 = ptr + dx;
+ for (; ptr < end2; ptr++)
+ {
+ ret_val += int(*ptr) * scaling;
+ }
+ ret_val += r_x1_i * int(*ptr);
+ }
+ // last row:
+ ret_val += D * int(*ptr);
+ ptr++;
+ const uchar* end3 = ptr + dx;
+ for (; ptr < end3; ptr++)
+ {
+ ret_val += r_y1_i * int(*ptr);
+ }
+ ret_val += C * int(*ptr);
+
+ return 0xFF & ((ret_val + scaling2 / 2) / scaling2 / 1024);
+}
+
+// half sampling
+inline void
+BriskLayer::halfsample(const cv::Mat& srcimg, cv::Mat& dstimg)
+{
+ // make sure the destination image is of the right size:
+ assert(srcimg.cols/2==dstimg.cols);
+ assert(srcimg.rows/2==dstimg.rows);
+
+ // handle non-SSE case
+ resize(srcimg, dstimg, dstimg.size(), 0, 0, INTER_AREA);
+}
+
+inline void
+BriskLayer::twothirdsample(const cv::Mat& srcimg, cv::Mat& dstimg)
+{
+ // make sure the destination image is of the right size:
+ assert((srcimg.cols/3)*2==dstimg.cols);
+ assert((srcimg.rows/3)*2==dstimg.rows);
+
+ resize(srcimg, dstimg, dstimg.size(), 0, 0, INTER_AREA);
+}
+
+}
projects / profile / ivi / opencv.git / commitdiff