From 13ded36ecb831d512a0f92a0baebdab8bedded6f Mon Sep 17 00:00:00 2001 From: Vincent Rabaud Date: Thu, 23 Aug 2012 14:52:01 +0200 Subject: [PATCH] initial addition of BRISK with some tests --- .../include/opencv2/features2d/features2d.hpp | 86 + modules/features2d/src/brisk.cpp | 2277 +++++++++++++++++ modules/features2d/src/features2d_init.cpp | 7 + modules/features2d/test/test_brisk.cpp | 95 + .../test/test_descriptors_regression.cpp | 7 + .../test/test_detectors_regression.cpp | 6 + 6 files changed, 2478 insertions(+) create mode 100755 modules/features2d/src/brisk.cpp create mode 100644 modules/features2d/test/test_brisk.cpp diff --git a/modules/features2d/include/opencv2/features2d/features2d.hpp b/modules/features2d/include/opencv2/features2d/features2d.hpp index ff52822ed4..a081fd4ee6 100644 --- a/modules/features2d/include/opencv2/features2d/features2d.hpp +++ b/modules/features2d/include/opencv2/features2d/features2d.hpp @@ -267,6 +267,92 @@ public: static Ptr create( const string& name ); }; +/*! + BRISK implementation +*/ +class CV_EXPORTS_W BRISK : public Feature2D +{ +public: + CV_WRAP explicit BRISK(int thresh=30, int octaves=3, float patternScale=1.0f); + + virtual ~BRISK(); + + // returns the descriptor size in bytes + int descriptorSize() const; + // returns the descriptor type + int descriptorType() const; + + // Compute the BRISK features on an image + void operator()(InputArray image, InputArray mask, vector& keypoints) const; + + // Compute the BRISK features and descriptors on an image + void operator()( InputArray image, InputArray mask, vector& keypoints, + OutputArray descriptors, bool useProvidedKeypoints=false ) const; + + AlgorithmInfo* info() const; + + // custom setup + CV_WRAP explicit BRISK(std::vector &radiusList, std::vector &numberList, + float dMax=5.85f, float dMin=8.2f, std::vector indexChange=std::vector()); + + // call this to generate the kernel: + // circle of radius r (pixels), with n points; + // short pairings with dMax, long pairings with dMin + CV_WRAP void generateKernel(std::vector &radiusList, + std::vector &numberList, float dMax=5.85f, float dMin=8.2f, + std::vector indexChange=std::vector()); + +protected: + + void computeImpl( const Mat& image, vector& keypoints, Mat& descriptors ) const; + void detectImpl( const Mat& image, vector& keypoints, const Mat& mask=Mat() ) const; + + // Feature parameters + CV_PROP_RW int threshold; + CV_PROP_RW int octaves; + + // some helper structures for the Brisk pattern representation + struct BriskPatternPoint{ + float x; // x coordinate relative to center + float y; // x coordinate relative to center + float sigma; // Gaussian smoothing sigma + }; + struct BriskShortPair{ + unsigned int i; // index of the first pattern point + unsigned int j; // index of other pattern point + }; + struct BriskLongPair{ + unsigned int i; // index of the first pattern point + unsigned int j; // index of other pattern point + int weighted_dx; // 1024.0/dx + int weighted_dy; // 1024.0/dy + }; + inline int 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; + // pattern properties + BriskPatternPoint* patternPoints_; //[i][rotation][scale] + unsigned int points_; // total number of collocation points + float* scaleList_; // lists the scaling per scale index [scale] + unsigned int* sizeList_; // lists the total pattern size per scale index [scale] + static const unsigned int scales_; // scales discretization + static const float scalerange_; // span of sizes 40->4 Octaves - else, this needs to be adjusted... + static const unsigned int n_rot_; // discretization of the rotation look-up + + // pairs + int strings_; // number of uchars the descriptor consists of + float dMax_; // short pair maximum distance + float dMin_; // long pair maximum distance + BriskShortPair* shortPairs_; // d<_dMax + BriskLongPair* longPairs_; // d>_dMin + unsigned int noShortPairs_; // number of shortParis + unsigned int noLongPairs_; // number of longParis + + // general + static const float basicSize_; +}; + /*! ORB implementation. diff --git a/modules/features2d/src/brisk.cpp b/modules/features2d/src/brisk.cpp new file mode 100755 index 0000000000..415ed78215 --- /dev/null +++ b/modules/features2d/src/brisk.cpp @@ -0,0 +1,2277 @@ +/********************************************************************* + * 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 +#include +#include +#include +#include + +#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& 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_ scores_; + // coordinate transformation + float scale_; + float offset_; + // agast + cv::Ptr 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& 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 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 rList; + std::vector 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 &radiusList, std::vector &numberList, float dMax, float dMin, + std::vector indexChange) +{ + generateKernel(radiusList, numberList, dMax, dMin, indexChange); +} + +void +BRISK::generateKernel(std::vector &radiusList, std::vector &numberList, float dMax, + float dMin, std::vector 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_ 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& 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 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::iterator beginning = keypoints.begin(); + std::vector::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& 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 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::iterator beginning = keypoints.begin(); + std::vector::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& keypoints, const Mat& mask) const +{ + (*this)(image, mask, keypoints); +} + +void +BRISK::computeImpl( const Mat& image, vector& 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& 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 > 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 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 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_::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& 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); +} + +} diff --git a/modules/features2d/src/features2d_init.cpp b/modules/features2d/src/features2d_init.cpp index c9abfefa9f..fa11b809cb 100644 --- a/modules/features2d/src/features2d_init.cpp +++ b/modules/features2d/src/features2d_init.cpp @@ -51,6 +51,12 @@ using namespace cv; Otherwise, linker may throw away some seemingly unused stuff. */ +CV_INIT_ALGORITHM(BRISK, "Feature2D.BRISK", + obj.info()->addParam(obj, "thres", obj.threshold); + obj.info()->addParam(obj, "octaves", obj.octaves)); + +/////////////////////////////////////////////////////////////////////////////////////////////////////////// + CV_INIT_ALGORITHM(BriefDescriptorExtractor, "Feature2D.BRIEF", obj.info()->addParam(obj, "bytes", obj.bytes_)); @@ -154,6 +160,7 @@ bool cv::initModule_features2d(void) { bool all = true; all &= !BriefDescriptorExtractor_info_auto.name().empty(); + all &= !BRISK_info_auto.name().empty(); all &= !FastFeatureDetector_info_auto.name().empty(); all &= !StarDetector_info_auto.name().empty(); all &= !MSER_info_auto.name().empty(); diff --git a/modules/features2d/test/test_brisk.cpp b/modules/features2d/test/test_brisk.cpp new file mode 100644 index 0000000000..10e71802a2 --- /dev/null +++ b/modules/features2d/test/test_brisk.cpp @@ -0,0 +1,95 @@ +/*M/////////////////////////////////////////////////////////////////////////////////////// +// +// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. +// +// By downloading, copying, installing or using the software you agree to this license. +// If you do not agree to this license, do not download, install, +// copy or use the software. +// +// +// License Agreement +// For Open Source Computer Vision Library +// +// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. +// Copyright (C) 2009, Willow Garage Inc., all rights reserved. +// Third party copyrights are property of their respective owners. +// +// Redistribution and use in source and binary forms, with or without modification, +// are permitted provided that the following conditions are met: +// +// * Redistribution's of source code must retain the above copyright notice, +// this list of conditions and the following disclaimer. +// +// * Redistribution's in binary form must reproduce the above copyright notice, +// this list of conditions and the following disclaimer in the documentation +// and/or other materials provided with the distribution. +// +// * The name of the copyright holders may not be used to endorse or promote products +// derived from this software without specific prior written permission. +// +// This software is provided by the copyright holders and contributors "as is" and +// any express or implied warranties, including, but not limited to, the implied +// warranties of merchantability and fitness for a particular purpose are disclaimed. +// In no event shall the Intel Corporation or contributors be liable for any direct, +// indirect, incidental, special, exemplary, or consequential damages +// (including, but not limited to, procurement of substitute goods or services; +// loss of use, data, or profits; or business interruption) however caused +// and on any theory of liability, whether in contract, strict liability, +// or tort (including negligence or otherwise) arising in any way out of +// the use of this software, even if advised of the possibility of such damage. +// +//M*/ + +#include "test_precomp.hpp" + +using namespace cv; + +class CV_BRISKTest : public cvtest::BaseTest +{ +public: + CV_BRISKTest(); + ~CV_BRISKTest(); +protected: + void run(int); +}; + +CV_BRISKTest::CV_BRISKTest() {} +CV_BRISKTest::~CV_BRISKTest() {} + +void CV_BRISKTest::run( int ) +{ + Mat image1 = imread(string(ts->get_data_path()) + "inpaint/orig.jpg"); + Mat image2 = imread(string(ts->get_data_path()) + "cameracalibration/chess9.jpg"); + + if (image1.empty() || image2.empty()) + { + ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); + return; + } + + Mat gray1, gray2; + cvtColor(image1, gray1, CV_BGR2GRAY); + cvtColor(image2, gray2, CV_BGR2GRAY); + + Ptr detector = Algorithm::create("Feature2D.BRISK"); + + vector keypoints1; + vector keypoints2; + detector->detect(image1, keypoints1); + detector->detect(image2, keypoints2); + + for(size_t i = 0; i < keypoints1.size(); ++i) + { + const KeyPoint& kp = keypoints1[i]; + ASSERT_NE(kp.angle, -1); + } + + for(size_t i = 0; i < keypoints2.size(); ++i) + { + const KeyPoint& kp = keypoints2[i]; + ASSERT_NE(kp.angle, -1); + } +} + +TEST(Features2d_BRISK, regression) { CV_BRISKTest test; test.safe_run(); } + diff --git a/modules/features2d/test/test_descriptors_regression.cpp b/modules/features2d/test/test_descriptors_regression.cpp index b53ef57f78..2185625ae9 100644 --- a/modules/features2d/test/test_descriptors_regression.cpp +++ b/modules/features2d/test/test_descriptors_regression.cpp @@ -301,6 +301,13 @@ private: * Tests registrations * \****************************************************************************************/ +TEST( Features2d_DescriptorExtractor_BRISK, regression ) +{ + CV_DescriptorExtractorTest test( "descriptor-brisk", (CV_DescriptorExtractorTest::DistanceType)2.f, + DescriptorExtractor::create("BRISK") ); + test.safe_run(); +} + TEST( Features2d_DescriptorExtractor_ORB, regression ) { // TODO adjust the parameters below diff --git a/modules/features2d/test/test_detectors_regression.cpp b/modules/features2d/test/test_detectors_regression.cpp index 0077f84cc0..e5e8712cef 100644 --- a/modules/features2d/test/test_detectors_regression.cpp +++ b/modules/features2d/test/test_detectors_regression.cpp @@ -247,6 +247,12 @@ void CV_FeatureDetectorTest::run( int /*start_from*/ ) * Tests registrations * \****************************************************************************************/ +TEST( Features2d_Detector_BRISK, regression ) +{ + CV_FeatureDetectorTest test( "detector-brisk", FeatureDetector::create("BRISK") ); + test.safe_run(); +} + TEST( Features2d_Detector_FAST, regression ) { CV_FeatureDetectorTest test( "detector-fast", FeatureDetector::create("FAST") );