opencv_contrib xfeatures2d boostdesc

/********************************************************************* * Software License Agreement (BSD License) * * Copyright (c) 2013, 2016 * * Tomasz Trzcinski <t dot trzcinski at ii dot pw dot edu dot pl> * Mario Christoudias <mariochristoudias at gmail dot com> * Vincent Lepetit <lepetit at icg dot tugraz dot at> * * * 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 copyright holders 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. *********************************************************************/ /* "Learning Image Descriptors with Boosting" T. Trzcinski, M. Christoudias and V. Lepetit IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 "Boosting Binary Keypoint Descriptors" T. Trzcinski, M. Christoudias, V. Lepetit and P. Fua Computer Vision and Pattern Recognition (CVPR), 2013 Original code: Tomasz Trzcinski <t dot trzcinski at ii dot pw dot edu dot pl> OpenCV port by: Cristian Balint <cristian dot balint at gmail dot com> */ #include <bitset> #include "precomp.hpp" using namespace cv; using namespace std; namespace cv { namespace xfeatures2d { /* !BoostDesc implementation */ class BoostDesc_Impl : public BoostDesc { public: // constructor explicit BoostDesc_Impl( int desc = BINBOOST_256, bool use_scale_orientation = true, float scale_factor = 6.25f ); // destructor virtual ~BoostDesc_Impl(); // returns the descriptor length in bytes virtual int descriptorSize() const { return m_descriptor_size; } // returns the descriptor type virtual int descriptorType() const { return m_descriptor_type; } // returns the default norm type virtual int defaultNorm() const { return m_descriptor_norm; } // compute descriptors given keypoints virtual void compute( InputArray image, vector<KeyPoint>& keypoints, OutputArray descriptors ); protected: /* * BoostDesc parameters */ // size, type, norm int m_descriptor_size; int m_descriptor_type; int m_descriptor_norm; // desc type int m_desc_type; // gradient // assignment types enum Assign { ASSIGN_HARD = 0, ASSIGN_BILINEAR = 1, ASSIGN_SOFT = 2, ASSIGN_HARD_MAGN = 3, ASSIGN_SOFT_MAGN = 4 }; // dims int m_Dims; // no. weak // learners int m_nWLs; // gradient type int m_grad_atype; // patch size int m_patch_size; // orient quantitiy int m_orient_q; // patch scale factor float m_scale_factor; /* * BoostDesc switches */ // switch to enable sample by keypoints orientation bool m_use_scale_orientation; /* * BoostDesc arrays */ // image Mat m_image; // parameters // weak learner Mat m_wl_thresh; Mat m_wl_orient; Mat m_wl_x_min, m_wl_x_max; Mat m_wl_y_min, m_wl_y_max; Mat m_wl_alpha, m_wl_beta; private: /* * BoostDesc functions */ // initialize parameters inline void ini_params( const int orientQuant, const int patchSize, const int iGradAssignType, const int nDim, const int nWLs, const unsigned int thresh[], const int orient[], const int x_min[], const int x_max[], const int y_min[], const int y_max[], const unsigned int alpha[], const unsigned int beta[] ); }; // END BoostDesc_Impl CLASS // ------------------------------------------------- /* BoostDesc internal routines */ static void computeGradientMaps( const Mat& im, const int gradAssignType, const int orientQuant, vector<Mat>& gradMap ) { enum Assign { ASSIGN_HARD = 0, ASSIGN_BILINEAR = 1, ASSIGN_SOFT = 2, ASSIGN_HARD_MAGN = 3, ASSIGN_SOFT_MAGN = 4 }; Mat derivx( im.size(), CV_32FC1 ); Mat derivy( im.size(), CV_32FC1 ); Sobel( im, derivx, derivx.depth(), 1, 0 ); Sobel( im, derivy, derivy.depth(), 0, 1 ); for ( int i = 0; i < orientQuant; i++ ) gradMap.push_back( Mat::zeros( im.size(), CV_8UC1 ) ); int index, index2; double binCenter, weight; double binSize = (2 * CV_PI) / orientQuant; // fill in temp matrices with // respones to edge detection const float* pDerivx = derivx.ptr<float>(); const float* pDerivy = derivy.ptr<float>(); for ( int i = 0; i < im.rows; i++ ) { for ( int j = 0; j < im.cols; j++ ) { float gradMagnitude = sqrt( (*pDerivx) * (*pDerivx) + (*pDerivy) * (*pDerivy) ); if ( gradMagnitude > 20 ) { double theta = atan2( *pDerivy, *pDerivx ); theta = ( theta < 0 ) ? theta + 2*CV_PI : theta; index = int( theta / binSize ); index = ( index == orientQuant ) ? 0 : index; switch ( gradAssignType ) { case ASSIGN_HARD: gradMap[index].at<uchar>(i,j) = 1; break; case ASSIGN_HARD_MAGN: gradMap[index].at<uchar>(i,j) = (uchar) cvRound( gradMagnitude ); break; case ASSIGN_BILINEAR: index2 = (int) ceil( theta / binSize ); index2 = ( index2 == orientQuant ) ? 0 : index2; binCenter = ( index + 0.5f ) * binSize; weight = 1 - abs( theta - binCenter ) / binSize; gradMap[index ].at<uchar>(i,j) = (uchar) cvRound( 255 * weight ); gradMap[index2].at<uchar>(i,j) = (uchar) cvRound( 255 * ( 1 - weight ) ); break; case ASSIGN_SOFT: for ( int binNum = 0; binNum < orientQuant/2 + 1; binNum++ ) { index2 = ( binNum + index + orientQuant - orientQuant/4 ) % orientQuant; binCenter = ( index2 + 0.5f ) * binSize; weight = cos( theta - binCenter ); weight = ( weight < 0 ) ? 0 : weight; gradMap[index2].at<uchar>(i,j) = (uchar) cvRound( 255 * weight ); } break; case ASSIGN_SOFT_MAGN: for ( int binNum = 0; binNum < orientQuant/2 + 1; binNum++ ) { index2 = ( binNum + index + orientQuant - orientQuant/4 ) % orientQuant; binCenter = ( index2 +