--- /dev/null
+// freak.cpp
+//
+// Copyright (C) 2011-2012 Signal processing laboratory 2, EPFL,
+// Kirell Benzi (kirell.benzi@epfl.ch),
+// Raphael Ortiz (raphael.ortiz@a3.epfl.ch)
+// Alexandre Alahi (alexandre.alahi@epfl.ch)
+// and Pierre Vandergheynst (pierre.vandergheynst@epfl.ch)
+//
+// 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.
+
+#include "precomp.hpp"
+#include <fstream>
+#include <stdlib.h>
+#include <algorithm>
+#include <iostream>
+#include <bitset>
+#include <sstream>
+#include <algorithm>
+#include <iomanip>
+#include <string.h>
+
+namespace cv
+{
+
+#if CV_SSSE3
+// binary: 10000000 => char: 128 or hex: 0x80
+static const __m128i binMask = _mm_set_epi8(0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80,
+ 0x80, 0x80, 0x80,
+ 0x80);
+#endif
+
+static const double FREAK_SQRT2 = 1.4142135623731;
+static const double FREAK_INV_SQRT2 = 1.0 / FREAK_SQRT2;
+static const double FREAK_LOG2 = 0.693147180559945;
+static const int FREAK_NB_ORIENTATION = 256;
+static const int FREAK_NB_POINTS = 43;
+static const int FREAK_SMALLEST_KP_SIZE = 7; // smallest size of keypoints
+static const int FREAK_NB_SCALES = FREAK::NB_SCALES;
+static const int FREAK_NB_PAIRS = FREAK::NB_PAIRS;
+static const int FREAK_NB_ORIENPAIRS = FREAK::NB_ORIENPAIRS;
+
+static const int FREAK_DEF_PAIRS[FREAK::NB_PAIRS] =
+{ // default pairs
+ 404,431,818,511,181,52,311,874,774,543,719,230,417,205,11,
+ 560,149,265,39,306,165,857,250,8,61,15,55,717,44,412,
+ 592,134,761,695,660,782,625,487,549,516,271,665,762,392,178,
+ 796,773,31,672,845,548,794,677,654,241,831,225,238,849,83,
+ 691,484,826,707,122,517,583,731,328,339,571,475,394,472,580,
+ 381,137,93,380,327,619,729,808,218,213,459,141,806,341,95,
+ 382,568,124,750,193,749,706,843,79,199,317,329,768,198,100,
+ 466,613,78,562,783,689,136,838,94,142,164,679,219,419,366,
+ 418,423,77,89,523,259,683,312,555,20,470,684,123,458,453,833,
+ 72,113,253,108,313,25,153,648,411,607,618,128,305,232,301,84,
+ 56,264,371,46,407,360,38,99,176,710,114,578,66,372,653,
+ 129,359,424,159,821,10,323,393,5,340,891,9,790,47,0,175,346,
+ 236,26,172,147,574,561,32,294,429,724,755,398,787,288,299,
+ 769,565,767,722,757,224,465,723,498,467,235,127,802,446,233,
+ 544,482,800,318,16,532,801,441,554,173,60,530,713,469,30,
+ 212,630,899,170,266,799,88,49,512,399,23,500,107,524,90,
+ 194,143,135,192,206,345,148,71,119,101,563,870,158,254,214,
+ 276,464,332,725,188,385,24,476,40,231,620,171,258,67,109,
+ 844,244,187,388,701,690,50,7,850,479,48,522,22,154,12,659,
+ 736,655,577,737,830,811,174,21,237,335,353,234,53,270,62,
+ 182,45,177,245,812,673,355,556,612,166,204,54,248,365,226,
+ 242,452,700,685,573,14,842,481,468,781,564,416,179,405,35,
+ 819,608,624,367,98,643,448,2,460,676,440,240,130,146,184,
+ 185,430,65,807,377,82,121,708,239,310,138,596,730,575,477,
+ 851,797,247,27,85,586,307,779,326,494,856,324,827,96,748,
+ 13,397,125,688,702,92,293,716,277,140,112,4,80,855,839,1,
+ 413,347,584,493,289,696,19,751,379,76,73,115,6,590,183,734,
+ 197,483,217,344,330,400,186,243,587,220,780,200,793,246,824,
+ 41,735,579,81,703,322,760,720,139,480,490,91,814,813,163,
+ 152,488,763,263,425,410,576,120,319,668,150,160,302,491,515,
+ 260,145,428,97,251,395,272,252,18,106,358,854,485,144,550,
+ 131,133,378,68,102,104,58,361,275,209,697,582,338,742,589,
+ 325,408,229,28,304,191,189,110,126,486,211,547,533,70,215,
+ 670,249,36,581,389,605,331,518,442,822
+};
+
+struct PairStat
+{ // used to sort pairs during pairs selection
+ double mean;
+ int idx;
+};
+
+struct sortMean
+{
+ bool operator()( const PairStat& a, const PairStat& b ) const {
+ return a.mean < b.mean;
+ }
+};
+
+void FREAK::buildPattern()
+{
+ if( patternScale == patternScale0 && nOctaves == nOctaves0 && !patternLookup.empty() )
+ return;
+
+ nOctaves0 = nOctaves;
+ patternScale0 = patternScale;
+
+ patternLookup.resize(FREAK_NB_SCALES*FREAK_NB_ORIENTATION*FREAK_NB_POINTS);
+ double scaleStep = pow(2.0, (double)(nOctaves)/FREAK_NB_SCALES ); // 2 ^ ( (nOctaves-1) /nbScales)
+ double scalingFactor, alpha, beta, theta = 0;
+
+ // pattern definition, radius normalized to 1.0 (outer point position+sigma=1.0)
+ const int n[8] = {6,6,6,6,6,6,6,1}; // number of points on each concentric circle (from outer to inner)
+ const double bigR(2.0/3.0); // bigger radius
+ const double smallR(2.0/24.0); // smaller radius
+ const double unitSpace( (bigR-smallR)/21.0 ); // define spaces between concentric circles (from center to outer: 1,2,3,4,5,6)
+ // radii of the concentric cirles (from outer to inner)
+ const double radius[8] = {bigR, bigR-6*unitSpace, bigR-11*unitSpace, bigR-15*unitSpace, bigR-18*unitSpace, bigR-20*unitSpace, smallR, 0.0};
+ // sigma of pattern points (each group of 6 points on a concentric cirle has the same sigma)
+ const double sigma[8] = {radius[0]/2.0, radius[1]/2.0, radius[2]/2.0,
+ radius[3]/2.0, radius[4]/2.0, radius[5]/2.0,
+ radius[6]/2.0, radius[6]/2.0
+ };
+ // fill the lookup table
+ for( int scaleIdx=0; scaleIdx < FREAK_NB_SCALES; ++scaleIdx ) {
+ patternSizes[scaleIdx] = 0; // proper initialization
+ scalingFactor = pow(scaleStep,scaleIdx); //scale of the pattern, scaleStep ^ scaleIdx
+
+ for( int orientationIdx = 0; orientationIdx < FREAK_NB_ORIENTATION; ++orientationIdx ) {
+ theta = double(orientationIdx)* 2*CV_PI/double(FREAK_NB_ORIENTATION); // orientation of the pattern
+ int pointIdx = 0;
+
+ PatternPoint* patternLookupPtr = &patternLookup[0];
+ for( size_t i = 0; i < 8; ++i ) {
+ for( int k = 0 ; k < n[i]; ++k ) {
+ beta = M_PI/n[i] * (i%2); // orientation offset so that groups of points on each circles are staggered
+ alpha = double(k)* 2*M_PI/double(n[i])+beta+theta;
+
+ // add the point to the look-up table
+ PatternPoint& point = patternLookupPtr[ scaleIdx*FREAK_NB_ORIENTATION*FREAK_NB_POINTS+orientationIdx*FREAK_NB_POINTS+pointIdx ];
+ point.x = radius[i] * cos(alpha) * scalingFactor * patternScale;
+ point.y = radius[i] * sin(alpha) * scalingFactor * patternScale;
+ point.sigma = sigma[i] * scalingFactor * patternScale;
+
+ // adapt the sizeList if necessary
+ const int sizeMax = ceil((radius[i]+sigma[i])*scalingFactor*patternScale) + 1;
+ if( patternSizes[scaleIdx] < sizeMax )
+ patternSizes[scaleIdx] = sizeMax;
+
+ ++pointIdx;
+ }
+ }
+ }
+ }
+
+ // build the list of orientation pairs
+ orientationPairs[0].i=0; orientationPairs[0].j=3; orientationPairs[1].i=1; orientationPairs[1].j=4; orientationPairs[2].i=2; orientationPairs[2].j=5;
+ orientationPairs[3].i=0; orientationPairs[3].j=2; orientationPairs[4].i=1; orientationPairs[4].j=3; orientationPairs[5].i=2; orientationPairs[5].j=4;
+ orientationPairs[6].i=3; orientationPairs[6].j=5; orientationPairs[7].i=4; orientationPairs[7].j=0; orientationPairs[8].i=5; orientationPairs[8].j=1;
+
+ orientationPairs[9].i=6; orientationPairs[9].j=9; orientationPairs[10].i=7; orientationPairs[10].j=10; orientationPairs[11].i=8; orientationPairs[11].j=11;
+ orientationPairs[12].i=6; orientationPairs[12].j=8; orientationPairs[13].i=7; orientationPairs[13].j=9; orientationPairs[14].i=8; orientationPairs[14].j=10;
+ orientationPairs[15].i=9; orientationPairs[15].j=11; orientationPairs[16].i=10; orientationPairs[16].j=6; orientationPairs[17].i=11; orientationPairs[17].j=7;
+
+ orientationPairs[18].i=12; orientationPairs[18].j=15; orientationPairs[19].i=13; orientationPairs[19].j=16; orientationPairs[20].i=14; orientationPairs[20].j=17;
+ orientationPairs[21].i=12; orientationPairs[21].j=14; orientationPairs[22].i=13; orientationPairs[22].j=15; orientationPairs[23].i=14; orientationPairs[23].j=16;
+ orientationPairs[24].i=15; orientationPairs[24].j=17; orientationPairs[25].i=16; orientationPairs[25].j=12; orientationPairs[26].i=17; orientationPairs[26].j=13;
+
+ orientationPairs[27].i=18; orientationPairs[27].j=21; orientationPairs[28].i=19; orientationPairs[28].j=22; orientationPairs[29].i=20; orientationPairs[29].j=23;
+ orientationPairs[30].i=18; orientationPairs[30].j=20; orientationPairs[31].i=19; orientationPairs[31].j=21; orientationPairs[32].i=20; orientationPairs[32].j=22;
+ orientationPairs[33].i=21; orientationPairs[33].j=23; orientationPairs[34].i=22; orientationPairs[34].j=18; orientationPairs[35].i=23; orientationPairs[35].j=19;
+
+ orientationPairs[36].i=24; orientationPairs[36].j=27; orientationPairs[37].i=25; orientationPairs[37].j=28; orientationPairs[38].i=26; orientationPairs[38].j=29;
+ orientationPairs[39].i=30; orientationPairs[39].j=33; orientationPairs[40].i=31; orientationPairs[40].j=34; orientationPairs[41].i=32; orientationPairs[41].j=35;
+ orientationPairs[42].i=36; orientationPairs[42].j=39; orientationPairs[43].i=37; orientationPairs[43].j=40; orientationPairs[44].i=38; orientationPairs[44].j=41;
+
+ for( unsigned m = FREAK_NB_ORIENPAIRS; m--; ) {
+ const float dx = patternLookup[orientationPairs[m].i].x-patternLookup[orientationPairs[m].j].x;
+ const float dy = patternLookup[orientationPairs[m].i].y-patternLookup[orientationPairs[m].j].y;
+ const float norm_sq = (dx*dx+dy*dy);
+ orientationPairs[m].weight_dx = int((dx/(norm_sq))*4096.0+0.5);
+ orientationPairs[m].weight_dy = int((dy/(norm_sq))*4096.0+0.5);
+ }
+
+ // build the list of description pairs
+ std::vector<DescriptionPair> allPairs;
+ for( unsigned int i = 1; i < (unsigned int)FREAK_NB_POINTS; ++i ) {
+ // (generate all the pairs)
+ for( unsigned int j = 0; (unsigned int)j < i; ++j ) {
+ DescriptionPair pair = {(uchar)i,(uchar)j};
+ allPairs.push_back(pair);
+ }
+ }
+ // Input vector provided
+ if( !selectedPairs0.empty() ) {
+ if( (int)selectedPairs0.size() == FREAK_NB_PAIRS ) {
+ for( int i = 0; i < FREAK_NB_PAIRS; ++i )
+ descriptionPairs[i] = allPairs[selectedPairs0.at(i)];
+ }
+ else {
+ CV_Error(CV_StsVecLengthErr, "Input vector does not match the required size");
+ }
+ }
+ else { // default selected pairs
+ for( int i = 0; i < FREAK_NB_PAIRS; ++i )
+ descriptionPairs[i] = allPairs[FREAK_DEF_PAIRS[i]];
+ }
+}
+
+void FREAK::computeImpl( const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors ) const {
+
+ if( image.empty() )
+ return;
+ if( keypoints.empty() )
+ return;
+
+ ((FREAK*)this)->buildPattern();
+
+#if CV_SSSE3
+ register __m128i operand1;
+ register __m128i operand2;
+ register __m128i workReg;
+ register __m128i result128;
+#endif
+
+ Mat imgIntegral;
+ integral(image, imgIntegral);
+ std::vector<int> kpScaleIdx(keypoints.size()); // used to save pattern scale index corresponding to each keypoints
+ const std::vector<int>::iterator ScaleIdxBegin = kpScaleIdx.begin(); // used in std::vector erase function
+ const std::vector<cv::KeyPoint>::iterator kpBegin = keypoints.begin(); // used in std::vector erase function
+ const float sizeCst = FREAK_NB_SCALES/(FREAK_LOG2* nOctaves );
+ uchar pointsValue[FREAK_NB_POINTS];
+ int thetaIdx = 0;
+ int direction0;
+ int direction1;
+
+ // compute the scale index corresponding to the keypoint size and remove keypoints close to the border
+ if( scaleNormalized ) {
+ for( size_t k = keypoints.size(); k--; ) {
+ //Is k non-zero? If so, decrement it and continue"
+ kpScaleIdx[k] = max( (int)(log(keypoints[k].size/FREAK_SMALLEST_KP_SIZE)*sizeCst+0.5) ,0);
+ if( kpScaleIdx[k] >= FREAK_NB_SCALES )
+ kpScaleIdx[k] = FREAK_NB_SCALES-1;
+
+ if( keypoints[k].pt.x <= patternSizes[kpScaleIdx[k]] || //check if the description at this specific position and scale fits inside the image
+ keypoints[k].pt.y <= patternSizes[kpScaleIdx[k]] ||
+ keypoints[k].pt.x >= image.cols-patternSizes[kpScaleIdx[k]] ||
+ keypoints[k].pt.y >= image.rows-patternSizes[kpScaleIdx[k]]
+ ) {
+ keypoints.erase(kpBegin+k);
+ kpScaleIdx.erase(ScaleIdxBegin+k);
+ }
+ }
+ }
+ else {
+ const int scIdx = max( (int)(1.0986122886681*sizeCst+0.5) ,0);
+ for( size_t k = keypoints.size(); k--; ) {
+ kpScaleIdx[k] = scIdx; // equivalent to the formule when the scale is normalized with a constant size of keypoints[k].size=3*SMALLEST_KP_SIZE
+ if( kpScaleIdx[k] >= FREAK_NB_SCALES ) {
+ kpScaleIdx[k] = FREAK_NB_SCALES-1;
+ }
+ if( keypoints[k].pt.x <= patternSizes[kpScaleIdx[k]] ||
+ keypoints[k].pt.y <= patternSizes[kpScaleIdx[k]] ||
+ keypoints[k].pt.x >= image.cols-patternSizes[kpScaleIdx[k]] ||
+ keypoints[k].pt.y >= image.rows-patternSizes[kpScaleIdx[k]]
+ ) {
+ keypoints.erase(kpBegin+k);
+ kpScaleIdx.erase(ScaleIdxBegin+k);
+ }
+ }
+ }
+
+ // allocate descriptor memory, estimate orientations, extract descriptors
+ if( !extAll ) {
+ // extract the best comparisons only
+ descriptors = cv::Mat::zeros(keypoints.size(), FREAK_NB_PAIRS/8, CV_8U);
+#if CV_SSSE3
+ __m128i* ptr= (__m128i*) (descriptors.data+(keypoints.size()-1)*descriptors.step[0]);
+#else
+ std::bitset<FREAK_NB_PAIRS>* ptr = (std::bitset<FREAK_NB_PAIRS>*) (descriptors.data+(keypoints.size()-1)*descriptors.step[0]);
+#endif
+ for( size_t k = keypoints.size(); k--; ) {
+ // estimate orientation (gradient)
+ if( !orientationNormalized ) {
+ thetaIdx = 0; // assign 0° to all keypoints
+ keypoints[k].angle = 0.0;
+ }
+ else {
+ // get the points intensity value in the un-rotated pattern
+ for( int i = FREAK_NB_POINTS; i--; ) {
+ pointsValue[i] = meanIntensity(image, imgIntegral, keypoints[k].pt.x,keypoints[k].pt.y, kpScaleIdx[k], 0, i);
+ }
+ direction0 = 0;
+ direction1 = 0;
+ for( int m = 45; m--; ) {
+ //iterate through the orientation pairs
+ const int delta = (pointsValue[ orientationPairs[m].i ]-pointsValue[ orientationPairs[m].j ]);
+ direction0 += delta*(orientationPairs[m].weight_dx)/2048;
+ direction1 += delta*(orientationPairs[m].weight_dy)/2048;
+ }
+
+ keypoints[k].angle = atan2((float)direction1,(float)direction0)*(180.0/M_PI);//estimate orientation
+ thetaIdx = int(FREAK_NB_ORIENTATION*keypoints[k].angle*(1/360.0)+0.5);
+ if( thetaIdx < 0 )
+ thetaIdx += FREAK_NB_ORIENTATION;
+
+ if( thetaIdx >= FREAK_NB_ORIENTATION )
+ thetaIdx -= FREAK_NB_ORIENTATION;
+ }
+ // extract descriptor at the computed orientation
+ for( int i = FREAK_NB_POINTS; i--; ) {
+ pointsValue[i] = meanIntensity(image, imgIntegral, keypoints[k].pt.x,keypoints[k].pt.y, kpScaleIdx[k], thetaIdx, i);
+ }
+#if CV_SSSE3
+ // extracting descriptor by blocks of 128 bits using SSE instructions
+ // note that comparisons order is modified in each block (but first 128 comparisons remain globally the same-->does not affect the 128,384 bits segmanted matching strategy)
+ int cnt(0);
+ for( int n = 4; n-- ; ) {
+ result128 = _mm_setzero_si128();
+ for( int m = 8; m--; cnt+=16 ) {
+ operand1 = _mm_set_epi8(pointsValue[descriptionPairs[cnt].i],pointsValue[descriptionPairs[cnt+1].i],pointsValue[descriptionPairs[cnt+2].i],pointsValue[descriptionPairs[cnt+3].i],
+ pointsValue[descriptionPairs[cnt+4].i],pointsValue[descriptionPairs[cnt+5].i],pointsValue[descriptionPairs[cnt+6].i],pointsValue[descriptionPairs[cnt+7].i],
+ pointsValue[descriptionPairs[cnt+8].i],pointsValue[descriptionPairs[cnt+9].i],pointsValue[descriptionPairs[cnt+10].i],pointsValue[descriptionPairs[cnt+11].i],
+ pointsValue[descriptionPairs[cnt+12].i],pointsValue[descriptionPairs[cnt+13].i],pointsValue[descriptionPairs[cnt+14].i],pointsValue[descriptionPairs[cnt+15].i]);
+
+ operand2 = _mm_set_epi8(pointsValue[descriptionPairs[cnt].j],pointsValue[descriptionPairs[cnt+1].j],pointsValue[descriptionPairs[cnt+2].j],pointsValue[descriptionPairs[cnt+3].j],
+ pointsValue[descriptionPairs[cnt+4].j],pointsValue[descriptionPairs[cnt+5].j],pointsValue[descriptionPairs[cnt+6].j],pointsValue[descriptionPairs[cnt+7].j],
+ pointsValue[descriptionPairs[cnt+8].j],pointsValue[descriptionPairs[cnt+9].j],pointsValue[descriptionPairs[cnt+10].j],pointsValue[descriptionPairs[cnt+11].j],
+ pointsValue[descriptionPairs[cnt+12].j],pointsValue[descriptionPairs[cnt+13].j],pointsValue[descriptionPairs[cnt+14].j],pointsValue[descriptionPairs[cnt+15].j]);
+
+ workReg = _mm_min_epu8(operand1, operand2); // emulated "greater than" for UNSIGNED int
+ workReg = _mm_cmpeq_epi8(workReg, operand2); // emulated "greater than" for UNSIGNED int
+
+ workReg = _mm_and_si128(_mm_srli_epi16(binMask, m), workReg); // merge the last 16 bits with the 128bits std::vector until full
+ result128 = _mm_or_si128(result128, workReg);
+ }
+ (*ptr) = result128;
+ ++ptr;
+ }
+ ptr-=8;
+#else
+ for( int m = FREAK_NB_PAIRS; m--; ) {
+ ptr->set(m, pointsValue[descriptionPairs[m].i]> pointsValue[descriptionPairs[m].j ] );
+ }
+ --ptr;
+#endif
+ }
+ }
+ else { // extract all possible comparisons for selection
+ descriptors = cv::Mat::zeros(keypoints.size(), 128, CV_8U);
+ std::bitset<1024>* ptr = (std::bitset<1024>*) (descriptors.data+(keypoints.size()-1)*descriptors.step[0]);
+
+ for( size_t k = keypoints.size(); k--; ) {
+ //estimate orientation (gradient)
+ if( !orientationNormalized ) {
+ thetaIdx = 0;//assign 0° to all keypoints
+ keypoints[k].angle = 0.0;
+ }
+ else {
+ //get the points intensity value in the un-rotated pattern
+ for( int i = FREAK_NB_POINTS;i--; )
+ pointsValue[i] = meanIntensity(image, imgIntegral, keypoints[k].pt.x,keypoints[k].pt.y, kpScaleIdx[k], 0, i);
+
+ direction0 = 0;
+ direction1 = 0;
+ for( int m = 45; m--; ) {
+ //iterate through the orientation pairs
+ const int delta = (pointsValue[ orientationPairs[m].i ]-pointsValue[ orientationPairs[m].j ]);
+ direction0 += delta*(orientationPairs[m].weight_dx)/2048;
+ direction1 += delta*(orientationPairs[m].weight_dy)/2048;
+ }
+
+ keypoints[k].angle = atan2((float)direction1,(float)direction0)*(180.0/M_PI); //estimate orientation
+ thetaIdx = int(FREAK_NB_ORIENTATION*keypoints[k].angle*(1/360.0)+0.5);
+
+ if( thetaIdx < 0 )
+ thetaIdx += FREAK_NB_ORIENTATION;
+
+ if( thetaIdx >= FREAK_NB_ORIENTATION )
+ thetaIdx -= FREAK_NB_ORIENTATION;
+ }
+ // get the points intensity value in the rotated pattern
+ for( int i = FREAK_NB_POINTS; i--; ) {
+ pointsValue[i] = meanIntensity(image, imgIntegral, keypoints[k].pt.x,
+ keypoints[k].pt.y, kpScaleIdx[k], thetaIdx, i);
+ }
+
+ int cnt(0);
+ for( int i = 1; i < FREAK_NB_POINTS; ++i ) {
+ //(generate all the pairs)
+ for( int j = 0; j < i; ++j ) {
+ ptr->set(cnt, pointsValue[i]>pointsValue[j] );
+ ++cnt;
+ }
+ }
+ --ptr;
+ }
+ }
+}
+
+// simply take average on a square patch, not even gaussian approx
+uchar FREAK::meanIntensity( const cv::Mat& image, const cv::Mat& integral,
+ const float kp_x,
+ const float kp_y,
+ const unsigned int scale,
+ const unsigned int rot,
+ const unsigned int point) const {
+ // get point position in image
+ const PatternPoint& FreakPoint = patternLookup[scale*FREAK_NB_ORIENTATION*FREAK_NB_POINTS + rot*FREAK_NB_POINTS + point];
+ const float xf = FreakPoint.x+kp_x;
+ const float yf = FreakPoint.y+kp_y;
+ const int x = int(xf);
+ const int y = int(yf);
+ const int& imagecols = image.cols;
+
+ // get the sigma:
+ const float radius = FreakPoint.sigma;
+
+ // calculate output:
+ int ret_val;
+ if( radius < 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;
+ // linear interpolation:
+ 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;
+ }
+
+ // expected case:
+
+ // calculate borders
+ const int x_left = int(xf-radius+0.5);
+ const int y_top = int(yf-radius+0.5);
+ const int x_right = int(xf+radius+1.5);//integral image is 1px wider
+ const int y_bottom = int(yf+radius+1.5);//integral image is 1px higher
+
+ ret_val = integral.at<int>(y_bottom,x_right);//bottom right corner
+ ret_val -= integral.at<int>(y_bottom,x_left);
+ ret_val += integral.at<int>(y_top,x_left);
+ ret_val -= integral.at<int>(y_top,x_right);
+ ret_val = ret_val/( (x_right-x_left)* (y_bottom-y_top) );
+ //~ std::cout<<integral.step[1]<<std::endl;
+ return ret_val;
+}
+
+// pair selection algorithm from a set of training images and corresponding keypoints
+vector<int> FREAK::selectPairs(const std::vector<Mat>& images
+ , std::vector<std::vector<KeyPoint> >& keypoints
+ , const double corrTresh
+ , bool verbose )
+{
+ extAll = true;
+ // compute descriptors with all pairs
+ Mat descriptors;
+
+ if( verbose )
+ std::cout << "Number of images: " << images.size() << std::endl;
+
+ for( size_t i = 0;i < images.size(); ++i ) {
+ Mat descriptorsTmp;
+ computeImpl(images[i],keypoints[i],descriptorsTmp);
+ descriptors.push_back(descriptorsTmp);
+ }
+
+ if( verbose )
+ std::cout << "number of keypoints: " << descriptors.rows << std::endl;
+
+ //descriptor in floating point format (each bit is a float)
+ Mat descriptorsFloat = Mat::zeros(descriptors.rows, 903, CV_32F);
+
+ std::bitset<1024>* ptr = (std::bitset<1024>*) (descriptors.data+(descriptors.rows-1)*descriptors.step[0]);
+ for( size_t m = descriptors.rows; m--; ) {
+ for( size_t n = 903; n--; ) {
+ if( ptr->test(n) == true )
+ descriptorsFloat.at<float>(m,n)=1.0;
+ }
+ --ptr;
+ }
+
+ std::vector<PairStat> pairStat;
+ for( size_t n = 903; n--; ) {
+ // the higher the variance, the better --> mean = 0.5
+ PairStat tmp = { fabs( mean(descriptorsFloat.col(n))[0]-0.5 ) ,n};
+ pairStat.push_back(tmp);
+ }
+
+ std::sort( pairStat.begin(),pairStat.end(), sortMean() );
+
+ std::vector<PairStat> bestPairs;
+ for( int m = 0; m < 903; ++m ) {
+ if( verbose )
+ std::cout << m << ":" << bestPairs.size() << " " << std::flush;
+ double corrMax(0);
+
+ for( size_t n = 0; n < bestPairs.size(); ++n ) {
+ int idxA = bestPairs[n].idx;
+ int idxB = pairStat[m].idx;
+ double corr(0);
+ // compute correlation between 2 pairs
+ corr = fabs(compareHist(descriptorsFloat.col(idxA), descriptorsFloat.col(idxB), CV_COMP_CORREL));
+
+ if( corr > corrMax ) {
+ corrMax = corr;
+ if( corrMax >= corrTresh )
+ break;
+ }
+ }
+
+ if( corrMax < corrTresh/*0.7*/ )
+ bestPairs.push_back(pairStat[m]);
+
+ if( bestPairs.size() >= 512 ) {
+ if( verbose )
+ std::cout << m << std::endl;
+ break;
+ }
+ }
+
+ std::vector<int> idxBestPairs;
+ if( (int)bestPairs.size() >= FREAK_NB_PAIRS ) {
+ for( int i = 0; i < FREAK_NB_PAIRS; ++i )
+ idxBestPairs.push_back(bestPairs[i].idx);
+ }
+ else {
+ if( verbose )
+ std::cout << "correlation threshold too small (restrictive)" << std::endl;
+ CV_Error(CV_StsError, "correlation threshold too small (restrictive)");
+ }
+ extAll = false;
+ return idxBestPairs;
+}
+
+
+/*
+void FREAKImpl::drawPattern()
+{ // create an image showing the brisk pattern
+ Mat pattern = Mat::zeros(1000, 1000, CV_8UC3) + Scalar(255,255,255);
+ int sFac = 500 / patternScale;
+ for( int n = 0; n < kNB_POINTS; ++n ) {
+ PatternPoint& pt = patternLookup[n];
+ circle(pattern, Point( pt.x*sFac,pt.y*sFac)+Point(500,500), pt.sigma*sFac, Scalar(0,0,255),2);
+ // rectangle(pattern, Point( (pt.x-pt.sigma)*sFac,(pt.y-pt.sigma)*sFac)+Point(500,500), Point( (pt.x+pt.sigma)*sFac,(pt.y+pt.sigma)*sFac)+Point(500,500), Scalar(0,0,255),2);
+
+ circle(pattern, Point( pt.x*sFac,pt.y*sFac)+Point(500,500), 1, Scalar(0,0,0),3);
+ std::ostringstream oss;
+ oss << n;
+ putText( pattern, oss.str(), Point( pt.x*sFac,pt.y*sFac)+Point(500,500), FONT_HERSHEY_SIMPLEX,0.5, Scalar(0,0,0), 1);
+ }
+ imshow( "FreakDescriptorExtractor pattern", pattern );
+ waitKey(0);
+}
+*/
+
+// -------------------------------------------------
+/* FREAK interface implementation */
+FREAK::FREAK( bool _orientationNormalized, bool _scaleNormalized
+ , float _patternScale, int _nOctaves, const std::vector<int>& _selectedPairs )
+ : orientationNormalized(_orientationNormalized), scaleNormalized(_scaleNormalized),
+ patternScale(_patternScale), nOctaves(_nOctaves), selectedPairs0(_selectedPairs)
+{
+}
+
+FREAK::~FREAK()
+{
+}
+
+int FREAK::descriptorSize() const {
+ return FREAK_NB_PAIRS / 8; // descriptor length in bytes
+}
+
+int FREAK::descriptorType() const {
+ return CV_8U;
+}
+
+} // END NAMESPACE CV
projects / profile / ivi / opencv.git / commitdiff