2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
949 lines
40 KiB
C++
949 lines
40 KiB
C++
/*********************************************************************
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* Software License Agreement (BSD License)
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*
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* Copyright (c) 2009, Willow Garage, Inc.
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the Willow Garage nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*********************************************************************/
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/** Authors: Ethan Rublee, Vincent Rabaud, Gary Bradski */
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#include "precomp.hpp"
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#include <iterator>
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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namespace cv
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{
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const float HARRIS_K = 0.04f;
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const int DESCRIPTOR_SIZE = 32;
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/**
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* Function that computes the Harris responses in a
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* blockSize x blockSize patch at given points in an image
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*/
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static void
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HarrisResponses(const Mat& img, std::vector<KeyPoint>& pts, int blockSize, float harris_k)
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{
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CV_Assert( img.type() == CV_8UC1 && blockSize*blockSize <= 2048 );
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size_t ptidx, ptsize = pts.size();
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const uchar* ptr00 = img.ptr<uchar>();
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int step = (int)(img.step/img.elemSize1());
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int r = blockSize/2;
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float scale = (1 << 2) * blockSize * 255.0f;
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scale = 1.0f / scale;
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float scale_sq_sq = scale * scale * scale * scale;
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AutoBuffer<int> ofsbuf(blockSize*blockSize);
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int* ofs = ofsbuf;
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for( int i = 0; i < blockSize; i++ )
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for( int j = 0; j < blockSize; j++ )
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ofs[i*blockSize + j] = (int)(i*step + j);
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for( ptidx = 0; ptidx < ptsize; ptidx++ )
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{
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int x0 = cvRound(pts[ptidx].pt.x - r);
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int y0 = cvRound(pts[ptidx].pt.y - r);
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const uchar* ptr0 = ptr00 + y0*step + x0;
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int a = 0, b = 0, c = 0;
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for( int k = 0; k < blockSize*blockSize; k++ )
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{
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const uchar* ptr = ptr0 + ofs[k];
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int Ix = (ptr[1] - ptr[-1])*2 + (ptr[-step+1] - ptr[-step-1]) + (ptr[step+1] - ptr[step-1]);
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int Iy = (ptr[step] - ptr[-step])*2 + (ptr[step-1] - ptr[-step-1]) + (ptr[step+1] - ptr[-step+1]);
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a += Ix*Ix;
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b += Iy*Iy;
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c += Ix*Iy;
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}
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pts[ptidx].response = ((float)a * b - (float)c * c -
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harris_k * ((float)a + b) * ((float)a + b))*scale_sq_sq;
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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static float IC_Angle(const Mat& image, const int half_k, Point2f pt,
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const std::vector<int> & u_max)
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{
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int m_01 = 0, m_10 = 0;
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const uchar* center = &image.at<uchar> (cvRound(pt.y), cvRound(pt.x));
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// Treat the center line differently, v=0
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for (int u = -half_k; u <= half_k; ++u)
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m_10 += u * center[u];
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// Go line by line in the circular patch
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int step = (int)image.step1();
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for (int v = 1; v <= half_k; ++v)
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{
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// Proceed over the two lines
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int v_sum = 0;
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int d = u_max[v];
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for (int u = -d; u <= d; ++u)
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{
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int val_plus = center[u + v*step], val_minus = center[u - v*step];
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v_sum += (val_plus - val_minus);
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m_10 += u * (val_plus + val_minus);
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}
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m_01 += v * v_sum;
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}
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return fastAtan2((float)m_01, (float)m_10);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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static void computeOrbDescriptor(const KeyPoint& kpt,
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const Mat& img, const Point* pattern,
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uchar* desc, int dsize, int WTA_K)
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{
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float angle = kpt.angle;
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//angle = cvFloor(angle/12)*12.f;
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angle *= (float)(CV_PI/180.f);
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float a = (float)cos(angle), b = (float)sin(angle);
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const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
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int step = (int)img.step;
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#if 1
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#define GET_VALUE(idx) \
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center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
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cvRound(pattern[idx].x*a - pattern[idx].y*b)]
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#else
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float x, y;
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int ix, iy;
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#define GET_VALUE(idx) \
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(x = pattern[idx].x*a - pattern[idx].y*b, \
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y = pattern[idx].x*b + pattern[idx].y*a, \
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ix = cvFloor(x), iy = cvFloor(y), \
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x -= ix, y -= iy, \
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cvRound(center[iy*step + ix]*(1-x)*(1-y) + center[(iy+1)*step + ix]*(1-x)*y + \
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center[iy*step + ix+1]*x*(1-y) + center[(iy+1)*step + ix+1]*x*y))
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#endif
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if( WTA_K == 2 )
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{
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for (int i = 0; i < dsize; ++i, pattern += 16)
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{
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int t0, t1, val;
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t0 = GET_VALUE(0); t1 = GET_VALUE(1);
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val = t0 < t1;
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t0 = GET_VALUE(2); t1 = GET_VALUE(3);
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val |= (t0 < t1) << 1;
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t0 = GET_VALUE(4); t1 = GET_VALUE(5);
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val |= (t0 < t1) << 2;
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t0 = GET_VALUE(6); t1 = GET_VALUE(7);
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val |= (t0 < t1) << 3;
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t0 = GET_VALUE(8); t1 = GET_VALUE(9);
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val |= (t0 < t1) << 4;
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t0 = GET_VALUE(10); t1 = GET_VALUE(11);
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val |= (t0 < t1) << 5;
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t0 = GET_VALUE(12); t1 = GET_VALUE(13);
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val |= (t0 < t1) << 6;
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t0 = GET_VALUE(14); t1 = GET_VALUE(15);
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val |= (t0 < t1) << 7;
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desc[i] = (uchar)val;
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}
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}
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else if( WTA_K == 3 )
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{
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for (int i = 0; i < dsize; ++i, pattern += 12)
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{
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int t0, t1, t2, val;
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t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
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val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);
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t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);
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val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;
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t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);
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val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;
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t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);
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val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;
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desc[i] = (uchar)val;
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}
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}
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else if( WTA_K == 4 )
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{
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for (int i = 0; i < dsize; ++i, pattern += 16)
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{
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int t0, t1, t2, t3, u, v, k, val;
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t0 = GET_VALUE(0); t1 = GET_VALUE(1);
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t2 = GET_VALUE(2); t3 = GET_VALUE(3);
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u = 0, v = 2;
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if( t1 > t0 ) t0 = t1, u = 1;
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if( t3 > t2 ) t2 = t3, v = 3;
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k = t0 > t2 ? u : v;
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val = k;
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t0 = GET_VALUE(4); t1 = GET_VALUE(5);
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t2 = GET_VALUE(6); t3 = GET_VALUE(7);
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u = 0, v = 2;
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if( t1 > t0 ) t0 = t1, u = 1;
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if( t3 > t2 ) t2 = t3, v = 3;
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k = t0 > t2 ? u : v;
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val |= k << 2;
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t0 = GET_VALUE(8); t1 = GET_VALUE(9);
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t2 = GET_VALUE(10); t3 = GET_VALUE(11);
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u = 0, v = 2;
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if( t1 > t0 ) t0 = t1, u = 1;
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if( t3 > t2 ) t2 = t3, v = 3;
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k = t0 > t2 ? u : v;
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val |= k << 4;
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t0 = GET_VALUE(12); t1 = GET_VALUE(13);
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t2 = GET_VALUE(14); t3 = GET_VALUE(15);
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u = 0, v = 2;
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if( t1 > t0 ) t0 = t1, u = 1;
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if( t3 > t2 ) t2 = t3, v = 3;
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k = t0 > t2 ? u : v;
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val |= k << 6;
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desc[i] = (uchar)val;
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}
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}
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else
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CV_Error( CV_StsBadSize, "Wrong WTA_K. It can be only 2, 3 or 4." );
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#undef GET_VALUE
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}
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static void initializeOrbPattern( const Point* pattern0, std::vector<Point>& pattern, int ntuples, int tupleSize, int poolSize )
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{
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RNG rng(0x12345678);
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int i, k, k1;
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pattern.resize(ntuples*tupleSize);
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for( i = 0; i < ntuples; i++ )
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{
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for( k = 0; k < tupleSize; k++ )
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{
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for(;;)
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{
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int idx = rng.uniform(0, poolSize);
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Point pt = pattern0[idx];
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for( k1 = 0; k1 < k; k1++ )
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if( pattern[tupleSize*i + k1] == pt )
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break;
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if( k1 == k )
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{
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pattern[tupleSize*i + k] = pt;
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break;
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}
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}
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}
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}
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}
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static int bit_pattern_31_[256*4] =
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{
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8,-3, 9,5/*mean (0), correlation (0)*/,
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4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
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-11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
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7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
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2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
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1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
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-2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
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-13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
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-13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
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10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
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-13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
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-11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
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7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
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-4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
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-13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
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-9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
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12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
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-3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
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-6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
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11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
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4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
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5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
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3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
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-8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
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-2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
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-13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
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-7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
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-4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
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-10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
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5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
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5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
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1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
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9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
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4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
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2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
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-4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
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-8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
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4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
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0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
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-13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
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-3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
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-6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
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8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
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0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
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7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
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-13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
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10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
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-6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
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10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
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-13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
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-13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
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3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
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5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
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-1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
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3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
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2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
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-13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
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-13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
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-13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
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-7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
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6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
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-9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
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-2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
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-12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
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3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
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-7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
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-3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
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2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
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-11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
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-1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
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5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
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-4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
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-9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
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-12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
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10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
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7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
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-7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
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-4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
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7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
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-7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
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-13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
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-3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
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7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
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-13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
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1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
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2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
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-4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
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-1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
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7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
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1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
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9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
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-1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
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-13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
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7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
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12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
|
|
6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
|
|
5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
|
|
2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
|
|
3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
|
|
2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
|
|
9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
|
|
-8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
|
|
-11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
|
|
1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
|
|
6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
|
|
2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
|
|
6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
|
|
3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
|
|
7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
|
|
-11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
|
|
-10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
|
|
-5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
|
|
-10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
|
|
8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
|
|
4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
|
|
-10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
|
|
4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
|
|
-2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
|
|
-5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
|
|
7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
|
|
-9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
|
|
-5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
|
|
8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
|
|
-9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
|
|
1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
|
|
7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
|
|
-2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
|
|
11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
|
|
-12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
|
|
3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
|
|
5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
|
|
0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
|
|
-9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
|
|
0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
|
|
-1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
|
|
5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
|
|
3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
|
|
-13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
|
|
-5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
|
|
-4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
|
|
6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
|
|
-7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
|
|
-13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
|
|
1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
|
|
4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
|
|
-2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
|
|
2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
|
|
-2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
|
|
4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
|
|
-6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
|
|
-3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
|
|
7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
|
|
4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
|
|
-13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
|
|
7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
|
|
7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
|
|
-7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
|
|
-8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
|
|
-13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
|
|
2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
|
|
10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
|
|
-6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
|
|
8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
|
|
2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
|
|
-11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
|
|
-12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
|
|
-11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
|
|
5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
|
|
-2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
|
|
-1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
|
|
-13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
|
|
-10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
|
|
-3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
|
|
2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
|
|
-9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
|
|
-4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
|
|
-4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
|
|
-6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
|
|
6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
|
|
-13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
|
|
11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
|
|
7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
|
|
-1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
|
|
-4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
|
|
-7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
|
|
-13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
|
|
-7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
|
|
-8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
|
|
-5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
|
|
-13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
|
|
1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
|
|
1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
|
|
9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
|
|
5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
|
|
-1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
|
|
-9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
|
|
-1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
|
|
-13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
|
|
8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
|
|
2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
|
|
7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
|
|
-10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
|
|
-10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
|
|
4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
|
|
3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
|
|
-4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
|
|
5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
|
|
4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
|
|
-9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
|
|
0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
|
|
-12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
|
|
3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
|
|
-10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
|
|
8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
|
|
-8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
|
|
2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
|
|
10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
|
|
6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
|
|
-7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
|
|
-3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
|
|
-1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
|
|
-3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
|
|
-8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
|
|
4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
|
|
2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
|
|
6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
|
|
3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
|
|
11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
|
|
-3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/,
|
|
4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/,
|
|
2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/,
|
|
-10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/,
|
|
-13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/,
|
|
-13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/,
|
|
6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/,
|
|
0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/,
|
|
-13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/,
|
|
-9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/,
|
|
-13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/,
|
|
5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/,
|
|
2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/,
|
|
-1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/,
|
|
9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/,
|
|
11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/,
|
|
3,0, 3,5/*mean (0.101147), correlation (0.525576)*/,
|
|
-1,4, 0,10/*mean (0.105263), correlation (0.531498)*/,
|
|
3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/,
|
|
-13,0, -10,5/*mean (0.112798), correlation (0.536582)*/,
|
|
5,8, 12,11/*mean (0.114181), correlation (0.555793)*/,
|
|
8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/,
|
|
7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/,
|
|
-10,4, -10,9/*mean (0.12094), correlation (0.554785)*/,
|
|
7,3, 12,4/*mean (0.122582), correlation (0.555825)*/,
|
|
9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/,
|
|
7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/,
|
|
-1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/
|
|
};
|
|
|
|
|
|
static void makeRandomPattern(int patchSize, Point* pattern, int npoints)
|
|
{
|
|
RNG rng(0x34985739); // we always start with a fixed seed,
|
|
// to make patterns the same on each run
|
|
for( int i = 0; i < npoints; i++ )
|
|
{
|
|
pattern[i].x = rng.uniform(-patchSize/2, patchSize/2+1);
|
|
pattern[i].y = rng.uniform(-patchSize/2, patchSize/2+1);
|
|
}
|
|
}
|
|
|
|
|
|
static inline float getScale(int level, int firstLevel, double scaleFactor)
|
|
{
|
|
return (float)std::pow(scaleFactor, (double)(level - firstLevel));
|
|
}
|
|
|
|
/** Constructor
|
|
* @param detector_params parameters to use
|
|
*/
|
|
ORB::ORB(int _nfeatures, float _scaleFactor, int _nlevels, int _edgeThreshold,
|
|
int _firstLevel, int _WTA_K, int _scoreType, int _patchSize) :
|
|
nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
|
|
edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(_WTA_K),
|
|
scoreType(_scoreType), patchSize(_patchSize)
|
|
{}
|
|
|
|
|
|
int ORB::descriptorSize() const
|
|
{
|
|
return kBytes;
|
|
}
|
|
|
|
int ORB::descriptorType() const
|
|
{
|
|
return CV_8U;
|
|
}
|
|
|
|
/** Compute the ORB features and descriptors on an image
|
|
* @param img the image to compute the features and descriptors on
|
|
* @param mask the mask to apply
|
|
* @param keypoints the resulting keypoints
|
|
*/
|
|
void ORB::operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const
|
|
{
|
|
(*this)(image, mask, keypoints, noArray(), false);
|
|
}
|
|
|
|
|
|
/** Compute the ORB keypoint orientations
|
|
* @param image the image to compute the features and descriptors on
|
|
* @param integral_image the integral image of the iamge (can be empty, but the computation will be slower)
|
|
* @param scale the scale at which we compute the orientation
|
|
* @param keypoints the resulting keypoints
|
|
*/
|
|
static void computeOrientation(const Mat& image, std::vector<KeyPoint>& keypoints,
|
|
int halfPatchSize, const std::vector<int>& umax)
|
|
{
|
|
// Process each keypoint
|
|
for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
|
|
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
|
|
{
|
|
keypoint->angle = IC_Angle(image, halfPatchSize, keypoint->pt, umax);
|
|
}
|
|
}
|
|
|
|
|
|
/** Compute the ORB keypoints on an image
|
|
* @param image_pyramid the image pyramid to compute the features and descriptors on
|
|
* @param mask_pyramid the masks to apply at every level
|
|
* @param keypoints the resulting keypoints, clustered per level
|
|
*/
|
|
static void computeKeyPoints(const std::vector<Mat>& imagePyramid,
|
|
const std::vector<Mat>& maskPyramid,
|
|
std::vector<std::vector<KeyPoint> >& allKeypoints,
|
|
int nfeatures, int firstLevel, double scaleFactor,
|
|
int edgeThreshold, int patchSize, int scoreType )
|
|
{
|
|
int nlevels = (int)imagePyramid.size();
|
|
std::vector<int> nfeaturesPerLevel(nlevels);
|
|
|
|
// fill the extractors and descriptors for the corresponding scales
|
|
float factor = (float)(1.0 / scaleFactor);
|
|
float ndesiredFeaturesPerScale = nfeatures*(1 - factor)/(1 - (float)std::pow((double)factor, (double)nlevels));
|
|
|
|
int sumFeatures = 0;
|
|
for( int level = 0; level < nlevels-1; level++ )
|
|
{
|
|
nfeaturesPerLevel[level] = cvRound(ndesiredFeaturesPerScale);
|
|
sumFeatures += nfeaturesPerLevel[level];
|
|
ndesiredFeaturesPerScale *= factor;
|
|
}
|
|
nfeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);
|
|
|
|
// Make sure we forget about what is too close to the boundary
|
|
//edge_threshold_ = std::max(edge_threshold_, patch_size_/2 + kKernelWidth / 2 + 2);
|
|
|
|
// pre-compute the end of a row in a circular patch
|
|
int halfPatchSize = patchSize / 2;
|
|
std::vector<int> umax(halfPatchSize + 2);
|
|
|
|
int v, v0, vmax = cvFloor(halfPatchSize * std::sqrt(2.f) / 2 + 1);
|
|
int vmin = cvCeil(halfPatchSize * std::sqrt(2.f) / 2);
|
|
for (v = 0; v <= vmax; ++v)
|
|
umax[v] = cvRound(std::sqrt((double)halfPatchSize * halfPatchSize - v * v));
|
|
|
|
// Make sure we are symmetric
|
|
for (v = halfPatchSize, v0 = 0; v >= vmin; --v)
|
|
{
|
|
while (umax[v0] == umax[v0 + 1])
|
|
++v0;
|
|
umax[v] = v0;
|
|
++v0;
|
|
}
|
|
|
|
allKeypoints.resize(nlevels);
|
|
|
|
for (int level = 0; level < nlevels; ++level)
|
|
{
|
|
int featuresNum = nfeaturesPerLevel[level];
|
|
allKeypoints[level].reserve(featuresNum*2);
|
|
|
|
std::vector<KeyPoint> & keypoints = allKeypoints[level];
|
|
|
|
// Detect FAST features, 20 is a good threshold
|
|
FastFeatureDetector fd(20, true);
|
|
fd.detect(imagePyramid[level], keypoints, maskPyramid[level]);
|
|
|
|
// Remove keypoints very close to the border
|
|
KeyPointsFilter::runByImageBorder(keypoints, imagePyramid[level].size(), edgeThreshold);
|
|
|
|
if( scoreType == ORB::HARRIS_SCORE )
|
|
{
|
|
// Keep more points than necessary as FAST does not give amazing corners
|
|
KeyPointsFilter::retainBest(keypoints, 2 * featuresNum);
|
|
|
|
// Compute the Harris cornerness (better scoring than FAST)
|
|
HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K);
|
|
}
|
|
|
|
//cull to the final desired level, using the new Harris scores or the original FAST scores.
|
|
KeyPointsFilter::retainBest(keypoints, featuresNum);
|
|
|
|
float sf = getScale(level, firstLevel, scaleFactor);
|
|
|
|
// Set the level of the coordinates
|
|
for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
|
|
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
|
|
{
|
|
keypoint->octave = level;
|
|
keypoint->size = patchSize*sf;
|
|
}
|
|
|
|
computeOrientation(imagePyramid[level], keypoints, halfPatchSize, umax);
|
|
}
|
|
}
|
|
|
|
|
|
/** Compute the ORB decriptors
|
|
* @param image the image to compute the features and descriptors on
|
|
* @param integral_image the integral image of the image (can be empty, but the computation will be slower)
|
|
* @param level the scale at which we compute the orientation
|
|
* @param keypoints the keypoints to use
|
|
* @param descriptors the resulting descriptors
|
|
*/
|
|
static void computeDescriptors(const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors,
|
|
const std::vector<Point>& pattern, int dsize, int WTA_K)
|
|
{
|
|
//convert to grayscale if more than one color
|
|
CV_Assert(image.type() == CV_8UC1);
|
|
//create the descriptor mat, keypoints.size() rows, BYTES cols
|
|
descriptors = Mat::zeros((int)keypoints.size(), dsize, CV_8UC1);
|
|
|
|
for (size_t i = 0; i < keypoints.size(); i++)
|
|
computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i), dsize, WTA_K);
|
|
}
|
|
|
|
|
|
/** Compute the ORB features and descriptors on an image
|
|
* @param img the image to compute the features and descriptors on
|
|
* @param mask the mask to apply
|
|
* @param keypoints the resulting keypoints
|
|
* @param descriptors the resulting descriptors
|
|
* @param do_keypoints if true, the keypoints are computed, otherwise used as an input
|
|
* @param do_descriptors if true, also computes the descriptors
|
|
*/
|
|
void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>& _keypoints,
|
|
OutputArray _descriptors, bool useProvidedKeypoints) const
|
|
{
|
|
CV_Assert(patchSize >= 2);
|
|
|
|
bool do_keypoints = !useProvidedKeypoints;
|
|
bool do_descriptors = _descriptors.needed();
|
|
|
|
if( (!do_keypoints && !do_descriptors) || _image.empty() )
|
|
return;
|
|
|
|
//ROI handling
|
|
const int HARRIS_BLOCK_SIZE = 9;
|
|
int halfPatchSize = patchSize / 2;
|
|
int border = std::max(edgeThreshold, std::max(halfPatchSize, HARRIS_BLOCK_SIZE/2))+1;
|
|
|
|
Mat image = _image.getMat(), mask = _mask.getMat();
|
|
if( image.type() != CV_8UC1 )
|
|
cvtColor(_image, image, CV_BGR2GRAY);
|
|
|
|
int levelsNum = this->nlevels;
|
|
|
|
if( !do_keypoints )
|
|
{
|
|
// if we have pre-computed keypoints, they may use more levels than it is set in parameters
|
|
// !!!TODO!!! implement more correct method, independent from the used keypoint detector.
|
|
// Namely, the detector should provide correct size of each keypoint. Based on the keypoint size
|
|
// and the algorithm used (i.e. BRIEF, running on 31x31 patches) we should compute the approximate
|
|
// scale-factor that we need to apply. Then we should cluster all the computed scale-factors and
|
|
// for each cluster compute the corresponding image.
|
|
//
|
|
// In short, ultimately the descriptor should
|
|
// ignore octave parameter and deal only with the keypoint size.
|
|
levelsNum = 0;
|
|
for( size_t i = 0; i < _keypoints.size(); i++ )
|
|
levelsNum = std::max(levelsNum, std::max(_keypoints[i].octave, 0));
|
|
levelsNum++;
|
|
}
|
|
|
|
// Pre-compute the scale pyramids
|
|
std::vector<Mat> imagePyramid(levelsNum), maskPyramid(levelsNum);
|
|
for (int level = 0; level < levelsNum; ++level)
|
|
{
|
|
float scale = 1/getScale(level, firstLevel, scaleFactor);
|
|
Size sz(cvRound(image.cols*scale), cvRound(image.rows*scale));
|
|
Size wholeSize(sz.width + border*2, sz.height + border*2);
|
|
Mat temp(wholeSize, image.type()), masktemp;
|
|
imagePyramid[level] = temp(Rect(border, border, sz.width, sz.height));
|
|
|
|
if( !mask.empty() )
|
|
{
|
|
masktemp = Mat(wholeSize, mask.type());
|
|
maskPyramid[level] = masktemp(Rect(border, border, sz.width, sz.height));
|
|
}
|
|
|
|
// Compute the resized image
|
|
if( level != firstLevel )
|
|
{
|
|
if( level < firstLevel )
|
|
{
|
|
resize(image, imagePyramid[level], sz, 0, 0, INTER_LINEAR);
|
|
if (!mask.empty())
|
|
resize(mask, maskPyramid[level], sz, 0, 0, INTER_LINEAR);
|
|
}
|
|
else
|
|
{
|
|
resize(imagePyramid[level-1], imagePyramid[level], sz, 0, 0, INTER_LINEAR);
|
|
if (!mask.empty())
|
|
{
|
|
resize(maskPyramid[level-1], maskPyramid[level], sz, 0, 0, INTER_LINEAR);
|
|
threshold(maskPyramid[level], maskPyramid[level], 254, 0, THRESH_TOZERO);
|
|
}
|
|
}
|
|
|
|
copyMakeBorder(imagePyramid[level], temp, border, border, border, border,
|
|
BORDER_REFLECT_101+BORDER_ISOLATED);
|
|
if (!mask.empty())
|
|
copyMakeBorder(maskPyramid[level], masktemp, border, border, border, border,
|
|
BORDER_CONSTANT+BORDER_ISOLATED);
|
|
}
|
|
else
|
|
{
|
|
copyMakeBorder(image, temp, border, border, border, border,
|
|
BORDER_REFLECT_101);
|
|
if( !mask.empty() )
|
|
copyMakeBorder(mask, masktemp, border, border, border, border,
|
|
BORDER_CONSTANT+BORDER_ISOLATED);
|
|
}
|
|
}
|
|
|
|
// Pre-compute the keypoints (we keep the best over all scales, so this has to be done beforehand
|
|
std::vector < std::vector<KeyPoint> > allKeypoints;
|
|
if( do_keypoints )
|
|
{
|
|
// Get keypoints, those will be far enough from the border that no check will be required for the descriptor
|
|
computeKeyPoints(imagePyramid, maskPyramid, allKeypoints,
|
|
nfeatures, firstLevel, scaleFactor,
|
|
edgeThreshold, patchSize, scoreType);
|
|
|
|
// make sure we have the right number of keypoints keypoints
|
|
/*std::vector<KeyPoint> temp;
|
|
|
|
for (int level = 0; level < n_levels; ++level)
|
|
{
|
|
std::vector<KeyPoint>& keypoints = all_keypoints[level];
|
|
temp.insert(temp.end(), keypoints.begin(), keypoints.end());
|
|
keypoints.clear();
|
|
}
|
|
|
|
KeyPoint::retainBest(temp, n_features_);
|
|
|
|
for (std::vector<KeyPoint>::iterator keypoint = temp.begin(),
|
|
keypoint_end = temp.end(); keypoint != keypoint_end; ++keypoint)
|
|
all_keypoints[keypoint->octave].push_back(*keypoint);*/
|
|
}
|
|
else
|
|
{
|
|
// Remove keypoints very close to the border
|
|
KeyPointsFilter::runByImageBorder(_keypoints, image.size(), edgeThreshold);
|
|
|
|
// Cluster the input keypoints depending on the level they were computed at
|
|
allKeypoints.resize(levelsNum);
|
|
for (std::vector<KeyPoint>::iterator keypoint = _keypoints.begin(),
|
|
keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)
|
|
allKeypoints[keypoint->octave].push_back(*keypoint);
|
|
|
|
// Make sure we rescale the coordinates
|
|
for (int level = 0; level < levelsNum; ++level)
|
|
{
|
|
if (level == firstLevel)
|
|
continue;
|
|
|
|
std::vector<KeyPoint> & keypoints = allKeypoints[level];
|
|
float scale = 1/getScale(level, firstLevel, scaleFactor);
|
|
for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
|
|
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
|
|
keypoint->pt *= scale;
|
|
}
|
|
}
|
|
|
|
Mat descriptors;
|
|
std::vector<Point> pattern;
|
|
|
|
if( do_descriptors )
|
|
{
|
|
int nkeypoints = 0;
|
|
for (int level = 0; level < levelsNum; ++level)
|
|
nkeypoints += (int)allKeypoints[level].size();
|
|
if( nkeypoints == 0 )
|
|
_descriptors.release();
|
|
else
|
|
{
|
|
_descriptors.create(nkeypoints, descriptorSize(), CV_8U);
|
|
descriptors = _descriptors.getMat();
|
|
}
|
|
|
|
const int npoints = 512;
|
|
Point patternbuf[npoints];
|
|
const Point* pattern0 = (const Point*)bit_pattern_31_;
|
|
|
|
if( patchSize != 31 )
|
|
{
|
|
pattern0 = patternbuf;
|
|
makeRandomPattern(patchSize, patternbuf, npoints);
|
|
}
|
|
|
|
CV_Assert( WTA_K == 2 || WTA_K == 3 || WTA_K == 4 );
|
|
|
|
if( WTA_K == 2 )
|
|
std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));
|
|
else
|
|
{
|
|
int ntuples = descriptorSize()*4;
|
|
initializeOrbPattern(pattern0, pattern, ntuples, WTA_K, npoints);
|
|
}
|
|
}
|
|
|
|
_keypoints.clear();
|
|
int offset = 0;
|
|
for (int level = 0; level < levelsNum; ++level)
|
|
{
|
|
// Get the features and compute their orientation
|
|
std::vector<KeyPoint>& keypoints = allKeypoints[level];
|
|
int nkeypoints = (int)keypoints.size();
|
|
|
|
// Compute the descriptors
|
|
if (do_descriptors)
|
|
{
|
|
Mat desc;
|
|
if (!descriptors.empty())
|
|
{
|
|
desc = descriptors.rowRange(offset, offset + nkeypoints);
|
|
}
|
|
|
|
offset += nkeypoints;
|
|
// preprocess the resized image
|
|
Mat& workingMat = imagePyramid[level];
|
|
//boxFilter(working_mat, working_mat, working_mat.depth(), Size(5,5), Point(-1,-1), true, BORDER_REFLECT_101);
|
|
GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);
|
|
computeDescriptors(workingMat, keypoints, desc, pattern, descriptorSize(), WTA_K);
|
|
}
|
|
|
|
// Copy to the output data
|
|
if (level != firstLevel)
|
|
{
|
|
float scale = getScale(level, firstLevel, scaleFactor);
|
|
for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
|
|
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
|
|
keypoint->pt *= scale;
|
|
}
|
|
// And add the keypoints to the output
|
|
_keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
|
|
}
|
|
}
|
|
|
|
void ORB::detectImpl( const Mat& image, std::vector<KeyPoint>& keypoints, const Mat& mask) const
|
|
{
|
|
(*this)(image, mask, keypoints, noArray(), false);
|
|
}
|
|
|
|
void ORB::computeImpl( const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors) const
|
|
{
|
|
(*this)(image, Mat(), keypoints, descriptors, true);
|
|
}
|
|
|
|
}
|