/*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*/ /**********************************************************************************************\ Implementation of SIFT is based on the code from http://blogs.oregonstate.edu/hess/code/sift/ Below is the original copyright. // Copyright (c) 2006-2010, Rob Hess // All rights reserved. // The following patent has been issued for methods embodied in this // software: "Method and apparatus for identifying scale invariant features // in an image and use of same for locating an object in an image," David // G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application // filed March 8, 1999. Asignee: The University of British Columbia. For // further details, contact David Lowe (lowe@cs.ubc.ca) or the // University-Industry Liaison Office of the University of British // Columbia. // Note that restrictions imposed by this patent (and possibly others) // exist independently of and may be in conflict with the freedoms granted // in this license, which refers to copyright of the program, not patents // for any methods that it implements. Both copyright and patent law must // be obeyed to legally use and redistribute this program and it is not the // purpose of this license to induce you to infringe any patents or other // property right claims or to contest validity of any such claims. If you // redistribute or use the program, then this license merely protects you // from committing copyright infringement. It does not protect you from // committing patent infringement. So, before you do anything with this // program, make sure that you have permission to do so not merely in terms // of copyright, but also in terms of patent law. // Please note that this license is not to be understood as a guarantee // either. If you use the program according to this license, but in // conflict with patent law, it does not mean that the licensor will refund // you for any losses that you incur if you are sued for your patent // infringement. // 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 and // patent notices, 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 Oregon State University 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 // HOLDER 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 #include namespace cv { /******************************* Defs and macros *****************************/ // default number of sampled intervals per octave static const int SIFT_INTVLS = 3; // default sigma for initial gaussian smoothing static const float SIFT_SIGMA = 1.6f; // default threshold on keypoint contrast |D(x)| static const float SIFT_CONTR_THR = 0.04f; // default threshold on keypoint ratio of principle curvatures static const float SIFT_CURV_THR = 10.f; // double image size before pyramid construction? static const bool SIFT_IMG_DBL = true; // default width of descriptor histogram array static const int SIFT_DESCR_WIDTH = 4; // default number of bins per histogram in descriptor array static const int SIFT_DESCR_HIST_BINS = 8; // assumed gaussian blur for input image static const float SIFT_INIT_SIGMA = 0.5f; // width of border in which to ignore keypoints static const int SIFT_IMG_BORDER = 5; // maximum steps of keypoint interpolation before failure static const int SIFT_MAX_INTERP_STEPS = 5; // default number of bins in histogram for orientation assignment static const int SIFT_ORI_HIST_BINS = 36; // determines gaussian sigma for orientation assignment static const float SIFT_ORI_SIG_FCTR = 1.5f; // determines the radius of the region used in orientation assignment static const float SIFT_ORI_RADIUS = 3 * SIFT_ORI_SIG_FCTR; // orientation magnitude relative to max that results in new feature static const float SIFT_ORI_PEAK_RATIO = 0.8f; // determines the size of a single descriptor orientation histogram static const float SIFT_DESCR_SCL_FCTR = 3.f; // threshold on magnitude of elements of descriptor vector static const float SIFT_DESCR_MAG_THR = 0.2f; // factor used to convert floating-point descriptor to unsigned char static const float SIFT_INT_DESCR_FCTR = 512.f; static const int SIFT_FIXPT_SCALE = 48; static Mat createInitialImage( const Mat& img, bool doubleImageSize, float sigma ) { Mat gray, gray_fpt; if( img.channels() == 3 || img.channels() == 4 ) cvtColor(img, gray, COLOR_BGR2GRAY); else img.copyTo(gray); gray.convertTo(gray_fpt, CV_16S, SIFT_FIXPT_SCALE, 0); float sig_diff; if( doubleImageSize ) { sig_diff = sqrtf( std::max(sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA * 4, 0.01f) ); Mat dbl; resize(gray_fpt, dbl, Size(gray.cols*2, gray.rows*2), 0, 0, INTER_LINEAR); GaussianBlur(dbl, dbl, Size(), sig_diff, sig_diff); return dbl; } else { sig_diff = sqrtf( std::max(sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA, 0.01f) ); GaussianBlur(gray_fpt, gray_fpt, Size(), sig_diff, sig_diff); return gray_fpt; } } void SIFT::buildGaussianPyramid( const Mat& base, vector& pyr, int nOctaves ) const { vector sig(nOctaveLayers + 3); pyr.resize(nOctaves*(nOctaveLayers + 3)); // precompute Gaussian sigmas using the following formula: // \sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2 sig[0] = sigma; double k = pow( 2., 1. / nOctaveLayers ); for( int i = 1; i < nOctaveLayers + 3; i++ ) { double sig_prev = pow(k, (double)(i-1))*sigma; double sig_total = sig_prev*k; sig[i] = std::sqrt(sig_total*sig_total - sig_prev*sig_prev); } for( int o = 0; o < nOctaves; o++ ) { for( int i = 0; i < nOctaveLayers + 3; i++ ) { Mat& dst = pyr[o*(nOctaveLayers + 3) + i]; if( o == 0 && i == 0 ) dst = base; // base of new octave is halved image from end of previous octave else if( i == 0 ) { const Mat& src = pyr[(o-1)*(nOctaveLayers + 3) + nOctaveLayers]; resize(src, dst, Size(src.cols/2, src.rows/2), 0, 0, INTER_NEAREST); } else { const Mat& src = pyr[o*(nOctaveLayers + 3) + i-1]; GaussianBlur(src, dst, Size(), sig[i], sig[i]); } } } } void SIFT::buildDoGPyramid( const vector& gpyr, vector& dogpyr ) const { int nOctaves = (int)gpyr.size()/(nOctaveLayers + 3); dogpyr.resize( nOctaves*(nOctaveLayers + 2) ); for( int o = 0; o < nOctaves; o++ ) { for( int i = 0; i < nOctaveLayers + 2; i++ ) { const Mat& src1 = gpyr[o*(nOctaveLayers + 3) + i]; const Mat& src2 = gpyr[o*(nOctaveLayers + 3) + i + 1]; Mat& dst = dogpyr[o*(nOctaveLayers + 2) + i]; subtract(src2, src1, dst, noArray(), CV_16S); } } } // Computes a gradient orientation histogram at a specified pixel static float calcOrientationHist( const Mat& img, Point pt, int radius, float sigma, float* hist, int n ) { int i, j, k, len = (radius*2+1)*(radius*2+1); float expf_scale = -1.f/(2.f * sigma * sigma); AutoBuffer buf(len*4 + n+4); float *X = buf, *Y = X + len, *Mag = X, *Ori = Y + len, *W = Ori + len; float* temphist = W + len + 2; for( i = 0; i < n; i++ ) temphist[i] = 0.f; for( i = -radius, k = 0; i <= radius; i++ ) { int y = pt.y + i; if( y <= 0 || y >= img.rows - 1 ) continue; for( j = -radius; j <= radius; j++ ) { int x = pt.x + j; if( x <= 0 || x >= img.cols - 1 ) continue; float dx = (float)(img.at(y, x+1) - img.at(y, x-1)); float dy = (float)(img.at(y-1, x) - img.at(y+1, x)); X[k] = dx; Y[k] = dy; W[k] = (i*i + j*j)*expf_scale; k++; } } len = k; // compute gradient values, orientations and the weights over the pixel neighborhood exp(W, W, len); fastAtan2(Y, X, Ori, len, true); magnitude(X, Y, Mag, len); for( k = 0; k < len; k++ ) { int bin = cvRound((n/360.f)*Ori[k]); if( bin >= n ) bin -= n; if( bin < 0 ) bin += n; temphist[bin] += W[k]*Mag[k]; } // smooth the histogram temphist[-1] = temphist[n-1]; temphist[-2] = temphist[n-2]; temphist[n] = temphist[0]; temphist[n+1] = temphist[1]; for( i = 0; i < n; i++ ) { hist[i] = (temphist[i-2] + temphist[i+2])*(1.f/16.f) + (temphist[i-1] + temphist[i+1])*(4.f/16.f) + temphist[i]*(6.f/16.f); } float maxval = hist[0]; for( i = 1; i < n; i++ ) maxval = std::max(maxval, hist[i]); return maxval; } // // Interpolates a scale-space extremum's location and scale to subpixel // accuracy to form an image feature. Rejects features with low contrast. // Based on Section 4 of Lowe's paper. static bool adjustLocalExtrema( const vector& dog_pyr, KeyPoint& kpt, int octv, int& layer, int& r, int& c, int nOctaveLayers, float contrastThreshold, float edgeThreshold, float sigma ) { const float img_scale = 1.f/(255*SIFT_FIXPT_SCALE); const float deriv_scale = img_scale*0.5f; const float second_deriv_scale = img_scale; const float cross_deriv_scale = img_scale*0.25f; float xi=0, xr=0, xc=0, contr; int i = 0; for( ; i < SIFT_MAX_INTERP_STEPS; i++ ) { int idx = octv*(nOctaveLayers+2) + layer; const Mat& img = dog_pyr[idx]; const Mat& prev = dog_pyr[idx-1]; const Mat& next = dog_pyr[idx+1]; Vec3f dD((img.at(r, c+1) - img.at(r, c-1))*deriv_scale, (img.at(r+1, c) - img.at(r-1, c))*deriv_scale, (next.at(r, c) - prev.at(r, c))*deriv_scale); float v2 = (float)img.at(r, c)*2; float dxx = (img.at(r, c+1) + img.at(r, c-1) - v2)*second_deriv_scale; float dyy = (img.at(r+1, c) + img.at(r-1, c) - v2)*second_deriv_scale; float dss = (next.at(r, c) + prev.at(r, c) - v2)*second_deriv_scale; float dxy = (img.at(r+1, c+1) - img.at(r+1, c-1) - img.at(r-1, c+1) + img.at(r-1, c-1))*cross_deriv_scale; float dxs = (next.at(r, c+1) - next.at(r, c-1) - prev.at(r, c+1) + prev.at(r, c-1))*cross_deriv_scale; float dys = (next.at(r+1, c) - next.at(r-1, c) - prev.at(r+1, c) + prev.at(r-1, c))*cross_deriv_scale; Matx33f H(dxx, dxy, dxs, dxy, dyy, dys, dxs, dys, dss); Vec3f X = H.solve(dD, DECOMP_LU); xi = -X[2]; xr = -X[1]; xc = -X[0]; if( std::abs( xi ) < 0.5f && std::abs( xr ) < 0.5f && std::abs( xc ) < 0.5f ) break; c += cvRound( xc ); r += cvRound( xr ); layer += cvRound( xi ); if( layer < 1 || layer > nOctaveLayers || c < SIFT_IMG_BORDER || c >= img.cols - SIFT_IMG_BORDER || r < SIFT_IMG_BORDER || r >= img.rows - SIFT_IMG_BORDER ) return false; } /* ensure convergence of interpolation */ if( i >= SIFT_MAX_INTERP_STEPS ) return false; { int idx = octv*(nOctaveLayers+2) + layer; const Mat& img = dog_pyr[idx]; const Mat& prev = dog_pyr[idx-1]; const Mat& next = dog_pyr[idx+1]; Matx31f dD((img.at(r, c+1) - img.at(r, c-1))*deriv_scale, (img.at(r+1, c) - img.at(r-1, c))*deriv_scale, (next.at(r, c) - prev.at(r, c))*deriv_scale); float t = dD.dot(Matx31f(xc, xr, xi)); contr = img.at(r, c)*img_scale + t * 0.5f; if( std::abs( contr ) * nOctaveLayers < contrastThreshold ) return false; /* principal curvatures are computed using the trace and det of Hessian */ float v2 = img.at(r, c)*2.f; float dxx = (img.at(r, c+1) + img.at(r, c-1) - v2)*second_deriv_scale; float dyy = (img.at(r+1, c) + img.at(r-1, c) - v2)*second_deriv_scale; float dxy = (img.at(r+1, c+1) - img.at(r+1, c-1) - img.at(r-1, c+1) + img.at(r-1, c-1)) * cross_deriv_scale; float tr = dxx + dyy; float det = dxx * dyy - dxy * dxy; if( det <= 0 || tr*tr*edgeThreshold >= (edgeThreshold + 1)*(edgeThreshold + 1)*det ) return false; } kpt.pt.x = (c + xc) * (1 << octv); kpt.pt.y = (r + xr) * (1 << octv); kpt.octave = octv + (layer << 8) + (cvRound((xi + 0.5)*255) << 16); kpt.size = sigma*powf(2.f, (layer + xi) / nOctaveLayers)*(1 << octv)*2; return true; } // // Detects features at extrema in DoG scale space. Bad features are discarded // based on contrast and ratio of principal curvatures. void SIFT::findScaleSpaceExtrema( const vector& gauss_pyr, const vector& dog_pyr, vector& keypoints ) const { int nOctaves = (int)gauss_pyr.size()/(nOctaveLayers + 3); int threshold = cvFloor(0.5 * contrastThreshold / nOctaveLayers * 255 * SIFT_FIXPT_SCALE); const int n = SIFT_ORI_HIST_BINS; float hist[n]; KeyPoint kpt; keypoints.clear(); for( int o = 0; o < nOctaves; o++ ) for( int i = 1; i <= nOctaveLayers; i++ ) { int idx = o*(nOctaveLayers+2)+i; const Mat& img = dog_pyr[idx]; const Mat& prev = dog_pyr[idx-1]; const Mat& next = dog_pyr[idx+1]; int step = (int)img.step1(); int rows = img.rows, cols = img.cols; for( int r = SIFT_IMG_BORDER; r < rows-SIFT_IMG_BORDER; r++) { const short* currptr = img.ptr(r); const short* prevptr = prev.ptr(r); const short* nextptr = next.ptr(r); for( int c = SIFT_IMG_BORDER; c < cols-SIFT_IMG_BORDER; c++) { int val = currptr[c]; // find local extrema with pixel accuracy if( std::abs(val) > threshold && ((val > 0 && val >= currptr[c-1] && val >= currptr[c+1] && val >= currptr[c-step-1] && val >= currptr[c-step] && val >= currptr[c-step+1] && val >= currptr[c+step-1] && val >= currptr[c+step] && val >= currptr[c+step+1] && val >= nextptr[c] && val >= nextptr[c-1] && val >= nextptr[c+1] && val >= nextptr[c-step-1] && val >= nextptr[c-step] && val >= nextptr[c-step+1] && val >= nextptr[c+step-1] && val >= nextptr[c+step] && val >= nextptr[c+step+1] && val >= prevptr[c] && val >= prevptr[c-1] && val >= prevptr[c+1] && val >= prevptr[c-step-1] && val >= prevptr[c-step] && val >= prevptr[c-step+1] && val >= prevptr[c+step-1] && val >= prevptr[c+step] && val >= prevptr[c+step+1]) || (val < 0 && val <= currptr[c-1] && val <= currptr[c+1] && val <= currptr[c-step-1] && val <= currptr[c-step] && val <= currptr[c-step+1] && val <= currptr[c+step-1] && val <= currptr[c+step] && val <= currptr[c+step+1] && val <= nextptr[c] && val <= nextptr[c-1] && val <= nextptr[c+1] && val <= nextptr[c-step-1] && val <= nextptr[c-step] && val <= nextptr[c-step+1] && val <= nextptr[c+step-1] && val <= nextptr[c+step] && val <= nextptr[c+step+1] && val <= prevptr[c] && val <= prevptr[c-1] && val <= prevptr[c+1] && val <= prevptr[c-step-1] && val <= prevptr[c-step] && val <= prevptr[c-step+1] && val <= prevptr[c+step-1] && val <= prevptr[c+step] && val <= prevptr[c+step+1]))) { int r1 = r, c1 = c, layer = i; if( !adjustLocalExtrema(dog_pyr, kpt, o, layer, r1, c1, nOctaveLayers, (float)contrastThreshold, (float)edgeThreshold, (float)sigma) ) continue; float scl_octv = kpt.size*0.5f/(1 << o); float omax = calcOrientationHist(gauss_pyr[o*(nOctaveLayers+3) + layer], Point(c1, r1), cvRound(SIFT_ORI_RADIUS * scl_octv), SIFT_ORI_SIG_FCTR * scl_octv, hist, n); float mag_thr = (float)(omax * SIFT_ORI_PEAK_RATIO); for( int j = 0; j < n; j++ ) { int l = j > 0 ? j - 1 : n - 1; int r2 = j < n-1 ? j + 1 : 0; if( hist[j] > hist[l] && hist[j] > hist[r2] && hist[j] >= mag_thr ) { float bin = j + 0.5f * (hist[l]-hist[r2]) / (hist[l] - 2*hist[j] + hist[r2]); bin = bin < 0 ? n + bin : bin >= n ? bin - n : bin; kpt.angle = (float)((360.f/n) * bin); keypoints.push_back(kpt); } } } } } } } static void calcSIFTDescriptor( const Mat& img, Point2f ptf, float ori, float scl, int d, int n, float* dst ) { Point pt(cvRound(ptf.x), cvRound(ptf.y)); float cos_t = cosf(ori*(float)(CV_PI/180)); float sin_t = sinf(ori*(float)(CV_PI/180)); float bins_per_rad = n / 360.f; float exp_scale = -1.f/(d * d * 0.5f); float hist_width = SIFT_DESCR_SCL_FCTR * scl; int radius = cvRound(hist_width * 1.4142135623730951f * (d + 1) * 0.5f); cos_t /= hist_width; sin_t /= hist_width; int i, j, k, len = (radius*2+1)*(radius*2+1), histlen = (d+2)*(d+2)*(n+2); int rows = img.rows, cols = img.cols; AutoBuffer buf(len*6 + histlen); float *X = buf, *Y = X + len, *Mag = Y, *Ori = Mag + len, *W = Ori + len; float *RBin = W + len, *CBin = RBin + len, *hist = CBin + len; for( i = 0; i < d+2; i++ ) { for( j = 0; j < d+2; j++ ) for( k = 0; k < n+2; k++ ) hist[(i*(d+2) + j)*(n+2) + k] = 0.; } for( i = -radius, k = 0; i <= radius; i++ ) for( j = -radius; j <= radius; j++ ) { /* Calculate sample's histogram array coords rotated relative to ori. Subtract 0.5 so samples that fall e.g. in the center of row 1 (i.e. r_rot = 1.5) have full weight placed in row 1 after interpolation. */ float c_rot = j * cos_t - i * sin_t; float r_rot = j * sin_t + i * cos_t; float rbin = r_rot + d/2 - 0.5f; float cbin = c_rot + d/2 - 0.5f; int r = pt.y + i, c = pt.x + j; if( rbin > -1 && rbin < d && cbin > -1 && cbin < d && r > 0 && r < rows - 1 && c > 0 && c < cols - 1 ) { float dx = (float)(img.at(r, c+1) - img.at(r, c-1)); float dy = (float)(img.at(r-1, c) - img.at(r+1, c)); X[k] = dx; Y[k] = dy; RBin[k] = rbin; CBin[k] = cbin; W[k] = (c_rot * c_rot + r_rot * r_rot)*exp_scale; k++; } } len = k; fastAtan2(Y, X, Ori, len, true); magnitude(X, Y, Mag, len); exp(W, W, len); for( k = 0; k < len; k++ ) { float rbin = RBin[k], cbin = CBin[k]; float obin = (Ori[k] - ori)*bins_per_rad; float mag = Mag[k]*W[k]; int r0 = cvFloor( rbin ); int c0 = cvFloor( cbin ); int o0 = cvFloor( obin ); rbin -= r0; cbin -= c0; obin -= o0; if( o0 < 0 ) o0 += n; if( o0 >= n ) o0 -= n; // histogram update using tri-linear interpolation float v_r1 = mag*rbin, v_r0 = mag - v_r1; float v_rc11 = v_r1*cbin, v_rc10 = v_r1 - v_rc11; float v_rc01 = v_r0*cbin, v_rc00 = v_r0 - v_rc01; float v_rco111 = v_rc11*obin, v_rco110 = v_rc11 - v_rco111; float v_rco101 = v_rc10*obin, v_rco100 = v_rc10 - v_rco101; float v_rco011 = v_rc01*obin, v_rco010 = v_rc01 - v_rco011; float v_rco001 = v_rc00*obin, v_rco000 = v_rc00 - v_rco001; int idx = ((r0+1)*(d+2) + c0+1)*(n+2) + o0; hist[idx] += v_rco000; hist[idx+1] += v_rco001; hist[idx+(n+2)] += v_rco010; hist[idx+(n+3)] += v_rco011; hist[idx+(d+2)*(n+2)] += v_rco100; hist[idx+(d+2)*(n+2)+1] += v_rco101; hist[idx+(d+3)*(n+2)] += v_rco110; hist[idx+(d+3)*(n+2)+1] += v_rco111; } // finalize histogram, since the orientation histograms are circular for( i = 0; i < d; i++ ) for( j = 0; j < d; j++ ) { int idx = ((i+1)*(d+2) + (j+1))*(n+2); hist[idx] += hist[idx+n]; hist[idx+1] += hist[idx+n+1]; for( k = 0; k < n; k++ ) dst[(i*d + j)*n + k] = hist[idx+k]; } // copy histogram to the descriptor, // apply hysteresis thresholding // and scale the result, so that it can be easily converted // to byte array float nrm2 = 0; len = d*d*n; for( k = 0; k < len; k++ ) nrm2 += dst[k]*dst[k]; float thr = std::sqrt(nrm2)*SIFT_DESCR_MAG_THR; for( i = 0, nrm2 = 0; i < k; i++ ) { float val = std::min(dst[i], thr); dst[i] = val; nrm2 += val*val; } nrm2 = SIFT_INT_DESCR_FCTR/std::max(std::sqrt(nrm2), FLT_EPSILON); for( k = 0; k < len; k++ ) { dst[k] = saturate_cast(dst[k]*nrm2); } } static void calcDescriptors(const vector& gpyr, const vector& keypoints, Mat& descriptors, int nOctaveLayers ) { int d = SIFT_DESCR_WIDTH, n = SIFT_DESCR_HIST_BINS; for( size_t i = 0; i < keypoints.size(); i++ ) { KeyPoint kpt = keypoints[i]; int octv=kpt.octave & 255, layer=(kpt.octave >> 8) & 255; float scale = 1.f/(1 << octv); float size=kpt.size*scale; Point2f ptf(kpt.pt.x*scale, kpt.pt.y*scale); const Mat& img = gpyr[octv*(nOctaveLayers + 3) + layer]; calcSIFTDescriptor(img, ptf, kpt.angle, size*0.5f, d, n, descriptors.ptr((int)i)); } } ////////////////////////////////////////////////////////////////////////////////////////// SIFT::SIFT( int _nfeatures, int _nOctaveLayers, double _contrastThreshold, double _edgeThreshold, double _sigma ) : nfeatures(_nfeatures), nOctaveLayers(_nOctaveLayers), contrastThreshold(_contrastThreshold), edgeThreshold(_edgeThreshold), sigma(_sigma) { } int SIFT::descriptorSize() const { return SIFT_DESCR_WIDTH*SIFT_DESCR_WIDTH*SIFT_DESCR_HIST_BINS; } int SIFT::descriptorType() const { return CV_32F; } void SIFT::operator()(InputArray _image, InputArray _mask, vector& keypoints) const { (*this)(_image, _mask, keypoints, noArray()); } void SIFT::operator()(InputArray _image, InputArray _mask, vector& keypoints, OutputArray _descriptors, bool useProvidedKeypoints) const { Mat image = _image.getMat(), mask = _mask.getMat(); if( image.empty() || image.depth() != CV_8U ) CV_Error( CV_StsBadArg, "image is empty or has incorrect depth (!=CV_8U)" ); if( !mask.empty() && mask.type() != CV_8UC1 ) CV_Error( CV_StsBadArg, "mask has incorrect type (!=CV_8UC1)" ); Mat base = createInitialImage(image, false, (float)sigma); vector gpyr, dogpyr; int nOctaves = cvRound(log( (double)std::min( base.cols, base.rows ) ) / log(2.) - 2); //double t, tf = getTickFrequency(); //t = (double)getTickCount(); buildGaussianPyramid(base, gpyr, nOctaves); buildDoGPyramid(gpyr, dogpyr); //t = (double)getTickCount() - t; //printf("pyramid construction time: %g\n", t*1000./tf); if( !useProvidedKeypoints ) { //t = (double)getTickCount(); findScaleSpaceExtrema(gpyr, dogpyr, keypoints); KeyPointsFilter::removeDuplicated( keypoints ); if( !mask.empty() ) KeyPointsFilter::runByPixelsMask( keypoints, mask ); if( nfeatures > 0 ) KeyPointsFilter::retainBest(keypoints, nfeatures); //t = (double)getTickCount() - t; //printf("keypoint detection time: %g\n", t*1000./tf); } else { // filter keypoints by mask //KeyPointsFilter::runByPixelsMask( keypoints, mask ); } if( _descriptors.needed() ) { //t = (double)getTickCount(); int dsize = descriptorSize(); _descriptors.create((int)keypoints.size(), dsize, CV_32F); Mat descriptors = _descriptors.getMat(); calcDescriptors(gpyr, keypoints, descriptors, nOctaveLayers); //t = (double)getTickCount() - t; //printf("descriptor extraction time: %g\n", t*1000./tf); } } void SIFT::detectImpl( const Mat& image, vector& keypoints, const Mat& mask) const { (*this)(image, mask, keypoints, noArray()); } void SIFT::computeImpl( const Mat& image, vector& keypoints, Mat& descriptors) const { (*this)(image, Mat(), keypoints, descriptors, true); } }