generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

lot's of changes; nonfree & photo modules added; SIFT & SURF -> nonfree module; Inpainting -> photo; refactored features2d (ORB is still failing tests), optimized brute-force matcher and made it non-template.

Browse Source
This commit is contained in:
Vadim Pisarevsky
2012-03-15 14:36:01 +00:00
parent 6300215b94
commit 957e80abbd
99 changed files with 6719 additions and 7240 deletions
Download Patch File Download Diff File Expand all files Collapse all files

44
modules/nonfree/src/precomp.cpp Normal file
View File

@@ -0,0 +1,44 @@
/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
/* End of file. */

58
modules/nonfree/src/precomp.hpp Normal file
View File

@@ -0,0 +1,58 @@
/*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*/
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#if _MSC_VER >= 1200
#pragma warning( disable: 4251 4512 4710 4711 4514 4996 )
#endif
#ifdef HAVE_CVCONFIG_H
#include "cvconfig.h"
#endif
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/core/internal.hpp"
#endif

772
modules/nonfree/src/sift.cpp Normal file
View File

@@ -0,0 +1,772 @@
/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 <hess@eecs.oregonstate.edu>
// 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 <iostream>
#include <stdarg.h>
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<Mat>& pyr, int nOctaves ) const
{
vector<double> 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<Mat>& gpyr, vector<Mat>& 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<float> 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 = img.at<short>(y, x+1) - img.at<short>(y, x-1);
float dy = img.at<short>(y-1, x) - img.at<short>(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<Mat>& 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];
Matx31f dD((img.at<short>(r, c+1) - img.at<short>(r, c-1))*deriv_scale,
(img.at<short>(r+1, c) - img.at<short>(r-1, c))*deriv_scale,
(next.at<short>(r, c) - prev.at<short>(r, c))*deriv_scale);
float v2 = img.at<short>(r, c)*2;
float dxx = (img.at<short>(r, c+1) + img.at<short>(r, c-1) - v2)*second_deriv_scale;
float dyy = (img.at<short>(r+1, c) + img.at<short>(r-1, c) - v2)*second_deriv_scale;
float dss = (next.at<short>(r, c) + prev.at<short>(r, c) - v2)*second_deriv_scale;
float dxy = (img.at<short>(r+1, c+1) - img.at<short>(r+1, c-1) -
img.at<short>(r-1, c+1) + img.at<short>(r-1, c-1))*cross_deriv_scale;
float dxs = (next.at<short>(r, c+1) - next.at<short>(r, c-1) -
prev.at<short>(r, c+1) + prev.at<short>(r, c-1))*cross_deriv_scale;
float dys = (next.at<short>(r+1, c) - next.at<short>(r-1, c) -
prev.at<short>(r+1, c) + prev.at<short>(r-1, c))*cross_deriv_scale;
Matx33f H(dxx, dxy, dxs,
dxy, dyy, dys,
dxs, dys, dss);
Matx31f X = H.solve<1>(dD, DECOMP_LU);
xi = -X(2, 0);
xr = -X(1, 0);
xc = -X(0, 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<short>(r, c+1) - img.at<short>(r, c-1))*deriv_scale,
(img.at<short>(r+1, c) - img.at<short>(r-1, c))*deriv_scale,
(next.at<short>(r, c) - prev.at<short>(r, c))*deriv_scale);
float t = dD.dot(Matx31f(xc, xr, xi));
contr = img.at<short>(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<short>(r, c)*2;
float dxx = (img.at<short>(r, c+1) + img.at<short>(r, c-1) - v2)*second_deriv_scale;
float dyy = (img.at<short>(r+1, c) + img.at<short>(r-1, c) - v2)*second_deriv_scale;
float dxy = (img.at<short>(r+1, c+1) - img.at<short>(r+1, c-1) -
img.at<short>(r-1, c+1) + img.at<short>(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<Mat>& gauss_pyr, const vector<Mat>& dog_pyr,
vector<KeyPoint>& 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<short>(r);
const short* prevptr = prev.ptr<short>(r);
const short* nextptr = next.ptr<short>(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,
contrastThreshold, edgeThreshold, 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 r = j < n-1 ? j + 1 : 0;
if( hist[j] > hist[l] && hist[j] > hist[r] && hist[j] >= mag_thr )
{
float bin = j + 0.5f * (hist[l]-hist[r]) / (hist[l] - 2*hist[j] + hist[r]);
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<float> 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 = img.at<short>(r, c+1) - img.at<short>(r, c-1);
float dy = img.at<short>(r-1, c) - img.at<short>(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<uchar>(dst[k]*nrm2);
}
}
static void calcDescriptors(const vector<Mat>& gpyr, const vector<KeyPoint>& 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<float>(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;
}
static Algorithm* createSIFT()
{
return new SIFT;
}
static AlgorithmInfo sift_info("Feature2D.SIFT", createSIFT);
AlgorithmInfo* SIFT::info() const
{
static volatile bool initialized = false;
if( !initialized )
{
sift_info.addParam(this, "nFeatures", nfeatures);
sift_info.addParam(this, "nOctaveLayers", nOctaveLayers);
sift_info.addParam(this, "contrastThreshold", contrastThreshold);
sift_info.addParam(this, "edgeThreshold", edgeThreshold);
sift_info.addParam(this, "sigma", sigma);
initialized = true;
}
return &sift_info;
}
void SIFT::operator()(InputArray _image, InputArray _mask,
vector<KeyPoint>& keypoints) const
{
(*this)(_image, _mask, keypoints, noArray());
}
void SIFT::operator()(InputArray _image, InputArray _mask,
vector<KeyPoint>& 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, sigma);
vector<Mat> 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<KeyPoint>& keypoints, const Mat& mask) const
{
(*this)(image, mask, keypoints, noArray());
}
void SIFT::computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors) const
{
(*this)(image, Mat(), keypoints, descriptors, true);
}
}

981
modules/nonfree/src/surf.cpp Normal file
View File

@@ -0,0 +1,981 @@
/* Original code has been submitted by Liu Liu. Here is the copyright.
----------------------------------------------------------------------------------
* An OpenCV Implementation of SURF
* Further Information Refer to "SURF: Speed-Up Robust Feature"
* Author: Liu Liu
* liuliu.1987+opencv@gmail.com
*
* There are still serveral lacks for this experimental implementation:
* 1.The interpolation of sub-pixel mentioned in article was not implemented yet;
* 2.A comparision with original libSurf.so shows that the hessian detector is not a 100% match to their implementation;
* 3.Due to above reasons, I recommanded the original one for study and reuse;
*
* However, the speed of this implementation is something comparable to original one.
*
* Copyright© 2008, Liu Liu All rights reserved.
*
* Redistribution and use in source and binary forms, with or
* without modification, are permitted provided that the following
* conditions are met:
* Redistributions of source code must retain the above
* copyright notice, this list of conditions and the following
* disclaimer.
* Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials
* provided with the distribution.
* The name of Contributor 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 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.
*/
/*
The following changes have been made, comparing to the original contribution:
1. A lot of small optimizations, less memory allocations, got rid of global buffers
2. Reversed order of cvGetQuadrangleSubPix and cvResize calls; probably less accurate, but much faster
3. The descriptor computing part (which is most expensive) is threaded using OpenMP
(subpixel-accurate keypoint localization and scale estimation are still TBD)
*/
/*
KeyPoint position and scale interpolation has been implemented as described in
the Brown and Lowe paper cited by the SURF paper.
The sampling step along the x and y axes of the image for the determinant of the
Hessian is now the same for each layer in an octave. While this increases the
computation time, it ensures that a true 3x3x3 neighbourhood exists, with
samples calculated at the same position in the layers above and below. This
results in improved maxima detection and non-maxima suppression, and I think it
is consistent with the description in the SURF paper.
The wavelet size sampling interval has also been made consistent. The wavelet
size at the first layer of the first octave is now 9 instead of 7. Along with
regular position sampling steps, this makes location and scale interpolation
easy. I think this is consistent with the SURF paper and original
implementation.
The scaling of the wavelet parameters has been fixed to ensure that the patterns
are symmetric around the centre. Previously the truncation caused by integer
division in the scaling ratio caused a bias towards the top left of the wavelet,
resulting in inconsistent keypoint positions.
The matrices for the determinant and trace of the Hessian are now reused in each
octave.
The extraction of the patch of pixels surrounding a keypoint used to build a
descriptor has been simplified.
KeyPoint descriptor normalisation has been changed from normalising each 4x4
cell (resulting in a descriptor of magnitude 16) to normalising the entire
descriptor to magnitude 1.
The default number of octaves has been increased from 3 to 4 to match the
original SURF binary default. The increase in computation time is minimal since
the higher octaves are sampled sparsely.
The default number of layers per octave has been reduced from 3 to 2, to prevent
redundant calculation of similar sizes in consecutive octaves. This decreases
computation time. The number of features extracted may be less, however the
additional features were mostly redundant.
The radius of the circle of gradient samples used to assign an orientation has
been increased from 4 to 6 to match the description in the SURF paper. This is
now defined by ORI_RADIUS, and could be made into a parameter.
The size of the sliding window used in orientation assignment has been reduced
from 120 to 60 degrees to match the description in the SURF paper. This is now
defined by ORI_WIN, and could be made into a parameter.
Other options like HAAR_SIZE0, HAAR_SIZE_INC, SAMPLE_STEP0, ORI_SEARCH_INC,
ORI_SIGMA and DESC_SIGMA have been separated from the code and documented.
These could also be made into parameters.
Modifications by Ian Mahon
*/
#include "precomp.hpp"
bool cv::initModule_nonfree(void) { return true; }
namespace cv
{
static const int SURF_ORI_SEARCH_INC = 5;
static const float SURF_ORI_SIGMA = 2.5f;
static const float SURF_DESC_SIGMA = 3.3f;
// Wavelet size at first layer of first octave.
static const int SURF_HAAR_SIZE0 = 9;
// Wavelet size increment between layers. This should be an even number,
// such that the wavelet sizes in an octave are either all even or all odd.
// This ensures that when looking for the neighbours of a sample, the layers
// above and below are aligned correctly.
static const int SURF_HAAR_SIZE_INC = 6;
struct SurfHF
{
int p0, p1, p2, p3;
float w;
};
inline float calcHaarPattern( const int* origin, const SurfHF* f, int n )
{
double d = 0;
for( int k = 0; k < n; k++ )
d += (origin[f[k].p0] + origin[f[k].p3] - origin[f[k].p1] - origin[f[k].p2])*f[k].w;
return (float)d;
}
static void
resizeHaarPattern( const int src[][5], SurfHF* dst, int n, int oldSize, int newSize, int widthStep )
{
float ratio = (float)newSize/oldSize;
for( int k = 0; k < n; k++ )
{
int dx1 = cvRound( ratio*src[k][0] );
int dy1 = cvRound( ratio*src[k][1] );
int dx2 = cvRound( ratio*src[k][2] );
int dy2 = cvRound( ratio*src[k][3] );
dst[k].p0 = dy1*widthStep + dx1;
dst[k].p1 = dy2*widthStep + dx1;
dst[k].p2 = dy1*widthStep + dx2;
dst[k].p3 = dy2*widthStep + dx2;
dst[k].w = src[k][4]/((float)(dx2-dx1)*(dy2-dy1));
}
}
/*
* Calculate the determinant and trace of the Hessian for a layer of the
* scale-space pyramid
*/
static void calcLayerDetAndTrace( const Mat& sum, int size, int sampleStep,
Mat& det, Mat& trace )
{
const int NX=3, NY=3, NXY=4;
const int dx_s[NX][5] = { {0, 2, 3, 7, 1}, {3, 2, 6, 7, -2}, {6, 2, 9, 7, 1} };
const int dy_s[NY][5] = { {2, 0, 7, 3, 1}, {2, 3, 7, 6, -2}, {2, 6, 7, 9, 1} };
const int dxy_s[NXY][5] = { {1, 1, 4, 4, 1}, {5, 1, 8, 4, -1}, {1, 5, 4, 8, -1}, {5, 5, 8, 8, 1} };
SurfHF Dx[NX], Dy[NY], Dxy[NXY];
if( size > sum.rows-1 || size > sum.cols-1 )
return;
resizeHaarPattern( dx_s , Dx , NX , 9, size, sum.cols );
resizeHaarPattern( dy_s , Dy , NY , 9, size, sum.cols );
resizeHaarPattern( dxy_s, Dxy, NXY, 9, size, sum.cols );
/* The integral image 'sum' is one pixel bigger than the source image */
int samples_i = 1+(sum.rows-1-size)/sampleStep;
int samples_j = 1+(sum.cols-1-size)/sampleStep;
/* Ignore pixels where some of the kernel is outside the image */
int margin = (size/2)/sampleStep;
for( int i = 0; i < samples_i; i++ )
{
const int* sum_ptr = sum.ptr<int>(i*sampleStep);
float* det_ptr = &det.at<float>(i+margin, margin);
float* trace_ptr = &trace.at<float>(i+margin, margin);
for( int j = 0; j < samples_j; j++ )
{
float dx = calcHaarPattern( sum_ptr, Dx , 3 );
float dy = calcHaarPattern( sum_ptr, Dy , 3 );
float dxy = calcHaarPattern( sum_ptr, Dxy, 4 );
sum_ptr += sampleStep;
det_ptr[j] = dx*dy - 0.81f*dxy*dxy;
trace_ptr[j] = dx + dy;
}
}
}
/*
* Maxima location interpolation as described in "Invariant Features from
* Interest Point Groups" by Matthew Brown and David Lowe. This is performed by
* fitting a 3D quadratic to a set of neighbouring samples.
*
* The gradient vector and Hessian matrix at the initial keypoint location are
* approximated using central differences. The linear system Ax = b is then
* solved, where A is the Hessian, b is the negative gradient, and x is the
* offset of the interpolated maxima coordinates from the initial estimate.
* This is equivalent to an iteration of Netwon's optimisation algorithm.
*
* N9 contains the samples in the 3x3x3 neighbourhood of the maxima
* dx is the sampling step in x
* dy is the sampling step in y
* ds is the sampling step in size
* point contains the keypoint coordinates and scale to be modified
*
* Return value is 1 if interpolation was successful, 0 on failure.
*/
static int
interpolateKeypoint( float N9[3][9], int dx, int dy, int ds, KeyPoint& kpt )
{
Matx31f b(-(N9[1][5]-N9[1][3])/2, // Negative 1st deriv with respect to x
-(N9[1][7]-N9[1][1])/2, // Negative 1st deriv with respect to y
-(N9[2][4]-N9[0][4])/2); // Negative 1st deriv with respect to s
Matx33f A(
N9[1][3]-2*N9[1][4]+N9[1][5], // 2nd deriv x, x
(N9[1][8]-N9[1][6]-N9[1][2]+N9[1][0])/4, // 2nd deriv x, y
(N9[2][5]-N9[2][3]-N9[0][5]+N9[0][3])/4, // 2nd deriv x, s
(N9[1][8]-N9[1][6]-N9[1][2]+N9[1][0])/4, // 2nd deriv x, y
N9[1][1]-2*N9[1][4]+N9[1][7], // 2nd deriv y, y
(N9[2][7]-N9[2][1]-N9[0][7]+N9[0][1])/4, // 2nd deriv y, s
(N9[2][5]-N9[2][3]-N9[0][5]+N9[0][3])/4, // 2nd deriv x, s
(N9[2][7]-N9[2][1]-N9[0][7]+N9[0][1])/4, // 2nd deriv y, s
N9[0][4]-2*N9[1][4]+N9[2][4]); // 2nd deriv s, s
Matx31f x = A.solve<1>(b, DECOMP_LU);
bool ok = (x(0,0) != 0 || x(1,0) != 0 || x(2,0) != 0) &&
std::abs(x(0,0)) <= 1 && std::abs(x(1,0)) <= 1 && std::abs(x(2,0)) <= 1;
if( ok )
{
kpt.pt.x += x(0,0)*dx;
kpt.pt.y += x(1,0)*dy;
kpt.size = cvRound( kpt.size + x(2,0)*ds );
}
return ok;
}
/*
* Find the maxima in the determinant of the Hessian in a layer of the
* scale-space pyramid
*/
static void
findMaximaInLayer( const Mat& sum, const Mat& mask_sum,
const vector<Mat>& dets, const vector<Mat>& traces,
const vector<int>& sizes, vector<KeyPoint>& keypoints,
int octave, int layer, float hessianThreshold, int sampleStep )
{
// Wavelet Data
const int NM=1;
const int dm[NM][5] = { {0, 0, 9, 9, 1} };
SurfHF Dm;
int size = sizes[layer];
// The integral image 'sum' is one pixel bigger than the source image
int layer_rows = (sum.rows-1)/sampleStep;
int layer_cols = (sum.cols-1)/sampleStep;
// Ignore pixels without a 3x3x3 neighbourhood in the layer above
int margin = (sizes[layer+1]/2)/sampleStep+1;
if( !mask_sum.empty() )
resizeHaarPattern( dm, &Dm, NM, 9, size, mask_sum.cols );
int step = (int)(dets[layer].step/dets[layer].elemSize());
for( int i = margin; i < layer_rows - margin; i++ )
{
const float* det_ptr = dets[layer].ptr<float>(i);
const float* trace_ptr = traces[layer].ptr<float>(i);
for( int j = margin; j < layer_cols-margin; j++ )
{
float val0 = det_ptr[j];
if( val0 > hessianThreshold )
{
/* Coordinates for the start of the wavelet in the sum image. There
is some integer division involved, so don't try to simplify this
(cancel out sampleStep) without checking the result is the same */
int sum_i = sampleStep*(i-(size/2)/sampleStep);
int sum_j = sampleStep*(j-(size/2)/sampleStep);
/* The 3x3x3 neighbouring samples around the maxima.
The maxima is included at N9[1][4] */
const float *det1 = &dets[layer-1].at<float>(i, j);
const float *det2 = &dets[layer].at<float>(i, j);
const float *det3 = &dets[layer+1].at<float>(i, j);
float N9[3][9] = { { det1[-step-1], det1[-step], det1[-step+1],
det1[-1] , det1[0] , det1[1],
det1[step-1] , det1[step] , det1[step+1] },
{ det2[-step-1], det2[-step], det2[-step+1],
det2[-1] , det2[0] , det2[1],
det2[step-1] , det2[step] , det2[step+1] },
{ det3[-step-1], det3[-step], det3[-step+1],
det3[-1] , det3[0] , det3[1],
det3[step-1] , det3[step] , det3[step+1] } };
/* Check the mask - why not just check the mask at the center of the wavelet? */
if( !mask_sum.empty() )
{
const int* mask_ptr = &mask_sum.at<int>(sum_i, sum_j);
float mval = calcHaarPattern( mask_ptr, &Dm, 1 );
if( mval < 0.5 )
continue;
}
/* Non-maxima suppression. val0 is at N9[1][4]*/
if( val0 > N9[0][0] && val0 > N9[0][1] && val0 > N9[0][2] &&
val0 > N9[0][3] && val0 > N9[0][4] && val0 > N9[0][5] &&
val0 > N9[0][6] && val0 > N9[0][7] && val0 > N9[0][8] &&
val0 > N9[1][0] && val0 > N9[1][1] && val0 > N9[1][2] &&
val0 > N9[1][3] && val0 > N9[1][5] &&
val0 > N9[1][6] && val0 > N9[1][7] && val0 > N9[1][8] &&
val0 > N9[2][0] && val0 > N9[2][1] && val0 > N9[2][2] &&
val0 > N9[2][3] && val0 > N9[2][4] && val0 > N9[2][5] &&
val0 > N9[2][6] && val0 > N9[2][7] && val0 > N9[2][8] )
{
/* Calculate the wavelet center coordinates for the maxima */
float center_i = sum_i + (size-1)*0.5f;
float center_j = sum_j + (size-1)*0.5f;
KeyPoint kpt( center_j, center_i, sizes[layer], -1, val0, octave, CV_SIGN(trace_ptr[j]) );
/* Interpolate maxima location within the 3x3x3 neighbourhood */
int ds = size - sizes[layer-1];
int interp_ok = interpolateKeypoint( N9, sampleStep, sampleStep, ds, kpt );
/* Sometimes the interpolation step gives a negative size etc. */
if( interp_ok )
{
/*printf( "KeyPoint %f %f %d\n", point.pt.x, point.pt.y, point.size );*/
#ifdef HAVE_TBB
static tbb::mutex m;
tbb::mutex::scoped_lock lock(m);
#endif
keypoints.push_back(kpt);
}
}
}
}
}
}
// Multi-threaded construction of the scale-space pyramid
struct SURFBuildInvoker
{
SURFBuildInvoker( const Mat& _sum, const vector<int>& _sizes,
const vector<int>& _sampleSteps,
vector<Mat>& _dets, vector<Mat>& _traces )
{
sum = &_sum;
sizes = &_sizes;
sampleSteps = &_sampleSteps;
dets = &_dets;
traces = &_traces;
}
void operator()(const BlockedRange& range) const
{
for( int i=range.begin(); i<range.end(); i++ )
calcLayerDetAndTrace( *sum, (*sizes)[i], (*sampleSteps)[i], (*dets)[i], (*traces)[i] );
}
const Mat *sum;
const vector<int> *sizes;
const vector<int> *sampleSteps;
vector<Mat>* dets;
vector<Mat>* traces;
};
// Multi-threaded search of the scale-space pyramid for keypoints
struct SURFFindInvoker
{
SURFFindInvoker( const Mat& _sum, const Mat& _mask_sum,
const vector<Mat>& _dets, const vector<Mat>& _traces,
const vector<int>& _sizes, const vector<int>& _sampleSteps,
const vector<int>& _middleIndices, vector<KeyPoint>& _keypoints,
int _nOctaveLayers, float _hessianThreshold )
{
sum = &_sum;
mask_sum = &_mask_sum;
dets = &_dets;
traces = &_traces;
sizes = &_sizes;
sampleSteps = &_sampleSteps;
middleIndices = &_middleIndices;
keypoints = &_keypoints;
nOctaveLayers = _nOctaveLayers;
hessianThreshold = _hessianThreshold;
}
void operator()(const BlockedRange& range) const
{
for( int i=range.begin(); i<range.end(); i++ )
{
int layer = (*middleIndices)[i];
int octave = i % nOctaveLayers;
findMaximaInLayer( *sum, *mask_sum, *dets, *traces, *sizes,
*keypoints, octave, layer, hessianThreshold,
(*sampleSteps)[layer] );
}
}
const Mat *sum;
const Mat *mask_sum;
const vector<Mat>* dets;
const vector<Mat>* traces;
const vector<int>* sizes;
const vector<int>* sampleSteps;
const vector<int>* middleIndices;
vector<KeyPoint>* keypoints;
int nOctaveLayers;
float hessianThreshold;
};
static void fastHessianDetector( const Mat& sum, const Mat& mask_sum, vector<KeyPoint>& keypoints,
int nOctaves, int nOctaveLayers, float hessianThreshold )
{
/* Sampling step along image x and y axes at first octave. This is doubled
for each additional octave. WARNING: Increasing this improves speed,
however keypoint extraction becomes unreliable. */
const int SAMPLE_STEP0 = 1;
int nTotalLayers = (nOctaveLayers+2)*nOctaves;
int nMiddleLayers = nOctaveLayers*nOctaves;
vector<Mat> dets(nTotalLayers);
vector<Mat> traces(nTotalLayers);
vector<int> sizes(nTotalLayers);
vector<int> sampleSteps(nTotalLayers);
vector<int> middleIndices(nMiddleLayers);
// Allocate space and calculate properties of each layer
int index = 0, middleIndex = 0, step = SAMPLE_STEP0;
for( int octave = 0; octave < nOctaves; octave++ )
{
for( int layer = 0; layer < nOctaveLayers+2; layer++ )
{
/* The integral image sum is one pixel bigger than the source image*/
dets[index].create( (sum.rows-1)/step, (sum.cols-1)/step, CV_32F );
traces[index].create( (sum.rows-1)/step, (sum.cols-1)/step, CV_32F );
sizes[index] = (SURF_HAAR_SIZE0 + SURF_HAAR_SIZE_INC*layer) << octave;
sampleSteps[index] = step;
if( 0 < layer && layer <= nOctaveLayers )
middleIndices[middleIndex++] = index;
index++;
}
step *= 2;
}
// Calculate hessian determinant and trace samples in each layer
parallel_for( BlockedRange(0, nTotalLayers),
SURFBuildInvoker(sum, sizes, sampleSteps, dets, traces) );
// Find maxima in the determinant of the hessian
parallel_for( BlockedRange(0, nMiddleLayers),
SURFFindInvoker(sum, mask_sum, dets, traces, sizes,
sampleSteps, middleIndices, keypoints,
nOctaveLayers, hessianThreshold) );
}
struct SURFInvoker
{
enum { ORI_RADIUS = 6, ORI_WIN = 60, PATCH_SZ = 20 };
SURFInvoker( const Mat& _img, const Mat& _sum,
vector<KeyPoint>& _keypoints, Mat& _descriptors,
bool _extended, bool _upright )
{
keypoints = &_keypoints;
descriptors = &_descriptors;
img = &_img;
sum = &_sum;
extended = _extended;
upright = _upright;
// Simple bound for number of grid points in circle of radius ORI_RADIUS
const int nOriSampleBound = (2*ORI_RADIUS+1)*(2*ORI_RADIUS+1);
// Allocate arrays
apt.resize(nOriSampleBound);
aptw.resize(nOriSampleBound);
DW.resize(PATCH_SZ*PATCH_SZ);
/* Coordinates and weights of samples used to calculate orientation */
Mat G_ori = getGaussianKernel( 2*ORI_RADIUS+1, SURF_ORI_SIGMA, CV_32F );
nOriSamples = 0;
for( int i = -ORI_RADIUS; i <= ORI_RADIUS; i++ )
{
for( int j = -ORI_RADIUS; j <= ORI_RADIUS; j++ )
{
if( i*i + j*j <= ORI_RADIUS*ORI_RADIUS )
{
apt[nOriSamples] = cvPoint(i,j);
aptw[nOriSamples++] = G_ori.at<float>(i+ORI_RADIUS,0) * G_ori.at<float>(j+ORI_RADIUS,0);
}
}
}
CV_Assert( nOriSamples <= nOriSampleBound );
/* Gaussian used to weight descriptor samples */
Mat G_desc = getGaussianKernel( PATCH_SZ, SURF_DESC_SIGMA, CV_32F );
for( int i = 0; i < PATCH_SZ; i++ )
{
for( int j = 0; j < PATCH_SZ; j++ )
DW[i*PATCH_SZ+j] = G_desc.at<float>(i,0) * G_desc.at<float>(j,0);
}
}
void operator()(const BlockedRange& range) const
{
/* X and Y gradient wavelet data */
const int NX=2, NY=2;
const int dx_s[NX][5] = {{0, 0, 2, 4, -1}, {2, 0, 4, 4, 1}};
const int dy_s[NY][5] = {{0, 0, 4, 2, 1}, {0, 2, 4, 4, -1}};
// Optimisation is better using nOriSampleBound than nOriSamples for
// array lengths. Maybe because it is a constant known at compile time
const int nOriSampleBound =(2*ORI_RADIUS+1)*(2*ORI_RADIUS+1);
float X[nOriSampleBound], Y[nOriSampleBound], angle[nOriSampleBound];
uchar PATCH[PATCH_SZ+1][PATCH_SZ+1];
float DX[PATCH_SZ][PATCH_SZ], DY[PATCH_SZ][PATCH_SZ];
CvMat matX = cvMat(1, nOriSampleBound, CV_32F, X);
CvMat matY = cvMat(1, nOriSampleBound, CV_32F, Y);
CvMat _angle = cvMat(1, nOriSampleBound, CV_32F, angle);
Mat _patch(PATCH_SZ+1, PATCH_SZ+1, CV_8U, PATCH);
int dsize = extended ? 128 : 64;
int k, k1 = range.begin(), k2 = range.end();
float maxSize = 0;
for( k = k1; k < k2; k++ )
{
maxSize = std::max(maxSize, (*keypoints)[k].size);
}
maxSize = cvCeil((PATCH_SZ+1)*maxSize*1.2f/9.0f);
Ptr<CvMat> winbuf = cvCreateMat( 1, maxSize > 0 ? maxSize*maxSize : 1, CV_8U );
for( k = k1; k < k2; k++ )
{
int i, j, kk, x, y, nangle;
float* vec;
SurfHF dx_t[NX], dy_t[NY];
KeyPoint& kp = (*keypoints)[k];
float size = kp.size;
Point2f center = kp.pt;
/* The sampling intervals and wavelet sized for selecting an orientation
and building the keypoint descriptor are defined relative to 's' */
float s = size*1.2f/9.0f;
/* To find the dominant orientation, the gradients in x and y are
sampled in a circle of radius 6s using wavelets of size 4s.
We ensure the gradient wavelet size is even to ensure the
wavelet pattern is balanced and symmetric around its center */
int grad_wav_size = 2*cvRound( 2*s );
if( sum->rows < grad_wav_size || sum->cols < grad_wav_size )
{
/* when grad_wav_size is too big,
* the sampling of gradient will be meaningless
* mark keypoint for deletion. */
kp.size = -1;
continue;
}
float descriptor_dir = 90.f;
if (upright == 0)
{
resizeHaarPattern( dx_s, dx_t, NX, 4, grad_wav_size, sum->cols );
resizeHaarPattern( dy_s, dy_t, NY, 4, grad_wav_size, sum->cols );
for( kk = 0, nangle = 0; kk < nOriSamples; kk++ )
{
x = cvRound( center.x + apt[kk].x*s - (float)(grad_wav_size-1)/2 );
y = cvRound( center.y + apt[kk].y*s - (float)(grad_wav_size-1)/2 );
if( y < 0 || y >= sum->rows - grad_wav_size ||
x < 0 || x >= sum->cols - grad_wav_size )
continue;
const int* ptr = &sum->at<int>(y, x);
float vx = calcHaarPattern( ptr, dx_t, 2 );
float vy = calcHaarPattern( ptr, dy_t, 2 );
X[nangle] = vx*aptw[kk];
Y[nangle] = vy*aptw[kk];
nangle++;
}
if( nangle == 0 )
{
// No gradient could be sampled because the keypoint is too
// near too one or more of the sides of the image. As we
// therefore cannot find a dominant direction, we skip this
// keypoint and mark it for later deletion from the sequence.
kp.size = -1;
continue;
}
matX.cols = matY.cols = _angle.cols = nangle;
cvCartToPolar( &matX, &matY, 0, &_angle, 1 );
float bestx = 0, besty = 0, descriptor_mod = 0;
for( i = 0; i < 360; i += SURF_ORI_SEARCH_INC )
{
float sumx = 0, sumy = 0, temp_mod;
for( j = 0; j < nangle; j++ )
{
int d = std::abs(cvRound(angle[j]) - i);
if( d < ORI_WIN/2 || d > 360-ORI_WIN/2 )
{
sumx += X[j];
sumy += Y[j];
}
}
temp_mod = sumx*sumx + sumy*sumy;
if( temp_mod > descriptor_mod )
{
descriptor_mod = temp_mod;
bestx = sumx;
besty = sumy;
}
}
descriptor_dir = fastAtan2( besty, bestx );
}
kp.angle = descriptor_dir;
if( !descriptors || !descriptors->data )
continue;
/* Extract a window of pixels around the keypoint of size 20s */
int win_size = (int)((PATCH_SZ+1)*s);
CV_Assert( winbuf->cols >= win_size*win_size );
Mat win(win_size, win_size, CV_8U, winbuf->data.ptr);
if( !upright )
{
descriptor_dir *= (float)(CV_PI/180);
float sin_dir = std::sin(descriptor_dir);
float cos_dir = std::cos(descriptor_dir);
/* Subpixel interpolation version (slower). Subpixel not required since
the pixels will all get averaged when we scale down to 20 pixels */
/*
float w[] = { cos_dir, sin_dir, center.x,
-sin_dir, cos_dir , center.y };
CvMat W = cvMat(2, 3, CV_32F, w);
cvGetQuadrangleSubPix( img, &win, &W );
*/
// Nearest neighbour version (faster)
float win_offset = -(float)(win_size-1)/2;
float start_x = center.x + win_offset*cos_dir + win_offset*sin_dir;
float start_y = center.y - win_offset*sin_dir + win_offset*cos_dir;
uchar* WIN = win.data;
for( i = 0; i < win_size; i++, start_x += sin_dir, start_y += cos_dir )
{
float pixel_x = start_x;
float pixel_y = start_y;
for( j = 0; j < win_size; j++, pixel_x += cos_dir, pixel_y -= sin_dir )
{
int x = std::min(std::max(cvRound(pixel_x), 0), img->cols-1);
int y = std::min(std::max(cvRound(pixel_y), 0), img->rows-1);
WIN[i*win_size + j] = img->at<uchar>(y, x);
}
}
}
else
{
// extract rect - slightly optimized version of the code above
// TODO: find faster code, as this is simply an extract rect operation,
// e.g. by using cvGetSubRect, problem is the border processing
// descriptor_dir == 90 grad
// sin_dir == 1
// cos_dir == 0
float win_offset = -(float)(win_size-1)/2;
int start_x = cvRound(center.x + win_offset);
int start_y = cvRound(center.y - win_offset);
uchar* WIN = win.data;
for( i = 0; i < win_size; i++, start_x++ )
{
int pixel_x = start_x;
int pixel_y = start_y;
for( j = 0; j < win_size; j++, pixel_y-- )
{
x = MAX( pixel_x, 0 );
y = MAX( pixel_y, 0 );
x = MIN( x, img->cols-1 );
y = MIN( y, img->rows-1 );
WIN[i*win_size + j] = img->at<uchar>(y, x);
}
}
}
// Scale the window to size PATCH_SZ so each pixel's size is s. This
// makes calculating the gradients with wavelets of size 2s easy
resize(win, _patch, _patch.size(), 0, 0, INTER_AREA);
// Calculate gradients in x and y with wavelets of size 2s
for( i = 0; i < PATCH_SZ; i++ )
for( j = 0; j < PATCH_SZ; j++ )
{
float dw = DW[i*PATCH_SZ + j];
float vx = (PATCH[i][j+1] - PATCH[i][j] + PATCH[i+1][j+1] - PATCH[i+1][j])*dw;
float vy = (PATCH[i+1][j] - PATCH[i][j] + PATCH[i+1][j+1] - PATCH[i][j+1])*dw;
DX[i][j] = vx;
DY[i][j] = vy;
}
// Construct the descriptor
vec = descriptors->ptr<float>(k);
for( kk = 0; kk < dsize; kk++ )
vec[kk] = 0;
double square_mag = 0;
if( extended )
{
// 128-bin descriptor
for( i = 0; i < 4; i++ )
for( j = 0; j < 4; j++ )
{
for( y = i*5; y < i*5+5; y++ )
{
for( x = j*5; x < j*5+5; x++ )
{
float tx = DX[y][x], ty = DY[y][x];
if( ty >= 0 )
{
vec[0] += tx;
vec[1] += (float)fabs(tx);
} else {
vec[2] += tx;
vec[3] += (float)fabs(tx);
}
if ( tx >= 0 )
{
vec[4] += ty;
vec[5] += (float)fabs(ty);
} else {
vec[6] += ty;
vec[7] += (float)fabs(ty);
}
}
}
for( kk = 0; kk < 8; kk++ )
square_mag += vec[kk]*vec[kk];
vec += 8;
}
}
else
{
// 64-bin descriptor
for( i = 0; i < 4; i++ )
for( j = 0; j < 4; j++ )
{
for( y = i*5; y < i*5+5; y++ )
{
for( x = j*5; x < j*5+5; x++ )
{
float tx = DX[y][x], ty = DY[y][x];
vec[0] += tx; vec[1] += ty;
vec[2] += (float)fabs(tx); vec[3] += (float)fabs(ty);
}
}
for( kk = 0; kk < 4; kk++ )
square_mag += vec[kk]*vec[kk];
vec+=4;
}
}
// unit vector is essential for contrast invariance
vec = descriptors->ptr<float>(k);
float scale = (float)(1./(sqrt(square_mag) + DBL_EPSILON));
for( kk = 0; kk < dsize; kk++ )
vec[kk] *= scale;
}
}
// Parameters
const Mat* img;
const Mat* sum;
vector<KeyPoint>* keypoints;
Mat* descriptors;
bool extended;
bool upright;
// Pre-calculated values
int nOriSamples;
vector<Point> apt;
vector<float> aptw;
vector<float> DW;
};
SURF::SURF()
{
hessianThreshold = 100;
extended = true;
upright = false;
nOctaves = 4;
nOctaveLayers = 2;
}
SURF::SURF(double _threshold, bool _extended, bool _upright, int _nOctaves, int _nOctaveLayers)
{
hessianThreshold = _threshold;
extended = _extended;
upright = _upright;
nOctaves = _nOctaves;
nOctaveLayers = _nOctaveLayers;
}
int SURF::descriptorSize() const { return extended ? 128 : 64; }
int SURF::descriptorType() const { return CV_32F; }
void SURF::operator()(InputArray imgarg, InputArray maskarg,
CV_OUT vector<KeyPoint>& keypoints) const
{
(*this)(imgarg, maskarg, keypoints, noArray(), false);
}
void SURF::operator()(InputArray _img, InputArray _mask,
CV_OUT vector<KeyPoint>& keypoints,
OutputArray _descriptors,
bool useProvidedKeypoints) const
{
Mat img = _img.getMat(), mask = _mask.getMat(), mask1, sum, msum;
bool doDescriptors = _descriptors.needed();
CV_Assert(!img.empty() && img.depth() == CV_8U);
if( img.channels() > 1 )
cvtColor(img, img, COLOR_BGR2GRAY);
CV_Assert(mask.empty() || (mask.type() == CV_8U && mask.size() == img.size()));
CV_Assert(hessianThreshold >= 0);
CV_Assert(nOctaves > 0);
CV_Assert(nOctaveLayers > 0);
integral(img, sum, CV_32S);
// Compute keypoints only if we are not asked for evaluating the descriptors are some given locations:
if( !useProvidedKeypoints )
{
if( !mask.empty() )
{
cv::min(mask, 1, mask1);
integral(mask1, msum, CV_32S);
}
fastHessianDetector( sum, msum, keypoints, nOctaves, nOctaveLayers, hessianThreshold );
}
int i, j, N = (int)keypoints.size();
if( N > 0 )
{
Mat descriptors;
if( doDescriptors )
{
_descriptors.create((int)keypoints.size(), (extended ? 128 : 64), CV_32F);
descriptors = _descriptors.getMat();
}
parallel_for(BlockedRange(0, N), SURFInvoker(img, sum, keypoints, descriptors, extended, upright) );
size_t dsize = descriptors.cols*descriptors.elemSize();
// remove keypoints that were marked for deletion
for( i = j = 0; i < N; i++ )
{
if( keypoints[i].size > 0 )
{
if( i > j )
{
keypoints[j] = keypoints[i];
if( doDescriptors )
memcpy( descriptors.ptr(j), descriptors.ptr(i), dsize);
}
j++;
}
}
if( N > j )
{
N = j;
keypoints.resize(N);
if( doDescriptors )
{
Mat d = descriptors.rowRange(0, N);
d.copyTo(_descriptors);
}
}
}
}
void SURF::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
{
(*this)(image, mask, keypoints, noArray(), false);
}
void SURF::computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors) const
{
(*this)(image, Mat(), keypoints, descriptors, true);
}
static Algorithm* createSURF()
{
return new SURF;
}
static AlgorithmInfo surf_info("Feature2D.SURF", createSURF);
AlgorithmInfo* SURF::info() const
{
static volatile bool initialized = false;
if( !initialized )
{
surf_info.addParam(this, "hessianThreshold", hessianThreshold);
surf_info.addParam(this, "nOctaves", nOctaves);
surf_info.addParam(this, "nOctaveLayers", nOctaveLayers);
surf_info.addParam(this, "extended", extended);
surf_info.addParam(this, "upright", upright);
initialized = true;
}
return &surf_info;
}
/*
// SurfFeatureDetector
SurfFeatureDetector::SurfFeatureDetector( double hessianThreshold, int octaves, int octaveLayers, bool upright )
: surf(hessianThreshold, octaves, octaveLayers, false, upright)
{}
void SurfFeatureDetector::read (const FileNode& fn)
{
double hessianThreshold = fn["hessianThreshold"];
int octaves = fn["octaves"];
int octaveLayers = fn["octaveLayers"];
bool upright = (int)fn["upright"] != 0;
surf = SURF( hessianThreshold, octaves, octaveLayers, false, upright );
}
void SurfFeatureDetector::write (FileStorage& fs) const
{
//fs << "algorithm" << getAlgorithmName ();
fs << "hessianThreshold" << surf.hessianThreshold;
fs << "octaves" << surf.nOctaves;
fs << "octaveLayers" << surf.nOctaveLayers;
fs << "upright" << surf.upright;
}
void SurfFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
Mat grayImage = image;
if( image.type() != CV_8U ) cvtColor( image, grayImage, CV_BGR2GRAY );
surf(grayImage, mask, keypoints);
}
*/
}