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/**********************************************************************************************\
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 <[email protected]>
// 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 ([email protected]) 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
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// patent notices, this list of conditions and the following
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// contributors may be used to endorse or promote products derived
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\**********************************************************************************************/
#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;
#if 0
// intermediate type used for DoG pyramids
typedef short sift_wt;
static const int SIFT_FIXPT_SCALE = 48;
#else
// intermediate type used for DoG pyramids
typedef float sift_wt;
static const int SIFT_FIXPT_SCALE = 1;
#endif
static inline void
unpackOctave(const KeyPoint& kpt, int& octave, int& layer, float& scale)
{
octave = kpt.octave & 255;
layer = (kpt.octave >> 8) & 255;
octave = octave < 128 ? octave : (-128 | octave);
scale = octave >= 0 ? 1.f/(1 << octave) : (float)(1 << -octave);
}
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, DataType<sift_wt>::type, 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(), DataType<sift_wt>::type);
}
}
}
// 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 = (float)(img.at<sift_wt>(y, x+1) - img.at<sift_wt>(y, x-1));
float dy = (float)(img.at<sift_wt>(y-1, x) - img.at<sift_wt>(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=0;
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<sift_wt>(r, c+1) - img.at<sift_wt>(r, c-1))*deriv_scale,
(img.at<sift_wt>(r+1, c) - img.at<sift_wt>(r-1, c))*deriv_scale,
(next.at<sift_wt>(r, c) - prev.at<sift_wt>(r, c))*deriv_scale);
float v2 = (float)img.at<sift_wt>(r, c)*2;
float dxx = (img.at<sift_wt>(r, c+1) + img.at<sift_wt>(r, c-1) - v2)*second_deriv_scale;
float dyy = (img.at<sift_wt>(r+1, c) + img.at<sift_wt>(r-1, c) - v2)*second_deriv_scale;
float dss = (next.at<sift_wt>(r, c) + prev.at<sift_wt>(r, c) - v2)*second_deriv_scale;
float dxy = (img.at<sift_wt>(r+1, c+1) - img.at<sift_wt>(r+1, c-1) -
img.at<sift_wt>(r-1, c+1) + img.at<sift_wt>(r-1, c-1))*cross_deriv_scale;
float dxs = (next.at<sift_wt>(r, c+1) - next.at<sift_wt>(r, c-1) -
prev.at<sift_wt>(r, c+1) + prev.at<sift_wt>(r, c-1))*cross_deriv_scale;
float dys = (next.at<sift_wt>(r+1, c) - next.at<sift_wt>(r-1, c) -
prev.at<sift_wt>(r+1, c) + prev.at<sift_wt>(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;
if( std::abs(xi) > (float)(INT_MAX/3) ||
std::abs(xr) > (float)(INT_MAX/3) ||
std::abs(xc) > (float)(INT_MAX/3) )
return false;
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<sift_wt>(r, c+1) - img.at<sift_wt>(r, c-1))*deriv_scale,
(img.at<sift_wt>(r+1, c) - img.at<sift_wt>(r-1, c))*deriv_scale,
(next.at<sift_wt>(r, c) - prev.at<sift_wt>(r, c))*deriv_scale);
float t = dD.dot(Matx31f(xc, xr, xi));
contr = img.at<sift_wt>(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<sift_wt>(r, c)*2.f;
float dxx = (img.at<sift_wt>(r, c+1) + img.at<sift_wt>(r, c-1) - v2)*second_deriv_scale;
float dyy = (img.at<sift_wt>(r+1, c) + img.at<sift_wt>(r-1, c) - v2)*second_deriv_scale;
float dxy = (img.at<sift_wt>(r+1, c+1) - img.at<sift_wt>(r+1, c-1) -
img.at<sift_wt>(r-1, c+1) + img.at<sift_wt>(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;
kpt.response = std::abs(contr);
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 sift_wt* currptr = img.ptr<sift_wt>(r);
const sift_wt* prevptr = prev.ptr<sift_wt>(r);
const sift_wt* nextptr = next.ptr<sift_wt>(r);
for( int c = SIFT_IMG_BORDER; c < cols-SIFT_IMG_BORDER; c++)
{
sift_wt 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 = 360.f - (float)((360.f/n) * bin);
if(std::abs(kpt.angle - 360.f) < FLT_EPSILON)
kpt.angle = 0.f;
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);
// Clip the radius to the diagonal of the image to avoid autobuffer too large exception
radius = std::min(radius, (int) sqrt((double) img.cols*img.cols + img.rows*img.rows));
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 = (float)(img.at<sift_wt>(r, c+1) - img.at<sift_wt>(r, c-1));
float dy = (float)(img.at<sift_wt>(r-1, c) - img.at<sift_wt>(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);
#if 1
for( k = 0; k < len; k++ )
{
dst[k] = saturate_cast<uchar>(dst[k]*nrm2);
}
#else
float nrm1 = 0;
for( k = 0; k < len; k++ )
{
dst[k] *= nrm2;
nrm1 += dst[k];
}
nrm1 = 1.f/std::max(nrm1, FLT_EPSILON);
for( k = 0; k < len; k++ )
{
dst[k] = std::sqrt(dst[k] * nrm1);//saturate_cast<uchar>(std::sqrt(dst[k] * nrm1)*SIFT_INT_DESCR_FCTR);
}
#endif
}
static void calcDescriptors(const vector<Mat>& gpyr, const vector<KeyPoint>& keypoints,
Mat& descriptors, int nOctaveLayers, int firstOctave )
{
int d = SIFT_DESCR_WIDTH, n = SIFT_DESCR_HIST_BINS;
for( size_t i = 0; i < keypoints.size(); i++ )
{
KeyPoint kpt = keypoints[i];
int octave, layer;
float scale;
unpackOctave(kpt, octave, layer, scale);
CV_Assert(octave >= firstOctave && layer <= nOctaveLayers+2);
float size=kpt.size*scale;
Point2f ptf(kpt.pt.x*scale, kpt.pt.y*scale);
const Mat& img = gpyr[(octave - firstOctave)*(nOctaveLayers + 3) + layer];
float angle = 360.f - kpt.angle;
if(std::abs(angle - 360.f) < FLT_EPSILON)
angle = 0.f;
calcSIFTDescriptor(img, ptf, angle, size*0.5f, d, n, descriptors.ptr<float>((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<KeyPoint>& keypoints) const
{
(*this)(_image, _mask, keypoints, noArray());
}
void SIFT::operator()(InputArray _image, InputArray _mask,
vector<KeyPoint>& keypoints,
OutputArray _descriptors,
bool useProvidedKeypoints) const
{
int firstOctave = -1, actualNOctaves = 0, actualNLayers = 0;
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)" );
if( useProvidedKeypoints )
{
firstOctave = 0;
int maxOctave = INT_MIN;
for( size_t i = 0; i < keypoints.size(); i++ )
{
int octave, layer;
float scale;
unpackOctave(keypoints[i], octave, layer, scale);
firstOctave = std::min(firstOctave, octave);
maxOctave = std::max(maxOctave, octave);
actualNLayers = std::max(actualNLayers, layer-2);
}
firstOctave = std::min(firstOctave, 0);
CV_Assert( firstOctave >= -1 && actualNLayers <= nOctaveLayers );
actualNOctaves = maxOctave - firstOctave + 1;
}
Mat base = createInitialImage(image, firstOctave < 0, (float)sigma);
vector<Mat> gpyr, dogpyr;
int nOctaves = actualNOctaves > 0 ? actualNOctaves : cvRound(log( (double)std::min( base.cols, base.rows ) ) / log(2.) - 2) - firstOctave;
//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( nfeatures > 0 )
KeyPointsFilter::retainBest(keypoints, nfeatures);
//t = (double)getTickCount() - t;
//printf("keypoint detection time: %g\n", t*1000./tf);
if( firstOctave < 0 )
for( size_t i = 0; i < keypoints.size(); i++ )
{
KeyPoint& kpt = keypoints[i];
float scale = 1.f/(float)(1 << -firstOctave);
kpt.octave = (kpt.octave & ~255) | ((kpt.octave + firstOctave) & 255);
kpt.pt *= scale;
kpt.size *= scale;
}
if( !mask.empty() )
KeyPointsFilter::runByPixelsMask( keypoints, mask );
}
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, firstOctave);
//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);
}
}