Chiebot-Mirror
/
opencv
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
27509 Commits
6 Branches
128 Tags
3.0 GiB
 
 
 
 
 
 
Tag: Branch: Tree: d41b20b268
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'd41b20b268'
${ noResults }
opencv/modules/features2d/src/sift.simd.hpp

822 lines
30 KiB
Raw Blame History

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (c) 2006-2010, Rob Hess <hess@eecs.oregonstate.edu>
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2020, Intel Corporation, all rights reserved.
/**********************************************************************************************\
Implementation of SIFT is based on the code from http://blogs.oregonstate.edu/hess/code/sift/
Below is the original copyright.
Patent US6711293 expired in March 2020.
// 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 <opencv2/core/hal/hal.hpp>
#include "opencv2/core/hal/intrin.hpp"
#include <opencv2/core/utils/buffer_area.private.hpp>
namespace cv {
#if !defined(CV_CPU_DISPATCH_MODE) || !defined(CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY)
/******************************* Defs and macros *****************************/
// 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 = 4.5f; // 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;
#define DoG_TYPE_SHORT 0
#if DoG_TYPE_SHORT
// 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
#endif // definitions and macros
CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN
void findScaleSpaceExtrema(
int octave,
int layer,
int threshold,
int idx,
int step,
int cols,
int nOctaveLayers,
double contrastThreshold,
double edgeThreshold,
double sigma,
const std::vector<Mat>& gauss_pyr,
const std::vector<Mat>& dog_pyr,
std::vector<KeyPoint>& kpts,
const cv::Range& range);
void calcSIFTDescriptor(
const Mat& img, Point2f ptf, float ori, float scl,
int d, int n, float* dst
);
#ifndef CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY
// 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
)
{
CV_TRACE_FUNCTION();
int i, j, k, len = (radius*2+1)*(radius*2+1);
float expf_scale = -1.f/(2.f * sigma * sigma);
cv::utils::BufferArea area;
float *X = 0, *Y = 0, *Mag, *Ori = 0, *W = 0, *temphist = 0;
area.allocate(X, len, CV_SIMD_WIDTH);
area.allocate(Y, len, CV_SIMD_WIDTH);
area.allocate(Ori, len, CV_SIMD_WIDTH);
area.allocate(W, len, CV_SIMD_WIDTH);
area.allocate(temphist, n+4, CV_SIMD_WIDTH);
area.commit();
temphist += 2;
Mag = X;
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
cv::hal::exp32f(W, W, len);
cv::hal::fastAtan2(Y, X, Ori, len, true);
cv::hal::magnitude32f(X, Y, Mag, len);
k = 0;
#if CV_SIMD
const int vecsize = v_float32::nlanes;
v_float32 nd360 = vx_setall_f32(n/360.f);
v_int32 __n = vx_setall_s32(n);
int CV_DECL_ALIGNED(CV_SIMD_WIDTH) bin_buf[vecsize];
float CV_DECL_ALIGNED(CV_SIMD_WIDTH) w_mul_mag_buf[vecsize];
for( ; k <= len - vecsize; k += vecsize )
{
v_float32 w = vx_load_aligned( W + k );
v_float32 mag = vx_load_aligned( Mag + k );
v_float32 ori = vx_load_aligned( Ori + k );
v_int32 bin = v_round( nd360 * ori );
bin = v_select(bin >= __n, bin - __n, bin);
bin = v_select(bin < vx_setzero_s32(), bin + __n, bin);
w = w * mag;
v_store_aligned(bin_buf, bin);
v_store_aligned(w_mul_mag_buf, w);
for(int vi = 0; vi < vecsize; vi++)
{
temphist[bin_buf[vi]] += w_mul_mag_buf[vi];
}
}
#endif
for( ; 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];
i = 0;
#if CV_SIMD
v_float32 d_1_16 = vx_setall_f32(1.f/16.f);
v_float32 d_4_16 = vx_setall_f32(4.f/16.f);
v_float32 d_6_16 = vx_setall_f32(6.f/16.f);
for( ; i <= n - v_float32::nlanes; i += v_float32::nlanes )
{
v_float32 tn2 = vx_load_aligned(temphist + i-2);
v_float32 tn1 = vx_load(temphist + i-1);
v_float32 t0 = vx_load(temphist + i);
v_float32 t1 = vx_load(temphist + i+1);
v_float32 t2 = vx_load(temphist + i+2);
v_float32 _hist = v_fma(tn2 + t2, d_1_16,
v_fma(tn1 + t1, d_4_16, t0 * d_6_16));
v_store(hist + i, _hist);
}
#endif
for( ; 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 std::vector<Mat>& dog_pyr, KeyPoint& kpt, int octv,
int& layer, int& r, int& c, int nOctaveLayers,
float contrastThreshold, float edgeThreshold, float sigma
)
{
CV_TRACE_FUNCTION();
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;
}
namespace {
class findScaleSpaceExtremaT
{
public:
findScaleSpaceExtremaT(
int _o,
int _i,
int _threshold,
int _idx,
int _step,
int _cols,
int _nOctaveLayers,
double _contrastThreshold,
double _edgeThreshold,
double _sigma,
const std::vector<Mat>& _gauss_pyr,
const std::vector<Mat>& _dog_pyr,
std::vector<KeyPoint>& kpts)
: o(_o),
i(_i),
threshold(_threshold),
idx(_idx),
step(_step),
cols(_cols),
nOctaveLayers(_nOctaveLayers),
contrastThreshold(_contrastThreshold),
edgeThreshold(_edgeThreshold),
sigma(_sigma),
gauss_pyr(_gauss_pyr),
dog_pyr(_dog_pyr),
kpts_(kpts)
{
// nothing
}
void process(const cv::Range& range)
{
CV_TRACE_FUNCTION();
const int begin = range.start;
const int end = range.end;
static const int n = SIFT_ORI_HIST_BINS;
float CV_DECL_ALIGNED(CV_SIMD_WIDTH) hist[n];
const Mat& img = dog_pyr[idx];
const Mat& prev = dog_pyr[idx-1];
const Mat& next = dog_pyr[idx+1];
for( int r = begin; r < end; 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])))
{
CV_TRACE_REGION("pixel_candidate");
KeyPoint kpt;
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;
kpts_.push_back(kpt);
}
}
}
}
}
}
private:
int o, i;
int threshold;
int idx, step, cols;
int nOctaveLayers;
double contrastThreshold;
double edgeThreshold;
double sigma;
const std::vector<Mat>& gauss_pyr;
const std::vector<Mat>& dog_pyr;
std::vector<KeyPoint>& kpts_;
};
} // namespace
void findScaleSpaceExtrema(
int octave,
int layer,
int threshold,
int idx,
int step,
int cols,
int nOctaveLayers,
double contrastThreshold,
double edgeThreshold,
double sigma,
const std::vector<Mat>& gauss_pyr,
const std::vector<Mat>& dog_pyr,
std::vector<KeyPoint>& kpts,
const cv::Range& range)
{
CV_TRACE_FUNCTION();
findScaleSpaceExtremaT(octave, layer, threshold, idx,
step, cols,
nOctaveLayers, contrastThreshold, edgeThreshold, sigma,
gauss_pyr, dog_pyr,
kpts)
.process(range);
}
void calcSIFTDescriptor(
const Mat& img, Point2f ptf, float ori, float scl,
int d, int n, float* dst
)
{
CV_TRACE_FUNCTION();
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)std::sqrt(((double) img.cols)*img.cols + ((double) 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.data(), *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;
cv::hal::fastAtan2(Y, X, Ori, len, true);
cv::hal::magnitude32f(X, Y, Mag, len);
cv::hal::exp32f(W, W, len);
k = 0;
#if CV_SIMD
{
const int vecsize = v_float32::nlanes;
int CV_DECL_ALIGNED(CV_SIMD_WIDTH) idx_buf[vecsize];
float CV_DECL_ALIGNED(CV_SIMD_WIDTH) rco_buf[8*vecsize];
const v_float32 __ori = vx_setall_f32(ori);
const v_float32 __bins_per_rad = vx_setall_f32(bins_per_rad);
const v_int32 __n = vx_setall_s32(n);
const v_int32 __1 = vx_setall_s32(1);
const v_int32 __d_plus_2 = vx_setall_s32(d+2);
const v_int32 __n_plus_2 = vx_setall_s32(n+2);
for( ; k <= len - vecsize; k += vecsize )
{
v_float32 rbin = vx_load(RBin + k);
v_float32 cbin = vx_load(CBin + k);
v_float32 obin = (vx_load(Ori + k) - __ori) * __bins_per_rad;
v_float32 mag = vx_load(Mag + k) * vx_load(W + k);
v_int32 r0 = v_floor(rbin);
v_int32 c0 = v_floor(cbin);
v_int32 o0 = v_floor(obin);
rbin -= v_cvt_f32(r0);
cbin -= v_cvt_f32(c0);
obin -= v_cvt_f32(o0);
o0 = v_select(o0 < vx_setzero_s32(), o0 + __n, o0);
o0 = v_select(o0 >= __n, o0 - __n, o0);
v_float32 v_r1 = mag*rbin, v_r0 = mag - v_r1;
v_float32 v_rc11 = v_r1*cbin, v_rc10 = v_r1 - v_rc11;
v_float32 v_rc01 = v_r0*cbin, v_rc00 = v_r0 - v_rc01;
v_float32 v_rco111 = v_rc11*obin, v_rco110 = v_rc11 - v_rco111;
v_float32 v_rco101 = v_rc10*obin, v_rco100 = v_rc10 - v_rco101;
v_float32 v_rco011 = v_rc01*obin, v_rco010 = v_rc01 - v_rco011;
v_float32 v_rco001 = v_rc00*obin, v_rco000 = v_rc00 - v_rco001;
v_int32 idx = v_muladd(v_muladd(r0+__1, __d_plus_2, c0+__1), __n_plus_2, o0);
v_store_aligned(idx_buf, idx);
v_store_aligned(rco_buf, v_rco000);
v_store_aligned(rco_buf+vecsize, v_rco001);
v_store_aligned(rco_buf+vecsize*2, v_rco010);
v_store_aligned(rco_buf+vecsize*3, v_rco011);
v_store_aligned(rco_buf+vecsize*4, v_rco100);
v_store_aligned(rco_buf+vecsize*5, v_rco101);
v_store_aligned(rco_buf+vecsize*6, v_rco110);
v_store_aligned(rco_buf+vecsize*7, v_rco111);
for(int id = 0; id < vecsize; id++)
{
hist[idx_buf[id]] += rco_buf[id];
hist[idx_buf[id]+1] += rco_buf[vecsize + id];
hist[idx_buf[id]+(n+2)] += rco_buf[2*vecsize + id];
hist[idx_buf[id]+(n+3)] += rco_buf[3*vecsize + id];
hist[idx_buf[id]+(d+2)*(n+2)] += rco_buf[4*vecsize + id];
hist[idx_buf[id]+(d+2)*(n+2)+1] += rco_buf[5*vecsize + id];
hist[idx_buf[id]+(d+3)*(n+2)] += rco_buf[6*vecsize + id];
hist[idx_buf[id]+(d+3)*(n+2)+1] += rco_buf[7*vecsize + id];
}
}
}
#endif
for( ; 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;
k = 0;
#if CV_SIMD
{
v_float32 __nrm2 = vx_setzero_f32();
v_float32 __dst;
for( ; k <= len - v_float32::nlanes; k += v_float32::nlanes )
{
__dst = vx_load(dst + k);
__nrm2 = v_fma(__dst, __dst, __nrm2);
}
nrm2 = (float)v_reduce_sum(__nrm2);
}
#endif
for( ; k < len; k++ )
nrm2 += dst[k]*dst[k];
float thr = std::sqrt(nrm2)*SIFT_DESCR_MAG_THR;
i = 0, nrm2 = 0;
#if 0 //CV_AVX2
// This code cannot be enabled because it sums nrm2 in a different order,
// thus producing slightly different results
{
float CV_DECL_ALIGNED(CV_SIMD_WIDTH) nrm2_buf[8];
__m256 __dst;
__m256 __nrm2 = _mm256_setzero_ps();
__m256 __thr = _mm256_set1_ps(thr);
for( ; i <= len - 8; i += 8 )
{
__dst = _mm256_loadu_ps(&dst[i]);
__dst = _mm256_min_ps(__dst, __thr);
_mm256_storeu_ps(&dst[i], __dst);
#if CV_FMA3
__nrm2 = _mm256_fmadd_ps(__dst, __dst, __nrm2);
#else
__nrm2 = _mm256_add_ps(__nrm2, _mm256_mul_ps(__dst, __dst));
#endif
}
_mm256_store_ps(nrm2_buf, __nrm2);
nrm2 = nrm2_buf[0] + nrm2_buf[1] + nrm2_buf[2] + nrm2_buf[3] +
nrm2_buf[4] + nrm2_buf[5] + nrm2_buf[6] + nrm2_buf[7];
}
#endif
for( ; i < len; 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
k = 0;
#if CV_SIMD
{
v_float32 __dst;
v_float32 __min = vx_setzero_f32();
v_float32 __max = vx_setall_f32(255.0f); // max of uchar
v_float32 __nrm2 = vx_setall_f32(nrm2);
for( k = 0; k <= len - v_float32::nlanes; k += v_float32::nlanes )
{
__dst = vx_load(dst + k);
__dst = v_min(v_max(v_cvt_f32(v_round(__dst * __nrm2)), __min), __max);
v_store(dst + k, __dst);
}
}
#endif
for( ; 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
}
#endif
CV_CPU_OPTIMIZATION_NAMESPACE_END
} // namespace