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// clang-format off
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//
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//
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// If you do not agree to this license, do not download, install,
// copy or use the software.
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// License Agreement
// For Open Source Computer Vision Library
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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 <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
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// * 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.
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// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
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\**********************************************************************************************/
// clang-format on
#include "y2020/vision/sift/sift971.h"
#include <cstdarg>
#include <iostream>
#include <mutex>
#include "absl/log/check.h"
#include "absl/log/log.h"
#include <opencv2/core/hal/hal.hpp>
#include <opencv2/imgproc.hpp>
#include "y2020/vision/sift/fast_gaussian.h"
using namespace cv;
namespace frc971::vision {
namespace {
#define USE_AVX2 0
/******************************* 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 = 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 1
#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
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);
}
constexpr bool kLogTiming = false;
} // namespace
void SIFT971_Impl::buildGaussianPyramid(const Mat &base, std::vector<Mat> &pyr,
int nOctaves) const {
std::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 = std::pow(2., 1. / nOctaveLayers);
for (int i = 1; i < nOctaveLayers + 3; i++) {
double sig_prev = std::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;
} else if (i == 0) {
// base of new octave is halved image from end of previous octave
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];
if (use_fast_gaussian_pyramid_) {
FastGaussian(src, &dst, sig[i]);
} else {
GaussianBlur(src, dst, Size(), sig[i], sig[i]);
}
}
}
}
}
namespace {
class buildDoGPyramidComputer : public ParallelLoopBody {
public:
buildDoGPyramidComputer(int _nOctaveLayers, const std::vector<Mat> &_gpyr,
std::vector<Mat> &_dogpyr,
bool use_fast_subtract_dogpyr)
: nOctaveLayers(_nOctaveLayers),
gpyr(_gpyr),
dogpyr(_dogpyr),
use_fast_subtract_dogpyr_(use_fast_subtract_dogpyr) {}
void operator()(const cv::Range &range) const override {
const int begin = range.start;
const int end = range.end;
for (int a = begin; a < end; a++) {
const int o = a / (nOctaveLayers + 2);
const int i = a % (nOctaveLayers + 2);
const Mat &src1 = gpyr[o * (nOctaveLayers + 3) + i];
const Mat &src2 = gpyr[o * (nOctaveLayers + 3) + i + 1];
CHECK_EQ(a, o * (nOctaveLayers + 2) + i);
Mat &dst = dogpyr[o * (nOctaveLayers + 2) + i];
if (use_fast_subtract_dogpyr_) {
FastSubtract(src2, src1, &dst);
} else {
subtract(src2, src1, dst, noArray(), DataType<sift_wt>::type);
}
}
}
private:
const int nOctaveLayers;
const std::vector<Mat> &gpyr;
std::vector<Mat> &dogpyr;
const bool use_fast_subtract_dogpyr_;
};
} // namespace
void SIFT971_Impl::buildDoGPyramid(const std::vector<Mat> &gpyr,
std::vector<Mat> &dogpyr) const {
int nOctaves = (int)gpyr.size() / (nOctaveLayers + 3);
dogpyr.resize(nOctaves * (nOctaveLayers + 2));
#if 0
parallel_for_(Range(0, nOctaves * (nOctaveLayers + 2)),
buildDoGPyramidComputer(nOctaveLayers, gpyr, dogpyr, use_fast_subtract_dogpyr_));
#else
buildDoGPyramidComputer(
nOctaveLayers, gpyr, dogpyr,
use_fast_subtract_dogpyr_)(Range(0, nOctaves * (nOctaveLayers + 2)));
#endif
}
// base is the image to start with.
// gpyr is the pyramid of gaussian blurs. This is both an output and a place
// where we store intermediates.
// dogpyr is the pyramid of gaussian differences which we fill out.
// number_octaves is the number of octaves to calculate.
void SIFT971_Impl::buildGaussianAndDifferencePyramid(
const cv::Mat &base, std::vector<cv::Mat> &gpyr,
std::vector<cv::Mat> &dogpyr, int number_octaves) const {
const int layers_per_octave = nOctaveLayers;
// We use the base (possibly after downscaling) as the first "blurred" image.
// Then we calculate 2 more than the number of octaves.
// TODO(Brian): Why are there 2 extra?
const int gpyr_layers_per_octave = layers_per_octave + 3;
// There is 1 less difference than the number of blurs.
const int dogpyr_layers_per_octave = gpyr_layers_per_octave - 1;
gpyr.resize(number_octaves * gpyr_layers_per_octave);
dogpyr.resize(number_octaves * dogpyr_layers_per_octave);
// Precompute Gaussian sigmas using the following formula:
// \sigma_{total}^2 = \sigma_{i}^2 + \sigma_{i-1}^2
// We need one for each of the layers in the pyramid we blur, which skips the
// first one because it's just the base image without any blurring.
std::vector<double> sig(gpyr_layers_per_octave - 1);
double k = std::pow(2., 1. / layers_per_octave);
for (int i = 0; i < gpyr_layers_per_octave - 1; i++) {
double sig_prev = std::pow<double>(k, i) * sigma;
double sig_total = sig_prev * k;
sig[i] = std::sqrt(sig_total * sig_total - sig_prev * sig_prev);
}
for (int octave = 0; octave < number_octaves; octave++) {
const int octave_gpyr_index = octave * gpyr_layers_per_octave;
const int octave_dogpyr_index = octave * dogpyr_layers_per_octave;
// At the beginning of each octave, calculate the new base image.
{
Mat &dst = gpyr[octave_gpyr_index];
if (octave == 0) {
// For the first octave, it's just the base image.
dst = base;
} else {
// For the other octaves, OpenCV's code claims that it's a halved
// version of the end of the previous octave.
// TODO(Brian): But this isn't really the end of the previous octave?
// But if you use the end, it finds way fewer features? Maybe this is
// just a arbitrarily-ish-somewhat-blurred thing from the previous
// octave??
const int gpyr_index = octave_gpyr_index - 3;
// Verify that the indexing in the original OpenCV code gives the same
// result. It's unclear which one makes more logical sense.
CHECK_EQ((octave - 1) * gpyr_layers_per_octave + layers_per_octave,
gpyr_index);
const Mat &src = gpyr[gpyr_index];
resize(src, dst, Size(src.cols / 2, src.rows / 2), 0, 0, INTER_NEAREST);
}
}
// Then, go through all the layers and calculate the appropriate
// differences.
for (int layer = 0; layer < dogpyr_layers_per_octave; layer++) {
// The index where the current layer starts.
const int layer_gpyr_index = octave_gpyr_index + layer;
const int layer_dogpyr_index = octave_dogpyr_index + layer;
if (use_fast_pyramid_difference_) {
const Mat &input = gpyr[layer_gpyr_index];
Mat &blurred = gpyr[layer_gpyr_index + 1];
Mat &difference = dogpyr[layer_dogpyr_index];
FastGaussianAndSubtract(input, &blurred, &difference, sig[layer]);
} else {
// First, calculate the new gaussian blur.
{
const Mat &src = gpyr[layer_gpyr_index];
Mat &dst = gpyr[layer_gpyr_index + 1];
if (use_fast_gaussian_pyramid_) {
FastGaussian(src, &dst, sig[layer]);
} else {
GaussianBlur(src, dst, Size(), sig[layer], sig[layer]);
}
}
// Then, calculate the difference from the previous one.
{
const Mat &src1 = gpyr[layer_gpyr_index];
const Mat &src2 = gpyr[layer_gpyr_index + 1];
Mat &dst = dogpyr[layer_dogpyr_index];
if (use_fast_subtract_dogpyr_) {
FastSubtract(src2, src1, &dst);
} else {
subtract(src2, src1, dst, noArray(), DataType<sift_wt>::type);
}
}
}
}
}
}
namespace {
// 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
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_AVX2
if (USE_AVX2) {
__m256 __nd360 = _mm256_set1_ps(n / 360.f);
__m256i __n = _mm256_set1_epi32(n);
int CV_DECL_ALIGNED(32) bin_buf[8];
float CV_DECL_ALIGNED(32) w_mul_mag_buf[8];
for (; k <= len - 8; k += 8) {
__m256i __bin =
_mm256_cvtps_epi32(_mm256_mul_ps(__nd360, _mm256_loadu_ps(&Ori[k])));
__bin = _mm256_sub_epi32(
__bin, _mm256_andnot_si256(_mm256_cmpgt_epi32(__n, __bin), __n));
__bin = _mm256_add_epi32(
__bin, _mm256_and_si256(
__n, _mm256_cmpgt_epi32(_mm256_setzero_si256(), __bin)));
__m256 __w_mul_mag =
_mm256_mul_ps(_mm256_loadu_ps(&W[k]), _mm256_loadu_ps(&Mag[k]));
_mm256_store_si256((__m256i *)bin_buf, __bin);
_mm256_store_ps(w_mul_mag_buf, __w_mul_mag);
temphist[bin_buf[0]] += w_mul_mag_buf[0];
temphist[bin_buf[1]] += w_mul_mag_buf[1];
temphist[bin_buf[2]] += w_mul_mag_buf[2];
temphist[bin_buf[3]] += w_mul_mag_buf[3];
temphist[bin_buf[4]] += w_mul_mag_buf[4];
temphist[bin_buf[5]] += w_mul_mag_buf[5];
temphist[bin_buf[6]] += w_mul_mag_buf[6];
temphist[bin_buf[7]] += w_mul_mag_buf[7];
}
}
#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_AVX2
if (USE_AVX2) {
__m256 __d_1_16 = _mm256_set1_ps(1.f / 16.f);
__m256 __d_4_16 = _mm256_set1_ps(4.f / 16.f);
__m256 __d_6_16 = _mm256_set1_ps(6.f / 16.f);
for (; i <= n - 8; i += 8) {
#if CV_FMA3
__m256 __hist = _mm256_fmadd_ps(
_mm256_add_ps(_mm256_loadu_ps(&temphist[i - 2]),
_mm256_loadu_ps(&temphist[i + 2])),
__d_1_16,
_mm256_fmadd_ps(
_mm256_add_ps(_mm256_loadu_ps(&temphist[i - 1]),
_mm256_loadu_ps(&temphist[i + 1])),
__d_4_16,
_mm256_mul_ps(_mm256_loadu_ps(&temphist[i]), __d_6_16)));
#else
__m256 __hist = _mm256_add_ps(
_mm256_mul_ps(_mm256_add_ps(_mm256_loadu_ps(&temphist[i - 2]),
_mm256_loadu_ps(&temphist[i + 2])),
__d_1_16),
_mm256_add_ps(
_mm256_mul_ps(_mm256_add_ps(_mm256_loadu_ps(&temphist[i - 1]),
_mm256_loadu_ps(&temphist[i + 1])),
__d_4_16),
_mm256_mul_ps(_mm256_loadu_ps(&temphist[i]), __d_6_16)));
#endif
_mm256_storeu_ps(&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) {
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;
}
template <typename T>
class PerThreadAccumulator {
public:
void Add(std::vector<T> &&data) {
std::unique_lock locker(mutex_);
result_.emplace_back(data);
}
std::vector<std::vector<T>> move_result() { return std::move(result_); }
private:
// Should we do something more intelligent with per-thread std::vector that we
// merge at the end?
std::vector<std::vector<T>> result_;
std::mutex mutex_;
};
class findScaleSpaceExtremaComputer : public ParallelLoopBody {
public:
findScaleSpaceExtremaComputer(
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,
PerThreadAccumulator<KeyPoint> &_tls_kpts_struct)
: 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),
tls_kpts_struct(_tls_kpts_struct) {}
void operator()(const cv::Range &range) const override {
const int begin = range.start;
const int end = range.end;
static const int n = SIFT_ORI_HIST_BINS;
float hist[n];
const Mat &img = dog_pyr[idx];
const Mat &prev = dog_pyr[idx - 1];
const Mat &next = dog_pyr[idx + 1];
std::vector<KeyPoint> tls_kpts;
KeyPoint kpt;
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]))) {
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;
{ tls_kpts.push_back(kpt); }
}
}
}
}
}
tls_kpts_struct.Add(std::move(tls_kpts));
}
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;
PerThreadAccumulator<KeyPoint> &tls_kpts_struct;
};
} // namespace
//
// Detects features at extrema in DoG scale space. Bad features are discarded
// based on contrast and ratio of principal curvatures.
void SIFT971_Impl::findScaleSpaceExtrema(
const std::vector<Mat> &gauss_pyr, const std::vector<Mat> &dog_pyr,
std::vector<KeyPoint> &keypoints) const {
const int nOctaves = (int)gauss_pyr.size() / (nOctaveLayers + 3);
const int threshold =
cvFloor(0.5 * contrastThreshold / nOctaveLayers * 255 * SIFT_FIXPT_SCALE);
keypoints.clear();
PerThreadAccumulator<KeyPoint> tls_kpts_struct;
for (int o = 0; o < nOctaves; o++)
for (int i = 1; i <= nOctaveLayers; i++) {
const int idx = o * (nOctaveLayers + 2) + i;
const Mat &img = dog_pyr[idx];
const int step = (int)img.step1();
const int rows = img.rows, cols = img.cols;
parallel_for_(Range(SIFT_IMG_BORDER, rows - SIFT_IMG_BORDER),
findScaleSpaceExtremaComputer(
o, i, threshold, idx, step, cols, nOctaveLayers,
contrastThreshold, edgeThreshold, sigma, gauss_pyr,
dog_pyr, tls_kpts_struct));
}
const std::vector<std::vector<KeyPoint>> kpt_vecs =
tls_kpts_struct.move_result();
for (size_t i = 0; i < kpt_vecs.size(); ++i) {
keypoints.insert(keypoints.end(), kpt_vecs[i].begin(), kpt_vecs[i].end());
}
}
namespace {
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 +
((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, *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_AVX2
if (USE_AVX2) {
int CV_DECL_ALIGNED(32) idx_buf[8];
float CV_DECL_ALIGNED(32) rco_buf[64];
const __m256 __ori = _mm256_set1_ps(ori);
const __m256 __bins_per_rad = _mm256_set1_ps(bins_per_rad);
const __m256i __n = _mm256_set1_epi32(n);
for (; k <= len - 8; k += 8) {
__m256 __rbin = _mm256_loadu_ps(&RBin[k]);
__m256 __cbin = _mm256_loadu_ps(&CBin[k]);
__m256 __obin = _mm256_mul_ps(
_mm256_sub_ps(_mm256_loadu_ps(&Ori[k]), __ori), __bins_per_rad);
__m256 __mag =
_mm256_mul_ps(_mm256_loadu_ps(&Mag[k]), _mm256_loadu_ps(&W[k]));
__m256 __r0 = _mm256_floor_ps(__rbin);
__rbin = _mm256_sub_ps(__rbin, __r0);
__m256 __c0 = _mm256_floor_ps(__cbin);
__cbin = _mm256_sub_ps(__cbin, __c0);
__m256 __o0 = _mm256_floor_ps(__obin);
__obin = _mm256_sub_ps(__obin, __o0);
__m256i __o0i = _mm256_cvtps_epi32(__o0);
__o0i = _mm256_add_epi32(
__o0i, _mm256_and_si256(
__n, _mm256_cmpgt_epi32(_mm256_setzero_si256(), __o0i)));
__o0i = _mm256_sub_epi32(
__o0i, _mm256_andnot_si256(_mm256_cmpgt_epi32(__n, __o0i), __n));
__m256 __v_r1 = _mm256_mul_ps(__mag, __rbin);
__m256 __v_r0 = _mm256_sub_ps(__mag, __v_r1);
__m256 __v_rc11 = _mm256_mul_ps(__v_r1, __cbin);
__m256 __v_rc10 = _mm256_sub_ps(__v_r1, __v_rc11);
__m256 __v_rc01 = _mm256_mul_ps(__v_r0, __cbin);
__m256 __v_rc00 = _mm256_sub_ps(__v_r0, __v_rc01);
__m256 __v_rco111 = _mm256_mul_ps(__v_rc11, __obin);
__m256 __v_rco110 = _mm256_sub_ps(__v_rc11, __v_rco111);
__m256 __v_rco101 = _mm256_mul_ps(__v_rc10, __obin);
__m256 __v_rco100 = _mm256_sub_ps(__v_rc10, __v_rco101);
__m256 __v_rco011 = _mm256_mul_ps(__v_rc01, __obin);
__m256 __v_rco010 = _mm256_sub_ps(__v_rc01, __v_rco011);
__m256 __v_rco001 = _mm256_mul_ps(__v_rc00, __obin);
__m256 __v_rco000 = _mm256_sub_ps(__v_rc00, __v_rco001);
__m256i __one = _mm256_set1_epi32(1);
__m256i __idx = _mm256_add_epi32(
_mm256_mullo_epi32(
_mm256_add_epi32(
_mm256_mullo_epi32(
_mm256_add_epi32(_mm256_cvtps_epi32(__r0), __one),
_mm256_set1_epi32(d + 2)),
_mm256_add_epi32(_mm256_cvtps_epi32(__c0), __one)),
_mm256_set1_epi32(n + 2)),
__o0i);
_mm256_store_si256((__m256i *)idx_buf, __idx);
_mm256_store_ps(&(rco_buf[0]), __v_rco000);
_mm256_store_ps(&(rco_buf[8]), __v_rco001);
_mm256_store_ps(&(rco_buf[16]), __v_rco010);
_mm256_store_ps(&(rco_buf[24]), __v_rco011);
_mm256_store_ps(&(rco_buf[32]), __v_rco100);
_mm256_store_ps(&(rco_buf[40]), __v_rco101);
_mm256_store_ps(&(rco_buf[48]), __v_rco110);
_mm256_store_ps(&(rco_buf[56]), __v_rco111);
#define HIST_SUM_HELPER(id) \
hist[idx_buf[(id)]] += rco_buf[(id)]; \
hist[idx_buf[(id)] + 1] += rco_buf[8 + (id)]; \
hist[idx_buf[(id)] + (n + 2)] += rco_buf[16 + (id)]; \
hist[idx_buf[(id)] + (n + 3)] += rco_buf[24 + (id)]; \
hist[idx_buf[(id)] + (d + 2) * (n + 2)] += rco_buf[32 + (id)]; \
hist[idx_buf[(id)] + (d + 2) * (n + 2) + 1] += rco_buf[40 + (id)]; \
hist[idx_buf[(id)] + (d + 3) * (n + 2)] += rco_buf[48 + (id)]; \
hist[idx_buf[(id)] + (d + 3) * (n + 2) + 1] += rco_buf[56 + (id)];
HIST_SUM_HELPER(0);
HIST_SUM_HELPER(1);
HIST_SUM_HELPER(2);
HIST_SUM_HELPER(3);
HIST_SUM_HELPER(4);
HIST_SUM_HELPER(5);
HIST_SUM_HELPER(6);
HIST_SUM_HELPER(7);
#undef HIST_SUM_HELPER
}
}
#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_AVX2
if (USE_AVX2) {
float CV_DECL_ALIGNED(32) nrm2_buf[8];
__m256 __nrm2 = _mm256_setzero_ps();
__m256 __dst;
for (; k <= len - 8; k += 8) {
__dst = _mm256_loadu_ps(&dst[k]);
#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 (; 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
if( USE_AVX2 )
{
float CV_DECL_ALIGNED(32) 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_AVX2
if (USE_AVX2) {
__m256 __dst;
__m256 __min = _mm256_setzero_ps();
__m256 __max = _mm256_set1_ps(255.0f); // max of uchar
__m256 __nrm2 = _mm256_set1_ps(nrm2);
for (k = 0; k <= len - 8; k += 8) {
__dst = _mm256_loadu_ps(&dst[k]);
__dst = _mm256_min_ps(
_mm256_max_ps(
_mm256_round_ps(_mm256_mul_ps(__dst, __nrm2),
_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC),
__min),
__max);
_mm256_storeu_ps(&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
}
class calcDescriptorsComputer : public ParallelLoopBody {
public:
calcDescriptorsComputer(const std::vector<Mat> &_gpyr,
const std::vector<KeyPoint> &_keypoints,
Mat &_descriptors, int _nOctaveLayers,
int _firstOctave)
: gpyr(_gpyr),
keypoints(_keypoints),
descriptors(_descriptors),
nOctaveLayers(_nOctaveLayers),
firstOctave(_firstOctave) {}
void operator()(const cv::Range &range) const override {
const int begin = range.start;
const int end = range.end;
static const int d = SIFT_DESCR_WIDTH, n = SIFT_DESCR_HIST_BINS;
for (int i = begin; i < end; 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));
}
}
private:
const std::vector<Mat> &gpyr;
const std::vector<KeyPoint> &keypoints;
Mat &descriptors;
int nOctaveLayers;
int firstOctave;
};
static void calcDescriptors(const std::vector<Mat> &gpyr,
const std::vector<KeyPoint> &keypoints,
Mat &descriptors, int nOctaveLayers,
int firstOctave) {
parallel_for_(Range(0, static_cast<int>(keypoints.size())),
calcDescriptorsComputer(gpyr, keypoints, descriptors,
nOctaveLayers, firstOctave));
}
} // namespace
//////////////////////////////////////////////////////////////////////////////////////////
SIFT971_Impl::SIFT971_Impl(int _nfeatures, int _nOctaveLayers,
double _contrastThreshold, double _edgeThreshold,
double _sigma)
: nfeatures(_nfeatures),
nOctaveLayers(_nOctaveLayers),
contrastThreshold(_contrastThreshold),
edgeThreshold(_edgeThreshold),
sigma(_sigma) {}
int SIFT971_Impl::descriptorSize() const {
return SIFT_DESCR_WIDTH * SIFT_DESCR_WIDTH * SIFT_DESCR_HIST_BINS;
}
int SIFT971_Impl::descriptorType() const { return CV_32F; }
int SIFT971_Impl::defaultNorm() const { return NORM_L2; }
void SIFT971_Impl::detectAndCompute(InputArray _image, InputArray _mask,
std::vector<KeyPoint> &keypoints,
OutputArray _descriptors,
bool useProvidedKeypoints) {
int firstOctave = -1, actualNOctaves = 0, actualNLayers = 0;
Mat image = _image.getMat(), mask = _mask.getMat();
if (image.empty() || image.depth() != CV_8U)
CV_Error(Error::StsBadArg,
"image is empty or has incorrect depth (!=CV_8U)");
if (!mask.empty() && mask.type() != CV_8UC1)
CV_Error(Error::StsBadArg, "mask has incorrect type (!=CV_8UC1)");
if (useProvidedKeypoints) {
LOG_IF(INFO, kLogTiming);
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;
}
LOG_IF(INFO, kLogTiming);
Mat base = createInitialImage(image, firstOctave < 0);
LOG_IF(INFO, kLogTiming);
std::vector<Mat> gpyr;
int nOctaves;
if (actualNOctaves > 0) {
nOctaves = actualNOctaves;
} else {
nOctaves = cvRound(std::log((double)std::min(base.cols, base.rows)) /
std::log(2.) -
2) -
firstOctave;
}
if (!useProvidedKeypoints) {
std::vector<Mat> dogpyr;
if (use_fused_pyramid_difference_) {
buildGaussianAndDifferencePyramid(base, gpyr, dogpyr, nOctaves);
LOG_IF(INFO, kLogTiming);
} else {
buildGaussianPyramid(base, gpyr, nOctaves);
LOG_IF(INFO, kLogTiming);
buildDoGPyramid(gpyr, dogpyr);
LOG_IF(INFO, kLogTiming);
}
findScaleSpaceExtrema(gpyr, dogpyr, keypoints);
// TODO(Brian): I think it can go faster by knowing they're sorted?
// KeyPointsFilter::removeDuplicatedSorted( keypoints );
KeyPointsFilter::removeDuplicated(keypoints);
if (nfeatures > 0) KeyPointsFilter::retainBest(keypoints, nfeatures);
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);
LOG_IF(INFO, kLogTiming);
} else {
buildGaussianPyramid(base, gpyr, nOctaves);
LOG_IF(INFO, kLogTiming);
// filter keypoints by mask
// KeyPointsFilter::runByPixelsMask( keypoints, mask );
}
if (_descriptors.needed()) {
int dsize = descriptorSize();
_descriptors.create((int)keypoints.size(), dsize, CV_32F);
Mat descriptors = _descriptors.getMat();
calcDescriptors(gpyr, keypoints, descriptors, nOctaveLayers, firstOctave);
LOG_IF(INFO, kLogTiming);
}
}
Mat SIFT971_Impl::createInitialImage(const Mat &img,
bool doubleImageSize) const {
Mat gray, gray_fpt;
if (img.channels() == 3 || img.channels() == 4) {
cvtColor(img, gray, COLOR_BGR2GRAY);
gray.convertTo(gray_fpt, DataType<sift_wt>::type, SIFT_FIXPT_SCALE, 0);
} else {
img.convertTo(gray_fpt, DataType<sift_wt>::type, SIFT_FIXPT_SCALE, 0);
}
float sig_diff;
Mat maybe_doubled;
if (doubleImageSize) {
sig_diff = std::sqrt(
std::max(sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA * 4, 0.01));
resize(gray_fpt, maybe_doubled, Size(gray_fpt.cols * 2, gray_fpt.rows * 2),
0, 0, INTER_LINEAR);
} else {
sig_diff = std::sqrt(
std::max(sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA, 0.01));
maybe_doubled = gray_fpt;
}
if (use_fast_guassian_initial_) {
Mat temp;
FastGaussian(maybe_doubled, &temp, sig_diff);
return temp;
} else {
GaussianBlur(maybe_doubled, maybe_doubled, Size(), sig_diff, sig_diff);
return maybe_doubled;
}
}
} // namespace frc971::vision