// clang-format off
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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
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\**********************************************************************************************/
// clang-format on

#include "y2020/vision/sift/sift971.h"

#include <cstdarg>
#include <iostream>
#include <mutex>

#include "glog/logging.h"
#include <opencv2/core/hal/hal.hpp>
#include <opencv2/imgproc.hpp>

#include "y2020/vision/sift/fast_gaussian.h"

using namespace cv;

namespace frc971 {
namespace 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 vision
}  // namespace frc971