ar_basalt/thirdparty/basalt-headers/include/basalt/image/image_pyr.h

/**
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This file is part of the Basalt project.
https://gitlab.com/VladyslavUsenko/basalt-headers.git

Copyright (c) 2019, Vladyslav Usenko and Nikolaus Demmel.
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@file
@brief Image pyramid implementation stored as mipmap
*/

#pragma once

#include <basalt/image/image.h>

namespace basalt {

/// @brief Image pyramid that stores levels as mipmap
///
/// \image html mipmap.jpeg
/// Computes image pyramid (see \ref subsample) and stores it as a mipmap
/// (https://en.wikipedia.org/wiki/Mipmap).
template <typename T, class Allocator = DefaultImageAllocator<T>>
class ManagedImagePyr {
 public:
  using PixelType = T;
  using Ptr = std::shared_ptr<ManagedImagePyr<T, Allocator>>;

  /// @brief Default constructor.
  inline ManagedImagePyr() {}

  /// @brief Construct image pyramid from other image.
  ///
  /// @param other image to use for the pyramid level 0
  /// @param num_level number of levels for the pyramid
  inline ManagedImagePyr(const ManagedImage<T>& other, size_t num_levels) {
    setFromImage(other, num_levels);
  }

  /// @brief Set image pyramid from other image.
  ///
  /// @param other image to use for the pyramid level 0
  /// @param num_level number of levels for the pyramid
  inline void setFromImage(const ManagedImage<T>& other, size_t num_levels) {
    orig_w = other.w;
    image.Reinitialise(other.w + other.w / 2, other.h);
    image.Fill(0);
    lvl_internal(0).CopyFrom(other);

    for (size_t i = 0; i < num_levels; i++) {
      const Image<const T> l = lvl(i);
      Image<T> lp1 = lvl_internal(i + 1);
      subsample(l, lp1);
    }
  }

  /// @brief Extrapolate image after border with reflection.
  static inline int border101(int x, int h) {
    return h - 1 - std::abs(h - 1 - x);
  }

  /// @brief Subsample the image twice in each direction.
  ///
  /// Subsampling is done by convolution with Gaussian kernel
  /// \f[
  /// \frac{1}{256}
  /// \begin{bmatrix}
  ///   1 & 4 & 6 & 4 & 1
  /// \\4 & 16 & 24 & 16 & 4
  /// \\6 & 24 & 36 & 24 & 6
  /// \\4 & 16 & 24 & 16 & 4
  /// \\1 & 4 & 6 & 4 & 1
  /// \\ \end{bmatrix}
  /// \f]
  /// and removing every even-numbered row and column.
  static void subsample(const Image<const T>& img, Image<T>& img_sub) {
    static_assert(std::is_same<T, uint16_t>::value ||
                  std::is_same<T, uint8_t>::value);

    constexpr int kernel[5] = {1, 4, 6, 4, 1};

    // accumulator
    ManagedImage<int> tmp(img_sub.h, img.w);

    // Vertical convolution
    {
      for (int r = 0; r < int(img_sub.h); r++) {
        const T* row_m2 = img.RowPtr(std::abs(2 * r - 2));
        const T* row_m1 = img.RowPtr(std::abs(2 * r - 1));
        const T* row = img.RowPtr(2 * r);
        const T* row_p1 = img.RowPtr(border101(2 * r + 1, img.h));
        const T* row_p2 = img.RowPtr(border101(2 * r + 2, img.h));

        for (int c = 0; c < int(img.w); c++) {
          tmp(r, c) = kernel[0] * int(row_m2[c]) + kernel[1] * int(row_m1[c]) +
                      kernel[2] * int(row[c]) + kernel[3] * int(row_p1[c]) +
                      kernel[4] * int(row_p2[c]);
        }
      }
    }

    // Horizontal convolution
    {
      for (int c = 0; c < int(img_sub.w); c++) {
        const int* row_m2 = tmp.RowPtr(std::abs(2 * c - 2));
        const int* row_m1 = tmp.RowPtr(std::abs(2 * c - 1));
        const int* row = tmp.RowPtr(2 * c);
        const int* row_p1 = tmp.RowPtr(border101(2 * c + 1, tmp.h));
        const int* row_p2 = tmp.RowPtr(border101(2 * c + 2, tmp.h));

        for (int r = 0; r < int(tmp.w); r++) {
          int val_int = kernel[0] * row_m2[r] + kernel[1] * row_m1[r] +
                        kernel[2] * row[r] + kernel[3] * row_p1[r] +
                        kernel[4] * row_p2[r];
          T val = ((val_int + (1 << 7)) >> 8);
          img_sub(c, r) = val;
        }
      }
    }
  }

  /// @brief Return const image of the certain level
  ///
  /// @param lvl level to return
  /// @return const image of with the pyramid level
  inline const Image<const T> lvl(size_t lvl) const {
    size_t x = (lvl == 0) ? 0 : orig_w;
    size_t y = (lvl <= 1) ? 0 : (image.h - (image.h >> (lvl - 1)));
    size_t width = (orig_w >> lvl);
    size_t height = (image.h >> lvl);

    return image.SubImage(x, y, width, height);
  }

  /// @brief Return const image of underlying mipmap
  ///
  /// @return const image of of the underlying mipmap representation which can
  /// be for example used for visualization
  inline const Image<const T> mipmap() const {
    return image.SubImage(0, 0, image.w, image.h);
  }

  /// @brief Return coordinate offset of the image in the mipmap image.
  ///
  /// @param lvl level to return
  /// @return offset coordinates (2x1 vector)
  template <typename S>
  inline Eigen::Matrix<S, 2, 1> lvl_offset(size_t lvl) {
    size_t x = (lvl == 0) ? 0 : orig_w;
    size_t y = (lvl <= 1) ? 0 : (image.h - (image.h >> (lvl - 1)));

    return Eigen::Matrix<S, 2, 1>(x, y);
  }

 protected:
  /// @brief Return image of the certain level
  ///
  /// @param lvl level to return
  /// @return image of with the pyramid level
  inline Image<T> lvl_internal(size_t lvl) {
    size_t x = (lvl == 0) ? 0 : orig_w;
    size_t y = (lvl <= 1) ? 0 : (image.h - (image.h >> (lvl - 1)));
    size_t width = (orig_w >> lvl);
    size_t height = (image.h >> lvl);

    return image.SubImage(x, y, width, height);
  }

  size_t orig_w;          ///< Width of the original image (level 0)
  ManagedImage<T> image;  ///< Pyramid image stored as a mipmap
};

}  // namespace basalt