data_2 = data[x_size, :] data_3 = data_2[array, :] selectData = data_3[:, [index1, index2]] 解释

这段代码是在从一个矩阵中选取特定的行和列，并组成一个新的矩阵。其中，data是原始矩阵，x_size是矩阵的行数，array是一个包含选取行的索引数组，index1和index2是选取的列的索引。首先，data_2选取了第x_size行的所有元素；接着，data_3选取了array中指定的行，并且选取了index1和index2列；最后，selectData就是选取出来的新矩阵。

相关问题

def crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical): K_o = R_o ** 2 / range_z K_i = R_i ** 2 / range_z for z in range(range_z): r_o = np.sqrt(z * K_o) data_layer = data_crop[:, :, z] d_o = np.sqrt(x_o ** 2 + y_o ** 2) d_i = np.sqrt(x_i ** 2 + y_i ** 2) if z < z_critical: r_i = 0 else: r_i = np.sqrt(z * K_i) data_crop[:, :, z] = np.where((d_o > r_o) | (d_i <= r_i), 0, data_layer) return data_crop data_crop = data[:, :, :400] range_x, range_y, range_z = data_crop.shape dx = 550 dy = 530 x, y = np.meshgrid(np.arange(range_x), np.arange(range_y)) x_o = x - range_x / 2 y_o = y - range_y / 2 x_i = x - dx y_i = y - dy z_critical = 50 R_o = 550 R_i = 200 data_crop = crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical) data_crop = data_crop[:, :, 10:] 转C++ Eigen::Tensor<uint8_t, 3, Eigen::RowMajor>

以下是转换后的C++代码：

#include <Eigen/Core>
#include <unsupported/Eigen/CXX11/Tensor>

Eigen::Tensor<uint8_t, 3, Eigen::RowMajor> crop_pointcloud(
    Eigen::Tensor<uint8_t, 3, Eigen::RowMajor>& data_crop,
    Eigen::Tensor<float, 2>& x_o,
    Eigen::Tensor<float, 2>& y_o,
    Eigen::Tensor<float, 2>& x_i,
    Eigen::Tensor<float, 2>& y_i,
    float R_o,
    float R_i,
    int z_critical) {
  int range_z = data_crop.dimension(2);
  float K_o = R_o * R_o / range_z;
  float K_i = R_i * R_i / range_z;
  for (int z = 0; z < range_z; ++z) {
    float r_o = std::sqrt(z * K_o);
    auto data_layer = data_crop.chip(z, 2);
    auto d_o = (x_o * x_o + y_o * y_o).sqrt();
    auto d_i = (x_i * x_i + y_i * y_i).sqrt();
    float r_i = (z < z_critical) ? 0 : std::sqrt(z * K_i);
    data_crop.chip(z, 2) = (d_o > r_o || d_i <= r_i).select(0, data_layer);
  }
  return data_crop;
}

int main() {
  Eigen::Tensor<uint8_t, 3, Eigen::RowMajor> data(100, 100, 500);
  // Initialize data
  Eigen::Tensor<uint8_t, 3, Eigen::RowMajor> data_crop = data.slice(Eigen::array<int, 3>({0, 0, 0}), Eigen::array<int, 3>({100, 100, 400}));
  int range_x = data_crop.dimension(0);
  int range_y = data_crop.dimension(1);
  int range_z = data_crop.dimension(2);
  Eigen::Tensor<float, 2> x(range_x, range_y);
  Eigen::Tensor<float, 2> y(range_x, range_y);
  for (int i = 0; i < range_x; ++i) {
    for (int j = 0; j < range_y; ++j) {
      x(i, j) = i - range_x / 2;
      y(i, j) = j - range_y / 2;
    }
  }
  Eigen::Tensor<float, 2> x_o = x;
  Eigen::Tensor<float, 2> y_o = y;
  Eigen::Tensor<float, 2> x_i = x - 550;
  Eigen::Tensor<float, 2> y_i = y - 530;
  int z_critical = 50;
  float R_o = 550;
  float R_i = 200;
  data_crop = crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical);
  data_crop = data_crop.slice(Eigen::array<int, 3>({0, 0, 10}), Eigen::array<int, 3>({100, 100, 390}));
  return 0;
}

注意，为了方便实现，我使用了Eigen库来处理多维数组。在C++中，需要手动遍历数组来初始化x和y，而在Python中，可以直接使用np.meshgrid来生成网格。此外，由于C++中没有np.where这个函数，我使用了Eigen的.select()函数来实现类似的功能。最后，注意将Python代码中的data_crop[:, :, 10:]转换为C++中的.slice()函数。

x, y = np.meshgrid(np.arange(range_x), np.arange(range_y)) # np.savetxt('reshape_data.txt', x, delimiter=' ', fmt="%i") x_o = x - range_x / 2 y_o = y - range_y / 2 x_i = x - dx y_i = y - dy z_critical = 50 R_o = 550 R_i = 200 def crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical): K_o = R_o ** 2 / range_z K_i = R_i ** 2 / range_z for z in range(range_z): r_o = np.sqrt(z * K_o) data_layer = data_crop[:, :, z] d_o = np.sqrt(x_o ** 2 + y_o ** 2) d_i = np.sqrt(x_i ** 2 + y_i ** 2) if z < z_critical: r_i = 0 else: r_i = np.sqrt(z * K_i) data_crop[:, :, z] = np.where((d_o > r_o) | (d_i <= r_i), 0, data_layer) return data_crop data_crop = crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical) data_crop = data_crop[:, :, 10:] 用 C++ Eigen::Tensor实现 ，data_crop的数据类型为Eigen::Tensor<uint8_t, 3, Eigen::RowMajor>

以下是使用Eigen::Tensor库实现的代码，其中使用了Eigen::TensorMap和Eigen::array类来实现张量的切片和元素访问：

#include <Eigen/Core>
#include <unsupported/Eigen/CXX11/Tensor>
#include <cmath>

using namespace Eigen;

template<typename T>
using Tensor3 = Tensor<T, 3, RowMajor>;

template<typename T>
Tensor3<T> crop_pointcloud(Tensor3<T>& data_crop, const Tensor<int, 2>& x_o, const Tensor<int, 2>& y_o, 
                           const Tensor<int, 2>& x_i, const Tensor<int, 2>& y_i, const T& R_o, const T& R_i, const int z_critical) {
    const int range_z = data_crop.dimension(2);
    const T K_o = R_o * R_o / range_z;
    const T K_i = R_i * R_i / range_z;
    for (int z = 0; z < range_z; ++z) {
        const T r_o = std::sqrt(z * K_o);
        TensorMap<Tensor2<T>> data_layer(data_crop.data() + z * data_crop.dimension(0) * data_crop.dimension(1), data_crop.dimension(0), data_crop.dimension(1));
        const Tensor<T, 2> d_o = (x_o * x_o + y_o * y_o).sqrt();
        const Tensor<T, 2> d_i = (x_i * x_i + y_i * y_i).sqrt();
        const T r_i = (z < z_critical) ? 0 : std::sqrt(z * K_i);
        data_layer = (d_o > r_o).select(T(0), (d_i <= r_i).select(T(0), data_layer));
    }
    return data_crop;
}

int main() {
    const int range_x = 100;
    const int range_y = 100;
    const int range_z = 200;
    Tensor<int, 2> x(range_x, range_y), y(range_x, range_y);
    for (int i = 0; i < range_x; ++i) {
        for (int j = 0; j < range_y; ++j) {
            x(i, j) = i;
            y(i, j) = j;
        }
    }
    const Tensor<int, 2> x_o = x.array() - range_x / 2;
    const Tensor<int, 2> y_o = y.array() - range_y / 2;
    const Tensor<int, 2> x_i = x.array() - 1;
    const Tensor<int, 2> y_i = y.array() - 1;
    const int z_critical = 50;
    const int R_o = 550;
    const int R_i = 200;
    Tensor3<uint8_t> data_crop(range_x, range_y, range_z);
    data_crop.setRandom();
    data_crop = crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical);
    data_crop = data_crop.slice(IndexList<0, 1, 2>(), IndexList<0, 0, 10>());
    return 0;
}

这段代码中，crop_pointcloud函数的输入和输出都是三维张量，数据类型为uint8_t，其中x_o、y_o、x_i、y_i均为二维张量，R_o、R_i、z_critical均为标量。在函数中，使用了Eigen::TensorMap将三维张量data_crop的数据映射到二维张量data_layer上，然后使用select函数实现了条件判断和赋值操作。