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 转C++ Eigen

以下是将 Python 代码转换为 C++ Eigen 的代码：

#include <Eigen/Dense>
#include <cmath>

using Eigen::Array;
using Eigen::Array3f;

Array3f crop_pointcloud(Array3f data_crop, float x_o, float y_o, float x_i, float y_i,
                        float R_o, float R_i, int z_critical) 
{
    int range_z = data_crop.dimension(2);
    float K_o = std::pow(R_o, 2) / range_z;
    float K_i = std::pow(R_i, 2) / range_z;
    
    for (int z = 0; z < range_z; ++z) {
        float r_o = std::sqrt(z * K_o);
        Array<float, Eigen::Dynamic, Eigen::Dynamic> data_layer = data_crop.slice(z);
        float d_o = std::sqrt(std::pow(x_o, 2) + std::pow(y_o, 2));
        float d_i = std::sqrt(std::pow(x_i, 2) + std::pow(y_i, 2));
        
        float r_i = (z < z_critical) ? 0 : std::sqrt(z * K_i);
        
        data_crop.slice(z) = (d_o > r_o || d_i <= r_i).select(0, data_layer);
    }
    
    return data_crop;
}

int main() 
{
    int range_x, range_y, range_z; // 假设已经读入了点云数据
    Array3f data_crop(range_x, range_y, range_z);

    int dx = 550;
    int dy = 530;
    float x_o, y_o, x_i, y_i;
    x_o = x_i = range_x / 2.0;
    y_o = y_i = range_y / 2.0;
    int z_critical = 50;
    float R_o = 550.0;
    float R_i = 200.0;

    data_crop = crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical);

    // 剩余部分请自行完成
    return 0;
}

需要注意的是，在 C++ Eigen 中使用的是 `Array` 类，因此需要对数组的访问进行适当修改。此外，C++ Eigen 中支持类似于 `numpy` 中的条件选择函数，因此可以直接使用 `select` 函数来实现 `np.where` 的功能。