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 Eigen::Tensor<uint8_t, 3, Eigen::RowMajor> c++

以下是将上述 Python 代码转换为 C++ 代码的示例：

#include <Eigen/Dense>
#include <Eigen/Tensor>

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

注意，上述代码需要使用 Eigen 3 库，并且使用了 C++11 特性。此外，输入数据类型应该是浮点类型（例如 `float` 或 `double`），输出数据类型是 `uint8_t`。