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 Tensor
时间: 2024-03-17 09:46:20 浏览: 12
以下是crop_pointcloud函数的C++ Eigen Tensor库实现:
```cpp
#include <cmath>
#include <unsupported/Eigen/CXX11/Tensor>
#include <vector>
using namespace Eigen;
using namespace std;
vector<Tensor<float, 3>> crop_pointcloud(vector<Tensor<float, 3>> 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[0].dimension(2);
float K_o = pow(R_o, 2) / range_z;
float K_i = pow(R_i, 2) / range_z;
for (int z = 0; z < range_z; ++z) {
float r_o = sqrt(z * K_o);
Tensor<float, 2> data_layer = data_crop[z].reshape({data_crop[z].dimension(0), data_crop[z].dimension(1)});
float d_o = sqrt(pow(x_o, 2) + pow(y_o, 2));
float d_i = sqrt(pow(x_i, 2) + pow(y_i, 2));
float r_i = z < z_critical ? 0 : sqrt(z * K_i);
data_crop[z] = (d_o > r_o || d_i <= r_i).select(Tensor<float, 3>::Zero(data_crop[z].dimension(0), data_crop[z].dimension(1), data_crop[z].dimension(2)), data_crop[z]);
}
return data_crop;
}
```