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:] 转成C++ Eigen
时间: 2024-03-20 13:42:55 浏览: 18
以下是将给定的代码转换为C++ Eigen的代码:
```
#include <iostream>
#include <Eigen/Dense>
using namespace Eigen;
int main()
{
int range_x = 100;
int range_y = 100;
int range_z = 50;
double dx = 10.0;
double dy = 10.0;
MatrixXd x, y;
x.resize(range_x, range_y);
y.resize(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;
}
}
x.array() -= range_x / 2;
y.array() -= range_y / 2;
MatrixXd x_i, y_i;
x_i.resize(range_x, range_y);
y_i.resize(range_x, range_y);
for (int i = 0; i < range_x; i++) {
for (int j = 0; j < range_y; j++) {
x_i(i, j) = i - dx;
y_i(i, j) = j - dy;
}
}
double z_critical = 50;
double R_o = 550;
double R_i = 200;
double K_o = R_o * R_o / range_z;
double K_i = R_i * R_i / range_z;
MatrixXd data_crop;
data_crop.resize(range_x, range_y * range_z);
for (int i = 0; i < range_x; i++) {
for (int j = 0; j < range_y; j++) {
for (int k = 0; k < range_z; k++) {
data_crop(i, j * range_z + k) = i * j * k; // replace with actual data
}
}
}
for (int z = 0; z < range_z; z++) {
double r_o = std::sqrt(z * K_o);
MatrixXd data_layer;
data_layer.resize(range_x, range_y);
for (int i = 0; i < range_x; i++) {
for (int j = 0; j < range_y; j++) {
data_layer(i, j) = data_crop(i, j * range_z + z);
}
}
MatrixXd d_o, d_i;
d_o.resize(range_x, range_y);
d_i.resize(range_x, range_y);
for (int i = 0; i < range_x; i++) {
for (int j = 0; j < range_y; j++) {
d_o(i, j) = std::sqrt(x(i, j) * x(i, j) + y(i, j) * y(i, j));
d_i(i, j) = std::sqrt(x_i(i, j) * x_i(i, j) + y_i(i, j) * y_i(i, j));
}
}
double r_i = (z < z_critical) ? 0 : std::sqrt(z * K_i);
for (int i = 0; i < range_x; i++) {
for (int j = 0; j < range_y; j++) {
if (d_o(i, j) > r_o || d_i(i, j) <= r_i) {
data_layer(i, j) = 0;
}
}
}
for (int i = 0; i < range_x; i++) {
for (int j = 0; j < range_y; j++) {
data_crop(i, j * range_z + z) = data_layer(i, j);
}
}
}
data_crop = data_crop.block(0, 0, range_x, range_y * (range_z - 10)); // remove first 10 layers
std::cout << data_crop << std::endl;
return 0;
}
```
请注意,由于无法确定实际数据的类型和形状,因此在代码示例中,我假设数据是大小为(range_x,range_y,range_z)的三维数据,并将其替换为简单的乘积。您需要根据实际情况修改此代码以适应您的数据。