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 翻译
时间: 2024-04-07 21:32:26 浏览: 42
这段 Python 代码的作用是裁剪一个点云数据。具体来说,它将在垂直方向(即沿着 z 轴)上分成多层,然后根据一些条件来判断每个点是否应该被保留。具体来说,对于每一层:
- 计算该层距离原点(x_o, y_o)的距离 r_o 和距离内部点(x_i, y_i)的距离 r_i。
- 如果该层小于 z_critical,则 r_i 被设为 0。
- 对于该层上的每个点,如果它距离原点的距离大于 r_o 或者距离内部点的距离小于等于 r_i,则该点被替换为 0。
最终返回裁剪后的点云数据。
需要注意的是,由于该代码中的变量 range_z 没有在代码中定义,因此无法确定其值。
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++
这段 Python 代码主要是对点云数据进行裁剪,并返回裁剪后的点云数据。以下是将其转换为 C++ 的代码:
```cpp
#include <cmath>
#include <vector>
#include <algorithm>
void crop_pointcloud(std::vector<std::vector<std::vector<float>>>& data_crop,
int x_o, int y_o, int x_i, int y_i, int R_o, int R_i, int z_critical)
{
int range_x = data_crop.size();
int range_y = data_crop[0].size();
int range_z = data_crop[0][0].size();
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);
std::vector<std::vector<float>>& data_layer = data_crop[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);
for (int i = 0; i < range_x; ++i) {
for (int j = 0; j < range_y; ++j) {
if (d_o > r_o || d_i <= r_i) {
data_layer[i][j] = 0;
}
}
}
}
}
int main()
{
std::vector<std::vector<std::vector<float>>> data_crop; // 假设已经读入了点云数据
int range_x = data_crop.size();
int range_y = data_crop[0].size();
int range_z = data_crop[0][0].size();
int dx = 550;
int dy = 530;
int x_o, y_o, x_i, y_i;
x_o = x_i = range_x / 2;
y_o = y_i = range_y / 2;
int z_critical = 50;
int R_o = 550;
int R_i = 200;
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++ 中使用多维数组需要使用嵌套的 `std::vector`,因此在 C++ 的实现中需要对数组的访问进行适当修改。此外,需要将 `numpy` 的一些函数,比如 `np.arange` 和 `np.where` 等等,转换为 C++ 中的等价函数。
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:] 转成C++ Eigen
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。
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