D_H = np.sqrt(np.square(distence_U2K.iloc[i,j])-np.square(UAV_Hight))这句代码代表的意思
时间: 2024-02-26 21:55:12 浏览: 115
这行代码是计算无人机高度和地面点之间的距离,其中distence_U2K是一个矩阵,代表地面上每个点到无人机的距离。i和j是矩阵中的索引,表示地面矩阵上的一个点,根据这个点和无人机的距离可以计算出它们之间的直线距离,即distence_U2K.iloc[i,j]。接着,代码中的np.square函数计算了无人机高度的平方,然后用np.sqrt函数对两者平方差进行开方,即可得到无人机和地面点之间的距离D_H。
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 转C++ Eigen
以下是将 Python 代码转换为 C++ Eigen 的代码:
```cpp
#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。
def crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical): range_z = 400; 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和c++代码
下面是crop_pointcloud函数的Eigen/C++代码实现:
```cpp
#include <Eigen/Core>
#include <cmath>
Eigen::ArrayXXd crop_pointcloud(Eigen::ArrayXXd data_crop, double x_o, double y_o, double x_i, double y_i, double R_o, double R_i, int z_critical)
{
const int range_z = 400;
const double K_o = std::pow(R_o, 2) / range_z;
const double K_i = std::pow(R_i, 2) / range_z;
for (int z = 0; z < range_z; ++z)
{
const double r_o = std::sqrt(z * K_o);
Eigen::ArrayXXd data_layer = data_crop.slice(z);
const double d_o = std::sqrt(std::pow(x_o, 2) + std::pow(y_o, 2));
const double d_i = std::sqrt(std::pow(x_i, 2) + std::pow(y_i, 2));
double r_i = 0;
if (z >= z_critical)
{
r_i = std::sqrt(z * K_i);
}
data_crop.slice(z) = (d_o > r_o || d_i <= r_i).select(0, data_layer);
}
return data_crop;
}
```
请注意,这里的Eigen数组是使用slice()方法进行切片来实现三维数组的效果。