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:] Eigen::Tensor<uint8_t, 3, Eigen::RowMajor>
时间: 2024-04-03 18:37:15 浏览: 15
这段代码是用来对三维点云数据进行裁剪的,其输入参数包括点云数据(data),裁剪的圆心位置(x_o, y_o)、内部圆半径(R_i)、外部圆半径(R_o)以及关键深度位置(z_critical)。其中,range_x、range_y和range_z分别表示点云数据在x、y、z方向上的尺寸大小,dx和dy是内部圆心相对于外部圆心的偏移量。最终输出裁剪后的点云数据(data_crop)。
这段代码中使用了numpy和Eigen库,分别用于处理多维数组和C++矩阵计算。
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:] C++ Eigen::Tensor实现
以下是使用C++ Eigen::Tensor实现上述代码的示例:
```cpp
#include <iostream>
#include <Eigen/Dense>
#include <unsupported/Eigen/CXX11/Tensor>
using namespace Eigen;
template<typename T>
void crop_pointcloud(Tensor<T, 3>& data_crop, const Tensor<T, 2>& x_o, const Tensor<T, 2>& y_o, const Tensor<T, 2>& x_i, const Tensor<T, 2>& y_i, const T& R_o, const T& R_i, const int& z_critical)
{
const int range_z = data_crop.dimension(2);
const T K_o = R_o * R_o / range_z;
const T K_i = R_i * R_i / range_z;
for (int z = 0; z < range_z; ++z)
{
const T r_o = std::sqrt(z * K_o);
auto data_layer = data_crop.chip(z, 2);
const auto d_o = (x_o.square() + y_o.square()).sqrt();
const auto d_i = (x_i.square() + y_i.square()).sqrt();
const T r_i = (z < z_critical) ? T(0) : std::sqrt(z * K_i);
data_crop.chip(z, 2) = (d_o > r_o || d_i <= r_i).select(T(0), data_layer);
}
}
int main()
{
const int range_x = 100;
const int range_y = 100;
const int range_z = 100;
Tensor<float, 3> data_crop(range_x, range_y, range_z);
Tensor<int, 2> x(range_x, range_y);
Tensor<int, 2> y(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;
for (int k = 0; k < range_z; ++k)
{
data_crop(i, j, k) = i + j + k; // 假设这是点云数据
}
}
}
Tensor<float, 2> x_o = x.cast<float>() - range_x / 2;
Tensor<float, 2> y_o = y.cast<float>() - range_y / 2;
Tensor<float, 2> x_i = x.cast<float>() - 1; // 假设dx和dy都是1
Tensor<float, 2> y_i = y.cast<float>() - 1;
const float R_o = 550;
const float R_i = 200;
const int z_critical = 50;
crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical);
data_crop = data_crop.slice({ 0, 0, 10 }, { range_x, range_y, range_z - 10 });
std::cout << data_crop << std::endl; // 输出处理后的点云数据
return 0;
}
```
这里使用了C++11的`auto`关键字和`Tensor`类来简化代码,并且使用了`slice`函数来去掉处理后的点云数据的前10层。