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:] file_name = str(subdir.path.split('/')[-1].split('_')[-2]) outpath0 = os.path.join(outpath_raw, file_name) # outpath1 = os.path.join(outpath_img, file_name) data_crop.astype('int8').tofile(outpath0) 翻译一下
时间: 2024-04-07 16:31:29 浏览: 47
这段代码是将点云数据进行裁剪,并将结果存储到文件中。具体步骤如下:
1. 使用函数`crop_pointcloud`对点云数据进行裁剪,该函数需要传入一些参数,包括裁剪的范围、关键高度等。
2. 对裁剪后的点云数据进行切片,只保留第10列及以后的数据。
3. 从文件路径中提取文件名。
4. 将裁剪后的点云数据转换为int8类型,并存储到指定的输出路径`outpath0`中。
相关问题
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 转为c++ Eigen,Tensor
Here's the equivalent code in C++ using the Eigen and Tensor libraries:
```
#include <Eigen/Dense>
#include <unsupported/Eigen/CXX11/Tensor>
Eigen::Tensor<double, 3> crop_pointcloud(Eigen::Tensor<double, 3> data_crop, double x_o, double y_o, double x_i, double y_i, double R_o, double R_i, int z_critical) {
int range_z = 400;
double K_o = pow(R_o, 2) / range_z;
double K_i = pow(R_i, 2) / range_z;
for (int z = 0; z < range_z; z++) {
double r_o = sqrt(z * K_o);
Eigen::Tensor<double, 2> data_layer = data_crop.chip(z, 2);
double d_o = sqrt(pow(x_o, 2) + pow(y_o, 2));
double d_i = sqrt(pow(x_i, 2) + pow(y_i, 2));
double r_i = z < z_critical ? 0 : sqrt(z * K_i);
data_crop.chip(z, 2) = (d_o > r_o || d_i <= r_i).select(Eigen::Tensor<double, 2>::Zero(data_layer.rows(), data_layer.cols()), data_layer);
}
return data_crop;
}
```
Note that I assumed the input `data_crop` is a 3D tensor with dimensions (rows, cols, depth), and I used the `chip` method to extract each 2D layer of the tensor as a 2D Eigen tensor. Also, I used the `select` method to apply the cropping condition on each element of the tensor and set it to zero if the condition is true.
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
以下是将给定的 Python 代码转换为 C++ Eigen 的代码:
```cpp
#include <cmath>
#include <Eigen/Dense>
Eigen::MatrixXd crop_pointcloud(Eigen::MatrixXd data_crop, double x_o, double y_o, double x_i, double y_i, double R_o, double R_i, int range_z, double z_critical) {
double K_o = std::pow(R_o, 2) / range_z;
double K_i = std::pow(R_i, 2) / range_z;
for (int z = 0; z < range_z; ++z) {
double r_o = std::sqrt(z * K_o);
Eigen::MatrixXd data_layer = data_crop.block(0, 0, data_crop.rows(), data_crop.cols()).col(z);
double d_o = std::sqrt(std::pow(x_o, 2) + std::pow(y_o, 2));
double d_i = std::sqrt(std::pow(x_i, 2) + std::pow(y_i, 2));
double r_i = (z < z_critical) ? 0 : std::sqrt(z * K_i);
data_crop.block(0, 0, data_crop.rows(), data_crop.cols()).col(z) = (d_o > r_o) || (d_i <= r_i) ? Eigen::MatrixXd::Zero(data_layer.rows(), data_layer.cols()) : data_layer;
}
return data_crop;
}
```
其中,`Eigen::MatrixXd` 表示一个动态大小的矩阵,`std::pow` 表示求幂函数,`std::sqrt` 表示求平方根函数,`.block` 表示从一个矩阵中提取一个子矩阵,`.col` 表示提取一个矩阵的某一列,`Eigen::MatrixXd::Zero` 表示一个指定大小的全零矩阵。