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] print(file_path) # np.savetxt('reshape_data.txt', data_crop, delimiter=',') range_x, range_y, range_z = data_crop.shape 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 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

Eigen::Tensor is a C++ library for tensor operations, developed by the Eigen team. It provides a high-level API for performing tensor operations, such as tensor addition, multiplication, and convolution. The library is designed to be fast and efficient, and provides support for both CPU and GPU acceleration. Eigen::Tensor is particularly useful for machine learning applications, where tensors are commonly used to represent data.

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：

#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。