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发表于 2023-2-19 19:26:07
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以下是使用Eigen和OpenMP进行并行计算的示例代码,用于拟合1万个点的三次B样条曲线:
#include <iostream>
#include <vector>
#include <Eigen/Dense>
#include <Eigen/Sparse>
#include <Eigen/SparseCholesky>
#include <omp.h>
using namespace Eigen;
int main()
{
const int n = 10000; // 数据点数
const int k = 4; // B样条的阶数
// 生成随机数据
MatrixXd points = MatrixXd::Random(n, 3);
// 创建B样条矩阵
SparseMatrix<double> M(n, n);
for (int i = 0; i < n; i++) {
for (int j = i - k + 1; j <= i; j++) {
if (j < 0 || j >= n) continue;
double t = (i - j) / (double)k;
double b = 0;
if (t >= 0 && t < 1) b = (t*t*t - 3*t*t + 3*t - 1) / 6.0;
else if (t >= 1 && t < 2) b = (4 - 6*t*t*t + 3*t*t + 3*t - 3*t*t*t*t) / 6.0;
else if (t >= 2 && t <= 3) b = (t*t*t - 3*t*t + 3*t - 1) / 6.0;
M.coeffRef(i, j) = b;
}
}
// 计算B样条系数
SparseMatrix<double> A = M.transpose() * M;
SparseLU<SparseMatrix<double>> solver;
solver.analyzePattern(A);
solver.factorize(A);
VectorXd coeffs = solver.solve(M.transpose() * points);
// 计算B样条曲线
const int npoints = 100; // 插值点数
MatrixXd interp_points(npoints, 3);
#pragma omp parallel for
for (int i = 0; i < npoints; i++) {
double t = i / (double)(npoints - 1);
MatrixXd B(1, k);
for (int j = 0; j < k; j++) {
double tt = t - j;
if (tt >= 0 && tt < 1) B(0, j) = (tt*tt*tt - 3*tt*tt + 3*tt - 1) / 6.0;
else if (tt >= 1 && tt < 2) B(0, j) = (4 - 6*tt*tt*tt + 3*tt*tt + 3*tt - 3*tt*tt*tt*tt) / 6.0;
else if (tt >= 2 && tt <= 3) B(0, j) = (tt*tt*tt - 3*tt*tt + 3*tt - 1) / 6.0;
}
interp_points.row(i) = B * coeffs;
}
// 打印结果
std::cout << "B-spline coefficients: " << coeffs.transpose() << std::endl;
std::cout << "Interpolated points: " << std::endl << interp_points << std::endl;
// 保存结果到文件
std:fstream outfile("interp_points.txt");
if (outfile.is_open()) {
for (int i = 0; i < npoints; i++) {
outfile << interp_points(i, 0) << " " << interp_points(i, 1) << " " << interp_points(i, 2) << std::endl;
}
outfile.close();
}
return 0;
}
在上面的代码中,我们将计算得到的B样条曲线写入到名为“interp_points.txt”的文件中,每行包含一个点的x、y、z坐标。这样,您可以使用其他工具(如MATLAB、Python等)加载数据并进行进一步的处理、可视化等操作。 |
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