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ceresCurveFitting.cpp
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//
// Created by xiang on 18-11-19.
//
#include <iostream>
#include <opencv2/core/core.hpp>
#include <ceres/ceres.h>
#include <chrono>
using namespace std;
// 代价函数的计算模型
struct CURVE_FITTING_COST {
CURVE_FITTING_COST(double x, double y) : _x(x), _y(y) {}
// 残差的计算
template<typename T>
bool operator()(
const T *const abc, // 模型参数,有3维
T *residual) const {
residual[0] = T(_y) - ceres::exp(abc[0] * T(_x) * T(_x) + abc[1] * T(_x) + abc[2]); // y-exp(ax^2+bx+c)
return true;
}
const double _x, _y; // x,y数据
};
int main(int argc, char **argv) {
double ar = 1.0, br = 2.0, cr = 1.0; // 真实参数值
double ae = 2.0, be = -1.0, ce = 5.0; // 估计参数值
int N = 100; // 数据点
double w_sigma = 1.0; // 噪声Sigma值
double inv_sigma = 1.0 / w_sigma;
cv::RNG rng; // OpenCV随机数产生器
vector<double> x_data, y_data; // 数据
for (int i = 0; i < N; i++) {
double x = i / 100.0;
x_data.push_back(x);
y_data.push_back(exp(ar * x * x + br * x + cr) + rng.gaussian(w_sigma * w_sigma));
}
double abc[3] = {ae, be, ce};
// 构建最小二乘问题
ceres::Problem problem;
for (int i = 0; i < N; i++) {
problem.AddResidualBlock( // 向问题中添加误差项
// 使用自动求导,模板参数:误差类型,输出维度,输入维度,维数要与前面struct中一致
new ceres::AutoDiffCostFunction<CURVE_FITTING_COST, 1, 3>(
new CURVE_FITTING_COST(x_data[i], y_data[i])
),
nullptr, // 核函数,这里不使用,为空
abc // 待估计参数
);
}
// 配置求解器
ceres::Solver::Options options; // 这里有很多配置项可以填
options.linear_solver_type = ceres::DENSE_NORMAL_CHOLESKY; // 增量方程如何求解
options.minimizer_progress_to_stdout = true; // 输出到cout
ceres::Solver::Summary summary; // 优化信息
chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
ceres::Solve(options, &problem, &summary); // 开始优化
chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
cout << "solve time cost = " << time_used.count() << " seconds. " << endl;
// 输出结果
cout << summary.BriefReport() << endl;
cout << "estimated a,b,c = ";
for (auto a:abc) cout << a << " ";
cout << endl;
return 0;
}