实现如下: bool polynomial_curve_fit(std::vector<cv::Point>& key_point, int n, cv::Mat& A) { //Number of key points int N = key_point.size(); //构造矩阵X cv::Mat X = cv::Mat::zeros(n + 1, n + 1, CV_64FC1); for (int i =
所谓正态分布,就是高斯分布,正态曲线是一种特殊的高斯曲线。 python的scipy.optimize包里的curve_fit函数来拟合曲线,当然还可以拟合很多类型的曲线。scipy.optimize提供了函数最小值(标量或多维)、曲线拟合和寻找等式的根的有用算法。 import numpy as np import matplotlib.pyplot as plt from scipy.optimize import...
实现如下: bool polynomial_curve_fit(std::vector<cv::Point>& key_point,int n, cv::Mat& A) { //Number of key points int N = key_point.size(); //构造矩阵X cv::Mat X = cv::Mat::zeros(n + 1, n + 1, CV_64FC1); for (int i = 0; i < n + 1; i++) { for (int ...
具体的实现方法可以使用python的curve_fit函数,即可拟合出合适的k0,k1,k2,k3,k4系数。上面提到opencv Kannala-Brandt与厂家给的畸变表之间相差一个系数:focal_length,因此,在做曲线拟合的时候,要把这部分考虑进去: theta_input = data[:,0]*3.14/180 theta_fit = np.arctan(self.data[:,1]/0.95)#focal_le...
cv::Mat A;polynomial_curve_fit(points,3, A); std::cout <<"A = "<< A << std::endl; std::vector<cv::Point> points_fitted;for(intx =0; x <400; x++) {doubley = A.at<double>(0,0) + A.at<double>(1,0) * x + ...
x = curve_points[:, 0] y = curve_points[:, 1] # 使用多项式拟合 coefficients = poly.polyfit(x, y, 3) # 使用三次多项式 y_fit = poly.polyval(x, coefficients) # y_fit 是拟合后的y坐标,可以用于绘制平滑的曲线 3.2 轮廓平滑 如果曲线是从图像中的轮廓提取的,可以直接在轮廓点集上应用平滑算...
plt.scatter(x_data, y_data, label='Data Points', color='red') plt.plot(x_fit, y_fit, label='Fitted Curve', color='blue') plt.title('Curve Fitting using OpenCV') plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.legend() plt.grid() plt.show() 通过上述步骤,我们可以使用OpenCV...
poly=np.poly1d(np.polyfit(x,y,3))print(poly)# 绘制拟合曲线fortinrange(30,250,1):y_=np.int(poly(t))cv.circle(image,(t,y_),1,(0,0,255),1,8,0)cv.imshow("fit curve",image)cv.imwrite("D:/fitcurve.png",image) 二:圆拟合与应用 ...
bool polynomial_curve_fit(std::vector<cv::Point>& key_point, int n, cv::Mat& A) { //Number of key points int N = key_point.size(); //构造矩阵X cv::Mat X = cv::Mat::zeros(n + 1, n + 1, CV_64FC1); for (int i = 0; i < n + 1; i++) ...
int fitCurve(std::vector<double> x, std::vector<double> y) { //columns is 3, rows is x.size() cv::Mat A = cv::Mat::zeros(cv::Size(3, x.size()), CV_64FC1); for (int i = 0; i < x.size(); i++) { A.at<double>(i, 0) = 1; ...