Python图像处理之直线和曲线的拟合与绘制【curve_fit()应用】

# -*- coding:utf-8 -*-import numpy as npimport matplotlib.pyplot as pltfrom scipy import optimize#直线方程函数def f_1(x, A, B):  return A*x + B#二次曲线方程def f_2(x, A, B, C):  return A*x*x + B*x + C#三次曲线方程def f_3(x, A, B, C, D):  return A*x*x*x + B*x*x + C*x + Ddef plot_test():  plt.figure()  #拟合点  x0 = [1, 2, 3, 4, 5]  y0 = [1, 3, 8, 18, 36]  #绘制散点  plt.scatter(x0[:], y0[:], 25, "red")  #直线拟合与绘制  A1, B1 = optimize.curve_fit(f_1, x0, y0)[0]  x1 = np.arange(0, 6, 0.01)  y1 = A1*x1 + B1  plt.plot(x1, y1, "blue")  #二次曲线拟合与绘制  A2, B2, C2 = optimize.curve_fit(f_2, x0, y0)[0]  x2 = np.arange(0, 6, 0.01)  y2 = A2*x2*x2 + B2*x2 + C2  plt.plot(x2, y2, "green")  #三次曲线拟合与绘制  A3, B3, C3, D3= optimize.curve_fit(f_3, x0, y0)[0]  x3 = np.arange(0, 6, 0.01)  y3 = A3*x3*x3*x3 + B3*x3*x3 + C3*x3 + D3  plt.plot(x3, y3, "purple")  plt.title("www.vevb.com test")  plt.xlabel('x')  plt.ylabel('y')  plt.show()  returnplot_test()

def f_gauss(x, A, B, C, sigma):  return A*np.exp(-(x-B)**2/(2*sigma**2)) + C