Python生成gussian图像

图像"/>

Python生成gussian图像

Python生成gussian图像

import numpy as np
import matplotlib.pyplot as pltdef dnorm(x, mu, sd):return 1/(np.sqrt(2 * np.pi) * sd) * np.e ** (-np.power((x - mu)/sd, 2)/2)def gaussian_kernel(size, sigma=1, verbose=False):kernel_1D = np.linspace(-(size // 2), size // 2, size)for i in range(size):kernel_1D[i] = dnorm(kernel_1D[i], 0, sigma)kernel_2D = np.outer(kernel_1D.T, kernel_1D.T)kernel_2D *= 1.0 / kernel_2D.max()if verbose:plt.imshow(kernel_2D, interpolation='none',cmap='gray')plt.title("Image")plt.show()return kernel_2Dgaussian_kernel(15, sigma=1, verbose=True)

升级版

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3Dx,y = np.mgrid[-5:5:200j,-5:5:200j]
sigma = 2
z = 1/(2 * np.pi * (sigma**2)) * np.exp(-(x**2+y**2)/(2 * sigma**2))fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(x, y, z, rstride=1, cstride=1, cmap='rainbow',alpha = 0.9)plt.show()
————————————————
版权声明：本文为CSDN博主「xuguofei2006」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：

def normalization(data):_range = np.max(data) - np.min(data)return (data - np.min(data)) / _range
x,y = np.mgrid[-8:8:16j,-8:8:16j]
sigma = 5
z = 1/(2 * np.pi * (sigma**2)) * np.exp(-(x**2+y**2)/(2 * sigma**2))
z2 = normalization(z)

fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(x, y, z2, rstride=1, cstride=1, cmap='rainbow',alpha = 0.9)
plt.show()