Python 在 matplotlib 中以 3d 形式绘制 imshow() 图像

Plotting a imshow() image in 3d in matplotlib

提问by Raj

How to plot a imshow()image in 3d axes? I was trying with this post. In that post, the surface plot looks same as imshow()plot but actually they are not. To demonstrate, here I took different data:

如何imshow()在 3d 轴中绘制图像？我正在尝试这篇文章。在那篇文章中，表面图看起来与imshow()图相同，但实际上并非如此。为了演示，这里我采用了不同的数据：

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

# create a 21 x 21 vertex mesh
xx, yy = np.meshgrid(np.linspace(0,1,21), np.linspace(0,1,21))

# create vertices for a rotated mesh (3D rotation matrix)
X =  xx 
Y =  yy
Z =  10*np.ones(X.shape)

# create some dummy data (20 x 20) for the image
data = np.cos(xx) * np.cos(xx) + np.sin(yy) * np.sin(yy)

# create the figure
fig = plt.figure()

# show the reference image
ax1 = fig.add_subplot(121)
ax1.imshow(data, cmap=plt.cm.BrBG, interpolation='nearest', origin='lower', extent=[0,1,0,1])

# show the 3D rotated projection
ax2 = fig.add_subplot(122, projection='3d')
ax2.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=plt.cm.BrBG(data), shade=False)

Here are my plots:

这是我的情节：

采纳答案by Ed Smith

I think your error in the 3D vs 2D surface colour is due to data normalisation in the surface colours. If you normalise the data passed to plot_surfacefacecolor with, facecolors=plt.cm.BrBG(data/data.max())the results are closer to what you'd expect.

我认为您在 3D 与 2D 表面颜色中的错误是由于表面颜色的数据标准化。如果您将传递给plot_surfacefacecolor的数据标准化， facecolors=plt.cm.BrBG(data/data.max())则结果更接近您的预期。

If you simply want a slice normal to a coordinate axis, instead of using imshow, you could use contourf, which is supported in 3D as of matplotlib 1.1.0,

如果你只是想要一个垂直于坐标轴的切片，而不是使用imshow，你可以使用contourf，它在 matplotlib 1.1.0 的 3D 中支持，

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from matplotlib import cm

# create a 21 x 21 vertex mesh
xx, yy = np.meshgrid(np.linspace(0,1,21), np.linspace(0,1,21))

# create vertices for a rotated mesh (3D rotation matrix)
X =  xx 
Y =  yy
Z =  10*np.ones(X.shape)

# create some dummy data (20 x 20) for the image
data = np.cos(xx) * np.cos(xx) + np.sin(yy) * np.sin(yy)

# create the figure
fig = plt.figure()

# show the reference image
ax1 = fig.add_subplot(121)
ax1.imshow(data, cmap=plt.cm.BrBG, interpolation='nearest', origin='lower', extent=[0,1,0,1])

# show the 3D rotated projection
ax2 = fig.add_subplot(122, projection='3d')
cset = ax2.contourf(X, Y, data, 100, zdir='z', offset=0.5, cmap=cm.BrBG)

ax2.set_zlim((0.,1.))

plt.colorbar(cset)
plt.show()

This code results in this image:

此代码导致此图像：

Although this won't work for a slice at an arbitrary position in 3D where the imshow solutionis better.

尽管这不适用于 imshow解决方案更好的3D 中任意位置的切片。