python - Plotting a imshow() image in 3d in matplotlib

Question

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:

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:

Answer 1

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_surface facecolor with, facecolors=plt.cm.BrBG(data/data.max()) the results are closer to what you'd expect.

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,

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:

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