Python plot - stacked image slices

Question

I have a series of basic 2D images (3 for simplicity for now) and these are related to each other, analogous to frames from a movie:

Within python how may I stack these slices on top of each other, as in image1->image2->image-3? I'm using pylab to display these images. Ideally an isometric view of the stacked frames would be good or a tool allowing me to rotate the view within code/in rendered image.

Any assistance appreciated. Code and images shown:

from PIL import Image
import pylab

fileName = "image1.png"
im = Image.open(fileName)
pylab.axis('off')
pylab.imshow(im)
pylab.show()

Answer 1

You can't do this with imshow, but you can with contourf, if that will work for you. It's a bit of a kludge though:

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

fig = plt.figure()
ax = fig.gca(projection='3d')

x = np.linspace(0, 1, 100)
X, Y = np.meshgrid(x, x)
Z = np.sin(X)*np.sin(Y)

levels = np.linspace(-1, 1, 40)

ax.contourf(X, Y, .1*np.sin(3*X)*np.sin(5*Y), zdir='z', levels=.1*levels)
ax.contourf(X, Y, 3+.1*np.sin(5*X)*np.sin(8*Y), zdir='z', levels=3+.1*levels)
ax.contourf(X, Y, 7+.1*np.sin(7*X)*np.sin(3*Y), zdir='z', levels=7+.1*levels)

ax.legend()
ax.set_xlim3d(0, 1)
ax.set_ylim3d(0, 1)
ax.set_zlim3d(0, 10)

plt.show()

The docs of what's implemented in 3D are here.

As ali_m suggested, if this won't work for you, if you can imagine it you can do it with VTk/MayaVi.

Answer 2

As far as I know, matplotlib has no 3D equivalent to imshow that would allow you to draw a 2D array as a plane within 3D axes. However, mayavi seems to have exactly the function you're looking for.

Answer 3

Here is a completely silly way to accomplish using matplotlib and shear transformations (you probably need to tweak the transform matrix some more so the stacked images look correct):

import numpy as np
import matplotlib.pyplot as plt

from scipy.ndimage.interpolation import affine_transform


nimages = 4
img_height, img_width = 512, 512
bg_val = -1 # Some flag value indicating the background.

# Random test images.
rs = np.random.RandomState(123)
img = rs.randn(img_height, img_width)*0.1
images = [img+(i+1) for i in range(nimages)]

stacked_height = 2*img_height
stacked_width  = img_width + (nimages-1)*img_width/2
stacked = np.full((stacked_height, stacked_width), bg_val)

# Affine transform matrix.
T = np.array([[1,-1],
              [0, 1]])

for i in range(nimages):
    # The first image will be right most and on the "bottom" of the stack.
    o = (nimages-i-1) * img_width/2
    out = affine_transform(images[i], T, offset=[o,-o],
                           output_shape=stacked.shape, cval=bg_val)
    stacked[out != bg_val] = out[out != bg_val]

plt.imshow(stacked, cmap=plt.cm.viridis)
plt.show()