Python：来自2D图像的3D轮廓 - pylab和contourf

Question

I have a question regarding Python(pylab) and plotting - I'm able to load and display an image (code below loads the image below), but I'm unable to plot this as a contour in 3D, I understand an array is required for pylab.contourf(x,y,z) though I'm unsure how to achieve this from the loaded image data.

Any suggestions and assistance please. My code:

from PIL import Image
import pylab

fileName = "image1.png"
im = Image.open(fileName)
#pylab.contourf(im) # don't work - needs an array but how
pylab.axis('off')
pylab.imshow(im)
pylab.show()

Answer 1

The reason your image can be represented in a contour plot is that it is clearly a pseudocolor image , that is, an image that uses the full RGB color spectrum to represent a single variable. Contour plots also represent data that have a single variable that determines the color (ie, the Z axis), and therefore you can probably represent your image data as a contour plot as well.

This is the reason I suggested that you use a contour plot in the first place. (What you're actually asking for in this question, though, generally does not exist: there is no generally valid way to convert a color image into a contour plot, since a color image in general has three independent colors, RGB, and a contour plot has only one (the Z-axis), ie , this only works for pseudocolor images.)

To specifically solve your problem:

1) If you have the z-axis data that's used to create the pseudocolor image that you show, just use this data in the contour plot. This is the best solution.

2) If you don't have the z-data, it's more of a hassle, since you need to invert the colors in the image to a z-value, and then put this into the contour plot. The image you show is almost certainly using the colormap matplotlib.cm.jet, and I can't see a better way to invert it than unubtu says here .

In the end, you will need to understand the difference between a contour plot and an image to get the details to work.

demo of why convert doesn't work :
Here I run through a full test case using a ramp of z-values from left to right. As is clear, the z-values are now totally messed up because the values that were the largest are now the smallest, etc.

That is, the goal is that fig. 2 matches fig. 4, but they are very different. The problem, of course, is that convert doesn't correctly map jet to the original set of z-values.

import numpy as np
import matplotlib.pyplot as plt
import Image

fig, axs = plt.subplots(4,1)

x = np.repeat(np.linspace(0, 1, 100)[np.newaxis,:], 20, axis=0)

axs[0].imshow(x, cmap=plt.cm.gray)
axs[0].set_title('1: original z-values as grayscale')

d = axs[1].imshow(x, cmap=plt.cm.jet)
axs[1].set_title('2:original z-values as jet')    
d.write_png('temp01.png')  # write to a file

im = Image.open('temp01.png').convert('L')  # use 'convert' on image to get grayscale
data = np.asarray(im)  # make image into numpy data
axs[2].imshow(data, cmap=plt.cm.gray)
axs[2].set_title("3: 'convert' applied to jet image")

img = Image.open('temp01.png').convert('L')
z   = np.asarray(img)
mydata = z[::1,::1]  # I don't know what this is here for
axs[3].imshow(mydata,interpolation='nearest',cmap=plt.cm.jet)
axs[3].set_title("4: the code that Jake French suggests")

plt.show()

But, it's not so hard to do this correctly, as I suggest above.

Answer 2

OK, some research and simplifying code, the key is convert('L'), ie rgb to greyscale, then Ali_m's code works:

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pylab as pl
from PIL import Image
import numpy as np
import pylab

img = Image.open('40.jpg').convert('L')
z   = np.asarray(img)
mydata = z[::1,::1]
fig = pl.figure(facecolor='w')
ax1 = fig.add_subplot(1,2,1)
im = ax1.imshow(mydata,interpolation='nearest',cmap=pl.cm.jet)
ax1.set_title('2D')

ax2 = fig.add_subplot(1,2,2,projection='3d')
x,y = np.mgrid[:mydata.shape[0],:mydata.shape[1]]
ax2.plot_surface(x,y,mydata,cmap=pl.cm.jet,rstride=1,cstride=1,linewidth=0.,antialiased=False)
ax2.set_title('3D')
ax2.set_zlim3d(0,100)
pl.show()

Answer 3

Edit : sorry, I misunderstood the OP's original question. To get a numpy array from a PIL Image object you can usually just call np.array(im) . However, I work with a lot of microscopy data, and I find that for some image formats (particularly 16bit TIFFs) this syntax doesn't always work, in which case I would use np.asarray(im.getdata()).reshape(*im.shape[::-1]) .

Here's a revised example:

import numpy as np
from matplotlib import pylab as pl
from mpl_toolkits.mplot3d import Axes3D
from PIL import Image

def getimarray(path):
    im = Image.open(path,'r')
    return np.array(im)

def doplots(path='tmp/cell.png'):

    mydata = getimarray(path)
    mydata = mydata[::5,::5]
    fig = pl.figure(facecolor='w')
    ax1 = fig.add_subplot(1,2,1)
    im = ax1.imshow(mydata,interpolation='nearest',cmap=pl.cm.jet)
    ax1.set_title('2D')
    ax2 = fig.add_subplot(1,2,2,projection='3d')
    x,y = np.mgrid[:mydata.shape[0],:mydata.shape[1]]
    ax2.plot_surface(x,y,mydata,cmap=pl.cm.jet,rstride=1,cstride=1,linewidth=0.,antialiased=False)
    ax2.set_title('3D')
    ax2.set_zlim3d(0,255)

    return fig,ax1,ax2

if __name__ == '__main__':
    doplots()