matplotlib绘制小图像，无需重新采样

Question

I'm trying to plot a small image in python using matplotlib and would like the displayed axes to have the same shape as the numpy array it was generated from, ie the data should not be resampled. In other words, each entry in the array should correspond to a pixel (or thereabouts) on the screen. This seems trivial, but even after trawling the internet for while, I can't seem to get it to work:

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

X = np.random.rand(30,40)

fig = plt.figure()
fig.add_axes(aspect="equal",extent=[0, X.shape[1], 0, X.shape[0]])
ax = fig.gca()
ax.autoscale_view(True, False, False)
ax.imshow(X, cmap = cm.gray)

plt.show()

Answer 1

I've had the same problem myself. If the interpolation='nearest' option to imshow isn't good enough, well if your main objective is to see raw, un-scaled, non-interpolated, un-mucked about pixels in matplotlib, then you can't beat figimage IMHO. Demo:

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

a=256*np.random.rand(64,64)

f0=plt.figure()
plt.imshow(a,cmap=plt.gray())
plt.suptitle("imshow")

f1=plt.figure()
plt.figimage(a,cmap=plt.gray())
plt.suptitle("figimage")

plt.show()

Of course it means giving up the axes (or drawing them yourself somehow). There are some options to figimage which let you move the image around the figure so I suppose it might be possible to manoeuvre them on top of some axes created by other means.

Answer 2

You can use the following code snippet to convert an array into a PIL ( Python Imaging Library ). The resulting image will have the same size as the input array. It can be displayed or saved as an image.

from PIL import Image
from numpy import linspace, array, fromfunction, sin, cos
from matplotlib import cm


# scale array between vmin and vmax and encode it to uint8 (256 values)
def scale(arr, vmin, vmax):
    return (255*(arr - vmin)/(vmax - vmin)).clip(0, 255).astype('uint8')

# convert a matplotlib colormap into a PIL palette
def getpalette(cmap):
    return (255.*array(map(lambda x: cmap(x)[0:3], linspace(0., 1.,256))).ravel()).astype('int')


# a sample array
data = fromfunction(lambda i,j: cos((i+j)/50)*sin(i/50.), (200, 300), dtype='float')

# convert the float array to a PIL image
im = Image.fromarray(scale(data, 0, 1))
im.putpalette(getpalette(cm.jet))
im.save('test.png') # or im.show()

The only thing is that im.show() is not very good, because it requires to have the image viewer xv and it writes a temporary image. So you can as well write a file and load it with your favorite image viewer.

Answer 3

I'm not sure I completely understand your question. Does this mean you want the x axis between 0 and X.shape[1] and the y axis between 0 and X.shape[0]? In this case, this code should do the trick:

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

X = np.random.rand(30,40)

fig = plt.figure()

plt.xlim(0, X.shape[1])
plt.ylim(0, X.shape[0])

ax = fig.gca()
ax.autoscale_view(True, False, False)
ax.imshow(X, cmap = cm.gray)

plt.show()

Hope it helps

Answer 4

If you know the native resolution of your image, you can set the dpi argument to what you want in the plt.figure(dpi=value) .

You can find more information about adjusting image size on this link .