Find median of list of images

Question

If I have a list of images represented by 3D ndarray such as [[x,y,color],...], what operations can I use to output an image with values that are median of all values? I am using a for loop and find it too slow.

Answer 1

This is my vectorized implementation using NumPy:

For my test I used these five images:

The relevant parts:

import numpy as np
import scipy.ndimage

# Load five images:
ims = [scipy.ndimage.imread(str(i + 1) + '.png', flatten=True) for i in range(5)]
# Stack the reshaped images (rows) vertically:
ims = np.vstack([im.reshape(1,im.shape[0] * im.shape[1]) for im in ims])
# Compute the median column by column and reshape to the original shape:
median = np.median(ims, axis=0).reshape(100, 100)

The complete script:

import numpy as np
import scipy.ndimage
import matplotlib.pyplot as plt

ims = [scipy.ndimage.imread(str(i + 1) + '.png', flatten=True) for i in range(5)]
print ims[0].shape # (100, 100)

ims = np.vstack([im.reshape(1,im.shape[0] * im.shape[1]) for im in ims])
print ims.shape # (5, 10000)

median = np.median(ims, axis=0).reshape(100, 100)

fig = plt.figure(figsize=(100./109., 100./109.), dpi=109, frameon=False)
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')
plt.imshow(median, cmap='Greys_r')
plt.show()

The median (numpy.median) result of the five images looks like this:

Fun part: The mean (numpy.mean) result looks like this:

Okay, science meets art. :-)