PIL: Create one-dimensional histogram of image col

I've been working on a script, and I need it to basically:

Make the image greyscale (or bitonal, I will play with both to see which one works better).
Process each individual column and create a net intensity value for each column.
Spit the results into an ordered list.

There is a really easy way to do this with ImageMagick (although you need a few Linux utilities to process the output text), but I'm not really seeing how to do this with Python and PIL.

Here's what I have so far:

from PIL import Image

image_file = 'test.tiff'

image = Image.open(image_file).convert('L')

histo = image.histogram()
histo_string = ''

for i in histo:
  histo_string += str(i) + "\n"

print(histo_string)

This outputs something (I am looking to graph the results), but it looks nothing like the ImageMagick output. I'm using this to detect the seam and content of a scanned book.

Thanks to anyone who helps!

I've got a (nasty-looking) solution that works, for now:

from PIL import Image
import numpy

def smoothListGaussian(list,degree=5):
  window=degree*2-1
  weight=numpy.array([1.0]*window)
  weightGauss=[]

  for i in range(window):
    i=i-degree+1
    frac=i/float(window)
    gauss=1/(numpy.exp((4*(frac))**2))
    weightGauss.append(gauss)

  weight=numpy.array(weightGauss)*weight
  smoothed=[0.0]*(len(list)-window)

  for i in range(len(smoothed)):
    smoothed[i]=sum(numpy.array(list[i:i+window])*weight)/sum(weight)

  return smoothed

image_file = 'verypurple.jpg'
out_file = 'out.tiff'

image = Image.open(image_file).convert('1')
image2 = image.load()
image.save(out_file)

intensities = []

for x in xrange(image.size[0]):
  intensities.append([])

  for y in xrange(image.size[1]):
    intensities[x].append(image2[x, y] )

plot = []

for x in xrange(image.size[0]):
  plot.append(0)

  for y in xrange(image.size[1]):
    plot[x] += intensities[x][y]

plot = smoothListGaussian(plot, 10)

plot_str = ''

for x in range(len(plot)):
  plot_str += str(plot[x]) + "\n"

print(plot_str)

回答1:

I see you are using numpy. I would convert the greyscale image to a numpy array first, then use numpy to sum along an axis. Bonus: You'll probably find your smoothing function runs a lot faster when you fix it to accept an 1D array as input.

>>> from PIL import Image
>>> import numpy as np
>>> i = Image.open(r'C:\Pictures\pics\test.png')
>>> a = np.array(i.convert('L'))
>>> a.shape
(2000, 2000)
>>> b = a.sum(0) # or 1 depending on the axis you want to sum across
>>> b.shape
(2000,)

回答2:

From the docs for PIL, histogram gives you a list of pixel counts for each pixel value in the image. If you have a grayscale image, there will be 256 different possible values, ranging from 0 to 255, and the list returned from image.histogram will have 256 entries.