python - PIL: Create one-dimensional histogram of image color lightness? -

i've been working on script, , need basically:

• make image greyscale (or bitonal, play both see 1 works better).
• process each individual column , create net intensity value each column.
• spit results ordered list.

there easy way imagemagick (although need few linux utilities process output text), i'm not seeing how python , pil.

here's have far:

from pil import image  image_file = 'test.tiff'  image = image.open(image_file).convert('l')  histo = image.histogram() histo_string = ''  in histo:   histo_string += str(i) + "\n"  print(histo_string) 

this outputs (i looking graph results), looks nothing imagemagick output. i'm using detect seam , content of scanned book.

thanks helps!

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i've got (nasty-looking) solution works, now:

from pil import image import numpy  def smoothlistgaussian(list,degree=5):   window=degree*2-1   weight=numpy.array([1.0]*window)   weightgauss=[]    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)    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 = []  x in xrange(image.size[0]):   intensities.append([])    y in xrange(image.size[1]):     intensities[x].append(image2[x, y] )  plot = []  x in xrange(image.size[0]):   plot.append(0)    y in xrange(image.size[1]):     plot[x] += intensities[x][y]  plot = smoothlistgaussian(plot, 10)  plot_str = ''  x in range(len(plot)):   plot_str += str(plot[x]) + "\n"  print(plot_str) 

i see using numpy. convert greyscale image numpy array first, use numpy sum along axis. bonus: you'll find smoothing function runs lot faster when fix accept 1d array input.

>>> pil import image >>> import numpy np >>> = image.open(r'c:\pictures\pics\test.png') >>> = np.array(i.convert('l')) >>> a.shape (2000, 2000) >>> b = a.sum(0) # or 1 depending on axis want sum across >>> b.shape (2000,)