Question

The input is a spectrum with colorful (sorry) vertical lines on a black background. Given the approximate x coordinate of that band (as marked by X), I want to find the width of that band.

I am unfamiliar with image processing. Please direct me to the correct method of image processing and a Python image processing package that can do the same.

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I am thinking PIL, OpenCV gave me an impression of being overkill for this particular application.

What if I want to make this an expert system that can classify them in the future?

Answer 1

I’ll give a complete minimal working example (as suggested by sega_sai). I don’t have access to your original image, but you’ll see it doesn’t really matter! The peak distributions found by the code below are:

Mean values at: 26.2840960523 80.8255092125

import Image
from scipy import *
from scipy.optimize import leastsq

# Load the picture with PIL, process if needed
pic         = asarray(Image.open("band2.png"))

# Average the pixel values along vertical axis
pic_avg     = pic.mean(axis=2)
projection  = pic_avg.sum(axis=0)

# Set the min value to zero for a nice fit
projection /= projection.mean()
projection -= projection.min()

# Fit function, two gaussians, adjust as needed
def fitfunc(p,x):
    return p[0]*exp(-(x-p[1])**2/(2.0*p[2]**2)) + \
        p[3]*exp(-(x-p[4])**2/(2.0*p[5]**2))
errfunc = lambda p, x, y: fitfunc(p,x)-y

# Use scipy to fit, p0 is inital guess
p0 = array([0,20,1,0,75,10])
X  = xrange(len(projection))
p1, success = leastsq(errfunc, p0, args=(X,projection))
Y = fitfunc(p1,X)

# Output the result
print "Mean values at: ", p1[1], p1[4]

# Plot the result
from pylab import *
subplot(211)
imshow(pic)
subplot(223)
plot(projection)
subplot(224)
plot(X,Y,'r',lw=5)
show()