Using PIL and NumPy to convert an image to Lab array, modify the values and then convert back

Question

I am trying to convert a PIL image into an array using NumPy. I then want to convert that array into Lab values, modify the values and then convert the array back in to an image and save the image. I have the following code:

import Image, color, numpy

# Open the image file
src = Image.open("face-him.jpg")

# Attempt to ensure image is RGB
src = src.convert(mode="RGB")

# Create array of image using numpy
srcArray = numpy.asarray(src)

# Convert array from RGB into Lab
srcArray = color.rgb2lab(srcArray)

# Modify array here

# Convert array back into Lab
end = color.lab2rgb(srcArray)

# Create image from array
final = Image.fromarray(end, "RGB")

# Save
final.save("out.jpg")

This code is dependent on PIL, NumPy and color. color can be found in the SciPy trunk here. I downloaded the color.py file along with certain colordata .txt files. I modified the color.py so that it can run independently from the SciPy source and it all seems to work fine - values in the array are changed when I run conversions.

My problem is that when I run the above code which simply converts an image to Lab, then back to RGB and saves it I get the following image back:

What is going wrong? Is it the fact I am using the functions from color.py?

For reference:
Source Image - face-him.jpg
All source files required to test - colour-test.zip

Answer 1

Without having tried it, scaling errors are common in converting colors:
RGB is bytes 0 .. 255, e.g. yellow [255,255,0], whereas rgb2xyz() etc. work on triples of floats, yellow [1.,1.,0].
(color.py has no range checks: lab2rgb( rgb2lab([255,255,0]) ) is junk.)

In IPython, %run main.py, then print corners of srcArray and end ?

Added 13July: for the record / for google, here are NumPy idioms to pack, unpack and convert RGB image arrays:

    # unpack image array, 10 x 5 x 3 -> r g b --
img = np.arange( 10*5*3 ).reshape(( 10,5,3 ))
print "img.shape:", img.shape
r,g,b = img.transpose( 2,0,1 )  # 3 10 5
print "r.shape:", r.shape

    # pack 10 x 5 r g b -> 10 x 5 x 3 again --
rgb = np.array(( r, g, b )).transpose( 1,2,0 )  # 10 5 3 again
print "rgb.shape:", rgb.shape
assert (rgb == img).all()

    # rgb 0 .. 255 <-> float 0 .. 1 --
imgfloat = img.astype(np.float32) / 255.
img8 = (imgfloat * 255).round().astype(np.uint8)  
assert (img == img8).all()