PIL (Python Imaging Library) and Numpy work well together.

I use the following functions.

from PIL import Image
import numpy as np

def load_image( infilename ) :
    img = Image.open( infilename )
    img.load()
    data = np.asarray( img, dtype="int32" )
    return data

def save_image( npdata, outfilename ) :
    img = Image.fromarray( np.asarray( np.clip(npdata,0,255), dtype="uint8"), "L" )
    img.save( outfilename )

The 'Image.fromarray' is a little ugly because I clip incoming data to [0,255], convert to bytes, then create a grayscale image. I mostly work in gray.

An RGB image would be something like:

out_img = Image.fromarray( ycc_uint8, "RGB" )
out_img.save( "ycc.tif" )

You can use the python library: PIL & numpy. Click here to learn more about image handling in python.

import numpy
import PIL

img = PIL.Image.open("foo.jpg").convert("L")
imgarr = numpy.array(img)

I think what you are looking for is:

list(im.getdata())

or, if the image is too big to load entirely into memory, so something like that:

for pixel in iter(im.getdata()):
    print pixel

from PIL documentation:

getdata

im.getdata() => sequence

Returns the contents of an image as a sequence object containing pixel values. The sequence object is flattened, so that values for line one follow directly after the values of line zero, and so on.

Note that the sequence object returned by this method is an internal PIL data type, which only supports certain sequence operations, including iteration and basic sequence access. To convert it to an ordinary sequence (e.g. for printing), use list(im.getdata()).